diff --git a/README.md b/README.md index e93889f..cc893d0 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@
-Search your codebase semantically or chat with it from cli. Keep the vector database superfast up to date to the latest code changes. +Generate datasets from code for finetuning, search your codebase semantically or chat with your code from cli. Keep the vector database superfast up to date to the latest code changes. 100% local support without any dataleaks. Built with [langchain](https://github.com/langchain-ai/langchain), [treesitter](https://github.com/tree-sitter/tree-sitter), [sentence-transformers](https://github.com/UKPLab/sentence-transformers), [instructor-embedding](https://github.com/xlang-ai/instructor-embedding), [faiss](https://github.com/facebookresearch/faiss), [lama.cpp](https://github.com/ggerganov/llama.cpp), [Ollama](https://github.com/jmorganca/ollama), [Streamlit](https://github.com/streamlit/streamlit). @@ -19,6 +19,8 @@ Built with [langchain](https://github.com/langchain-ai/langchain), [treesitter]( ## ✨ Features +- 🗒️  Finetuning dataset generation + - export in Alpaca, conversational, instruction or completionn format - 🔎  Semantic code search - 💬  GPT-like chat with your codebase - ⚙️  Synchronize vector store and latest code changes with ease @@ -32,6 +34,19 @@ Built with [langchain](https://github.com/langchain-ai/langchain), [treesitter]( ## 🚀 Usage +#### Export finetuning dataset from codebase in conversational format: +``` +codeqai dataset +``` +Export in different format like Alpaca with: +``` +codeqai dataset --format alpaca +``` +Export dataset with model distillation +``` +codeqai dataset --distillation doc +``` + #### Start semantic search: ``` diff --git a/codeqai/app.py b/codeqai/app.py index 22b6e81..74e2b37 100644 --- a/codeqai/app.py +++ b/codeqai/app.py @@ -15,7 +15,8 @@ from codeqai.bootstrap import bootstrap from codeqai.cache import create_cache_dir, get_cache_path, save_vector_cache from codeqai.config import create_config, get_config_path, load_config -from codeqai.constants import EmbeddingsModel, LlmHost +from codeqai.constants import DistillationMode, EmbeddingsModel, LlmHost +from codeqai.dataset_extractor import DatasetExtractor from codeqai.embeddings import Embeddings from codeqai.vector_store import VectorStore @@ -81,20 +82,33 @@ def run(): "chat", "configure", "sync", - "export-dataset (experimental)", + "dataset", ], - help="Action to perform. 'search' will semantically search the codebase. 'chat' will chat with the codebase.", + help="Action to perform. 'app' to start the streamlit app, 'search' to search the codebase, " + + "'chat' to chat with the model, 'configure' to start config wizard, " + + "'sync' to sync the vector store with the current git checkout, 'dataset' to export a dataset for model distillation.", ) parser.add_argument( "--distillation", - action="store_true", - help="Use model distillation for finetuning dataset extraction.", + type=DistillationMode, + default=DistillationMode.NONE, + help="Use model distillation for finetuning dataset extraction. Default is None." + + "Supported modes are, 'full', 'doc', 'code'.\n" + + "doc - Extracts only documentation for distillation.\n" + + "code - Extracts will chunk code blocks with inlined comments for distillation.\n" + + "full - Uses both doc and code mode", ) parser.add_argument( "--format", type=str, - default="Conversational", - help="Format of the finetuning dataset. Supported formats are Conversational and Alpaca. Default is Conversational format.", + default="conversational", + help="Format of the finetuning dataset. Supported formats are conversational and alpaca. Default is Conversational format.", + ) + parser.add_argument( + "--max-tokens", + type=int, + default=1024, + help="Token limit per code block for distillation dataset extraction. Default is 1024.", ) args = parser.parse_args() @@ -149,10 +163,26 @@ def run(): ), ) - if args.action == "extract-dataset": + if args.action == "dataset": + print(args.distillation) + spinner = yaspin( + text=f"Parsing codebase for {args.format} dataset export...", + color="green", + ) + spinner.start() repo_name = repo.repo_name() files = repo.load_files() - documents = codeparser.parse_code_files_for_finetuning(files) + documents = codeparser.parse_code_files_for_finetuning( + files, args.max_tokens, spinner + ) + dateset_extractor = DatasetExtractor( + args.format, + args.distillation, + documents, + config, + args.max_tokens, + ) + dateset_extractor.export() exit() # check if faiss.index exists diff --git a/codeqai/bootstrap.py b/codeqai/bootstrap.py index 53fff1b..478bb5b 100644 --- a/codeqai/bootstrap.py +++ b/codeqai/bootstrap.py @@ -8,6 +8,17 @@ def bootstrap(config, repo_name, embeddings_model=None): + """ + Initializes the necessary components for the application. + + Args: + config (dict): Configuration dictionary containing settings for embeddings and LLM. + repo_name (str): The name of the repository. + embeddings_model (Embeddings, optional): Pre-initialized embeddings model. Defaults to None. + + Returns: + tuple: A tuple containing the vector store, memory, and QA chain. + """ if embeddings_model is None: embeddings_model = Embeddings( model=EmbeddingsModel[config["embeddings"].upper().replace("-", "_")], diff --git a/codeqai/cache.py b/codeqai/cache.py index 7c73719..4384eb0 100644 --- a/codeqai/cache.py +++ b/codeqai/cache.py @@ -27,6 +27,15 @@ def to_json(self): def load_vector_cache(filename) -> Dict[str, VectorCache]: + """ + Loads a vector cache from a JSON file. + + Args: + filename (str): The name of the file containing the vector cache. + + Returns: + Dict[str, VectorCache]: A dictionary where the keys are strings and the values are VectorCache objects. + """ with open( get_cache_path() + "/" + filename, "r", encoding="utf-8" ) as vector_cache_file: @@ -38,6 +47,13 @@ def load_vector_cache(filename) -> Dict[str, VectorCache]: def save_vector_cache(vector_cache, filename): + """ + Saves a vector cache to a JSON file. + + Args: + vector_cache (Dict[str, VectorCache]): A dictionary where the keys are strings and the values are VectorCache objects. + filename (str): The name of the file to save the vector cache to. + """ with open( get_cache_path() + "/" + filename, "w", encoding="utf-8" ) as vector_cache_file: @@ -45,6 +61,15 @@ def save_vector_cache(vector_cache, filename): def get_cache_path(): + """ + Returns the cache directory path based on the operating system. + + Returns: + str: The path to the cache directory. + + Raises: + NotImplementedError: If the operating system is not supported. + """ system = platform.system() if system == "Linux" or system == "Darwin": @@ -60,6 +85,12 @@ def get_cache_path(): def create_cache_dir(): + """ + Creates the cache directory if it does not already exist. + + This function checks if the cache directory exists at the path returned by get_cache_path(). + If the directory does not exist, it creates the directory and any necessary parent directories. + """ if not os.path.exists(get_cache_path()): path = Path(get_cache_path()) path.mkdir(parents=True, exist_ok=True) diff --git a/codeqai/codeparser.py b/codeqai/codeparser.py index 89f133a..5a829c3 100644 --- a/codeqai/codeparser.py +++ b/codeqai/codeparser.py @@ -1,8 +1,9 @@ -import ast import os +import inquirer from langchain.schema import Document from langchain.text_splitter import RecursiveCharacterTextSplitter +from yaspin import yaspin from codeqai import repo, utils from codeqai.constants import Language @@ -10,6 +11,15 @@ def parse_code_files_for_db(code_files: list[str]) -> list[Document]: + """ + Parses a list of code files and returns a list of Document objects for database storage. + + Args: + code_files (list[str]): List of paths to code files to be parsed. + + Returns: + list[Document]: List of Document objects containing parsed code information. + """ documents = [] code_splitter = None for code_file in code_files: @@ -60,7 +70,21 @@ def parse_code_files_for_db(code_files: list[str]) -> list[Document]: return documents -def parse_code_files_for_finetuning(code_files: list[str]) -> list[dict]: +def parse_code_files_for_finetuning( + code_files: list[str], max_tokens, spinner +) -> list[dict]: + """ + Parses a list of code files for fine-tuning and returns a list of dictionaries containing method information. + + Args: + code_files (list[str]): List of paths to code files to be parsed. + max_tokens (int): Maximum number of tokens allowed for output. + + Returns: + list[dict]: List of dictionaries containing method information, including method name, code, description, and language. + """ + input_tokens = 0 + output_tokens = 0 documents = [] for code_file in code_files: with open(code_file, "r", encoding="utf-8") as file: @@ -84,10 +108,34 @@ def parse_code_files_for_finetuning(code_files: list[str]) -> list[dict]: ) document = { + "method_name": node.name, "code": method_source_code, "description": node.doc_comment, - "language": programming_language, + "language": programming_language.value, } documents.append(document) + if node.doc_comment is not None: + input_tokens += utils.count_tokens(node.doc_comment) + output_tokens += max_tokens + + spinner.stop() + + print(f"Estimated input tokens for distillation needed: {input_tokens}.") + print(f"Maximum output tokens for distillation nedeed: {output_tokens}.") + questions = [ + inquirer.Confirm( + "confirm", + message="Proceed?", + default=True, + ), + ] + + confirm = inquirer.prompt(questions) + + if confirm and confirm["confirm"]: + pass + else: + exit() + return documents diff --git a/codeqai/config.py b/codeqai/config.py index e10c7ad..032edb0 100644 --- a/codeqai/config.py +++ b/codeqai/config.py @@ -8,6 +8,18 @@ def get_config_path(): + """ + Returns the configuration file path based on the operating system. + + This function determines the appropriate configuration directory based on the operating system + and constructs the full path to the configuration file. + + Returns: + str: The path to the configuration file. + + Raises: + NotImplementedError: If the operating system is not supported. + """ system = platform.system() if system == "Linux" or system == "Darwin": @@ -25,17 +37,44 @@ def get_config_path(): def load_config(): + """ + Loads the configuration from the configuration file. + + This function reads the configuration file specified by get_config_path() and parses its content + using the YAML parser. + + Returns: + dict: The configuration dictionary loaded from the file. + """ with open(get_config_path(), "r", encoding="utf-8") as config_file: config = yaml.safe_load(config_file) return config def save_config(config): + """ + Saves the configuration to the configuration file. + + Args: + config (dict): The configuration dictionary to be saved. + + This function writes the provided configuration dictionary to the configuration file specified by get_config_path() + using the YAML format. + """ with open(get_config_path(), "w", encoding="utf-8") as config_file: yaml.dump(config, config_file, default_flow_style=False) def create_config(): + """ + Creates a new configuration interactively by prompting the user for input. + + This function prompts the user with a series of questions to configure the embeddings model and LLM host. + Based on the user's responses, it constructs a configuration dictionary and saves it to the configuration file. + + Returns: + dict: The configuration dictionary created based on user input. + """ os.makedirs(os.path.dirname(get_config_path()), exist_ok=True) questions = [ diff --git a/codeqai/constants.py b/codeqai/constants.py index d60daee..e80b416 100644 --- a/codeqai/constants.py +++ b/codeqai/constants.py @@ -39,5 +39,14 @@ class LlmHost(Enum): class DatasetFormat(Enum): - ALPACA = "Alpaca" - CONVERSATIONAL = "Conversational" + ALPACA = "alpaca" + CONVERSATIONAL = "conversational" + INSTRUCTION = "instruction" + COMPLETION = "completion" + + +class DistillationMode(Enum): + NONE = "none" + FULL = "full" + DOCUMENTATION = "doc" + CODE = "code" diff --git a/codeqai/dataset-extractor.py b/codeqai/dataset-extractor.py deleted file mode 100644 index 057e71c..0000000 --- a/codeqai/dataset-extractor.py +++ /dev/null @@ -1,53 +0,0 @@ -import json - -from codeqai.constants import DatasetFormat - - -class DatesetExtractor: - def __init__( - self, format: DatasetFormat, distillation: bool, code_snippets: list[dict] - ): - self.format = format - self.distillation = distillation - self.code_snippets = code_snippets - - def export(self): - if self.format == DatasetFormat.CONVERSATIONAL: - return self.export_conversational() - elif self.format == DatasetFormat.ALPACA: - return self.export_alpaca() - - def export_conversational(self): - messages_list = [] - for code_snippet in self.code_snippets: - messages = { - "messages": [ - { - "role": "system", - "content": "You are a " - + (code_snippet.get("language") or "programming") - + " expert.", - }, - {"role": "user", "content": code_snippet.get("description")}, - {"role": "assistant", "content": code_snippet.get("code")}, - ] - } - messages_list.append(messages) - - with open("conversational_dataset.json", "w") as f: - for messages in messages_list: - json.dump(messages, f) - f.write("\n") - - def export_alpaca(self): - alpaca_list = [] - for code_snippet in self.code_snippets: - alpaca_entry = { - "instruction": code_snippet.get("description"), - "input": "", - "output": code_snippet.get("code"), - } - alpaca_list.append(alpaca_entry) - - with open("alpaca_dataset.json", "w") as f: - json.dump(alpaca_list, f, indent=4) diff --git a/codeqai/dataset_extractor.py b/codeqai/dataset_extractor.py new file mode 100644 index 0000000..74ac8b0 --- /dev/null +++ b/codeqai/dataset_extractor.py @@ -0,0 +1,320 @@ +import json + +from yaspin import yaspin + +from codeqai.constants import DatasetFormat, DistillationMode, LlmHost +from codeqai.llm import LLM + + +class DatasetExtractor: + def __init__( + self, + format: DatasetFormat, + distillation_mode: DistillationMode, + code_snippets: list[dict], + config, + max_tokens, + ): + self.format = format + self.distillation_mode = distillation_mode + self.code_snippets = code_snippets + self.llm = LLM( + llm_host=LlmHost[config["llm-host"].upper().replace("-", "_")], + chat_model=config["chat-model"], + max_tokens=max_tokens, + deployment=( + config["model-deployment"] if "model-deployment" in config else None + ), + ) + + def export(self): + """ + Exports the dataset based on the specified format. + + This method checks the format of the dataset and calls the appropriate export method. + It also prints messages indicating the progress and completion of the export process. + + Supported formats: + - CONVERSATIONAL: Exports to conversational_dataset.json + - ALPACA: Exports to alpaca_dataset.json + - INSTRUCTION: Exports to instruction_dataset.json + """ + print("Exporting dataset...") + if self.format == DatasetFormat.CONVERSATIONAL.value: + self.export_conversational() + print("Dataset exported to conversational_dataset.json") + elif self.format == DatasetFormat.ALPACA.value: + self.export_alpaca() + print("Dataset exported to alpaca_dataset.json") + elif self.format == DatasetFormat.INSTRUCTION.value: + self.export_instruction() + print("Dataset exported to instruction_dataset.json") + elif self.format == DatasetFormat.COMPLETION.value: + self.export_completion() + print("Dataset exported to completion_dataset.json") + + def export_conversational(self): + """ + Exports the code snippets in a conversational format. + + This method processes each code snippet in the dataset and creates conversational messages + for both implementation and explanation tasks. The messages are then saved to a JSON file. + """ + messages_list = [] + for code_snippet in self.code_snippets: + if code_snippet.get("description") is None: + if ( + self.distillation_mode == DistillationMode.DOCUMENTATION + or self.distillation_mode == DistillationMode.FULL + ): + docstring = self.distill_docstring(code_snippet) + else: + continue + else: + docstring = code_snippet.get("description") + + if ( + self.distillation_mode == DistillationMode.CODE + or self.distillation_mode == DistillationMode.FULL + ): + pass + + message = { + "messages": [ + { + "role": "system", + "content": "You are a " + + (code_snippet.get("language") or "programming") + + " expert. Write an implementation for the following description.", + }, + {"role": "user", "content": docstring}, + {"role": "assistant", "content": code_snippet.get("code")}, + ] + } + + messages_list.append(message) + message = { + "messages": [ + { + "role": "system", + "content": "You are a " + + (code_snippet.get("language") or "programming") + + " expert. Explain the following code.", + }, + {"role": "user", "content": code_snippet.get("code")}, + { + "role": "assistant", + "content": docstring, + }, + ] + } + + messages_list.append(message) + + with open("conversational_dataset.json", "w") as f: + for messages in messages_list: + json.dump(messages, f) + f.write("\n") + + def export_alpaca(self): + """ + Exports the code snippets in an Alpaca format. + + This method processes each code snippet in the dataset and creates Alpaca entries + for both implementation and explanation tasks. The entries are then saved to a JSON file. + """ + alpaca_list = [] + for code_snippet in self.code_snippets: + if code_snippet.get("description") is None: + if ( + self.distillation_mode == DistillationMode.DOCUMENTATION + or self.distillation_mode == DistillationMode.FULL + ): + docstring = self.distill_docstring(code_snippet) + else: + continue + else: + docstring = code_snippet.get("description") + + if ( + self.distillation_mode == DistillationMode.CODE + or self.distillation_mode == DistillationMode.FULL + ): + pass + + alpaca_entry = { + "instruction": "You are a " + + (code_snippet.get("language") or "programming") + + " expert. Write an implementation for the following description.", + "input": docstring, + "output": code_snippet.get("code"), + } + alpaca_list.append(alpaca_entry) + alpaca_entry = { + "instruction": "You are a " + + (code_snippet.get("language") or "programming") + + " expert. Explain the following code.", + "input": code_snippet.get("code"), + "output": docstring, + } + alpaca_list.append(alpaca_entry) + + with open("alpaca_dataset.json", "w") as f: + json.dump(alpaca_list, f, indent=4) + + def export_instruction(self): + """ + Exports the code snippets in an instruction format. + + This method processes each code snippet in the dataset and creates instruction entries + for both implementation and explanation tasks. The entries are then saved to a JSON file. + """ + instructions_list = [] + for code_snippet in self.code_snippets: + if code_snippet.get("description") is None: + if ( + self.distillation_mode == DistillationMode.DOCUMENTATION + or self.distillation_mode == DistillationMode.FULL + ): + result = self.distill_docstring(code_snippet) + if type(result) is str: + docstring = result + else: + continue + else: + continue + else: + docstring = code_snippet.get("description") + + if ( + self.distillation_mode == DistillationMode.CODE + or self.distillation_mode == DistillationMode.FULL + ): + pass + + instruction = { + "prompt": "You are a " + + (code_snippet.get("language") or "programming") + + " expert. Write an implementation for the following description:\n" + + (docstring or ""), + "completion": code_snippet.get("code"), + } + instructions_list.append(instruction) + instruction = { + "prompt": "You are a " + + (code_snippet.get("language") or "programming") + + " expert. Explain the following code:\n" + + (code_snippet.get("code") or ""), + "completion": docstring, + } + instructions_list.append(instruction) + + with open("instruction_dataset.json", "w") as f: + json.dump(instructions_list, f, indent=4) + + def export_completion(self): + """ + Exports the code snippets in a completion format. + + This method processes each code snippet in the dataset and creates completion entries. + The entries are then saved to a JSON file. + """ + completions_list = [] + for code_snippet in self.code_snippets: + if code_snippet.get("description") is None: + if ( + self.distillation_mode == DistillationMode.DOCUMENTATION + or self.distillation_mode == DistillationMode.FULL + ): + docstring = self.distill_docstring(code_snippet) + else: + continue + else: + docstring = code_snippet.get("description") + + if ( + self.distillation_mode == DistillationMode.CODE + or self.distillation_mode == DistillationMode.FULL + ): + pass + + completion = { + "input": docstring, + "output": code_snippet.get("code"), + } + completions_list.append(completion) + + with open("completion_dataset.json", "w") as f: + json.dump(completions_list, f, indent=4) + + def distill_docstring(self, code_snippet): + """ + Distills a concise description from the given code snippet. + + Args: + code_snippet (dict): A dictionary containing details about the code snippet, such as method name, programming language, and the actual code. + + Returns: + str: A concise description of the code snippet. + """ + spinner = yaspin( + text=f"Distilling {code_snippet.get('method_name')}...", + color="green", + ) + spinner.start() + prompt = ( + "You are a " + + (code_snippet.get("language") or "programming") + + " expert. Write a short and concise description for the following code. Return only the description." + ) + docstring = self.llm.chat_model.invoke( + [ + ("system", prompt), + ("human", code_snippet.get("code") or ""), + ] + ) + spinner.stop() + return docstring.content + + def distill_code(self, code_snippet): + """ + Distills a given code snippet into smaller chunks and provides explanations for each chunk. + + Args: + code_snippet (dict): A dictionary containing details about the code snippet, such as method name, programming language, and the actual code. + + Returns: + dict: A dictionary containing the distilled code chunks and their explanations. + """ + spinner = yaspin( + text=f"Distilling {code_snippet.get('method_name')}...", + color="green", + ) + spinner.start() + prompt = ( + "You are a " + + (code_snippet.get("language") or "programming") + + " expert. Split the following code into reasonable chunks and explain each chunk. " + + "Return a JSON object with a list of objects containing the code chunk with key 'code' and the explanation with key 'explanation'." + ) + code = self.llm.chat_model.invoke( + [ + ("system", prompt), + ("human", code_snippet.get("code") or ""), + ] + ) + spinner.stop() + + try: + # Ensure the content is a string before parsing + if isinstance(code.content, str): + # Parse the output to a JSON object + code_json = json.loads(code.content) + else: + raise ValueError("Content is not a valid JSON string") + except (json.JSONDecodeError, ValueError) as e: + # Handle JSON parsing error + print(f"Error parsing response JSON: {e}") + return {} + + return code_json diff --git a/codeqai/distillation.py b/codeqai/distillation.py deleted file mode 100644 index df09558..0000000 --- a/codeqai/distillation.py +++ /dev/null @@ -1,14 +0,0 @@ -def generate_code(): - pass - - -def generate_description(): - pass - - -def generate_inline_comments(): - pass - - -def split_by_inline_comments(): - pass diff --git a/codeqai/embeddings.py b/codeqai/embeddings.py index 23434d9..a5eef6e 100644 --- a/codeqai/embeddings.py +++ b/codeqai/embeddings.py @@ -1,5 +1,5 @@ import inquirer -from langchain_community.embeddings import HuggingFaceEmbeddings +from langchain_huggingface import HuggingFaceEmbeddings from langchain_openai import AzureOpenAIEmbeddings, OpenAIEmbeddings from codeqai import utils @@ -12,12 +12,21 @@ def __init__( model=EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002, deployment=None, ): + """ + Initializes the Embeddings class with the specified model and deployment. + + Args: + model (EmbeddingsModel): The embeddings model to use. Defaults to OPENAI_TEXT_EMBEDDING_ADA_002. + deployment (str, optional): The deployment name for Azure OpenAI embeddings. Defaults to None. + """ if model == EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002: self.embeddings = OpenAIEmbeddings( client=None, model="text-embedding-ada-002" ) elif model == EmbeddingsModel.AZURE_OPENAI and deployment: - self.embeddings = AzureOpenAIEmbeddings(client=None, deployment=deployment) + self.embeddings = AzureOpenAIEmbeddings( + client=None, azure_deployment=deployment + ) else: try: import sentence_transformers # noqa: F401 diff --git a/codeqai/llm.py b/codeqai/llm.py index c1249c3..5f6399e 100644 --- a/codeqai/llm.py +++ b/codeqai/llm.py @@ -6,19 +6,32 @@ from langchain.callbacks.manager import CallbackManager from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain_anthropic import ChatAnthropic -from langchain_community.chat_models import AzureChatOpenAI from langchain_community.llms import LlamaCpp, Ollama -from langchain_openai import ChatOpenAI +from langchain_openai import AzureChatOpenAI, ChatOpenAI from codeqai import utils from codeqai.constants import LlmHost class LLM: - def __init__(self, llm_host: LlmHost, chat_model: str, deployment=None): + def __init__( + self, llm_host: LlmHost, chat_model: str, max_tokens=2048, deployment=None + ): + """ + Initializes the LLM class with the specified parameters. + + Args: + llm_host (LlmHost): The host for the language model (e.g., OPENAI, AZURE_OPENAI, ANTHROPIC, LLAMACPP, OLLAMA). + chat_model (str): The chat model to use. + max_tokens (int, optional): The maximum number of tokens for the model. Defaults to 2048. + deployment (str, optional): The deployment name for Azure OpenAI. Defaults to None. + + Raises: + ValueError: If the required environment variable for Azure OpenAI is not set. + """ if llm_host == LlmHost.OPENAI: self.chat_model = ChatOpenAI( - temperature=0.9, max_tokens=2048, model=chat_model + temperature=0.9, max_tokens=max_tokens, model=chat_model ) elif llm_host == LlmHost.AZURE_OPENAI and deployment: azure_openai_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") @@ -26,8 +39,7 @@ def __init__(self, llm_host: LlmHost, chat_model: str, deployment=None): self.chat_model = AzureChatOpenAI( azure_endpoint=azure_openai_endpoint, temperature=0.9, - max_tokens=2048, - deployment_name=deployment, + max_tokens=max_tokens, model=chat_model, ) else: @@ -36,14 +48,18 @@ def __init__(self, llm_host: LlmHost, chat_model: str, deployment=None): ) elif llm_host == LlmHost.ANTHROPIC: self.chat_model = ChatAnthropic( - temperature=0.9, max_tokens=2048, model_name=chat_model + temperature=0.9, + max_tokens_to_sample=max_tokens, + model_name=chat_model, + timeout=30, + api_key=None, # API key is set to environment variable ANTHROPIC_API_KEY ) elif llm_host == LlmHost.LLAMACPP: self.install_llama_cpp() self.chat_model = LlamaCpp( model_path=chat_model, temperature=0.9, - max_tokens=2048, + max_tokens=max_tokens, verbose=False, ) elif llm_host == LlmHost.OLLAMA: diff --git a/codeqai/repo.py b/codeqai/repo.py index b0693a3..6d6851e 100644 --- a/codeqai/repo.py +++ b/codeqai/repo.py @@ -5,16 +5,43 @@ def repo_name(): + """ + Retrieves the name of the current Git repository. + + This function gets the root directory of the current Git repository based on the current working directory, + and extracts the repository name from the root directory path. + + Returns: + str: The name of the current Git repository. + """ return get_git_root(os.getcwd()).split("/")[-1] def get_git_root(path): + """ + Retrieves the root directory of the Git repository for the given path. + + Args: + path (str): The path to a directory within the Git repository. + + Returns: + str: The root directory of the Git repository. + """ git_repo = Repo(path, search_parent_directories=True) git_root = git_repo.git.rev_parse("--show-toplevel") return git_root def find_file_in_git_repo(file_name): + """ + Searches for a file with the given name in the current Git repository. + + Args: + file_name (str): The name of the file to search for. + + Returns: + str or None: The full path to the file if found, otherwise None. + """ git_root = get_git_root(os.getcwd()) for root, dirs, files in os.walk(git_root): @@ -26,6 +53,15 @@ def find_file_in_git_repo(file_name): def load_files(): + """ + Loads files from the current Git repository based on whitelist and blacklist criteria. + + This function walks through the directory structure of the Git repository, + and collects files that match the whitelist extensions and are not in the blacklist directories or files. + + Returns: + list: A list of file paths that meet the criteria. + """ git_root = get_git_root(os.getcwd()) file_list = [] @@ -42,6 +78,15 @@ def load_files(): def get_commit_hash(file_path): + """ + Retrieves the latest commit hash for the specified file. + + Args: + file_path (str): The path to the file for which to retrieve the commit hash. + + Returns: + str or None: The latest commit hash if found, otherwise None. + """ try: # Run the git log command result = subprocess.run( diff --git a/codeqai/treesitter/treesitter.py b/codeqai/treesitter/treesitter.py index 0bc69aa..2e62cac 100644 --- a/codeqai/treesitter/treesitter.py +++ b/codeqai/treesitter/treesitter.py @@ -40,6 +40,15 @@ def create_treesitter(language: Language) -> "Treesitter": return TreesitterRegistry.create_treesitter(language) def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]: + """ + Parses the given file bytes and extracts method nodes. + + Args: + file_bytes (bytes): The content of the file to be parsed. + + Returns: + list[TreesitterMethodNode]: A list of TreesitterMethodNode objects representing the methods in the file. + """ self.tree = self.parser.parse(file_bytes) result = [] methods = self._query_all_methods(self.tree.root_node) @@ -55,6 +64,15 @@ def _query_all_methods( self, node: tree_sitter.Node, ): + """ + Recursively queries all method nodes in the given syntax tree node. + + Args: + node (tree_sitter.Node): The root node to start the query from. + + Returns: + list: A list of dictionaries, each containing a method node and its associated doc comment (if any). + """ methods = [] if node.type == self.method_declaration_identifier: doc_comment_node = None @@ -70,6 +88,15 @@ def _query_all_methods( return methods def _query_method_name(self, node: tree_sitter.Node): + """ + Queries the method name from the given syntax tree node. + + Args: + node (tree_sitter.Node): The syntax tree node to query. + + Returns: + str or None: The method name if found, otherwise None. + """ if node.type == self.method_declaration_identifier: for child in node.children: if child.type == self.method_name_identifier: diff --git a/codeqai/treesitter/treesitter_c.py b/codeqai/treesitter/treesitter_c.py index 2380046..4647c49 100644 --- a/codeqai/treesitter/treesitter_c.py +++ b/codeqai/treesitter/treesitter_c.py @@ -10,6 +10,15 @@ def __init__(self): super().__init__(Language.C, "function_definition", "identifier", "comment") def _query_method_name(self, node: tree_sitter.Node): + """ + Queries the method name from the given syntax tree node. + + Args: + node (tree_sitter.Node): The syntax tree node to query. + + Returns: + str or None: The method name if found, otherwise None. + """ if node.type == self.method_declaration_identifier: for child in node.children: # if method returns pointer, skip pointer declarator diff --git a/codeqai/treesitter/treesitter_cpp.py b/codeqai/treesitter/treesitter_cpp.py index a2b9e4b..8428bf7 100644 --- a/codeqai/treesitter/treesitter_cpp.py +++ b/codeqai/treesitter/treesitter_cpp.py @@ -10,6 +10,15 @@ def __init__(self): super().__init__(Language.CPP, "function_definition", "identifier", "comment") def _query_method_name(self, node: tree_sitter.Node): + """ + Queries the method name from the given syntax tree node. + + Args: + node (tree_sitter.Node): The syntax tree node to query. + + Returns: + str or None: The method name if found, otherwise None. + """ if node.type == self.method_declaration_identifier: for child in node.children: # if method returns pointer, skip pointer declarator diff --git a/codeqai/treesitter/treesitter_cs.py b/codeqai/treesitter/treesitter_cs.py index ca0aed8..dbf6ede 100644 --- a/codeqai/treesitter/treesitter_cs.py +++ b/codeqai/treesitter/treesitter_cs.py @@ -12,6 +12,15 @@ def __init__(self): ) def _query_method_name(self, node: tree_sitter.Node): + """ + Queries the method name from the given syntax tree node. + + Args: + node (tree_sitter.Node): The syntax tree node to query. + + Returns: + str or None: The method name if found, otherwise None. + """ first_match = None if node.type == self.method_declaration_identifier: for child in node.children: @@ -24,6 +33,15 @@ def _query_method_name(self, node: tree_sitter.Node): return first_match def _query_all_methods(self, node: tree_sitter.Node): + """ + Recursively queries all method nodes in the given syntax tree node. + + Args: + node (tree_sitter.Node): The root node to start the query from. + + Returns: + list: A list of dictionaries, each containing a method node and its associated doc comment (if any). + """ methods = [] if node.type == self.method_declaration_identifier: doc_comment_nodes = [] diff --git a/codeqai/treesitter/treesitter_hs.py b/codeqai/treesitter/treesitter_hs.py index e0547d5..bcd30b7 100644 --- a/codeqai/treesitter/treesitter_hs.py +++ b/codeqai/treesitter/treesitter_hs.py @@ -1,5 +1,3 @@ -from typing import Dict, List - import tree_sitter from codeqai.constants import Language @@ -12,6 +10,15 @@ def __init__(self): super().__init__(Language.HASKELL, "function", "variable", "comment") def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]: + """ + Parses the given file bytes and extracts method nodes. + + Args: + file_bytes (bytes): The content of the file to be parsed. + + Returns: + list[TreesitterMethodNode]: A list of TreesitterMethodNode objects representing the methods in the file. + """ self.tree = self.parser.parse(file_bytes) result = [] methods = self._query_all_methods(self.tree.root_node) @@ -36,6 +43,15 @@ def _query_all_methods( self, node: tree_sitter.Node, ): + """ + Recursively queries all method nodes in the given syntax tree node. + + Args: + node (tree_sitter.Node): The root node to start the query from. + + Returns: + list: A list of dictionaries, each containing a method node and its associated doc comment (if any). + """ methods = [] if node.type == self.method_declaration_identifier: doc_comment_node = None @@ -79,6 +95,15 @@ def _query_all_methods( return methods def _query_method_name(self, node: tree_sitter.Node): + """ + Queries the method name from the given syntax tree node. + + Args: + node (tree_sitter.Node): The syntax tree node to query. + + Returns: + str or None: The method name if found, otherwise None. + """ if node.type == "signature" or node.type == self.method_declaration_identifier: for child in node.children: if child.type == self.method_name_identifier: diff --git a/codeqai/treesitter/treesitter_py.py b/codeqai/treesitter/treesitter_py.py index 0c35a32..2d14be7 100644 --- a/codeqai/treesitter/treesitter_py.py +++ b/codeqai/treesitter/treesitter_py.py @@ -12,6 +12,15 @@ def __init__(self): ) def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]: + """ + Parses the given file bytes and extracts method nodes. + + Args: + file_bytes (bytes): The content of the file to be parsed. + + Returns: + list[TreesitterMethodNode]: A list of TreesitterMethodNode objects representing the methods in the file. + """ self.tree = self.parser.parse(file_bytes) result = [] methods = self._query_all_methods(self.tree.root_node) @@ -22,6 +31,15 @@ def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]: return result def _query_method_name(self, node: tree_sitter.Node): + """ + Queries the method name from the given syntax tree node. + + Args: + node (tree_sitter.Node): The syntax tree node to query. + + Returns: + str or None: The method name if found, otherwise None. + """ if node.type == self.method_declaration_identifier: for child in node.children: if child.type == self.method_name_identifier: @@ -29,6 +47,15 @@ def _query_method_name(self, node: tree_sitter.Node): return None def _query_all_methods(self, node: tree_sitter.Node): + """ + Queries all method nodes within the given syntax tree node, including those within class definitions. + + Args: + node (tree_sitter.Node): The root node to start the query from. + + Returns: + list: A list of method nodes found within the given node. + """ methods = [] for child in node.children: if child.type == self.method_declaration_identifier: @@ -41,6 +68,15 @@ def _query_all_methods(self, node: tree_sitter.Node): return methods def _query_doc_comment(self, node: tree_sitter.Node): + """ + Queries the documentation comment for the given function definition node. + + Args: + node (tree_sitter.Node): The syntax tree node representing a function definition. + + Returns: + str or None: The documentation comment string if found, otherwise None. + """ query_code = """ (function_definition body: (block . (expression_statement (string)) @function_doc_str)) diff --git a/codeqai/treesitter/treesitter_rb.py b/codeqai/treesitter/treesitter_rb.py index 7ba25e7..b25dac9 100644 --- a/codeqai/treesitter/treesitter_rb.py +++ b/codeqai/treesitter/treesitter_rb.py @@ -7,9 +7,7 @@ class TreesitterRuby(Treesitter): def __init__(self): - super().__init__( - Language.RUBY, "method", "identifier", "comment" - ) + super().__init__(Language.RUBY, "method", "identifier", "comment") def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]: return super().parse(file_bytes) @@ -18,13 +16,23 @@ def _query_all_methods( self, node: tree_sitter.Node, ): + """ + Recursively queries all method nodes in the given syntax tree node. + + Args: + node (tree_sitter.Node): The root node to start the query from. + + Returns: + list: A list of dictionaries, each containing a method node and its associated doc comment (if any). + """ methods = [] if node.type == self.method_declaration_identifier: doc_comment = [] doc_comment_node = node while ( doc_comment_node.prev_named_sibling - and doc_comment_node.prev_named_sibling.type == self.doc_comment_identifier + and doc_comment_node.prev_named_sibling.type + == self.doc_comment_identifier ): doc_comment_node = doc_comment_node.prev_named_sibling doc_comment.insert(0, doc_comment_node.text.decode()) @@ -34,5 +42,6 @@ def _query_all_methods( methods.extend(self._query_all_methods(child)) return methods + # Register the TreesitterRuby class in the registry TreesitterRegistry.register_treesitter(Language.RUBY, TreesitterRuby) diff --git a/codeqai/treesitter/treesitter_rs.py b/codeqai/treesitter/treesitter_rs.py index d1c4170..eea2f8a 100644 --- a/codeqai/treesitter/treesitter_rs.py +++ b/codeqai/treesitter/treesitter_rs.py @@ -10,6 +10,15 @@ def __init__(self): super().__init__(Language.RUST, "function_item", "identifier", "line_comment") def _query_all_methods(self, node: tree_sitter.Node): + """ + Recursively queries all method nodes in the given syntax tree node. + + Args: + node (tree_sitter.Node): The root node to start the query from. + + Returns: + list: A list of dictionaries, each containing a method node and its associated doc comment (if any). + """ methods = [] if node.type == self.method_declaration_identifier: doc_comment_nodes = [] diff --git a/codeqai/utils.py b/codeqai/utils.py index aa1e253..a654343 100644 --- a/codeqai/utils.py +++ b/codeqai/utils.py @@ -1,6 +1,7 @@ import os import langchain.text_splitter as text_splitter +import tiktoken from codeqai.constants import Language from codeqai.repo import find_file_in_git_repo @@ -80,10 +81,30 @@ def get_langchain_language(language: Language): def get_bold_text(text): + """ + Returns the given text formatted in bold. + + Args: + text (str): The text to be formatted. + + Returns: + str: The text formatted in bold using ANSI escape codes. + """ return f"\033[01m{text}\033[0m" def find_starting_line_and_indent(filename, code_snippet): + """ + Finds the starting line number and indentation level of a code snippet within a file. + + Args: + filename (str): The name of the file to search within. + code_snippet (str): The code snippet to find in the file. + + Returns: + tuple: A tuple containing the starting line number (int) and the indentation level (str) of the code snippet. + If the file is not found or the code snippet is not found, returns (1, ""). + """ file_path = find_file_in_git_repo(filename) if file_path is not None: with open(file_path, "r") as file: @@ -94,3 +115,18 @@ def find_starting_line_and_indent(filename, code_snippet): file_content[:start_pos].split("\n")[-1], ) return 1, "" + + +def count_tokens(text, model="gpt-4"): + """ + Counts the number of tokens in the given text using the specified model's tokenizer. + + Args: + text (str): The text to be tokenized and counted. + model (str, optional): The model to use for tokenization. Defaults to "gpt-4". + + Returns: + int: The number of tokens in the text. + """ + enc = tiktoken.encoding_for_model(model) + return len(enc.encode(text)) diff --git a/codeqai/vector_store.py b/codeqai/vector_store.py index 9948b2a..497abb2 100644 --- a/codeqai/vector_store.py +++ b/codeqai/vector_store.py @@ -3,7 +3,7 @@ import inquirer from langchain.embeddings.base import Embeddings from langchain.schema import Document -from langchain_community.vectorstores import FAISS +from langchain_community.vectorstores.faiss import FAISS from codeqai import utils from codeqai.cache import VectorCache, get_cache_path, load_vector_cache @@ -18,6 +18,12 @@ def __init__(self, name: str, embeddings: Embeddings): self.install_faiss() def load_documents(self): + """ + Loads documents into the vector store. + + This method reads the serialized FAISS index from a file, deserializes it, and loads it into the FAISS database. + It also loads the vector cache from a JSON file and initializes the retriever with the specified search parameters. + """ with open( os.path.join(get_cache_path(), f"{self.name}.faiss.bytes"), "rb" ) as file: @@ -30,6 +36,15 @@ def load_documents(self): self.retriever = self.db.as_retriever(search_type="mmr", search_kwargs={"k": 8}) def index_documents(self, documents: list[Document]): + """ + Indexes the given documents and stores them in the vector store. + + This method creates a FAISS index from the provided documents and serializes it to a file. + It also creates a vector cache for quick lookup of document vectors and initializes the retriever. + + Args: + documents (list[Document]): A list of Document objects to be indexed. + """ self.vector_cache = {} self.db = FAISS.from_documents(documents, self.embeddings) index = self.db.serialize_to_bytes() @@ -41,7 +56,7 @@ def index_documents(self, documents: list[Document]): index_to_docstore_id = self.db.index_to_docstore_id for i in range(len(documents)): document = self.db.docstore.search(index_to_docstore_id[i]) - if document: + if document and document is type(Document): # Check if the document is already present in the vector cache # if yes, then add the vector id to the vector cache entry if self.vector_cache.get(document.metadata["filename"]): @@ -59,6 +74,17 @@ def index_documents(self, documents: list[Document]): self.retriever = self.db.as_retriever(search_type="mmr", search_kwargs={"k": 8}) def sync_documents(self, files): + """ + Synchronizes the documents in the vector store with the provided files. + + This method checks if the documents in the vector store are up-to-date with the provided files. + If a document has been modified, it deletes the old vectors and adds new vectors. + If a document is new, it adds the document to the vector store. + It also removes old documents that are no longer present in the provided files. + + Args: + files (list[str]): List of file paths to synchronize with the vector store. + """ new_filenames = set() for file in files: filename = os.path.basename(file) diff --git a/datasets/alpaca_dataset.json b/datasets/alpaca_dataset.json new file mode 100644 index 0000000..e9c3b3b --- /dev/null +++ b/datasets/alpaca_dataset.json @@ -0,0 +1,502 @@ +[ + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Initializes the necessary components for the application.\n\n Args:\n config (dict): Configuration dictionary containing settings for embeddings and LLM.\n repo_name (str): The name of the repository.\n embeddings_model (Embeddings, optional): Pre-initialized embeddings model. Defaults to None.\n\n Returns:\n tuple: A tuple containing the vector store, memory, and QA chain.\n \"\"\"", + "output": "def bootstrap(config, repo_name, embeddings_model=None):\n \n if embeddings_model is None:\n embeddings_model = Embeddings(\n model=EmbeddingsModel[config[\"embeddings\"].upper().replace(\"-\", \"_\")],\n deployment=(\n config[\"embeddings-deployment\"]\n if \"embeddings-deployment\" in config\n else None\n ),\n )\n\n vector_store = VectorStore(repo_name, embeddings=embeddings_model.embeddings)\n vector_store.load_documents()\n\n llm = LLM(\n llm_host=LlmHost[config[\"llm-host\"].upper().replace(\"-\", \"_\")],\n chat_model=config[\"chat-model\"],\n deployment=config[\"model-deployment\"] if \"model-deployment\" in config else None,\n )\n memory = ConversationSummaryMemory(\n llm=llm.chat_model, memory_key=\"chat_history\", return_messages=True\n )\n qa = ConversationalRetrievalChain.from_llm(\n llm.chat_model, retriever=vector_store.retriever, memory=memory\n )\n\n return vector_store, memory, qa" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def bootstrap(config, repo_name, embeddings_model=None):\n \n if embeddings_model is None:\n embeddings_model = Embeddings(\n model=EmbeddingsModel[config[\"embeddings\"].upper().replace(\"-\", \"_\")],\n deployment=(\n config[\"embeddings-deployment\"]\n if \"embeddings-deployment\" in config\n else None\n ),\n )\n\n vector_store = VectorStore(repo_name, embeddings=embeddings_model.embeddings)\n vector_store.load_documents()\n\n llm = LLM(\n llm_host=LlmHost[config[\"llm-host\"].upper().replace(\"-\", \"_\")],\n chat_model=config[\"chat-model\"],\n deployment=config[\"model-deployment\"] if \"model-deployment\" in config else None,\n )\n memory = ConversationSummaryMemory(\n llm=llm.chat_model, memory_key=\"chat_history\", return_messages=True\n )\n qa = ConversationalRetrievalChain.from_llm(\n llm.chat_model, retriever=vector_store.retriever, memory=memory\n )\n\n return vector_store, memory, qa", + "output": "\"\"\"\n Initializes the necessary components for the application.\n\n Args:\n config (dict): Configuration dictionary containing settings for embeddings and LLM.\n repo_name (str): The name of the repository.\n embeddings_model (Embeddings, optional): Pre-initialized embeddings model. Defaults to None.\n\n Returns:\n tuple: A tuple containing the vector store, memory, and QA chain.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Returns the configuration file path based on the operating system.\n\n This function determines the appropriate configuration directory based on the operating system\n and constructs the full path to the configuration file.\n\n Returns:\n str: The path to the configuration file.\n\n Raises:\n NotImplementedError: If the operating system is not supported.\n \"\"\"", + "output": "def get_config_path():\n \n system = platform.system()\n\n if system == \"Linux\" or system == \"Darwin\":\n user_home = os.path.expanduser(\"~\")\n config_dir = os.path.join(user_home, \".config\", \"codeqai\")\n elif system == \"Windows\":\n user_home = os.path.expanduser(\"~\")\n config_dir = os.path.join(user_home, \"AppData\", \"Roaming\", \"codeqai\")\n else:\n raise NotImplementedError(f\"Unsupported platform: {system}\")\n\n config_file_path = os.path.join(config_dir, \"config.yaml\")\n\n return config_file_path" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def get_config_path():\n \n system = platform.system()\n\n if system == \"Linux\" or system == \"Darwin\":\n user_home = os.path.expanduser(\"~\")\n config_dir = os.path.join(user_home, \".config\", \"codeqai\")\n elif system == \"Windows\":\n user_home = os.path.expanduser(\"~\")\n config_dir = os.path.join(user_home, \"AppData\", \"Roaming\", \"codeqai\")\n else:\n raise NotImplementedError(f\"Unsupported platform: {system}\")\n\n config_file_path = os.path.join(config_dir, \"config.yaml\")\n\n return config_file_path", + "output": "\"\"\"\n Returns the configuration file path based on the operating system.\n\n This function determines the appropriate configuration directory based on the operating system\n and constructs the full path to the configuration file.\n\n Returns:\n str: The path to the configuration file.\n\n Raises:\n NotImplementedError: If the operating system is not supported.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Loads the configuration from the configuration file.\n\n This function reads the configuration file specified by get_config_path() and parses its content\n using the YAML parser.\n\n Returns:\n dict: The configuration dictionary loaded from the file.\n \"\"\"", + "output": "def load_config():\n \n with open(get_config_path(), \"r\", encoding=\"utf-8\") as config_file:\n config = yaml.safe_load(config_file)\n return config" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def load_config():\n \n with open(get_config_path(), \"r\", encoding=\"utf-8\") as config_file:\n config = yaml.safe_load(config_file)\n return config", + "output": "\"\"\"\n Loads the configuration from the configuration file.\n\n This function reads the configuration file specified by get_config_path() and parses its content\n using the YAML parser.\n\n Returns:\n dict: The configuration dictionary loaded from the file.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Saves the configuration to the configuration file.\n\n Args:\n config (dict): The configuration dictionary to be saved.\n\n This function writes the provided configuration dictionary to the configuration file specified by get_config_path()\n using the YAML format.\n \"\"\"", + "output": "def save_config(config):\n \n with open(get_config_path(), \"w\", encoding=\"utf-8\") as config_file:\n yaml.dump(config, config_file, default_flow_style=False)" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def save_config(config):\n \n with open(get_config_path(), \"w\", encoding=\"utf-8\") as config_file:\n yaml.dump(config, config_file, default_flow_style=False)", + "output": "\"\"\"\n Saves the configuration to the configuration file.\n\n Args:\n config (dict): The configuration dictionary to be saved.\n\n This function writes the provided configuration dictionary to the configuration file specified by get_config_path()\n using the YAML format.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Creates a new configuration interactively by prompting the user for input.\n\n This function prompts the user with a series of questions to configure the embeddings model and LLM host.\n Based on the user's responses, it constructs a configuration dictionary and saves it to the configuration file.\n\n Returns:\n dict: The configuration dictionary created based on user input.\n \"\"\"", + "output": "def create_config():\n \n os.makedirs(os.path.dirname(get_config_path()), exist_ok=True)\n\n questions = [\n inquirer.Confirm(\n \"confirm\",\n message=\"Do you want to use local embedding models?\",\n default=False,\n ),\n ]\n\n confirm = inquirer.prompt(questions)\n\n if confirm and confirm[\"confirm\"]:\n questions = [\n inquirer.List(\n \"embeddings\",\n message=\"Which local embeddings model do you want to use?\",\n choices=[\n EmbeddingsModel.INSTRUCTOR_LARGE.value,\n EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MPNET_BASE_V2.value,\n EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MINILM_L6_V2.value,\n ],\n default=EmbeddingsModel.INSTRUCTOR_LARGE.value,\n ),\n ]\n else:\n questions = [\n inquirer.List(\n \"embeddings\",\n message=\"Which remote embeddings do you want to use?\",\n choices=[\n EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002.value,\n EmbeddingsModel.AZURE_OPENAI.value,\n ],\n default=EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002.value,\n ),\n ]\n\n answersEmbedding = inquirer.prompt(questions)\n\n questions = [\n inquirer.Confirm(\n \"confirm\", message=\"Do you want to use local chat models?\", default=False\n ),\n ]\n\n confirm = inquirer.prompt(questions)\n\n if confirm and confirm[\"confirm\"]:\n questions = [\n inquirer.List(\n \"llm-host\",\n message=\"Which local LLM host do you want to use?\",\n choices=[\n LlmHost.LLAMACPP.value,\n LlmHost.OLLAMA.value,\n ],\n default=LlmHost.LLAMACPP.value,\n ),\n ]\n else:\n questions = [\n inquirer.List(\n \"llm-host\",\n message=\"Which remote LLM provider do you want to use?\",\n choices=[\n LlmHost.OPENAI.value,\n LlmHost.AZURE_OPENAI.value,\n LlmHost.ANTHROPIC.value,\n ],\n default=LlmHost.OPENAI.value,\n ),\n ]\n\n answersLlm = inquirer.prompt(questions)\n\n if confirm and answersEmbedding and answersLlm:\n config = {\n \"embeddings\": answersEmbedding[\"embeddings\"],\n \"llm-host\": answersLlm[\"llm-host\"],\n }\n\n if config[\"embeddings\"] == EmbeddingsModel.AZURE_OPENAI.value:\n questions = [\n inquirer.Text(\n \"deployment\",\n message=\"Please enter the Azure OpenAI embeddings deployment name.\",\n default=\"\",\n ),\n ]\n deployment_answer = inquirer.prompt(questions)\n if deployment_answer and deployment_answer[\"deployment\"]:\n config[\"embeddings-deployment\"] = deployment_answer[\"deployment\"]\n\n if config[\"llm-host\"] == LlmHost.AZURE_OPENAI.value:\n questions = [\n inquirer.Text(\n \"deployment\",\n message=\"Please enter the Azure OpenAI model deployment name\",\n default=\"\",\n ),\n ]\n deployment_answer = inquirer.prompt(questions)\n if deployment_answer and deployment_answer[\"deployment\"]:\n config[\"model-deployment\"] = deployment_answer[\"deployment\"]\n config[\"chat-model\"] = deployment_answer[\"deployment\"]\n\n elif config[\"llm-host\"] == LlmHost.LLAMACPP.value:\n questions = [\n inquirer.Text(\n \"chat-model\",\n message=\"Please enter the path to the LLM\",\n default=\"\",\n ),\n ]\n\n elif config[\"llm-host\"] == LlmHost.OLLAMA.value:\n questions = [\n inquirer.List(\n \"chat-model\",\n message=\"Which model do you want to use with Ollama?\",\n choices=[\n \"llama2:7b\",\n \"llama2:13b\",\n \"llama3.2:1b\",\n \"llama3.2:3b\",\n \"llama3.1:8b\",\n \"codellama:7b\",\n \"codellama:13b\",\n \"gemma2:9b\",\n \"gemma2:2b\",\n \"deepseek-r1:1.5b\",\n \"deepseek-r1:7b\",\n \"deepseek-r1:8b\",\n \"qwen2.5-coder:7b\",\n \"qwen2.5-coder:3b\",\n ],\n default=\"llama2:13b\",\n ),\n ]\n\n elif config[\"llm-host\"] == LlmHost.OPENAI.value:\n questions = [\n inquirer.List(\n \"chat-model\",\n message=\"Which OpenAI model do you want to use?\",\n choices=[\n \"gpt-3.5-turbo\",\n \"gpt-4\",\n \"gpt-4-turbo\",\n \"gpt-4o\",\n \"gpt-4o-mini\",\n \"o1\",\n \"o1-mini\",\n \"o3-mini\",\n ],\n default=\"gpt-3.5-turbo\",\n ),\n ]\n\n elif config[\"llm-host\"] == LlmHost.ANTHROPIC.value:\n questions = [\n inquirer.List(\n \"chat-model\",\n message=\"Which Anthropic model do you want to use?\",\n choices=[\n \"claude-3-opus-latest\",\n \"claude-3-5-sonnet-latest\",\n \"claude-3-5-haiku-latest\",\n ],\n default=\"claude-3-opus-latest\",\n ),\n ]\n\n # Check if \"chat-model\" is already present in the case of Azure_OpenAI\n if \"chat-model\" not in config:\n answersChatmodel = inquirer.prompt(questions)\n if answersChatmodel and answersChatmodel[\"chat-model\"]:\n config[\"chat-model\"] = answersChatmodel[\"chat-model\"]\n\n save_config(config)\n\n return config\n\n return {}" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def create_config():\n \n os.makedirs(os.path.dirname(get_config_path()), exist_ok=True)\n\n questions = [\n inquirer.Confirm(\n \"confirm\",\n message=\"Do you want to use local embedding models?\",\n default=False,\n ),\n ]\n\n confirm = inquirer.prompt(questions)\n\n if confirm and confirm[\"confirm\"]:\n questions = [\n inquirer.List(\n \"embeddings\",\n message=\"Which local embeddings model do you want to use?\",\n choices=[\n EmbeddingsModel.INSTRUCTOR_LARGE.value,\n EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MPNET_BASE_V2.value,\n EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MINILM_L6_V2.value,\n ],\n default=EmbeddingsModel.INSTRUCTOR_LARGE.value,\n ),\n ]\n else:\n questions = [\n inquirer.List(\n \"embeddings\",\n message=\"Which remote embeddings do you want to use?\",\n choices=[\n EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002.value,\n EmbeddingsModel.AZURE_OPENAI.value,\n ],\n default=EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002.value,\n ),\n ]\n\n answersEmbedding = inquirer.prompt(questions)\n\n questions = [\n inquirer.Confirm(\n \"confirm\", message=\"Do you want to use local chat models?\", default=False\n ),\n ]\n\n confirm = inquirer.prompt(questions)\n\n if confirm and confirm[\"confirm\"]:\n questions = [\n inquirer.List(\n \"llm-host\",\n message=\"Which local LLM host do you want to use?\",\n choices=[\n LlmHost.LLAMACPP.value,\n LlmHost.OLLAMA.value,\n ],\n default=LlmHost.LLAMACPP.value,\n ),\n ]\n else:\n questions = [\n inquirer.List(\n \"llm-host\",\n message=\"Which remote LLM provider do you want to use?\",\n choices=[\n LlmHost.OPENAI.value,\n LlmHost.AZURE_OPENAI.value,\n LlmHost.ANTHROPIC.value,\n ],\n default=LlmHost.OPENAI.value,\n ),\n ]\n\n answersLlm = inquirer.prompt(questions)\n\n if confirm and answersEmbedding and answersLlm:\n config = {\n \"embeddings\": answersEmbedding[\"embeddings\"],\n \"llm-host\": answersLlm[\"llm-host\"],\n }\n\n if config[\"embeddings\"] == EmbeddingsModel.AZURE_OPENAI.value:\n questions = [\n inquirer.Text(\n \"deployment\",\n message=\"Please enter the Azure OpenAI embeddings deployment name.\",\n default=\"\",\n ),\n ]\n deployment_answer = inquirer.prompt(questions)\n if deployment_answer and deployment_answer[\"deployment\"]:\n config[\"embeddings-deployment\"] = deployment_answer[\"deployment\"]\n\n if config[\"llm-host\"] == LlmHost.AZURE_OPENAI.value:\n questions = [\n inquirer.Text(\n \"deployment\",\n message=\"Please enter the Azure OpenAI model deployment name\",\n default=\"\",\n ),\n ]\n deployment_answer = inquirer.prompt(questions)\n if deployment_answer and deployment_answer[\"deployment\"]:\n config[\"model-deployment\"] = deployment_answer[\"deployment\"]\n config[\"chat-model\"] = deployment_answer[\"deployment\"]\n\n elif config[\"llm-host\"] == LlmHost.LLAMACPP.value:\n questions = [\n inquirer.Text(\n \"chat-model\",\n message=\"Please enter the path to the LLM\",\n default=\"\",\n ),\n ]\n\n elif config[\"llm-host\"] == LlmHost.OLLAMA.value:\n questions = [\n inquirer.List(\n \"chat-model\",\n message=\"Which model do you want to use with Ollama?\",\n choices=[\n \"llama2:7b\",\n \"llama2:13b\",\n \"llama3.2:1b\",\n \"llama3.2:3b\",\n \"llama3.1:8b\",\n \"codellama:7b\",\n \"codellama:13b\",\n \"gemma2:9b\",\n \"gemma2:2b\",\n \"deepseek-r1:1.5b\",\n \"deepseek-r1:7b\",\n \"deepseek-r1:8b\",\n \"qwen2.5-coder:7b\",\n \"qwen2.5-coder:3b\",\n ],\n default=\"llama2:13b\",\n ),\n ]\n\n elif config[\"llm-host\"] == LlmHost.OPENAI.value:\n questions = [\n inquirer.List(\n \"chat-model\",\n message=\"Which OpenAI model do you want to use?\",\n choices=[\n \"gpt-3.5-turbo\",\n \"gpt-4\",\n \"gpt-4-turbo\",\n \"gpt-4o\",\n \"gpt-4o-mini\",\n \"o1\",\n \"o1-mini\",\n \"o3-mini\",\n ],\n default=\"gpt-3.5-turbo\",\n ),\n ]\n\n elif config[\"llm-host\"] == LlmHost.ANTHROPIC.value:\n questions = [\n inquirer.List(\n \"chat-model\",\n message=\"Which Anthropic model do you want to use?\",\n choices=[\n \"claude-3-opus-latest\",\n \"claude-3-5-sonnet-latest\",\n \"claude-3-5-haiku-latest\",\n ],\n default=\"claude-3-opus-latest\",\n ),\n ]\n\n # Check if \"chat-model\" is already present in the case of Azure_OpenAI\n if \"chat-model\" not in config:\n answersChatmodel = inquirer.prompt(questions)\n if answersChatmodel and answersChatmodel[\"chat-model\"]:\n config[\"chat-model\"] = answersChatmodel[\"chat-model\"]\n\n save_config(config)\n\n return config\n\n return {}", + "output": "\"\"\"\n Creates a new configuration interactively by prompting the user for input.\n\n This function prompts the user with a series of questions to configure the embeddings model and LLM host.\n Based on the user's responses, it constructs a configuration dictionary and saves it to the configuration file.\n\n Returns:\n dict: The configuration dictionary created based on user input.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Loads documents into the vector store.\n\n This method reads the serialized FAISS index from a file, deserializes it, and loads it into the FAISS database.\n It also loads the vector cache from a JSON file and initializes the retriever with the specified search parameters.\n \"\"\"", + "output": "def load_documents(self):\n \n with open(\n os.path.join(get_cache_path(), f\"{self.name}.faiss.bytes\"), \"rb\"\n ) as file:\n index = file.read()\n\n self.db = FAISS.deserialize_from_bytes(\n embeddings=self.embeddings, serialized=index\n )\n self.vector_cache = load_vector_cache(f\"{self.name}.json\")\n self.retriever = self.db.as_retriever(search_type=\"mmr\", search_kwargs={\"k\": 8})" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def load_documents(self):\n \n with open(\n os.path.join(get_cache_path(), f\"{self.name}.faiss.bytes\"), \"rb\"\n ) as file:\n index = file.read()\n\n self.db = FAISS.deserialize_from_bytes(\n embeddings=self.embeddings, serialized=index\n )\n self.vector_cache = load_vector_cache(f\"{self.name}.json\")\n self.retriever = self.db.as_retriever(search_type=\"mmr\", search_kwargs={\"k\": 8})", + "output": "\"\"\"\n Loads documents into the vector store.\n\n This method reads the serialized FAISS index from a file, deserializes it, and loads it into the FAISS database.\n It also loads the vector cache from a JSON file and initializes the retriever with the specified search parameters.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Indexes the given documents and stores them in the vector store.\n\n This method creates a FAISS index from the provided documents and serializes it to a file.\n It also creates a vector cache for quick lookup of document vectors and initializes the retriever.\n\n Args:\n documents (list[Document]): A list of Document objects to be indexed.\n \"\"\"", + "output": "def index_documents(self, documents: list[Document]):\n \n self.vector_cache = {}\n self.db = FAISS.from_documents(documents, self.embeddings)\n index = self.db.serialize_to_bytes()\n with open(\n os.path.join(get_cache_path(), f\"{self.name}.faiss.bytes\"), \"wb\"\n ) as binary_file:\n binary_file.write(index)\n # Create vector cache\n index_to_docstore_id = self.db.index_to_docstore_id\n for i in range(len(documents)):\n document = self.db.docstore.search(index_to_docstore_id[i])\n if document and document is type(Document):\n # Check if the document is already present in the vector cache\n # if yes, then add the vector id to the vector cache entry\n if self.vector_cache.get(document.metadata[\"filename\"]):\n self.vector_cache[document.metadata[\"filename\"]].vector_ids.append(\n index_to_docstore_id[i]\n )\n # if no, then create a new entry in the vector cache\n else:\n self.vector_cache[document.metadata[\"filename\"]] = VectorCache(\n document.metadata[\"filename\"],\n [index_to_docstore_id[i]],\n document.metadata[\"commit_hash\"],\n )\n\n self.retriever = self.db.as_retriever(search_type=\"mmr\", search_kwargs={\"k\": 8})" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def index_documents(self, documents: list[Document]):\n \n self.vector_cache = {}\n self.db = FAISS.from_documents(documents, self.embeddings)\n index = self.db.serialize_to_bytes()\n with open(\n os.path.join(get_cache_path(), f\"{self.name}.faiss.bytes\"), \"wb\"\n ) as binary_file:\n binary_file.write(index)\n # Create vector cache\n index_to_docstore_id = self.db.index_to_docstore_id\n for i in range(len(documents)):\n document = self.db.docstore.search(index_to_docstore_id[i])\n if document and document is type(Document):\n # Check if the document is already present in the vector cache\n # if yes, then add the vector id to the vector cache entry\n if self.vector_cache.get(document.metadata[\"filename\"]):\n self.vector_cache[document.metadata[\"filename\"]].vector_ids.append(\n index_to_docstore_id[i]\n )\n # if no, then create a new entry in the vector cache\n else:\n self.vector_cache[document.metadata[\"filename\"]] = VectorCache(\n document.metadata[\"filename\"],\n [index_to_docstore_id[i]],\n document.metadata[\"commit_hash\"],\n )\n\n self.retriever = self.db.as_retriever(search_type=\"mmr\", search_kwargs={\"k\": 8})", + "output": "\"\"\"\n Indexes the given documents and stores them in the vector store.\n\n This method creates a FAISS index from the provided documents and serializes it to a file.\n It also creates a vector cache for quick lookup of document vectors and initializes the retriever.\n\n Args:\n documents (list[Document]): A list of Document objects to be indexed.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Synchronizes the documents in the vector store with the provided files.\n\n This method checks if the documents in the vector store are up-to-date with the provided files.\n If a document has been modified, it deletes the old vectors and adds new vectors.\n If a document is new, it adds the document to the vector store.\n It also removes old documents that are no longer present in the provided files.\n\n Args:\n files (list[str]): List of file paths to synchronize with the vector store.\n \"\"\"", + "output": "def sync_documents(self, files):\n \n new_filenames = set()\n for file in files:\n filename = os.path.basename(file)\n new_filenames.add(filename)\n commit_hash = get_commit_hash(file)\n # Check if the document is already present in the vector cache\n # if yes, then check if the document has been modified or not\n if filename in self.vector_cache:\n # Check if the document has been modified, if yes delete all old vectors and add new vector\n if self.vector_cache[filename].commit_hash != commit_hash:\n # This will delete all the vectors associated with the document\n # incluing db.index_to_docstore_id, db.docstore and db.index\n try:\n self.db.delete(self.vector_cache[filename].vector_ids)\n except Exception as e:\n print(f\"Error deleting vectors for file {filename}: {e}\")\n\n # Add the new document to the vector store and recreate the vector cache entry\n self.vector_cache[filename] = VectorCache(\n filename,\n [],\n commit_hash,\n )\n documents = parse_code_files_for_db([file])\n for document in documents:\n self.db.add_documents([document])\n self.vector_cache[filename].vector_ids.append(\n self.db.index_to_docstore_id[\n len(self.db.index_to_docstore_id) - 1\n ]\n )\n\n # if no, then create a new entry in the vector cache and add the document to the vector store\n else:\n self.vector_cache[filename] = VectorCache(\n filename,\n [],\n commit_hash,\n )\n documents = parse_code_files_for_db([file])\n for document in documents:\n self.db.add_documents([document])\n self.vector_cache[filename].vector_ids.append(\n self.db.index_to_docstore_id[\n len(self.db.index_to_docstore_id) - 1\n ]\n )\n\n # Remove old documents from the vector store\n deleted_files = []\n for cache_item in self.vector_cache.values():\n if cache_item.filename not in new_filenames:\n try:\n self.db.delete(cache_item.vector_ids)\n except Exception as e:\n print(f\"Error deleting vectors for file {cache_item.filename}: {e}\")\n deleted_files.append(cache_item.filename)\n\n # Remove old filenames from the vector cache\n for deleted_file in deleted_files:\n self.vector_cache.pop(deleted_file)\n\n index = self.db.serialize_to_bytes()\n with open(\n os.path.join(get_cache_path(), f\"{self.name}.faiss.bytes\"), \"wb\"\n ) as binary_file:\n binary_file.write(index)" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def sync_documents(self, files):\n \n new_filenames = set()\n for file in files:\n filename = os.path.basename(file)\n new_filenames.add(filename)\n commit_hash = get_commit_hash(file)\n # Check if the document is already present in the vector cache\n # if yes, then check if the document has been modified or not\n if filename in self.vector_cache:\n # Check if the document has been modified, if yes delete all old vectors and add new vector\n if self.vector_cache[filename].commit_hash != commit_hash:\n # This will delete all the vectors associated with the document\n # incluing db.index_to_docstore_id, db.docstore and db.index\n try:\n self.db.delete(self.vector_cache[filename].vector_ids)\n except Exception as e:\n print(f\"Error deleting vectors for file {filename}: {e}\")\n\n # Add the new document to the vector store and recreate the vector cache entry\n self.vector_cache[filename] = VectorCache(\n filename,\n [],\n commit_hash,\n )\n documents = parse_code_files_for_db([file])\n for document in documents:\n self.db.add_documents([document])\n self.vector_cache[filename].vector_ids.append(\n self.db.index_to_docstore_id[\n len(self.db.index_to_docstore_id) - 1\n ]\n )\n\n # if no, then create a new entry in the vector cache and add the document to the vector store\n else:\n self.vector_cache[filename] = VectorCache(\n filename,\n [],\n commit_hash,\n )\n documents = parse_code_files_for_db([file])\n for document in documents:\n self.db.add_documents([document])\n self.vector_cache[filename].vector_ids.append(\n self.db.index_to_docstore_id[\n len(self.db.index_to_docstore_id) - 1\n ]\n )\n\n # Remove old documents from the vector store\n deleted_files = []\n for cache_item in self.vector_cache.values():\n if cache_item.filename not in new_filenames:\n try:\n self.db.delete(cache_item.vector_ids)\n except Exception as e:\n print(f\"Error deleting vectors for file {cache_item.filename}: {e}\")\n deleted_files.append(cache_item.filename)\n\n # Remove old filenames from the vector cache\n for deleted_file in deleted_files:\n self.vector_cache.pop(deleted_file)\n\n index = self.db.serialize_to_bytes()\n with open(\n os.path.join(get_cache_path(), f\"{self.name}.faiss.bytes\"), \"wb\"\n ) as binary_file:\n binary_file.write(index)", + "output": "\"\"\"\n Synchronizes the documents in the vector store with the provided files.\n\n This method checks if the documents in the vector store are up-to-date with the provided files.\n If a document has been modified, it deletes the old vectors and adds new vectors.\n If a document is new, it adds the document to the vector store.\n It also removes old documents that are no longer present in the provided files.\n\n Args:\n files (list[str]): List of file paths to synchronize with the vector store.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Exports the dataset based on the specified format.\n\n This method checks the format of the dataset and calls the appropriate export method.\n It also prints messages indicating the progress and completion of the export process.\n\n Supported formats:\n - CONVERSATIONAL: Exports to conversational_dataset.json\n - ALPACA: Exports to alpaca_dataset.json\n - INSTRUCTION: Exports to instruction_dataset.json\n \"\"\"", + "output": "def export(self):\n \n print(\"Exporting dataset...\")\n if self.format == DatasetFormat.CONVERSATIONAL.value:\n self.export_conversational()\n print(\"Dataset exported to conversational_dataset.json\")\n elif self.format == DatasetFormat.ALPACA.value:\n self.export_alpaca()\n print(\"Dataset exported to alpaca_dataset.json\")\n elif self.format == DatasetFormat.INSTRUCTION.value:\n self.export_instruction()\n print(\"Dataset exported to instruction_dataset.json\")" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def export(self):\n \n print(\"Exporting dataset...\")\n if self.format == DatasetFormat.CONVERSATIONAL.value:\n self.export_conversational()\n print(\"Dataset exported to conversational_dataset.json\")\n elif self.format == DatasetFormat.ALPACA.value:\n self.export_alpaca()\n print(\"Dataset exported to alpaca_dataset.json\")\n elif self.format == DatasetFormat.INSTRUCTION.value:\n self.export_instruction()\n print(\"Dataset exported to instruction_dataset.json\")", + "output": "\"\"\"\n Exports the dataset based on the specified format.\n\n This method checks the format of the dataset and calls the appropriate export method.\n It also prints messages indicating the progress and completion of the export process.\n\n Supported formats:\n - CONVERSATIONAL: Exports to conversational_dataset.json\n - ALPACA: Exports to alpaca_dataset.json\n - INSTRUCTION: Exports to instruction_dataset.json\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Exports the code snippets in a conversational format.\n\n This method processes each code snippet in the dataset and creates conversational messages\n for both implementation and explanation tasks. The messages are then saved to a JSON file.\n \"\"\"", + "output": "def export_conversational(self):\n \n messages_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if self.doc_distillation:\n docstring = self.distill_docstring(code_snippet)\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n if self.code_distillation:\n pass\n\n message = {\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write an implementation for the following description.\",\n },\n {\"role\": \"user\", \"content\": docstring},\n {\"role\": \"assistant\", \"content\": code_snippet.get(\"code\")},\n ]\n }\n\n messages_list.append(message)\n message = {\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Explain the following code.\",\n },\n {\"role\": \"user\", \"content\": code_snippet.get(\"code\")},\n {\n \"role\": \"assistant\",\n \"content\": docstring,\n },\n ]\n }\n\n messages_list.append(message)\n\n with open(\"conversational_dataset.json\", \"w\") as f:\n for messages in messages_list:\n json.dump(messages, f)\n f.write(\"\\n\")" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def export_conversational(self):\n \n messages_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if self.doc_distillation:\n docstring = self.distill_docstring(code_snippet)\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n if self.code_distillation:\n pass\n\n message = {\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write an implementation for the following description.\",\n },\n {\"role\": \"user\", \"content\": docstring},\n {\"role\": \"assistant\", \"content\": code_snippet.get(\"code\")},\n ]\n }\n\n messages_list.append(message)\n message = {\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Explain the following code.\",\n },\n {\"role\": \"user\", \"content\": code_snippet.get(\"code\")},\n {\n \"role\": \"assistant\",\n \"content\": docstring,\n },\n ]\n }\n\n messages_list.append(message)\n\n with open(\"conversational_dataset.json\", \"w\") as f:\n for messages in messages_list:\n json.dump(messages, f)\n f.write(\"\\n\")", + "output": "\"\"\"\n Exports the code snippets in a conversational format.\n\n This method processes each code snippet in the dataset and creates conversational messages\n for both implementation and explanation tasks. The messages are then saved to a JSON file.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Exports the code snippets in an Alpaca format.\n\n This method processes each code snippet in the dataset and creates Alpaca entries\n for both implementation and explanation tasks. The entries are then saved to a JSON file.\n \"\"\"", + "output": "def export_alpaca(self):\n \n alpaca_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if self.doc_distillation:\n docstring = self.distill_docstring(code_snippet)\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n if self.code_distillation:\n pass\n\n alpaca_entry = {\n \"instruction\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write an implementation for the following description.\",\n \"input\": docstring,\n \"output\": code_snippet.get(\"code\"),\n }\n alpaca_list.append(alpaca_entry)\n alpaca_entry = {\n \"instruction\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Explain the following code.\",\n \"input\": code_snippet.get(\"code\"),\n \"output\": docstring,\n }\n alpaca_list.append(alpaca_entry)\n\n with open(\"alpaca_dataset.json\", \"w\") as f:\n json.dump(alpaca_list, f, indent=4)" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def export_alpaca(self):\n \n alpaca_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if self.doc_distillation:\n docstring = self.distill_docstring(code_snippet)\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n if self.code_distillation:\n pass\n\n alpaca_entry = {\n \"instruction\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write an implementation for the following description.\",\n \"input\": docstring,\n \"output\": code_snippet.get(\"code\"),\n }\n alpaca_list.append(alpaca_entry)\n alpaca_entry = {\n \"instruction\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Explain the following code.\",\n \"input\": code_snippet.get(\"code\"),\n \"output\": docstring,\n }\n alpaca_list.append(alpaca_entry)\n\n with open(\"alpaca_dataset.json\", \"w\") as f:\n json.dump(alpaca_list, f, indent=4)", + "output": "\"\"\"\n Exports the code snippets in an Alpaca format.\n\n This method processes each code snippet in the dataset and creates Alpaca entries\n for both implementation and explanation tasks. The entries are then saved to a JSON file.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Exports the code snippets in an instruction format.\n\n This method processes each code snippet in the dataset and creates instruction entries\n for both implementation and explanation tasks. The entries are then saved to a JSON file.\n \"\"\"", + "output": "def export_instruction(self):\n \n instructions_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if self.doc_distillation:\n result = self.distill_docstring(code_snippet)\n if type(result) is str:\n docstring = result\n else:\n continue\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n instruction = {\n \"prompt\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write an implementation for the following description:\\n\"\n + (docstring or \"\"),\n \"completion\": code_snippet.get(\"code\"),\n }\n instructions_list.append(instruction)\n instruction = {\n \"prompt\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Explain the following code:\\n\"\n + (code_snippet.get(\"code\") or \"\"),\n \"completion\": docstring,\n }\n instructions_list.append(instruction)\n\n with open(\"instruction_dataset.json\", \"w\") as f:\n json.dump(instructions_list, f, indent=4)" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def export_instruction(self):\n \n instructions_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if self.doc_distillation:\n result = self.distill_docstring(code_snippet)\n if type(result) is str:\n docstring = result\n else:\n continue\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n instruction = {\n \"prompt\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write an implementation for the following description:\\n\"\n + (docstring or \"\"),\n \"completion\": code_snippet.get(\"code\"),\n }\n instructions_list.append(instruction)\n instruction = {\n \"prompt\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Explain the following code:\\n\"\n + (code_snippet.get(\"code\") or \"\"),\n \"completion\": docstring,\n }\n instructions_list.append(instruction)\n\n with open(\"instruction_dataset.json\", \"w\") as f:\n json.dump(instructions_list, f, indent=4)", + "output": "\"\"\"\n Exports the code snippets in an instruction format.\n\n This method processes each code snippet in the dataset and creates instruction entries\n for both implementation and explanation tasks. The entries are then saved to a JSON file.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Exports the code snippets in a completion format.\n\n This method processes each code snippet in the dataset and creates completion entries.\n The entries are then saved to a JSON file.\n \"\"\"", + "output": "def export_completion(self):\n \n completions_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if self.doc_distillation:\n docstring = self.distill_docstring(code_snippet)\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n completion = {\n \"input\": docstring,\n \"output\": code_snippet.get(\"code\"),\n }\n completions_list.append(completion)\n\n with open(\"completion_dataset.json\", \"w\") as f:\n json.dump(completions_list, f, indent=4)" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def export_completion(self):\n \n completions_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if self.doc_distillation:\n docstring = self.distill_docstring(code_snippet)\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n completion = {\n \"input\": docstring,\n \"output\": code_snippet.get(\"code\"),\n }\n completions_list.append(completion)\n\n with open(\"completion_dataset.json\", \"w\") as f:\n json.dump(completions_list, f, indent=4)", + "output": "\"\"\"\n Exports the code snippets in a completion format.\n\n This method processes each code snippet in the dataset and creates completion entries.\n The entries are then saved to a JSON file.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Distills a concise description from the given code snippet.\n\n Args:\n code_snippet (dict): A dictionary containing details about the code snippet, such as method name, programming language, and the actual code.\n\n Returns:\n str: A concise description of the code snippet.\n \"\"\"", + "output": "def distill_docstring(self, code_snippet):\n \n spinner = yaspin(\n text=f\"Distilling {code_snippet.get('method_name')}...\",\n color=\"green\",\n )\n spinner.start()\n prompt = (\n \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write a short and concise description for the following code. Return only the description.\"\n )\n docstring = self.llm.chat_model.invoke(\n [\n (\"system\", prompt),\n (\"human\", code_snippet.get(\"code\") or \"\"),\n ]\n )\n spinner.stop()\n return docstring.content" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def distill_docstring(self, code_snippet):\n \n spinner = yaspin(\n text=f\"Distilling {code_snippet.get('method_name')}...\",\n color=\"green\",\n )\n spinner.start()\n prompt = (\n \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write a short and concise description for the following code. Return only the description.\"\n )\n docstring = self.llm.chat_model.invoke(\n [\n (\"system\", prompt),\n (\"human\", code_snippet.get(\"code\") or \"\"),\n ]\n )\n spinner.stop()\n return docstring.content", + "output": "\"\"\"\n Distills a concise description from the given code snippet.\n\n Args:\n code_snippet (dict): A dictionary containing details about the code snippet, such as method name, programming language, and the actual code.\n\n Returns:\n str: A concise description of the code snippet.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Distills a given code snippet into smaller chunks and provides explanations for each chunk.\n\n Args:\n code_snippet (dict): A dictionary containing details about the code snippet, such as method name, programming language, and the actual code.\n\n Returns:\n dict: A dictionary containing the distilled code chunks and their explanations.\n \"\"\"", + "output": "def distill_code(self, code_snippet):\n \n spinner = yaspin(\n text=f\"Distilling {code_snippet.get('method_name')}...\",\n color=\"green\",\n )\n spinner.start()\n prompt = (\n \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Split the following code into reasonable chunks and explain each chunk. \"\n + \"Return a JSON object with a list of objects containing the code chunk with key 'code' and the explanation with key 'explanation'.\"\n )\n code = self.llm.chat_model.invoke(\n [\n (\"system\", prompt),\n (\"human\", code_snippet.get(\"code\") or \"\"),\n ]\n )\n spinner.stop()\n\n try:\n # Ensure the content is a string before parsing\n if isinstance(code.content, str):\n # Parse the output to a JSON object\n code_json = json.loads(code.content)\n else:\n raise ValueError(\"Content is not a valid JSON string\")\n except (json.JSONDecodeError, ValueError) as e:\n # Handle JSON parsing error\n print(f\"Error parsing response JSON: {e}\")\n return {}\n\n return code_json" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def distill_code(self, code_snippet):\n \n spinner = yaspin(\n text=f\"Distilling {code_snippet.get('method_name')}...\",\n color=\"green\",\n )\n spinner.start()\n prompt = (\n \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Split the following code into reasonable chunks and explain each chunk. \"\n + \"Return a JSON object with a list of objects containing the code chunk with key 'code' and the explanation with key 'explanation'.\"\n )\n code = self.llm.chat_model.invoke(\n [\n (\"system\", prompt),\n (\"human\", code_snippet.get(\"code\") or \"\"),\n ]\n )\n spinner.stop()\n\n try:\n # Ensure the content is a string before parsing\n if isinstance(code.content, str):\n # Parse the output to a JSON object\n code_json = json.loads(code.content)\n else:\n raise ValueError(\"Content is not a valid JSON string\")\n except (json.JSONDecodeError, ValueError) as e:\n # Handle JSON parsing error\n print(f\"Error parsing response JSON: {e}\")\n return {}\n\n return code_json", + "output": "\"\"\"\n Distills a given code snippet into smaller chunks and provides explanations for each chunk.\n\n Args:\n code_snippet (dict): A dictionary containing details about the code snippet, such as method name, programming language, and the actual code.\n\n Returns:\n dict: A dictionary containing the distilled code chunks and their explanations.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Loads a vector cache from a JSON file.\n\n Args:\n filename (str): The name of the file containing the vector cache.\n\n Returns:\n Dict[str, VectorCache]: A dictionary where the keys are strings and the values are VectorCache objects.\n \"\"\"", + "output": "def load_vector_cache(filename) -> Dict[str, VectorCache]:\n \n with open(\n get_cache_path() + \"/\" + filename, \"r\", encoding=\"utf-8\"\n ) as vector_cache_file:\n vector_cache_json = json.load(vector_cache_file)\n vector_cache = {}\n for key, value in vector_cache_json.items():\n vector_cache[key] = VectorCache.from_json(value)\n return vector_cache" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def load_vector_cache(filename) -> Dict[str, VectorCache]:\n \n with open(\n get_cache_path() + \"/\" + filename, \"r\", encoding=\"utf-8\"\n ) as vector_cache_file:\n vector_cache_json = json.load(vector_cache_file)\n vector_cache = {}\n for key, value in vector_cache_json.items():\n vector_cache[key] = VectorCache.from_json(value)\n return vector_cache", + "output": "\"\"\"\n Loads a vector cache from a JSON file.\n\n Args:\n filename (str): The name of the file containing the vector cache.\n\n Returns:\n Dict[str, VectorCache]: A dictionary where the keys are strings and the values are VectorCache objects.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Saves a vector cache to a JSON file.\n\n Args:\n vector_cache (Dict[str, VectorCache]): A dictionary where the keys are strings and the values are VectorCache objects.\n filename (str): The name of the file to save the vector cache to.\n \"\"\"", + "output": "def save_vector_cache(vector_cache, filename):\n \n with open(\n get_cache_path() + \"/\" + filename, \"w\", encoding=\"utf-8\"\n ) as vector_cache_file:\n json.dump(vector_cache, default=VectorCache.to_json, fp=vector_cache_file)" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def save_vector_cache(vector_cache, filename):\n \n with open(\n get_cache_path() + \"/\" + filename, \"w\", encoding=\"utf-8\"\n ) as vector_cache_file:\n json.dump(vector_cache, default=VectorCache.to_json, fp=vector_cache_file)", + "output": "\"\"\"\n Saves a vector cache to a JSON file.\n\n Args:\n vector_cache (Dict[str, VectorCache]): A dictionary where the keys are strings and the values are VectorCache objects.\n filename (str): The name of the file to save the vector cache to.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Returns the cache directory path based on the operating system.\n\n Returns:\n str: The path to the cache directory.\n\n Raises:\n NotImplementedError: If the operating system is not supported.\n \"\"\"", + "output": "def get_cache_path():\n \n system = platform.system()\n\n if system == \"Linux\" or system == \"Darwin\":\n user_home = os.path.expanduser(\"~\")\n cache_dir = os.path.join(user_home, \".cache\", \"codeqai\")\n elif system == \"Windows\":\n user_home = os.path.expanduser(\"~\")\n cache_dir = os.path.join(user_home, \"AppData\", \"Local\", \"codeqai\")\n else:\n raise NotImplementedError(f\"Unsupported platform: {system}\")\n\n return cache_dir" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def get_cache_path():\n \n system = platform.system()\n\n if system == \"Linux\" or system == \"Darwin\":\n user_home = os.path.expanduser(\"~\")\n cache_dir = os.path.join(user_home, \".cache\", \"codeqai\")\n elif system == \"Windows\":\n user_home = os.path.expanduser(\"~\")\n cache_dir = os.path.join(user_home, \"AppData\", \"Local\", \"codeqai\")\n else:\n raise NotImplementedError(f\"Unsupported platform: {system}\")\n\n return cache_dir", + "output": "\"\"\"\n Returns the cache directory path based on the operating system.\n\n Returns:\n str: The path to the cache directory.\n\n Raises:\n NotImplementedError: If the operating system is not supported.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Creates the cache directory if it does not already exist.\n\n This function checks if the cache directory exists at the path returned by get_cache_path().\n If the directory does not exist, it creates the directory and any necessary parent directories.\n \"\"\"", + "output": "def create_cache_dir():\n \n if not os.path.exists(get_cache_path()):\n path = Path(get_cache_path())\n path.mkdir(parents=True, exist_ok=True)" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def create_cache_dir():\n \n if not os.path.exists(get_cache_path()):\n path = Path(get_cache_path())\n path.mkdir(parents=True, exist_ok=True)", + "output": "\"\"\"\n Creates the cache directory if it does not already exist.\n\n This function checks if the cache directory exists at the path returned by get_cache_path().\n If the directory does not exist, it creates the directory and any necessary parent directories.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Initializes the LLM class with the specified parameters.\n\n Args:\n llm_host (LlmHost): The host for the language model (e.g., OPENAI, AZURE_OPENAI, ANTHROPIC, LLAMACPP, OLLAMA).\n chat_model (str): The chat model to use.\n max_tokens (int, optional): The maximum number of tokens for the model. Defaults to 2048.\n deployment (str, optional): The deployment name for Azure OpenAI. Defaults to None.\n\n Raises:\n ValueError: If the required environment variable for Azure OpenAI is not set.\n \"\"\"", + "output": "def __init__(\n self, llm_host: LlmHost, chat_model: str, max_tokens=2048, deployment=None\n ):\n \n if llm_host == LlmHost.OPENAI:\n self.chat_model = ChatOpenAI(\n temperature=0.9, max_tokens=max_tokens, model=chat_model\n )\n elif llm_host == LlmHost.AZURE_OPENAI and deployment:\n azure_openai_endpoint = os.getenv(\"AZURE_OPENAI_ENDPOINT\")\n if azure_openai_endpoint:\n self.chat_model = AzureChatOpenAI(\n azure_endpoint=azure_openai_endpoint,\n temperature=0.9,\n max_tokens=max_tokens,\n model=chat_model,\n )\n else:\n raise ValueError(\n \"Azure OpenAI requires environment variable AZURE_OPENAI_ENDPOINT to be set.\"\n )\n elif llm_host == LlmHost.ANTHROPIC:\n self.chat_model = ChatAnthropic(\n temperature=0.9,\n max_tokens_to_sample=max_tokens,\n model_name=chat_model,\n timeout=30,\n api_key=None, # API key is set to environment variable ANTHROPIC_API_KEY\n )\n elif llm_host == LlmHost.LLAMACPP:\n self.install_llama_cpp()\n self.chat_model = LlamaCpp(\n model_path=chat_model,\n temperature=0.9,\n max_tokens=max_tokens,\n verbose=False,\n )\n elif llm_host == LlmHost.OLLAMA:\n self.chat_model = Ollama(\n base_url=\"http://localhost:11434\",\n model=chat_model,\n callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n )" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def __init__(\n self, llm_host: LlmHost, chat_model: str, max_tokens=2048, deployment=None\n ):\n \n if llm_host == LlmHost.OPENAI:\n self.chat_model = ChatOpenAI(\n temperature=0.9, max_tokens=max_tokens, model=chat_model\n )\n elif llm_host == LlmHost.AZURE_OPENAI and deployment:\n azure_openai_endpoint = os.getenv(\"AZURE_OPENAI_ENDPOINT\")\n if azure_openai_endpoint:\n self.chat_model = AzureChatOpenAI(\n azure_endpoint=azure_openai_endpoint,\n temperature=0.9,\n max_tokens=max_tokens,\n model=chat_model,\n )\n else:\n raise ValueError(\n \"Azure OpenAI requires environment variable AZURE_OPENAI_ENDPOINT to be set.\"\n )\n elif llm_host == LlmHost.ANTHROPIC:\n self.chat_model = ChatAnthropic(\n temperature=0.9,\n max_tokens_to_sample=max_tokens,\n model_name=chat_model,\n timeout=30,\n api_key=None, # API key is set to environment variable ANTHROPIC_API_KEY\n )\n elif llm_host == LlmHost.LLAMACPP:\n self.install_llama_cpp()\n self.chat_model = LlamaCpp(\n model_path=chat_model,\n temperature=0.9,\n max_tokens=max_tokens,\n verbose=False,\n )\n elif llm_host == LlmHost.OLLAMA:\n self.chat_model = Ollama(\n base_url=\"http://localhost:11434\",\n model=chat_model,\n callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n )", + "output": "\"\"\"\n Initializes the LLM class with the specified parameters.\n\n Args:\n llm_host (LlmHost): The host for the language model (e.g., OPENAI, AZURE_OPENAI, ANTHROPIC, LLAMACPP, OLLAMA).\n chat_model (str): The chat model to use.\n max_tokens (int, optional): The maximum number of tokens for the model. Defaults to 2048.\n deployment (str, optional): The deployment name for Azure OpenAI. Defaults to None.\n\n Raises:\n ValueError: If the required environment variable for Azure OpenAI is not set.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Initializes the Embeddings class with the specified model and deployment.\n\n Args:\n model (EmbeddingsModel): The embeddings model to use. Defaults to OPENAI_TEXT_EMBEDDING_ADA_002.\n deployment (str, optional): The deployment name for Azure OpenAI embeddings. Defaults to None.\n \"\"\"", + "output": "def __init__(\n self,\n model=EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002,\n deployment=None,\n ):\n \n if model == EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002:\n self.embeddings = OpenAIEmbeddings(\n client=None, model=\"text-embedding-ada-002\"\n )\n elif model == EmbeddingsModel.AZURE_OPENAI and deployment:\n self.embeddings = AzureOpenAIEmbeddings(\n client=None, azure_deployment=deployment\n )\n else:\n try:\n import sentence_transformers # noqa: F401\n except ImportError:\n self._install_sentence_transformers()\n\n if model == EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MPNET_BASE_V2:\n self.embeddings = HuggingFaceEmbeddings()\n elif model == EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MINILM_L6_V2:\n self.embeddings = HuggingFaceEmbeddings(\n model_name=EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MINILM_L6_V2.value.replace(\n \"SentenceTransformers-\", \"\"\n )\n )\n elif model == EmbeddingsModel.INSTRUCTOR_LARGE:\n try:\n from InstructorEmbedding import INSTRUCTOR # noqa: F401\n except ImportError:\n self._install_instructor_embedding()\n\n self.embeddings = HuggingFaceEmbeddings(\n model_name=\"hkunlp/instructor-xl\"\n )" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def __init__(\n self,\n model=EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002,\n deployment=None,\n ):\n \n if model == EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002:\n self.embeddings = OpenAIEmbeddings(\n client=None, model=\"text-embedding-ada-002\"\n )\n elif model == EmbeddingsModel.AZURE_OPENAI and deployment:\n self.embeddings = AzureOpenAIEmbeddings(\n client=None, azure_deployment=deployment\n )\n else:\n try:\n import sentence_transformers # noqa: F401\n except ImportError:\n self._install_sentence_transformers()\n\n if model == EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MPNET_BASE_V2:\n self.embeddings = HuggingFaceEmbeddings()\n elif model == EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MINILM_L6_V2:\n self.embeddings = HuggingFaceEmbeddings(\n model_name=EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MINILM_L6_V2.value.replace(\n \"SentenceTransformers-\", \"\"\n )\n )\n elif model == EmbeddingsModel.INSTRUCTOR_LARGE:\n try:\n from InstructorEmbedding import INSTRUCTOR # noqa: F401\n except ImportError:\n self._install_instructor_embedding()\n\n self.embeddings = HuggingFaceEmbeddings(\n model_name=\"hkunlp/instructor-xl\"\n )", + "output": "\"\"\"\n Initializes the Embeddings class with the specified model and deployment.\n\n Args:\n model (EmbeddingsModel): The embeddings model to use. Defaults to OPENAI_TEXT_EMBEDDING_ADA_002.\n deployment (str, optional): The deployment name for Azure OpenAI embeddings. Defaults to None.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Returns the programming language based on the provided file extension.\n\n Args:\n file_extension (str): The file extension to determine the programming language of.\n\n Returns:\n Language: The programming language corresponding to the file extension. If the file extension is not found\n in the language mapping, returns Language.UNKNOWN.\n \"\"\"", + "output": "def get_programming_language(file_extension: str) -> Language:\n \n language_mapping = {\n \".py\": Language.PYTHON,\n \".js\": Language.JAVASCRIPT,\n \".jsx\": Language.JAVASCRIPT,\n \".mjs\": Language.JAVASCRIPT,\n \".cjs\": Language.JAVASCRIPT,\n \".ts\": Language.TYPESCRIPT,\n \".tsx\": Language.TYPESCRIPT,\n \".java\": Language.JAVA,\n \".kt\": Language.KOTLIN,\n \".rs\": Language.RUST,\n \".go\": Language.GO,\n \".cpp\": Language.CPP,\n \".c\": Language.C,\n \".cs\": Language.C_SHARP,\n \".hs\": Language.HASKELL,\n \".rb\": Language.RUBY,\n }\n return language_mapping.get(file_extension, Language.UNKNOWN)" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def get_programming_language(file_extension: str) -> Language:\n \n language_mapping = {\n \".py\": Language.PYTHON,\n \".js\": Language.JAVASCRIPT,\n \".jsx\": Language.JAVASCRIPT,\n \".mjs\": Language.JAVASCRIPT,\n \".cjs\": Language.JAVASCRIPT,\n \".ts\": Language.TYPESCRIPT,\n \".tsx\": Language.TYPESCRIPT,\n \".java\": Language.JAVA,\n \".kt\": Language.KOTLIN,\n \".rs\": Language.RUST,\n \".go\": Language.GO,\n \".cpp\": Language.CPP,\n \".c\": Language.C,\n \".cs\": Language.C_SHARP,\n \".hs\": Language.HASKELL,\n \".rb\": Language.RUBY,\n }\n return language_mapping.get(file_extension, Language.UNKNOWN)", + "output": "\"\"\"\n Returns the programming language based on the provided file extension.\n\n Args:\n file_extension (str): The file extension to determine the programming language of.\n\n Returns:\n Language: The programming language corresponding to the file extension. If the file extension is not found\n in the language mapping, returns Language.UNKNOWN.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Returns the extension of a file from its given name.\n\n Parameters:\n file_name (str): The name of the file.\n\n Returns:\n str: The extension of the file.\n\n \"\"\"", + "output": "def get_file_extension(file_name: str) -> str:\n \n return os.path.splitext(file_name)[-1]" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def get_file_extension(file_name: str) -> str:\n \n return os.path.splitext(file_name)[-1]", + "output": "\"\"\"\n Returns the extension of a file from its given name.\n\n Parameters:\n file_name (str): The name of the file.\n\n Returns:\n str: The extension of the file.\n\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Returns the given text formatted in bold.\n\n Args:\n text (str): The text to be formatted.\n\n Returns:\n str: The text formatted in bold using ANSI escape codes.\n \"\"\"", + "output": "def get_bold_text(text):\n \n return f\"\\033[01m{text}\\033[0m\"" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def get_bold_text(text):\n \n return f\"\\033[01m{text}\\033[0m\"", + "output": "\"\"\"\n Returns the given text formatted in bold.\n\n Args:\n text (str): The text to be formatted.\n\n Returns:\n str: The text formatted in bold using ANSI escape codes.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Finds the starting line number and indentation level of a code snippet within a file.\n\n Args:\n filename (str): The name of the file to search within.\n code_snippet (str): The code snippet to find in the file.\n\n Returns:\n tuple: A tuple containing the starting line number (int) and the indentation level (str) of the code snippet.\n If the file is not found or the code snippet is not found, returns (1, \"\").\n \"\"\"", + "output": "def find_starting_line_and_indent(filename, code_snippet):\n \n file_path = find_file_in_git_repo(filename)\n if file_path is not None:\n with open(file_path, \"r\") as file:\n file_content = file.read()\n start_pos = file_content.find(code_snippet)\n return (\n file_content.count(\"\\n\", 0, start_pos) + 1,\n file_content[:start_pos].split(\"\\n\")[-1],\n )\n return 1, \"\"" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def find_starting_line_and_indent(filename, code_snippet):\n \n file_path = find_file_in_git_repo(filename)\n if file_path is not None:\n with open(file_path, \"r\") as file:\n file_content = file.read()\n start_pos = file_content.find(code_snippet)\n return (\n file_content.count(\"\\n\", 0, start_pos) + 1,\n file_content[:start_pos].split(\"\\n\")[-1],\n )\n return 1, \"\"", + "output": "\"\"\"\n Finds the starting line number and indentation level of a code snippet within a file.\n\n Args:\n filename (str): The name of the file to search within.\n code_snippet (str): The code snippet to find in the file.\n\n Returns:\n tuple: A tuple containing the starting line number (int) and the indentation level (str) of the code snippet.\n If the file is not found or the code snippet is not found, returns (1, \"\").\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Counts the number of tokens in the given text using the specified model's tokenizer.\n\n Args:\n text (str): The text to be tokenized and counted.\n model (str, optional): The model to use for tokenization. Defaults to \"gpt-4\".\n\n Returns:\n int: The number of tokens in the text.\n \"\"\"", + "output": "def count_tokens(text, model=\"gpt-4\"):\n \n enc = tiktoken.encoding_for_model(model)\n return len(enc.encode(text))" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def count_tokens(text, model=\"gpt-4\"):\n \n enc = tiktoken.encoding_for_model(model)\n return len(enc.encode(text))", + "output": "\"\"\"\n Counts the number of tokens in the given text using the specified model's tokenizer.\n\n Args:\n text (str): The text to be tokenized and counted.\n model (str, optional): The model to use for tokenization. Defaults to \"gpt-4\".\n\n Returns:\n int: The number of tokens in the text.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Retrieves the name of the current Git repository.\n\n This function gets the root directory of the current Git repository based on the current working directory,\n and extracts the repository name from the root directory path.\n\n Returns:\n str: The name of the current Git repository.\n \"\"\"", + "output": "def repo_name():\n \n return get_git_root(os.getcwd()).split(\"/\")[-1]" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def repo_name():\n \n return get_git_root(os.getcwd()).split(\"/\")[-1]", + "output": "\"\"\"\n Retrieves the name of the current Git repository.\n\n This function gets the root directory of the current Git repository based on the current working directory,\n and extracts the repository name from the root directory path.\n\n Returns:\n str: The name of the current Git repository.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Retrieves the root directory of the Git repository for the given path.\n\n Args:\n path (str): The path to a directory within the Git repository.\n\n Returns:\n str: The root directory of the Git repository.\n \"\"\"", + "output": "def get_git_root(path):\n \n git_repo = Repo(path, search_parent_directories=True)\n git_root = git_repo.git.rev_parse(\"--show-toplevel\")\n return git_root" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def get_git_root(path):\n \n git_repo = Repo(path, search_parent_directories=True)\n git_root = git_repo.git.rev_parse(\"--show-toplevel\")\n return git_root", + "output": "\"\"\"\n Retrieves the root directory of the Git repository for the given path.\n\n Args:\n path (str): The path to a directory within the Git repository.\n\n Returns:\n str: The root directory of the Git repository.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Searches for a file with the given name in the current Git repository.\n\n Args:\n file_name (str): The name of the file to search for.\n\n Returns:\n str or None: The full path to the file if found, otherwise None.\n \"\"\"", + "output": "def find_file_in_git_repo(file_name):\n \n git_root = get_git_root(os.getcwd())\n\n for root, dirs, files in os.walk(git_root):\n if any(blacklist in root for blacklist in BLACKLIST_DIR):\n continue\n for file in files:\n if file == file_name:\n return os.path.join(root, file)" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def find_file_in_git_repo(file_name):\n \n git_root = get_git_root(os.getcwd())\n\n for root, dirs, files in os.walk(git_root):\n if any(blacklist in root for blacklist in BLACKLIST_DIR):\n continue\n for file in files:\n if file == file_name:\n return os.path.join(root, file)", + "output": "\"\"\"\n Searches for a file with the given name in the current Git repository.\n\n Args:\n file_name (str): The name of the file to search for.\n\n Returns:\n str or None: The full path to the file if found, otherwise None.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Loads files from the current Git repository based on whitelist and blacklist criteria.\n\n This function walks through the directory structure of the Git repository,\n and collects files that match the whitelist extensions and are not in the blacklist directories or files.\n\n Returns:\n list: A list of file paths that meet the criteria.\n \"\"\"", + "output": "def load_files():\n \n git_root = get_git_root(os.getcwd())\n file_list = []\n\n for root, dirs, files in os.walk(git_root):\n if any(blacklist in root for blacklist in BLACKLIST_DIR):\n continue\n for file in files:\n file_ext = os.path.splitext(file)[1]\n if any(whitelist == file_ext for whitelist in WHITELIST_FILES):\n if file not in BLACKLIST_FILES:\n file_list.append(os.path.join(root, file))\n\n return file_list" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def load_files():\n \n git_root = get_git_root(os.getcwd())\n file_list = []\n\n for root, dirs, files in os.walk(git_root):\n if any(blacklist in root for blacklist in BLACKLIST_DIR):\n continue\n for file in files:\n file_ext = os.path.splitext(file)[1]\n if any(whitelist == file_ext for whitelist in WHITELIST_FILES):\n if file not in BLACKLIST_FILES:\n file_list.append(os.path.join(root, file))\n\n return file_list", + "output": "\"\"\"\n Loads files from the current Git repository based on whitelist and blacklist criteria.\n\n This function walks through the directory structure of the Git repository,\n and collects files that match the whitelist extensions and are not in the blacklist directories or files.\n\n Returns:\n list: A list of file paths that meet the criteria.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Retrieves the latest commit hash for the specified file.\n\n Args:\n file_path (str): The path to the file for which to retrieve the commit hash.\n\n Returns:\n str or None: The latest commit hash if found, otherwise None.\n \"\"\"", + "output": "def get_commit_hash(file_path):\n \n try:\n # Run the git log command\n result = subprocess.run(\n [\"git\", \"log\", \"-n\", \"1\", \"--pretty=format:%H\", \"--\", file_path],\n stdout=subprocess.PIPE,\n text=True,\n check=True,\n )\n\n # Extract the commit hash from the command output\n commit_hash = result.stdout.strip()\n return commit_hash\n\n except subprocess.CalledProcessError as e:\n print(f\"Error executing git command: {e}\")\n return None" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def get_commit_hash(file_path):\n \n try:\n # Run the git log command\n result = subprocess.run(\n [\"git\", \"log\", \"-n\", \"1\", \"--pretty=format:%H\", \"--\", file_path],\n stdout=subprocess.PIPE,\n text=True,\n check=True,\n )\n\n # Extract the commit hash from the command output\n commit_hash = result.stdout.strip()\n return commit_hash\n\n except subprocess.CalledProcessError as e:\n print(f\"Error executing git command: {e}\")\n return None", + "output": "\"\"\"\n Retrieves the latest commit hash for the specified file.\n\n Args:\n file_path (str): The path to the file for which to retrieve the commit hash.\n\n Returns:\n str or None: The latest commit hash if found, otherwise None.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Args :\n env_path = source path of .env file.\n required_keys = [\"OPENAI_KEY\"] #change this according to need\n\n #running/calling the function.\n configs = env_loader('.env', required_keys)\n \"\"\"", + "output": "def env_loader(env_path, required_keys=None):\n \n\n # create env file if does not exists\n # parse required keys in the file if it's not None\n if not os.path.exists(env_path) or os.path.getsize(env_path) == 0:\n with open(env_path, \"w\") as env_f:\n if required_keys:\n for key in required_keys:\n env_f.write(f'{key}=\"\"\\n')\n else:\n pass\n\n configs = dotenv_values(env_path)\n changed = False\n for key, value in configs.items():\n env_key = os.getenv(key)\n if not value and not env_key:\n value = input(\n f\"[+] Key {utils.get_bold_text(key)} is required. Please enter it's value: \"\n )\n configs[key] = value\n changed = True\n elif not value and env_key:\n value = env_key\n configs[key] = value\n changed = True\n\n # update the .env file if config is changed/taken from user\n if changed:\n with open(env_path, \"w\") as env_f:\n for key, value in configs.items():\n env_f.write(f'{key}=\"{value}\"\\n')\n\n load_dotenv(env_path, override=True)" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def env_loader(env_path, required_keys=None):\n \n\n # create env file if does not exists\n # parse required keys in the file if it's not None\n if not os.path.exists(env_path) or os.path.getsize(env_path) == 0:\n with open(env_path, \"w\") as env_f:\n if required_keys:\n for key in required_keys:\n env_f.write(f'{key}=\"\"\\n')\n else:\n pass\n\n configs = dotenv_values(env_path)\n changed = False\n for key, value in configs.items():\n env_key = os.getenv(key)\n if not value and not env_key:\n value = input(\n f\"[+] Key {utils.get_bold_text(key)} is required. Please enter it's value: \"\n )\n configs[key] = value\n changed = True\n elif not value and env_key:\n value = env_key\n configs[key] = value\n changed = True\n\n # update the .env file if config is changed/taken from user\n if changed:\n with open(env_path, \"w\") as env_f:\n for key, value in configs.items():\n env_f.write(f'{key}=\"{value}\"\\n')\n\n load_dotenv(env_path, override=True)", + "output": "\"\"\"\n Args :\n env_path = source path of .env file.\n required_keys = [\"OPENAI_KEY\"] #change this according to need\n\n #running/calling the function.\n configs = env_loader('.env', required_keys)\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Parses a list of code files and returns a list of Document objects for database storage.\n\n Args:\n code_files (list[str]): List of paths to code files to be parsed.\n\n Returns:\n list[Document]: List of Document objects containing parsed code information.\n \"\"\"", + "output": "def parse_code_files_for_db(code_files: list[str]) -> list[Document]:\n \n documents = []\n code_splitter = None\n for code_file in code_files:\n with open(code_file, \"r\", encoding=\"utf-8\") as file:\n file_bytes = file.read().encode()\n commit_hash = repo.get_commit_hash(code_file)\n\n file_extension = utils.get_file_extension(code_file)\n programming_language = utils.get_programming_language(file_extension)\n if programming_language == Language.UNKNOWN:\n continue\n\n langchain_language = utils.get_langchain_language(programming_language)\n\n if langchain_language:\n code_splitter = RecursiveCharacterTextSplitter.from_language(\n language=langchain_language,\n chunk_size=512,\n chunk_overlap=128,\n )\n\n treesitter_parser = Treesitter.create_treesitter(programming_language)\n treesitterNodes: list[TreesitterMethodNode] = treesitter_parser.parse(\n file_bytes\n )\n for node in treesitterNodes:\n method_source_code = node.method_source_code\n filename = os.path.basename(code_file)\n\n if node.doc_comment and programming_language != Language.PYTHON:\n method_source_code = node.doc_comment + \"\\n\" + method_source_code\n\n splitted_documents = [method_source_code]\n if code_splitter:\n splitted_documents = code_splitter.split_text(method_source_code)\n\n for splitted_document in splitted_documents:\n document = Document(\n page_content=splitted_document,\n metadata={\n \"filename\": filename,\n \"method_name\": node.name,\n \"commit_hash\": commit_hash,\n },\n )\n documents.append(document)\n\n return documents" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def parse_code_files_for_db(code_files: list[str]) -> list[Document]:\n \n documents = []\n code_splitter = None\n for code_file in code_files:\n with open(code_file, \"r\", encoding=\"utf-8\") as file:\n file_bytes = file.read().encode()\n commit_hash = repo.get_commit_hash(code_file)\n\n file_extension = utils.get_file_extension(code_file)\n programming_language = utils.get_programming_language(file_extension)\n if programming_language == Language.UNKNOWN:\n continue\n\n langchain_language = utils.get_langchain_language(programming_language)\n\n if langchain_language:\n code_splitter = RecursiveCharacterTextSplitter.from_language(\n language=langchain_language,\n chunk_size=512,\n chunk_overlap=128,\n )\n\n treesitter_parser = Treesitter.create_treesitter(programming_language)\n treesitterNodes: list[TreesitterMethodNode] = treesitter_parser.parse(\n file_bytes\n )\n for node in treesitterNodes:\n method_source_code = node.method_source_code\n filename = os.path.basename(code_file)\n\n if node.doc_comment and programming_language != Language.PYTHON:\n method_source_code = node.doc_comment + \"\\n\" + method_source_code\n\n splitted_documents = [method_source_code]\n if code_splitter:\n splitted_documents = code_splitter.split_text(method_source_code)\n\n for splitted_document in splitted_documents:\n document = Document(\n page_content=splitted_document,\n metadata={\n \"filename\": filename,\n \"method_name\": node.name,\n \"commit_hash\": commit_hash,\n },\n )\n documents.append(document)\n\n return documents", + "output": "\"\"\"\n Parses a list of code files and returns a list of Document objects for database storage.\n\n Args:\n code_files (list[str]): List of paths to code files to be parsed.\n\n Returns:\n list[Document]: List of Document objects containing parsed code information.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Parses a list of code files for fine-tuning and returns a list of dictionaries containing method information.\n\n Args:\n code_files (list[str]): List of paths to code files to be parsed.\n max_tokens (int): Maximum number of tokens allowed for output.\n\n Returns:\n list[dict]: List of dictionaries containing method information, including method name, code, description, and language.\n \"\"\"", + "output": "def parse_code_files_for_finetuning(\n code_files: list[str], max_tokens, spinner\n) -> list[dict]:\n \n input_tokens = 0\n output_tokens = 0\n documents = []\n for code_file in code_files:\n with open(code_file, \"r\", encoding=\"utf-8\") as file:\n file_bytes = file.read().encode()\n\n file_extension = utils.get_file_extension(code_file)\n programming_language = utils.get_programming_language(file_extension)\n if programming_language == Language.UNKNOWN:\n continue\n\n treesitter_parser = Treesitter.create_treesitter(programming_language)\n treesitterNodes: list[TreesitterMethodNode] = treesitter_parser.parse(\n file_bytes\n )\n for node in treesitterNodes:\n method_source_code = node.method_source_code\n\n if node.doc_comment and programming_language == Language.PYTHON:\n method_source_code = method_source_code.replace(\n node.doc_comment, \"\"\n )\n\n document = {\n \"method_name\": node.name,\n \"code\": method_source_code,\n \"description\": node.doc_comment,\n \"language\": programming_language.value,\n }\n documents.append(document)\n\n if node.doc_comment is not None:\n input_tokens += utils.count_tokens(node.doc_comment)\n output_tokens += max_tokens\n\n spinner.stop()\n\n print(f\"Estimated input tokens for distillation needed: {input_tokens}.\")\n print(f\"Maximum output tokens for distillation nedeed: {output_tokens}.\")\n questions = [\n inquirer.Confirm(\n \"confirm\",\n message=\"Proceed?\",\n default=True,\n ),\n ]\n\n confirm = inquirer.prompt(questions)\n\n if confirm and confirm[\"confirm\"]:\n pass\n else:\n exit()\n\n return documents" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def parse_code_files_for_finetuning(\n code_files: list[str], max_tokens, spinner\n) -> list[dict]:\n \n input_tokens = 0\n output_tokens = 0\n documents = []\n for code_file in code_files:\n with open(code_file, \"r\", encoding=\"utf-8\") as file:\n file_bytes = file.read().encode()\n\n file_extension = utils.get_file_extension(code_file)\n programming_language = utils.get_programming_language(file_extension)\n if programming_language == Language.UNKNOWN:\n continue\n\n treesitter_parser = Treesitter.create_treesitter(programming_language)\n treesitterNodes: list[TreesitterMethodNode] = treesitter_parser.parse(\n file_bytes\n )\n for node in treesitterNodes:\n method_source_code = node.method_source_code\n\n if node.doc_comment and programming_language == Language.PYTHON:\n method_source_code = method_source_code.replace(\n node.doc_comment, \"\"\n )\n\n document = {\n \"method_name\": node.name,\n \"code\": method_source_code,\n \"description\": node.doc_comment,\n \"language\": programming_language.value,\n }\n documents.append(document)\n\n if node.doc_comment is not None:\n input_tokens += utils.count_tokens(node.doc_comment)\n output_tokens += max_tokens\n\n spinner.stop()\n\n print(f\"Estimated input tokens for distillation needed: {input_tokens}.\")\n print(f\"Maximum output tokens for distillation nedeed: {output_tokens}.\")\n questions = [\n inquirer.Confirm(\n \"confirm\",\n message=\"Proceed?\",\n default=True,\n ),\n ]\n\n confirm = inquirer.prompt(questions)\n\n if confirm and confirm[\"confirm\"]:\n pass\n else:\n exit()\n\n return documents", + "output": "\"\"\"\n Parses a list of code files for fine-tuning and returns a list of dictionaries containing method information.\n\n Args:\n code_files (list[str]): List of paths to code files to be parsed.\n max_tokens (int): Maximum number of tokens allowed for output.\n\n Returns:\n list[dict]: List of dictionaries containing method information, including method name, code, description, and language.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Parses the given file bytes and extracts method nodes.\n\n Args:\n file_bytes (bytes): The content of the file to be parsed.\n\n Returns:\n list[TreesitterMethodNode]: A list of TreesitterMethodNode objects representing the methods in the file.\n \"\"\"", + "output": "def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]:\n \n self.tree = self.parser.parse(file_bytes)\n result = []\n methods = self._query_all_methods(self.tree.root_node)\n for method in methods:\n method_name = self._query_method_name(method)\n doc_comment = self._query_doc_comment(method)\n result.append(TreesitterMethodNode(method_name, doc_comment, None, method))\n return result" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]:\n \n self.tree = self.parser.parse(file_bytes)\n result = []\n methods = self._query_all_methods(self.tree.root_node)\n for method in methods:\n method_name = self._query_method_name(method)\n doc_comment = self._query_doc_comment(method)\n result.append(TreesitterMethodNode(method_name, doc_comment, None, method))\n return result", + "output": "\"\"\"\n Parses the given file bytes and extracts method nodes.\n\n Args:\n file_bytes (bytes): The content of the file to be parsed.\n\n Returns:\n list[TreesitterMethodNode]: A list of TreesitterMethodNode objects representing the methods in the file.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\"", + "output": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None", + "output": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Queries all method nodes within the given syntax tree node, including those within class definitions.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of method nodes found within the given node.\n \"\"\"", + "output": "def _query_all_methods(self, node: tree_sitter.Node):\n \n methods = []\n for child in node.children:\n if child.type == self.method_declaration_identifier:\n methods.append(child)\n if child.type == \"class_definition\":\n class_body = child.children[-1]\n for child_node in class_body.children:\n if child_node.type == self.method_declaration_identifier:\n methods.append(child_node)\n return methods" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def _query_all_methods(self, node: tree_sitter.Node):\n \n methods = []\n for child in node.children:\n if child.type == self.method_declaration_identifier:\n methods.append(child)\n if child.type == \"class_definition\":\n class_body = child.children[-1]\n for child_node in class_body.children:\n if child_node.type == self.method_declaration_identifier:\n methods.append(child_node)\n return methods", + "output": "\"\"\"\n Queries all method nodes within the given syntax tree node, including those within class definitions.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of method nodes found within the given node.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Queries the documentation comment for the given function definition node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node representing a function definition.\n\n Returns:\n str or None: The documentation comment string if found, otherwise None.\n \"\"\"", + "output": "def _query_doc_comment(self, node: tree_sitter.Node):\n \n query_code = \"\"\"\n (function_definition\n body: (block . (expression_statement (string)) @function_doc_str))\n \"\"\"\n doc_str_query = self.language.query(query_code)\n doc_strs = doc_str_query.captures(node)\n\n if doc_strs:\n return doc_strs[0][0].text.decode()\n else:\n return None" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def _query_doc_comment(self, node: tree_sitter.Node):\n \n query_code = \"\"\"\n (function_definition\n body: (block . (expression_statement (string)) @function_doc_str))\n \"\"\"\n doc_str_query = self.language.query(query_code)\n doc_strs = doc_str_query.captures(node)\n\n if doc_strs:\n return doc_strs[0][0].text.decode()\n else:\n return None", + "output": "\"\"\"\n Queries the documentation comment for the given function definition node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node representing a function definition.\n\n Returns:\n str or None: The documentation comment string if found, otherwise None.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\"", + "output": "def _query_method_name(self, node: tree_sitter.Node):\n \n first_match = None\n if node.type == self.method_declaration_identifier:\n for child in node.children:\n # if the return type is an object type, then the method name\n # is the second match\n if child.type == self.method_name_identifier and not first_match:\n first_match = child.text.decode()\n elif child.type == self.method_name_identifier and first_match:\n return child.text.decode()\n return first_match" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def _query_method_name(self, node: tree_sitter.Node):\n \n first_match = None\n if node.type == self.method_declaration_identifier:\n for child in node.children:\n # if the return type is an object type, then the method name\n # is the second match\n if child.type == self.method_name_identifier and not first_match:\n first_match = child.text.decode()\n elif child.type == self.method_name_identifier and first_match:\n return child.text.decode()\n return first_match", + "output": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\"", + "output": "def _query_all_methods(self, node: tree_sitter.Node):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_nodes = []\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n current_doc_comment_node = node.prev_named_sibling\n while (\n current_doc_comment_node\n and current_doc_comment_node.type == self.doc_comment_identifier\n ):\n doc_comment_nodes.append(current_doc_comment_node.text.decode())\n if current_doc_comment_node.prev_named_sibling:\n current_doc_comment_node = (\n current_doc_comment_node.prev_named_sibling\n )\n else:\n current_doc_comment_node = None\n\n doc_comment_str = \"\"\n doc_comment_nodes.reverse()\n for doc_comment_node in doc_comment_nodes:\n doc_comment_str += doc_comment_node + \"\\n\"\n if doc_comment_str.strip() != \"\":\n methods.append({\"method\": node, \"doc_comment\": doc_comment_str.strip()})\n else:\n methods.append({\"method\": node, \"doc_comment\": None})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def _query_all_methods(self, node: tree_sitter.Node):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_nodes = []\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n current_doc_comment_node = node.prev_named_sibling\n while (\n current_doc_comment_node\n and current_doc_comment_node.type == self.doc_comment_identifier\n ):\n doc_comment_nodes.append(current_doc_comment_node.text.decode())\n if current_doc_comment_node.prev_named_sibling:\n current_doc_comment_node = (\n current_doc_comment_node.prev_named_sibling\n )\n else:\n current_doc_comment_node = None\n\n doc_comment_str = \"\"\n doc_comment_nodes.reverse()\n for doc_comment_node in doc_comment_nodes:\n doc_comment_str += doc_comment_node + \"\\n\"\n if doc_comment_str.strip() != \"\":\n methods.append({\"method\": node, \"doc_comment\": doc_comment_str.strip()})\n else:\n methods.append({\"method\": node, \"doc_comment\": None})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods", + "output": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\"", + "output": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n # if method returns pointer, skip pointer declarator\n if child.type == \"pointer_declarator\":\n child = child.children[1]\n if child.type == \"function_declarator\":\n for child in child.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n # if method returns pointer, skip pointer declarator\n if child.type == \"pointer_declarator\":\n child = child.children[1]\n if child.type == \"function_declarator\":\n for child in child.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None", + "output": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\"", + "output": "def _query_all_methods(self, node: tree_sitter.Node):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_nodes = []\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n current_doc_comment_node = node.prev_named_sibling\n while (\n current_doc_comment_node\n and current_doc_comment_node.type == self.doc_comment_identifier\n ):\n doc_comment_nodes.append(current_doc_comment_node.text.decode())\n if current_doc_comment_node.prev_named_sibling:\n current_doc_comment_node = (\n current_doc_comment_node.prev_named_sibling\n )\n else:\n current_doc_comment_node = None\n\n doc_comment_str = \"\"\n doc_comment_nodes.reverse()\n for doc_comment_node in doc_comment_nodes:\n doc_comment_str += doc_comment_node + \"\\n\"\n if doc_comment_str.strip() != \"\":\n methods.append({\"method\": node, \"doc_comment\": doc_comment_str.strip()})\n else:\n methods.append({\"method\": node, \"doc_comment\": None})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def _query_all_methods(self, node: tree_sitter.Node):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_nodes = []\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n current_doc_comment_node = node.prev_named_sibling\n while (\n current_doc_comment_node\n and current_doc_comment_node.type == self.doc_comment_identifier\n ):\n doc_comment_nodes.append(current_doc_comment_node.text.decode())\n if current_doc_comment_node.prev_named_sibling:\n current_doc_comment_node = (\n current_doc_comment_node.prev_named_sibling\n )\n else:\n current_doc_comment_node = None\n\n doc_comment_str = \"\"\n doc_comment_nodes.reverse()\n for doc_comment_node in doc_comment_nodes:\n doc_comment_str += doc_comment_node + \"\\n\"\n if doc_comment_str.strip() != \"\":\n methods.append({\"method\": node, \"doc_comment\": doc_comment_str.strip()})\n else:\n methods.append({\"method\": node, \"doc_comment\": None})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods", + "output": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\"", + "output": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n # if method returns pointer, skip pointer declarator\n if child.type == \"pointer_declarator\":\n child = child.children[1]\n if child.type == \"function_declarator\":\n for child in child.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n # if method returns pointer, skip pointer declarator\n if child.type == \"pointer_declarator\":\n child = child.children[1]\n if child.type == \"function_declarator\":\n for child in child.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None", + "output": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Parses the given file bytes and extracts method nodes.\n\n Args:\n file_bytes (bytes): The content of the file to be parsed.\n\n Returns:\n list[TreesitterMethodNode]: A list of TreesitterMethodNode objects representing the methods in the file.\n \"\"\"", + "output": "def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]:\n \n self.tree = self.parser.parse(file_bytes)\n result = []\n methods = self._query_all_methods(self.tree.root_node)\n for method in methods:\n method_name = self._query_method_name(method[\"method\"])\n doc_comment = method[\"doc_comment\"]\n source_code = None\n if method[\"method\"].type == \"signature\":\n sc = map(\n lambda x: \"\\n\" + x.text.decode() if x.type == \"function\" else \"\",\n method[\"method\"].children,\n )\n source_code = method[\"method\"].text.decode() + \"\".join(sc)\n result.append(\n TreesitterMethodNode(\n method_name, doc_comment, source_code, method[\"method\"]\n )\n )\n return result" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]:\n \n self.tree = self.parser.parse(file_bytes)\n result = []\n methods = self._query_all_methods(self.tree.root_node)\n for method in methods:\n method_name = self._query_method_name(method[\"method\"])\n doc_comment = method[\"doc_comment\"]\n source_code = None\n if method[\"method\"].type == \"signature\":\n sc = map(\n lambda x: \"\\n\" + x.text.decode() if x.type == \"function\" else \"\",\n method[\"method\"].children,\n )\n source_code = method[\"method\"].text.decode() + \"\".join(sc)\n result.append(\n TreesitterMethodNode(\n method_name, doc_comment, source_code, method[\"method\"]\n )\n )\n return result", + "output": "\"\"\"\n Parses the given file bytes and extracts method nodes.\n\n Args:\n file_bytes (bytes): The content of the file to be parsed.\n\n Returns:\n list[TreesitterMethodNode]: A list of TreesitterMethodNode objects representing the methods in the file.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\"", + "output": "def _query_all_methods(\n self,\n node: tree_sitter.Node,\n ):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_node = None\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n doc_comment_node = node.prev_named_sibling.text.decode()\n else:\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == \"signature\"\n ):\n prev_node = node.prev_named_sibling\n if (\n prev_node.prev_named_sibling\n and prev_node.prev_named_sibling.type\n == self.doc_comment_identifier\n ):\n doc_comment_node = prev_node.prev_named_sibling.text.decode()\n prev_node.children.append(node)\n node = prev_node\n methods.append({\"method\": node, \"doc_comment\": doc_comment_node})\n else:\n for child in node.children:\n current = self._query_all_methods(child)\n if methods and current:\n previous = methods[-1]\n if self._query_method_name(\n previous[\"method\"]\n ) == self._query_method_name(current[0][\"method\"]):\n previous[\"method\"].children.extend(\n map(lambda x: x[\"method\"], current)\n )\n methods = methods[:-1]\n methods.append(previous)\n else:\n methods.extend(current)\n else:\n methods.extend(current)\n return methods" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def _query_all_methods(\n self,\n node: tree_sitter.Node,\n ):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_node = None\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n doc_comment_node = node.prev_named_sibling.text.decode()\n else:\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == \"signature\"\n ):\n prev_node = node.prev_named_sibling\n if (\n prev_node.prev_named_sibling\n and prev_node.prev_named_sibling.type\n == self.doc_comment_identifier\n ):\n doc_comment_node = prev_node.prev_named_sibling.text.decode()\n prev_node.children.append(node)\n node = prev_node\n methods.append({\"method\": node, \"doc_comment\": doc_comment_node})\n else:\n for child in node.children:\n current = self._query_all_methods(child)\n if methods and current:\n previous = methods[-1]\n if self._query_method_name(\n previous[\"method\"]\n ) == self._query_method_name(current[0][\"method\"]):\n previous[\"method\"].children.extend(\n map(lambda x: x[\"method\"], current)\n )\n methods = methods[:-1]\n methods.append(previous)\n else:\n methods.extend(current)\n else:\n methods.extend(current)\n return methods", + "output": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\"", + "output": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == \"signature\" or node.type == self.method_declaration_identifier:\n for child in node.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == \"signature\" or node.type == self.method_declaration_identifier:\n for child in node.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None", + "output": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Parses the given file bytes and extracts method nodes.\n\n Args:\n file_bytes (bytes): The content of the file to be parsed.\n\n Returns:\n list[TreesitterMethodNode]: A list of TreesitterMethodNode objects representing the methods in the file.\n \"\"\"", + "output": "def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]:\n \n self.tree = self.parser.parse(file_bytes)\n result = []\n methods = self._query_all_methods(self.tree.root_node)\n for method in methods:\n method_name = self._query_method_name(method[\"method\"])\n doc_comment = method[\"doc_comment\"]\n result.append(\n TreesitterMethodNode(method_name, doc_comment, None, method[\"method\"])\n )\n return result" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]:\n \n self.tree = self.parser.parse(file_bytes)\n result = []\n methods = self._query_all_methods(self.tree.root_node)\n for method in methods:\n method_name = self._query_method_name(method[\"method\"])\n doc_comment = method[\"doc_comment\"]\n result.append(\n TreesitterMethodNode(method_name, doc_comment, None, method[\"method\"])\n )\n return result", + "output": "\"\"\"\n Parses the given file bytes and extracts method nodes.\n\n Args:\n file_bytes (bytes): The content of the file to be parsed.\n\n Returns:\n list[TreesitterMethodNode]: A list of TreesitterMethodNode objects representing the methods in the file.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\"", + "output": "def _query_all_methods(\n self,\n node: tree_sitter.Node,\n ):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_node = None\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n doc_comment_node = node.prev_named_sibling.text.decode()\n methods.append({\"method\": node, \"doc_comment\": doc_comment_node})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def _query_all_methods(\n self,\n node: tree_sitter.Node,\n ):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_node = None\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n doc_comment_node = node.prev_named_sibling.text.decode()\n methods.append({\"method\": node, \"doc_comment\": doc_comment_node})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods", + "output": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\"", + "output": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None", + "output": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\"" + }, + { + "instruction": "You are a python expert. Write an implementation for the following description.", + "input": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\"", + "output": "def _query_all_methods(\n self,\n node: tree_sitter.Node,\n ):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment = []\n doc_comment_node = node\n while (\n doc_comment_node.prev_named_sibling\n and doc_comment_node.prev_named_sibling.type\n == self.doc_comment_identifier\n ):\n doc_comment_node = doc_comment_node.prev_named_sibling\n doc_comment.insert(0, doc_comment_node.text.decode())\n methods.append({\"method\": node, \"doc_comment\": \"\\n\".join(doc_comment)})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods" + }, + { + "instruction": "You are a python expert. Explain the following code.", + "input": "def _query_all_methods(\n self,\n node: tree_sitter.Node,\n ):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment = []\n doc_comment_node = node\n while (\n doc_comment_node.prev_named_sibling\n and doc_comment_node.prev_named_sibling.type\n == self.doc_comment_identifier\n ):\n doc_comment_node = doc_comment_node.prev_named_sibling\n doc_comment.insert(0, doc_comment_node.text.decode())\n methods.append({\"method\": node, \"doc_comment\": \"\\n\".join(doc_comment)})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods", + "output": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\"" + } +] diff --git a/datasets/completion_dataset.json b/datasets/completion_dataset.json new file mode 100644 index 0000000..fbc686f --- /dev/null +++ b/datasets/completion_dataset.json @@ -0,0 +1,202 @@ +[ + { + "input": "\"\"\"\n Initializes the necessary components for the application.\n\n Args:\n config (dict): Configuration dictionary containing settings for embeddings and LLM.\n repo_name (str): The name of the repository.\n embeddings_model (Embeddings, optional): Pre-initialized embeddings model. Defaults to None.\n\n Returns:\n tuple: A tuple containing the vector store, memory, and QA chain.\n \"\"\"", + "output": "def bootstrap(config, repo_name, embeddings_model=None):\n \n if embeddings_model is None:\n embeddings_model = Embeddings(\n model=EmbeddingsModel[config[\"embeddings\"].upper().replace(\"-\", \"_\")],\n deployment=(\n config[\"embeddings-deployment\"]\n if \"embeddings-deployment\" in config\n else None\n ),\n )\n\n vector_store = VectorStore(repo_name, embeddings=embeddings_model.embeddings)\n vector_store.load_documents()\n\n llm = LLM(\n llm_host=LlmHost[config[\"llm-host\"].upper().replace(\"-\", \"_\")],\n chat_model=config[\"chat-model\"],\n deployment=config[\"model-deployment\"] if \"model-deployment\" in config else None,\n )\n memory = ConversationSummaryMemory(\n llm=llm.chat_model, memory_key=\"chat_history\", return_messages=True\n )\n qa = ConversationalRetrievalChain.from_llm(\n llm.chat_model, retriever=vector_store.retriever, memory=memory\n )\n\n return vector_store, memory, qa" + }, + { + "input": "\"\"\"\n Returns the configuration file path based on the operating system.\n\n This function determines the appropriate configuration directory based on the operating system\n and constructs the full path to the configuration file.\n\n Returns:\n str: The path to the configuration file.\n\n Raises:\n NotImplementedError: If the operating system is not supported.\n \"\"\"", + "output": "def get_config_path():\n \n system = platform.system()\n\n if system == \"Linux\" or system == \"Darwin\":\n user_home = os.path.expanduser(\"~\")\n config_dir = os.path.join(user_home, \".config\", \"codeqai\")\n elif system == \"Windows\":\n user_home = os.path.expanduser(\"~\")\n config_dir = os.path.join(user_home, \"AppData\", \"Roaming\", \"codeqai\")\n else:\n raise NotImplementedError(f\"Unsupported platform: {system}\")\n\n config_file_path = os.path.join(config_dir, \"config.yaml\")\n\n return config_file_path" + }, + { + "input": "\"\"\"\n Loads the configuration from the configuration file.\n\n This function reads the configuration file specified by get_config_path() and parses its content\n using the YAML parser.\n\n Returns:\n dict: The configuration dictionary loaded from the file.\n \"\"\"", + "output": "def load_config():\n \n with open(get_config_path(), \"r\", encoding=\"utf-8\") as config_file:\n config = yaml.safe_load(config_file)\n return config" + }, + { + "input": "\"\"\"\n Saves the configuration to the configuration file.\n\n Args:\n config (dict): The configuration dictionary to be saved.\n\n This function writes the provided configuration dictionary to the configuration file specified by get_config_path()\n using the YAML format.\n \"\"\"", + "output": "def save_config(config):\n \n with open(get_config_path(), \"w\", encoding=\"utf-8\") as config_file:\n yaml.dump(config, config_file, default_flow_style=False)" + }, + { + "input": "\"\"\"\n Creates a new configuration interactively by prompting the user for input.\n\n This function prompts the user with a series of questions to configure the embeddings model and LLM host.\n Based on the user's responses, it constructs a configuration dictionary and saves it to the configuration file.\n\n Returns:\n dict: The configuration dictionary created based on user input.\n \"\"\"", + "output": "def create_config():\n \n os.makedirs(os.path.dirname(get_config_path()), exist_ok=True)\n\n questions = [\n inquirer.Confirm(\n \"confirm\",\n message=\"Do you want to use local embedding models?\",\n default=False,\n ),\n ]\n\n confirm = inquirer.prompt(questions)\n\n if confirm and confirm[\"confirm\"]:\n questions = [\n inquirer.List(\n \"embeddings\",\n message=\"Which local embeddings model do you want to use?\",\n choices=[\n EmbeddingsModel.INSTRUCTOR_LARGE.value,\n EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MPNET_BASE_V2.value,\n EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MINILM_L6_V2.value,\n ],\n default=EmbeddingsModel.INSTRUCTOR_LARGE.value,\n ),\n ]\n else:\n questions = [\n inquirer.List(\n \"embeddings\",\n message=\"Which remote embeddings do you want to use?\",\n choices=[\n EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002.value,\n EmbeddingsModel.AZURE_OPENAI.value,\n ],\n default=EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002.value,\n ),\n ]\n\n answersEmbedding = inquirer.prompt(questions)\n\n questions = [\n inquirer.Confirm(\n \"confirm\", message=\"Do you want to use local chat models?\", default=False\n ),\n ]\n\n confirm = inquirer.prompt(questions)\n\n if confirm and confirm[\"confirm\"]:\n questions = [\n inquirer.List(\n \"llm-host\",\n message=\"Which local LLM host do you want to use?\",\n choices=[\n LlmHost.LLAMACPP.value,\n LlmHost.OLLAMA.value,\n ],\n default=LlmHost.LLAMACPP.value,\n ),\n ]\n else:\n questions = [\n inquirer.List(\n \"llm-host\",\n message=\"Which remote LLM provider do you want to use?\",\n choices=[\n LlmHost.OPENAI.value,\n LlmHost.AZURE_OPENAI.value,\n LlmHost.ANTHROPIC.value,\n ],\n default=LlmHost.OPENAI.value,\n ),\n ]\n\n answersLlm = inquirer.prompt(questions)\n\n if confirm and answersEmbedding and answersLlm:\n config = {\n \"embeddings\": answersEmbedding[\"embeddings\"],\n \"llm-host\": answersLlm[\"llm-host\"],\n }\n\n if config[\"embeddings\"] == EmbeddingsModel.AZURE_OPENAI.value:\n questions = [\n inquirer.Text(\n \"deployment\",\n message=\"Please enter the Azure OpenAI embeddings deployment name.\",\n default=\"\",\n ),\n ]\n deployment_answer = inquirer.prompt(questions)\n if deployment_answer and deployment_answer[\"deployment\"]:\n config[\"embeddings-deployment\"] = deployment_answer[\"deployment\"]\n\n if config[\"llm-host\"] == LlmHost.AZURE_OPENAI.value:\n questions = [\n inquirer.Text(\n \"deployment\",\n message=\"Please enter the Azure OpenAI model deployment name\",\n default=\"\",\n ),\n ]\n deployment_answer = inquirer.prompt(questions)\n if deployment_answer and deployment_answer[\"deployment\"]:\n config[\"model-deployment\"] = deployment_answer[\"deployment\"]\n config[\"chat-model\"] = deployment_answer[\"deployment\"]\n\n elif config[\"llm-host\"] == LlmHost.LLAMACPP.value:\n questions = [\n inquirer.Text(\n \"chat-model\",\n message=\"Please enter the path to the LLM\",\n default=\"\",\n ),\n ]\n\n elif config[\"llm-host\"] == LlmHost.OLLAMA.value:\n questions = [\n inquirer.List(\n \"chat-model\",\n message=\"Which model do you want to use with Ollama?\",\n choices=[\n \"llama2:7b\",\n \"llama2:13b\",\n \"llama3.2:1b\",\n \"llama3.2:3b\",\n \"llama3.1:8b\",\n \"codellama:7b\",\n \"codellama:13b\",\n \"gemma2:9b\",\n \"gemma2:2b\",\n \"deepseek-r1:1.5b\",\n \"deepseek-r1:7b\",\n \"deepseek-r1:8b\",\n \"qwen2.5-coder:7b\",\n \"qwen2.5-coder:3b\",\n ],\n default=\"llama2:13b\",\n ),\n ]\n\n elif config[\"llm-host\"] == LlmHost.OPENAI.value:\n questions = [\n inquirer.List(\n \"chat-model\",\n message=\"Which OpenAI model do you want to use?\",\n choices=[\n \"gpt-3.5-turbo\",\n \"gpt-4\",\n \"gpt-4-turbo\",\n \"gpt-4o\",\n \"gpt-4o-mini\",\n \"o1\",\n \"o1-mini\",\n \"o3-mini\",\n ],\n default=\"gpt-3.5-turbo\",\n ),\n ]\n\n elif config[\"llm-host\"] == LlmHost.ANTHROPIC.value:\n questions = [\n inquirer.List(\n \"chat-model\",\n message=\"Which Anthropic model do you want to use?\",\n choices=[\n \"claude-3-opus-latest\",\n \"claude-3-5-sonnet-latest\",\n \"claude-3-5-haiku-latest\",\n ],\n default=\"claude-3-opus-latest\",\n ),\n ]\n\n # Check if \"chat-model\" is already present in the case of Azure_OpenAI\n if \"chat-model\" not in config:\n answersChatmodel = inquirer.prompt(questions)\n if answersChatmodel and answersChatmodel[\"chat-model\"]:\n config[\"chat-model\"] = answersChatmodel[\"chat-model\"]\n\n save_config(config)\n\n return config\n\n return {}" + }, + { + "input": "\"\"\"\n Loads documents into the vector store.\n\n This method reads the serialized FAISS index from a file, deserializes it, and loads it into the FAISS database.\n It also loads the vector cache from a JSON file and initializes the retriever with the specified search parameters.\n \"\"\"", + "output": "def load_documents(self):\n \n with open(\n os.path.join(get_cache_path(), f\"{self.name}.faiss.bytes\"), \"rb\"\n ) as file:\n index = file.read()\n\n self.db = FAISS.deserialize_from_bytes(\n embeddings=self.embeddings, serialized=index\n )\n self.vector_cache = load_vector_cache(f\"{self.name}.json\")\n self.retriever = self.db.as_retriever(search_type=\"mmr\", search_kwargs={\"k\": 8})" + }, + { + "input": "\"\"\"\n Indexes the given documents and stores them in the vector store.\n\n This method creates a FAISS index from the provided documents and serializes it to a file.\n It also creates a vector cache for quick lookup of document vectors and initializes the retriever.\n\n Args:\n documents (list[Document]): A list of Document objects to be indexed.\n \"\"\"", + "output": "def index_documents(self, documents: list[Document]):\n \n self.vector_cache = {}\n self.db = FAISS.from_documents(documents, self.embeddings)\n index = self.db.serialize_to_bytes()\n with open(\n os.path.join(get_cache_path(), f\"{self.name}.faiss.bytes\"), \"wb\"\n ) as binary_file:\n binary_file.write(index)\n # Create vector cache\n index_to_docstore_id = self.db.index_to_docstore_id\n for i in range(len(documents)):\n document = self.db.docstore.search(index_to_docstore_id[i])\n if document and document is type(Document):\n # Check if the document is already present in the vector cache\n # if yes, then add the vector id to the vector cache entry\n if self.vector_cache.get(document.metadata[\"filename\"]):\n self.vector_cache[document.metadata[\"filename\"]].vector_ids.append(\n index_to_docstore_id[i]\n )\n # if no, then create a new entry in the vector cache\n else:\n self.vector_cache[document.metadata[\"filename\"]] = VectorCache(\n document.metadata[\"filename\"],\n [index_to_docstore_id[i]],\n document.metadata[\"commit_hash\"],\n )\n\n self.retriever = self.db.as_retriever(search_type=\"mmr\", search_kwargs={\"k\": 8})" + }, + { + "input": "\"\"\"\n Synchronizes the documents in the vector store with the provided files.\n\n This method checks if the documents in the vector store are up-to-date with the provided files.\n If a document has been modified, it deletes the old vectors and adds new vectors.\n If a document is new, it adds the document to the vector store.\n It also removes old documents that are no longer present in the provided files.\n\n Args:\n files (list[str]): List of file paths to synchronize with the vector store.\n \"\"\"", + "output": "def sync_documents(self, files):\n \n new_filenames = set()\n for file in files:\n filename = os.path.basename(file)\n new_filenames.add(filename)\n commit_hash = get_commit_hash(file)\n # Check if the document is already present in the vector cache\n # if yes, then check if the document has been modified or not\n if filename in self.vector_cache:\n # Check if the document has been modified, if yes delete all old vectors and add new vector\n if self.vector_cache[filename].commit_hash != commit_hash:\n # This will delete all the vectors associated with the document\n # incluing db.index_to_docstore_id, db.docstore and db.index\n try:\n self.db.delete(self.vector_cache[filename].vector_ids)\n except Exception as e:\n print(f\"Error deleting vectors for file {filename}: {e}\")\n\n # Add the new document to the vector store and recreate the vector cache entry\n self.vector_cache[filename] = VectorCache(\n filename,\n [],\n commit_hash,\n )\n documents = parse_code_files_for_db([file])\n for document in documents:\n self.db.add_documents([document])\n self.vector_cache[filename].vector_ids.append(\n self.db.index_to_docstore_id[\n len(self.db.index_to_docstore_id) - 1\n ]\n )\n\n # if no, then create a new entry in the vector cache and add the document to the vector store\n else:\n self.vector_cache[filename] = VectorCache(\n filename,\n [],\n commit_hash,\n )\n documents = parse_code_files_for_db([file])\n for document in documents:\n self.db.add_documents([document])\n self.vector_cache[filename].vector_ids.append(\n self.db.index_to_docstore_id[\n len(self.db.index_to_docstore_id) - 1\n ]\n )\n\n # Remove old documents from the vector store\n deleted_files = []\n for cache_item in self.vector_cache.values():\n if cache_item.filename not in new_filenames:\n try:\n self.db.delete(cache_item.vector_ids)\n except Exception as e:\n print(f\"Error deleting vectors for file {cache_item.filename}: {e}\")\n deleted_files.append(cache_item.filename)\n\n # Remove old filenames from the vector cache\n for deleted_file in deleted_files:\n self.vector_cache.pop(deleted_file)\n\n index = self.db.serialize_to_bytes()\n with open(\n os.path.join(get_cache_path(), f\"{self.name}.faiss.bytes\"), \"wb\"\n ) as binary_file:\n binary_file.write(index)" + }, + { + "input": "\"\"\"\n Exports the dataset based on the specified format.\n\n This method checks the format of the dataset and calls the appropriate export method.\n It also prints messages indicating the progress and completion of the export process.\n\n Supported formats:\n - CONVERSATIONAL: Exports to conversational_dataset.json\n - ALPACA: Exports to alpaca_dataset.json\n - INSTRUCTION: Exports to instruction_dataset.json\n \"\"\"", + "output": "def export(self):\n \n print(\"Exporting dataset...\")\n if self.format == DatasetFormat.CONVERSATIONAL.value:\n self.export_conversational()\n print(\"Dataset exported to conversational_dataset.json\")\n elif self.format == DatasetFormat.ALPACA.value:\n self.export_alpaca()\n print(\"Dataset exported to alpaca_dataset.json\")\n elif self.format == DatasetFormat.INSTRUCTION.value:\n self.export_instruction()\n print(\"Dataset exported to instruction_dataset.json\")\n elif self.format == DatasetFormat.COMPLETION.value:\n self.export_completion()\n print(\"Dataset exported to completion_dataset.json\")" + }, + { + "input": "\"\"\"\n Exports the code snippets in a conversational format.\n\n This method processes each code snippet in the dataset and creates conversational messages\n for both implementation and explanation tasks. The messages are then saved to a JSON file.\n \"\"\"", + "output": "def export_conversational(self):\n \n messages_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if (\n self.distillation_mode == DistillationMode.DOCUMENTATION\n or self.distillation_mode == DistillationMode.FULL\n ):\n docstring = self.distill_docstring(code_snippet)\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n if (\n self.distillation_mode == DistillationMode.CODE\n or self.distillation_mode == DistillationMode.FULL\n ):\n pass\n\n message = {\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write an implementation for the following description.\",\n },\n {\"role\": \"user\", \"content\": docstring},\n {\"role\": \"assistant\", \"content\": code_snippet.get(\"code\")},\n ]\n }\n\n messages_list.append(message)\n message = {\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Explain the following code.\",\n },\n {\"role\": \"user\", \"content\": code_snippet.get(\"code\")},\n {\n \"role\": \"assistant\",\n \"content\": docstring,\n },\n ]\n }\n\n messages_list.append(message)\n\n with open(\"conversational_dataset.json\", \"w\") as f:\n for messages in messages_list:\n json.dump(messages, f)\n f.write(\"\\n\")" + }, + { + "input": "\"\"\"\n Exports the code snippets in an Alpaca format.\n\n This method processes each code snippet in the dataset and creates Alpaca entries\n for both implementation and explanation tasks. The entries are then saved to a JSON file.\n \"\"\"", + "output": "def export_alpaca(self):\n \n alpaca_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if (\n self.distillation_mode == DistillationMode.DOCUMENTATION\n or self.distillation_mode == DistillationMode.FULL\n ):\n docstring = self.distill_docstring(code_snippet)\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n if (\n self.distillation_mode == DistillationMode.CODE\n or self.distillation_mode == DistillationMode.FULL\n ):\n pass\n\n alpaca_entry = {\n \"instruction\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write an implementation for the following description.\",\n \"input\": docstring,\n \"output\": code_snippet.get(\"code\"),\n }\n alpaca_list.append(alpaca_entry)\n alpaca_entry = {\n \"instruction\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Explain the following code.\",\n \"input\": code_snippet.get(\"code\"),\n \"output\": docstring,\n }\n alpaca_list.append(alpaca_entry)\n\n with open(\"alpaca_dataset.json\", \"w\") as f:\n json.dump(alpaca_list, f, indent=4)" + }, + { + "input": "\"\"\"\n Exports the code snippets in an instruction format.\n\n This method processes each code snippet in the dataset and creates instruction entries\n for both implementation and explanation tasks. The entries are then saved to a JSON file.\n \"\"\"", + "output": "def export_instruction(self):\n \n instructions_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if (\n self.distillation_mode == DistillationMode.DOCUMENTATION\n or self.distillation_mode == DistillationMode.FULL\n ):\n result = self.distill_docstring(code_snippet)\n if type(result) is str:\n docstring = result\n else:\n continue\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n if (\n self.distillation_mode == DistillationMode.CODE\n or self.distillation_mode == DistillationMode.FULL\n ):\n pass\n\n instruction = {\n \"prompt\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write an implementation for the following description:\\n\"\n + (docstring or \"\"),\n \"completion\": code_snippet.get(\"code\"),\n }\n instructions_list.append(instruction)\n instruction = {\n \"prompt\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Explain the following code:\\n\"\n + (code_snippet.get(\"code\") or \"\"),\n \"completion\": docstring,\n }\n instructions_list.append(instruction)\n\n with open(\"instruction_dataset.json\", \"w\") as f:\n json.dump(instructions_list, f, indent=4)" + }, + { + "input": "\"\"\"\n Exports the code snippets in a completion format.\n\n This method processes each code snippet in the dataset and creates completion entries.\n The entries are then saved to a JSON file.\n \"\"\"", + "output": "def export_completion(self):\n \n completions_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if (\n self.distillation_mode == DistillationMode.DOCUMENTATION\n or self.distillation_mode == DistillationMode.FULL\n ):\n docstring = self.distill_docstring(code_snippet)\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n if (\n self.distillation_mode == DistillationMode.CODE\n or self.distillation_mode == DistillationMode.FULL\n ):\n pass\n\n completion = {\n \"input\": docstring,\n \"output\": code_snippet.get(\"code\"),\n }\n completions_list.append(completion)\n\n with open(\"completion_dataset.json\", \"w\") as f:\n json.dump(completions_list, f, indent=4)" + }, + { + "input": "\"\"\"\n Distills a concise description from the given code snippet.\n\n Args:\n code_snippet (dict): A dictionary containing details about the code snippet, such as method name, programming language, and the actual code.\n\n Returns:\n str: A concise description of the code snippet.\n \"\"\"", + "output": "def distill_docstring(self, code_snippet):\n \n spinner = yaspin(\n text=f\"Distilling {code_snippet.get('method_name')}...\",\n color=\"green\",\n )\n spinner.start()\n prompt = (\n \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write a short and concise description for the following code. Return only the description.\"\n )\n docstring = self.llm.chat_model.invoke(\n [\n (\"system\", prompt),\n (\"human\", code_snippet.get(\"code\") or \"\"),\n ]\n )\n spinner.stop()\n return docstring.content" + }, + { + "input": "\"\"\"\n Distills a given code snippet into smaller chunks and provides explanations for each chunk.\n\n Args:\n code_snippet (dict): A dictionary containing details about the code snippet, such as method name, programming language, and the actual code.\n\n Returns:\n dict: A dictionary containing the distilled code chunks and their explanations.\n \"\"\"", + "output": "def distill_code(self, code_snippet):\n \n spinner = yaspin(\n text=f\"Distilling {code_snippet.get('method_name')}...\",\n color=\"green\",\n )\n spinner.start()\n prompt = (\n \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Split the following code into reasonable chunks and explain each chunk. \"\n + \"Return a JSON object with a list of objects containing the code chunk with key 'code' and the explanation with key 'explanation'.\"\n )\n code = self.llm.chat_model.invoke(\n [\n (\"system\", prompt),\n (\"human\", code_snippet.get(\"code\") or \"\"),\n ]\n )\n spinner.stop()\n\n try:\n # Ensure the content is a string before parsing\n if isinstance(code.content, str):\n # Parse the output to a JSON object\n code_json = json.loads(code.content)\n else:\n raise ValueError(\"Content is not a valid JSON string\")\n except (json.JSONDecodeError, ValueError) as e:\n # Handle JSON parsing error\n print(f\"Error parsing response JSON: {e}\")\n return {}\n\n return code_json" + }, + { + "input": "\"\"\"\n Loads a vector cache from a JSON file.\n\n Args:\n filename (str): The name of the file containing the vector cache.\n\n Returns:\n Dict[str, VectorCache]: A dictionary where the keys are strings and the values are VectorCache objects.\n \"\"\"", + "output": "def load_vector_cache(filename) -> Dict[str, VectorCache]:\n \n with open(\n get_cache_path() + \"/\" + filename, \"r\", encoding=\"utf-8\"\n ) as vector_cache_file:\n vector_cache_json = json.load(vector_cache_file)\n vector_cache = {}\n for key, value in vector_cache_json.items():\n vector_cache[key] = VectorCache.from_json(value)\n return vector_cache" + }, + { + "input": "\"\"\"\n Saves a vector cache to a JSON file.\n\n Args:\n vector_cache (Dict[str, VectorCache]): A dictionary where the keys are strings and the values are VectorCache objects.\n filename (str): The name of the file to save the vector cache to.\n \"\"\"", + "output": "def save_vector_cache(vector_cache, filename):\n \n with open(\n get_cache_path() + \"/\" + filename, \"w\", encoding=\"utf-8\"\n ) as vector_cache_file:\n json.dump(vector_cache, default=VectorCache.to_json, fp=vector_cache_file)" + }, + { + "input": "\"\"\"\n Returns the cache directory path based on the operating system.\n\n Returns:\n str: The path to the cache directory.\n\n Raises:\n NotImplementedError: If the operating system is not supported.\n \"\"\"", + "output": "def get_cache_path():\n \n system = platform.system()\n\n if system == \"Linux\" or system == \"Darwin\":\n user_home = os.path.expanduser(\"~\")\n cache_dir = os.path.join(user_home, \".cache\", \"codeqai\")\n elif system == \"Windows\":\n user_home = os.path.expanduser(\"~\")\n cache_dir = os.path.join(user_home, \"AppData\", \"Local\", \"codeqai\")\n else:\n raise NotImplementedError(f\"Unsupported platform: {system}\")\n\n return cache_dir" + }, + { + "input": "\"\"\"\n Creates the cache directory if it does not already exist.\n\n This function checks if the cache directory exists at the path returned by get_cache_path().\n If the directory does not exist, it creates the directory and any necessary parent directories.\n \"\"\"", + "output": "def create_cache_dir():\n \n if not os.path.exists(get_cache_path()):\n path = Path(get_cache_path())\n path.mkdir(parents=True, exist_ok=True)" + }, + { + "input": "\"\"\"\n Initializes the LLM class with the specified parameters.\n\n Args:\n llm_host (LlmHost): The host for the language model (e.g., OPENAI, AZURE_OPENAI, ANTHROPIC, LLAMACPP, OLLAMA).\n chat_model (str): The chat model to use.\n max_tokens (int, optional): The maximum number of tokens for the model. Defaults to 2048.\n deployment (str, optional): The deployment name for Azure OpenAI. Defaults to None.\n\n Raises:\n ValueError: If the required environment variable for Azure OpenAI is not set.\n \"\"\"", + "output": "def __init__(\n self, llm_host: LlmHost, chat_model: str, max_tokens=2048, deployment=None\n ):\n \n if llm_host == LlmHost.OPENAI:\n self.chat_model = ChatOpenAI(\n temperature=0.9, max_tokens=max_tokens, model=chat_model\n )\n elif llm_host == LlmHost.AZURE_OPENAI and deployment:\n azure_openai_endpoint = os.getenv(\"AZURE_OPENAI_ENDPOINT\")\n if azure_openai_endpoint:\n self.chat_model = AzureChatOpenAI(\n azure_endpoint=azure_openai_endpoint,\n temperature=0.9,\n max_tokens=max_tokens,\n model=chat_model,\n )\n else:\n raise ValueError(\n \"Azure OpenAI requires environment variable AZURE_OPENAI_ENDPOINT to be set.\"\n )\n elif llm_host == LlmHost.ANTHROPIC:\n self.chat_model = ChatAnthropic(\n temperature=0.9,\n max_tokens_to_sample=max_tokens,\n model_name=chat_model,\n timeout=30,\n api_key=None, # API key is set to environment variable ANTHROPIC_API_KEY\n )\n elif llm_host == LlmHost.LLAMACPP:\n self.install_llama_cpp()\n self.chat_model = LlamaCpp(\n model_path=chat_model,\n temperature=0.9,\n max_tokens=max_tokens,\n verbose=False,\n )\n elif llm_host == LlmHost.OLLAMA:\n self.chat_model = Ollama(\n base_url=\"http://localhost:11434\",\n model=chat_model,\n callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n )" + }, + { + "input": "\"\"\"\n Initializes the Embeddings class with the specified model and deployment.\n\n Args:\n model (EmbeddingsModel): The embeddings model to use. Defaults to OPENAI_TEXT_EMBEDDING_ADA_002.\n deployment (str, optional): The deployment name for Azure OpenAI embeddings. Defaults to None.\n \"\"\"", + "output": "def __init__(\n self,\n model=EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002,\n deployment=None,\n ):\n \n if model == EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002:\n self.embeddings = OpenAIEmbeddings(\n client=None, model=\"text-embedding-ada-002\"\n )\n elif model == EmbeddingsModel.AZURE_OPENAI and deployment:\n self.embeddings = AzureOpenAIEmbeddings(\n client=None, azure_deployment=deployment\n )\n else:\n try:\n import sentence_transformers # noqa: F401\n except ImportError:\n self._install_sentence_transformers()\n\n if model == EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MPNET_BASE_V2:\n self.embeddings = HuggingFaceEmbeddings()\n elif model == EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MINILM_L6_V2:\n self.embeddings = HuggingFaceEmbeddings(\n model_name=EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MINILM_L6_V2.value.replace(\n \"SentenceTransformers-\", \"\"\n )\n )\n elif model == EmbeddingsModel.INSTRUCTOR_LARGE:\n try:\n from InstructorEmbedding import INSTRUCTOR # noqa: F401\n except ImportError:\n self._install_instructor_embedding()\n\n self.embeddings = HuggingFaceEmbeddings(\n model_name=\"hkunlp/instructor-xl\"\n )" + }, + { + "input": "\"\"\"\n Returns the programming language based on the provided file extension.\n\n Args:\n file_extension (str): The file extension to determine the programming language of.\n\n Returns:\n Language: The programming language corresponding to the file extension. If the file extension is not found\n in the language mapping, returns Language.UNKNOWN.\n \"\"\"", + "output": "def get_programming_language(file_extension: str) -> Language:\n \n language_mapping = {\n \".py\": Language.PYTHON,\n \".js\": Language.JAVASCRIPT,\n \".jsx\": Language.JAVASCRIPT,\n \".mjs\": Language.JAVASCRIPT,\n \".cjs\": Language.JAVASCRIPT,\n \".ts\": Language.TYPESCRIPT,\n \".tsx\": Language.TYPESCRIPT,\n \".java\": Language.JAVA,\n \".kt\": Language.KOTLIN,\n \".rs\": Language.RUST,\n \".go\": Language.GO,\n \".cpp\": Language.CPP,\n \".c\": Language.C,\n \".cs\": Language.C_SHARP,\n \".hs\": Language.HASKELL,\n \".rb\": Language.RUBY,\n }\n return language_mapping.get(file_extension, Language.UNKNOWN)" + }, + { + "input": "\"\"\"\n Returns the extension of a file from its given name.\n\n Parameters:\n file_name (str): The name of the file.\n\n Returns:\n str: The extension of the file.\n\n \"\"\"", + "output": "def get_file_extension(file_name: str) -> str:\n \n return os.path.splitext(file_name)[-1]" + }, + { + "input": "\"\"\"\n Returns the given text formatted in bold.\n\n Args:\n text (str): The text to be formatted.\n\n Returns:\n str: The text formatted in bold using ANSI escape codes.\n \"\"\"", + "output": "def get_bold_text(text):\n \n return f\"\\033[01m{text}\\033[0m\"" + }, + { + "input": "\"\"\"\n Finds the starting line number and indentation level of a code snippet within a file.\n\n Args:\n filename (str): The name of the file to search within.\n code_snippet (str): The code snippet to find in the file.\n\n Returns:\n tuple: A tuple containing the starting line number (int) and the indentation level (str) of the code snippet.\n If the file is not found or the code snippet is not found, returns (1, \"\").\n \"\"\"", + "output": "def find_starting_line_and_indent(filename, code_snippet):\n \n file_path = find_file_in_git_repo(filename)\n if file_path is not None:\n with open(file_path, \"r\") as file:\n file_content = file.read()\n start_pos = file_content.find(code_snippet)\n return (\n file_content.count(\"\\n\", 0, start_pos) + 1,\n file_content[:start_pos].split(\"\\n\")[-1],\n )\n return 1, \"\"" + }, + { + "input": "\"\"\"\n Counts the number of tokens in the given text using the specified model's tokenizer.\n\n Args:\n text (str): The text to be tokenized and counted.\n model (str, optional): The model to use for tokenization. Defaults to \"gpt-4\".\n\n Returns:\n int: The number of tokens in the text.\n \"\"\"", + "output": "def count_tokens(text, model=\"gpt-4\"):\n \n enc = tiktoken.encoding_for_model(model)\n return len(enc.encode(text))" + }, + { + "input": "\"\"\"\n Retrieves the name of the current Git repository.\n\n This function gets the root directory of the current Git repository based on the current working directory,\n and extracts the repository name from the root directory path.\n\n Returns:\n str: The name of the current Git repository.\n \"\"\"", + "output": "def repo_name():\n \n return get_git_root(os.getcwd()).split(\"/\")[-1]" + }, + { + "input": "\"\"\"\n Retrieves the root directory of the Git repository for the given path.\n\n Args:\n path (str): The path to a directory within the Git repository.\n\n Returns:\n str: The root directory of the Git repository.\n \"\"\"", + "output": "def get_git_root(path):\n \n git_repo = Repo(path, search_parent_directories=True)\n git_root = git_repo.git.rev_parse(\"--show-toplevel\")\n return git_root" + }, + { + "input": "\"\"\"\n Searches for a file with the given name in the current Git repository.\n\n Args:\n file_name (str): The name of the file to search for.\n\n Returns:\n str or None: The full path to the file if found, otherwise None.\n \"\"\"", + "output": "def find_file_in_git_repo(file_name):\n \n git_root = get_git_root(os.getcwd())\n\n for root, dirs, files in os.walk(git_root):\n if any(blacklist in root for blacklist in BLACKLIST_DIR):\n continue\n for file in files:\n if file == file_name:\n return os.path.join(root, file)" + }, + { + "input": "\"\"\"\n Loads files from the current Git repository based on whitelist and blacklist criteria.\n\n This function walks through the directory structure of the Git repository,\n and collects files that match the whitelist extensions and are not in the blacklist directories or files.\n\n Returns:\n list: A list of file paths that meet the criteria.\n \"\"\"", + "output": "def load_files():\n \n git_root = get_git_root(os.getcwd())\n file_list = []\n\n for root, dirs, files in os.walk(git_root):\n if any(blacklist in root for blacklist in BLACKLIST_DIR):\n continue\n for file in files:\n file_ext = os.path.splitext(file)[1]\n if any(whitelist == file_ext for whitelist in WHITELIST_FILES):\n if file not in BLACKLIST_FILES:\n file_list.append(os.path.join(root, file))\n\n return file_list" + }, + { + "input": "\"\"\"\n Retrieves the latest commit hash for the specified file.\n\n Args:\n file_path (str): The path to the file for which to retrieve the commit hash.\n\n Returns:\n str or None: The latest commit hash if found, otherwise None.\n \"\"\"", + "output": "def get_commit_hash(file_path):\n \n try:\n # Run the git log command\n result = subprocess.run(\n [\"git\", \"log\", \"-n\", \"1\", \"--pretty=format:%H\", \"--\", file_path],\n stdout=subprocess.PIPE,\n text=True,\n check=True,\n )\n\n # Extract the commit hash from the command output\n commit_hash = result.stdout.strip()\n return commit_hash\n\n except subprocess.CalledProcessError as e:\n print(f\"Error executing git command: {e}\")\n return None" + }, + { + "input": "\"\"\"\n Args :\n env_path = source path of .env file.\n required_keys = [\"OPENAI_KEY\"] #change this according to need\n\n #running/calling the function.\n configs = env_loader('.env', required_keys)\n \"\"\"", + "output": "def env_loader(env_path, required_keys=None):\n \n\n # create env file if does not exists\n # parse required keys in the file if it's not None\n if not os.path.exists(env_path) or os.path.getsize(env_path) == 0:\n with open(env_path, \"w\") as env_f:\n if required_keys:\n for key in required_keys:\n env_f.write(f'{key}=\"\"\\n')\n else:\n pass\n\n configs = dotenv_values(env_path)\n changed = False\n for key, value in configs.items():\n env_key = os.getenv(key)\n if not value and not env_key:\n value = input(\n f\"[+] Key {utils.get_bold_text(key)} is required. Please enter it's value: \"\n )\n configs[key] = value\n changed = True\n elif not value and env_key:\n value = env_key\n configs[key] = value\n changed = True\n\n # update the .env file if config is changed/taken from user\n if changed:\n with open(env_path, \"w\") as env_f:\n for key, value in configs.items():\n env_f.write(f'{key}=\"{value}\"\\n')\n\n load_dotenv(env_path, override=True)" + }, + { + "input": "\"\"\"\n Parses a list of code files and returns a list of Document objects for database storage.\n\n Args:\n code_files (list[str]): List of paths to code files to be parsed.\n\n Returns:\n list[Document]: List of Document objects containing parsed code information.\n \"\"\"", + "output": "def parse_code_files_for_db(code_files: list[str]) -> list[Document]:\n \n documents = []\n code_splitter = None\n for code_file in code_files:\n with open(code_file, \"r\", encoding=\"utf-8\") as file:\n file_bytes = file.read().encode()\n commit_hash = repo.get_commit_hash(code_file)\n\n file_extension = utils.get_file_extension(code_file)\n programming_language = utils.get_programming_language(file_extension)\n if programming_language == Language.UNKNOWN:\n continue\n\n langchain_language = utils.get_langchain_language(programming_language)\n\n if langchain_language:\n code_splitter = RecursiveCharacterTextSplitter.from_language(\n language=langchain_language,\n chunk_size=512,\n chunk_overlap=128,\n )\n\n treesitter_parser = Treesitter.create_treesitter(programming_language)\n treesitterNodes: list[TreesitterMethodNode] = treesitter_parser.parse(\n file_bytes\n )\n for node in treesitterNodes:\n method_source_code = node.method_source_code\n filename = os.path.basename(code_file)\n\n if node.doc_comment and programming_language != Language.PYTHON:\n method_source_code = node.doc_comment + \"\\n\" + method_source_code\n\n splitted_documents = [method_source_code]\n if code_splitter:\n splitted_documents = code_splitter.split_text(method_source_code)\n\n for splitted_document in splitted_documents:\n document = Document(\n page_content=splitted_document,\n metadata={\n \"filename\": filename,\n \"method_name\": node.name,\n \"commit_hash\": commit_hash,\n },\n )\n documents.append(document)\n\n return documents" + }, + { + "input": "\"\"\"\n Parses a list of code files for fine-tuning and returns a list of dictionaries containing method information.\n\n Args:\n code_files (list[str]): List of paths to code files to be parsed.\n max_tokens (int): Maximum number of tokens allowed for output.\n\n Returns:\n list[dict]: List of dictionaries containing method information, including method name, code, description, and language.\n \"\"\"", + "output": "def parse_code_files_for_finetuning(\n code_files: list[str], max_tokens, spinner\n) -> list[dict]:\n \n input_tokens = 0\n output_tokens = 0\n documents = []\n for code_file in code_files:\n with open(code_file, \"r\", encoding=\"utf-8\") as file:\n file_bytes = file.read().encode()\n\n file_extension = utils.get_file_extension(code_file)\n programming_language = utils.get_programming_language(file_extension)\n if programming_language == Language.UNKNOWN:\n continue\n\n treesitter_parser = Treesitter.create_treesitter(programming_language)\n treesitterNodes: list[TreesitterMethodNode] = treesitter_parser.parse(\n file_bytes\n )\n for node in treesitterNodes:\n method_source_code = node.method_source_code\n\n if node.doc_comment and programming_language == Language.PYTHON:\n method_source_code = method_source_code.replace(\n node.doc_comment, \"\"\n )\n\n document = {\n \"method_name\": node.name,\n \"code\": method_source_code,\n \"description\": node.doc_comment,\n \"language\": programming_language.value,\n }\n documents.append(document)\n\n if node.doc_comment is not None:\n input_tokens += utils.count_tokens(node.doc_comment)\n output_tokens += max_tokens\n\n spinner.stop()\n\n print(f\"Estimated input tokens for distillation needed: {input_tokens}.\")\n print(f\"Maximum output tokens for distillation nedeed: {output_tokens}.\")\n questions = [\n inquirer.Confirm(\n \"confirm\",\n message=\"Proceed?\",\n default=True,\n ),\n ]\n\n confirm = inquirer.prompt(questions)\n\n if confirm and confirm[\"confirm\"]:\n pass\n else:\n exit()\n\n return documents" + }, + { + "input": "\"\"\"\n Parses the given file bytes and extracts method nodes.\n\n Args:\n file_bytes (bytes): The content of the file to be parsed.\n\n Returns:\n list[TreesitterMethodNode]: A list of TreesitterMethodNode objects representing the methods in the file.\n \"\"\"", + "output": "def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]:\n \n self.tree = self.parser.parse(file_bytes)\n result = []\n methods = self._query_all_methods(self.tree.root_node)\n for method in methods:\n method_name = self._query_method_name(method)\n doc_comment = self._query_doc_comment(method)\n result.append(TreesitterMethodNode(method_name, doc_comment, None, method))\n return result" + }, + { + "input": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\"", + "output": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None" + }, + { + "input": "\"\"\"\n Queries all method nodes within the given syntax tree node, including those within class definitions.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of method nodes found within the given node.\n \"\"\"", + "output": "def _query_all_methods(self, node: tree_sitter.Node):\n \n methods = []\n for child in node.children:\n if child.type == self.method_declaration_identifier:\n methods.append(child)\n if child.type == \"class_definition\":\n class_body = child.children[-1]\n for child_node in class_body.children:\n if child_node.type == self.method_declaration_identifier:\n methods.append(child_node)\n return methods" + }, + { + "input": "\"\"\"\n Queries the documentation comment for the given function definition node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node representing a function definition.\n\n Returns:\n str or None: The documentation comment string if found, otherwise None.\n \"\"\"", + "output": "def _query_doc_comment(self, node: tree_sitter.Node):\n \n query_code = \"\"\"\n (function_definition\n body: (block . (expression_statement (string)) @function_doc_str))\n \"\"\"\n doc_str_query = self.language.query(query_code)\n doc_strs = doc_str_query.captures(node)\n\n if doc_strs:\n return doc_strs[0][0].text.decode()\n else:\n return None" + }, + { + "input": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\"", + "output": "def _query_method_name(self, node: tree_sitter.Node):\n \n first_match = None\n if node.type == self.method_declaration_identifier:\n for child in node.children:\n # if the return type is an object type, then the method name\n # is the second match\n if child.type == self.method_name_identifier and not first_match:\n first_match = child.text.decode()\n elif child.type == self.method_name_identifier and first_match:\n return child.text.decode()\n return first_match" + }, + { + "input": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\"", + "output": "def _query_all_methods(self, node: tree_sitter.Node):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_nodes = []\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n current_doc_comment_node = node.prev_named_sibling\n while (\n current_doc_comment_node\n and current_doc_comment_node.type == self.doc_comment_identifier\n ):\n doc_comment_nodes.append(current_doc_comment_node.text.decode())\n if current_doc_comment_node.prev_named_sibling:\n current_doc_comment_node = (\n current_doc_comment_node.prev_named_sibling\n )\n else:\n current_doc_comment_node = None\n\n doc_comment_str = \"\"\n doc_comment_nodes.reverse()\n for doc_comment_node in doc_comment_nodes:\n doc_comment_str += doc_comment_node + \"\\n\"\n if doc_comment_str.strip() != \"\":\n methods.append({\"method\": node, \"doc_comment\": doc_comment_str.strip()})\n else:\n methods.append({\"method\": node, \"doc_comment\": None})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods" + }, + { + "input": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\"", + "output": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n # if method returns pointer, skip pointer declarator\n if child.type == \"pointer_declarator\":\n child = child.children[1]\n if child.type == \"function_declarator\":\n for child in child.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None" + }, + { + "input": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\"", + "output": "def _query_all_methods(self, node: tree_sitter.Node):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_nodes = []\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n current_doc_comment_node = node.prev_named_sibling\n while (\n current_doc_comment_node\n and current_doc_comment_node.type == self.doc_comment_identifier\n ):\n doc_comment_nodes.append(current_doc_comment_node.text.decode())\n if current_doc_comment_node.prev_named_sibling:\n current_doc_comment_node = (\n current_doc_comment_node.prev_named_sibling\n )\n else:\n current_doc_comment_node = None\n\n doc_comment_str = \"\"\n doc_comment_nodes.reverse()\n for doc_comment_node in doc_comment_nodes:\n doc_comment_str += doc_comment_node + \"\\n\"\n if doc_comment_str.strip() != \"\":\n methods.append({\"method\": node, \"doc_comment\": doc_comment_str.strip()})\n else:\n methods.append({\"method\": node, \"doc_comment\": None})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods" + }, + { + "input": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\"", + "output": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n # if method returns pointer, skip pointer declarator\n if child.type == \"pointer_declarator\":\n child = child.children[1]\n if child.type == \"function_declarator\":\n for child in child.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None" + }, + { + "input": "\"\"\"\n Parses the given file bytes and extracts method nodes.\n\n Args:\n file_bytes (bytes): The content of the file to be parsed.\n\n Returns:\n list[TreesitterMethodNode]: A list of TreesitterMethodNode objects representing the methods in the file.\n \"\"\"", + "output": "def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]:\n \n self.tree = self.parser.parse(file_bytes)\n result = []\n methods = self._query_all_methods(self.tree.root_node)\n for method in methods:\n method_name = self._query_method_name(method[\"method\"])\n doc_comment = method[\"doc_comment\"]\n source_code = None\n if method[\"method\"].type == \"signature\":\n sc = map(\n lambda x: \"\\n\" + x.text.decode() if x.type == \"function\" else \"\",\n method[\"method\"].children,\n )\n source_code = method[\"method\"].text.decode() + \"\".join(sc)\n result.append(\n TreesitterMethodNode(\n method_name, doc_comment, source_code, method[\"method\"]\n )\n )\n return result" + }, + { + "input": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\"", + "output": "def _query_all_methods(\n self,\n node: tree_sitter.Node,\n ):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_node = None\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n doc_comment_node = node.prev_named_sibling.text.decode()\n else:\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == \"signature\"\n ):\n prev_node = node.prev_named_sibling\n if (\n prev_node.prev_named_sibling\n and prev_node.prev_named_sibling.type\n == self.doc_comment_identifier\n ):\n doc_comment_node = prev_node.prev_named_sibling.text.decode()\n prev_node.children.append(node)\n node = prev_node\n methods.append({\"method\": node, \"doc_comment\": doc_comment_node})\n else:\n for child in node.children:\n current = self._query_all_methods(child)\n if methods and current:\n previous = methods[-1]\n if self._query_method_name(\n previous[\"method\"]\n ) == self._query_method_name(current[0][\"method\"]):\n previous[\"method\"].children.extend(\n map(lambda x: x[\"method\"], current)\n )\n methods = methods[:-1]\n methods.append(previous)\n else:\n methods.extend(current)\n else:\n methods.extend(current)\n return methods" + }, + { + "input": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\"", + "output": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == \"signature\" or node.type == self.method_declaration_identifier:\n for child in node.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None" + }, + { + "input": "\"\"\"\n Parses the given file bytes and extracts method nodes.\n\n Args:\n file_bytes (bytes): The content of the file to be parsed.\n\n Returns:\n list[TreesitterMethodNode]: A list of TreesitterMethodNode objects representing the methods in the file.\n \"\"\"", + "output": "def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]:\n \n self.tree = self.parser.parse(file_bytes)\n result = []\n methods = self._query_all_methods(self.tree.root_node)\n for method in methods:\n method_name = self._query_method_name(method[\"method\"])\n doc_comment = method[\"doc_comment\"]\n result.append(\n TreesitterMethodNode(method_name, doc_comment, None, method[\"method\"])\n )\n return result" + }, + { + "input": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\"", + "output": "def _query_all_methods(\n self,\n node: tree_sitter.Node,\n ):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_node = None\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n doc_comment_node = node.prev_named_sibling.text.decode()\n methods.append({\"method\": node, \"doc_comment\": doc_comment_node})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods" + }, + { + "input": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\"", + "output": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None" + }, + { + "input": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\"", + "output": "def _query_all_methods(\n self,\n node: tree_sitter.Node,\n ):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment = []\n doc_comment_node = node\n while (\n doc_comment_node.prev_named_sibling\n and doc_comment_node.prev_named_sibling.type\n == self.doc_comment_identifier\n ):\n doc_comment_node = doc_comment_node.prev_named_sibling\n doc_comment.insert(0, doc_comment_node.text.decode())\n methods.append({\"method\": node, \"doc_comment\": \"\\n\".join(doc_comment)})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods" + } +] diff --git a/datasets/conversational_dataset.json b/datasets/conversational_dataset.json new file mode 100644 index 0000000..7195b06 --- /dev/null +++ b/datasets/conversational_dataset.json @@ -0,0 +1,100 @@ +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Initializes the necessary components for the application.\n\n Args:\n config (dict): Configuration dictionary containing settings for embeddings and LLM.\n repo_name (str): The name of the repository.\n embeddings_model (Embeddings, optional): Pre-initialized embeddings model. Defaults to None.\n\n Returns:\n tuple: A tuple containing the vector store, memory, and QA chain.\n \"\"\""}, {"role": "assistant", "content": "def bootstrap(config, repo_name, embeddings_model=None):\n \n if embeddings_model is None:\n embeddings_model = Embeddings(\n model=EmbeddingsModel[config[\"embeddings\"].upper().replace(\"-\", \"_\")],\n deployment=(\n config[\"embeddings-deployment\"]\n if \"embeddings-deployment\" in config\n else None\n ),\n )\n\n vector_store = VectorStore(repo_name, embeddings=embeddings_model.embeddings)\n vector_store.load_documents()\n\n llm = LLM(\n llm_host=LlmHost[config[\"llm-host\"].upper().replace(\"-\", \"_\")],\n chat_model=config[\"chat-model\"],\n deployment=config[\"model-deployment\"] if \"model-deployment\" in config else None,\n )\n memory = ConversationSummaryMemory(\n llm=llm.chat_model, memory_key=\"chat_history\", return_messages=True\n )\n qa = ConversationalRetrievalChain.from_llm(\n llm.chat_model, retriever=vector_store.retriever, memory=memory\n )\n\n return vector_store, memory, qa"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def bootstrap(config, repo_name, embeddings_model=None):\n \n if embeddings_model is None:\n embeddings_model = Embeddings(\n model=EmbeddingsModel[config[\"embeddings\"].upper().replace(\"-\", \"_\")],\n deployment=(\n config[\"embeddings-deployment\"]\n if \"embeddings-deployment\" in config\n else None\n ),\n )\n\n vector_store = VectorStore(repo_name, embeddings=embeddings_model.embeddings)\n vector_store.load_documents()\n\n llm = LLM(\n llm_host=LlmHost[config[\"llm-host\"].upper().replace(\"-\", \"_\")],\n chat_model=config[\"chat-model\"],\n deployment=config[\"model-deployment\"] if \"model-deployment\" in config else None,\n )\n memory = ConversationSummaryMemory(\n llm=llm.chat_model, memory_key=\"chat_history\", return_messages=True\n )\n qa = ConversationalRetrievalChain.from_llm(\n llm.chat_model, retriever=vector_store.retriever, memory=memory\n )\n\n return vector_store, memory, qa"}, {"role": "assistant", "content": "\"\"\"\n Initializes the necessary components for the application.\n\n Args:\n config (dict): Configuration dictionary containing settings for embeddings and LLM.\n repo_name (str): The name of the repository.\n embeddings_model (Embeddings, optional): Pre-initialized embeddings model. Defaults to None.\n\n Returns:\n tuple: A tuple containing the vector store, memory, and QA chain.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Returns the configuration file path based on the operating system.\n\n This function determines the appropriate configuration directory based on the operating system\n and constructs the full path to the configuration file.\n\n Returns:\n str: The path to the configuration file.\n\n Raises:\n NotImplementedError: If the operating system is not supported.\n \"\"\""}, {"role": "assistant", "content": "def get_config_path():\n \n system = platform.system()\n\n if system == \"Linux\" or system == \"Darwin\":\n user_home = os.path.expanduser(\"~\")\n config_dir = os.path.join(user_home, \".config\", \"codeqai\")\n elif system == \"Windows\":\n user_home = os.path.expanduser(\"~\")\n config_dir = os.path.join(user_home, \"AppData\", \"Roaming\", \"codeqai\")\n else:\n raise NotImplementedError(f\"Unsupported platform: {system}\")\n\n config_file_path = os.path.join(config_dir, \"config.yaml\")\n\n return config_file_path"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def get_config_path():\n \n system = platform.system()\n\n if system == \"Linux\" or system == \"Darwin\":\n user_home = os.path.expanduser(\"~\")\n config_dir = os.path.join(user_home, \".config\", \"codeqai\")\n elif system == \"Windows\":\n user_home = os.path.expanduser(\"~\")\n config_dir = os.path.join(user_home, \"AppData\", \"Roaming\", \"codeqai\")\n else:\n raise NotImplementedError(f\"Unsupported platform: {system}\")\n\n config_file_path = os.path.join(config_dir, \"config.yaml\")\n\n return config_file_path"}, {"role": "assistant", "content": "\"\"\"\n Returns the configuration file path based on the operating system.\n\n This function determines the appropriate configuration directory based on the operating system\n and constructs the full path to the configuration file.\n\n Returns:\n str: The path to the configuration file.\n\n Raises:\n NotImplementedError: If the operating system is not supported.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Loads the configuration from the configuration file.\n\n This function reads the configuration file specified by get_config_path() and parses its content\n using the YAML parser.\n\n Returns:\n dict: The configuration dictionary loaded from the file.\n \"\"\""}, {"role": "assistant", "content": "def load_config():\n \n with open(get_config_path(), \"r\", encoding=\"utf-8\") as config_file:\n config = yaml.safe_load(config_file)\n return config"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def load_config():\n \n with open(get_config_path(), \"r\", encoding=\"utf-8\") as config_file:\n config = yaml.safe_load(config_file)\n return config"}, {"role": "assistant", "content": "\"\"\"\n Loads the configuration from the configuration file.\n\n This function reads the configuration file specified by get_config_path() and parses its content\n using the YAML parser.\n\n Returns:\n dict: The configuration dictionary loaded from the file.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Saves the configuration to the configuration file.\n\n Args:\n config (dict): The configuration dictionary to be saved.\n\n This function writes the provided configuration dictionary to the configuration file specified by get_config_path()\n using the YAML format.\n \"\"\""}, {"role": "assistant", "content": "def save_config(config):\n \n with open(get_config_path(), \"w\", encoding=\"utf-8\") as config_file:\n yaml.dump(config, config_file, default_flow_style=False)"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def save_config(config):\n \n with open(get_config_path(), \"w\", encoding=\"utf-8\") as config_file:\n yaml.dump(config, config_file, default_flow_style=False)"}, {"role": "assistant", "content": "\"\"\"\n Saves the configuration to the configuration file.\n\n Args:\n config (dict): The configuration dictionary to be saved.\n\n This function writes the provided configuration dictionary to the configuration file specified by get_config_path()\n using the YAML format.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Creates a new configuration interactively by prompting the user for input.\n\n This function prompts the user with a series of questions to configure the embeddings model and LLM host.\n Based on the user's responses, it constructs a configuration dictionary and saves it to the configuration file.\n\n Returns:\n dict: The configuration dictionary created based on user input.\n \"\"\""}, {"role": "assistant", "content": "def create_config():\n \n os.makedirs(os.path.dirname(get_config_path()), exist_ok=True)\n\n questions = [\n inquirer.Confirm(\n \"confirm\",\n message=\"Do you want to use local embedding models?\",\n default=False,\n ),\n ]\n\n confirm = inquirer.prompt(questions)\n\n if confirm and confirm[\"confirm\"]:\n questions = [\n inquirer.List(\n \"embeddings\",\n message=\"Which local embeddings model do you want to use?\",\n choices=[\n EmbeddingsModel.INSTRUCTOR_LARGE.value,\n EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MPNET_BASE_V2.value,\n EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MINILM_L6_V2.value,\n ],\n default=EmbeddingsModel.INSTRUCTOR_LARGE.value,\n ),\n ]\n else:\n questions = [\n inquirer.List(\n \"embeddings\",\n message=\"Which remote embeddings do you want to use?\",\n choices=[\n EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002.value,\n EmbeddingsModel.AZURE_OPENAI.value,\n ],\n default=EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002.value,\n ),\n ]\n\n answersEmbedding = inquirer.prompt(questions)\n\n questions = [\n inquirer.Confirm(\n \"confirm\", message=\"Do you want to use local chat models?\", default=False\n ),\n ]\n\n confirm = inquirer.prompt(questions)\n\n if confirm and confirm[\"confirm\"]:\n questions = [\n inquirer.List(\n \"llm-host\",\n message=\"Which local LLM host do you want to use?\",\n choices=[\n LlmHost.LLAMACPP.value,\n LlmHost.OLLAMA.value,\n ],\n default=LlmHost.LLAMACPP.value,\n ),\n ]\n else:\n questions = [\n inquirer.List(\n \"llm-host\",\n message=\"Which remote LLM provider do you want to use?\",\n choices=[\n LlmHost.OPENAI.value,\n LlmHost.AZURE_OPENAI.value,\n LlmHost.ANTHROPIC.value,\n ],\n default=LlmHost.OPENAI.value,\n ),\n ]\n\n answersLlm = inquirer.prompt(questions)\n\n if confirm and answersEmbedding and answersLlm:\n config = {\n \"embeddings\": answersEmbedding[\"embeddings\"],\n \"llm-host\": answersLlm[\"llm-host\"],\n }\n\n if config[\"embeddings\"] == EmbeddingsModel.AZURE_OPENAI.value:\n questions = [\n inquirer.Text(\n \"deployment\",\n message=\"Please enter the Azure OpenAI embeddings deployment name.\",\n default=\"\",\n ),\n ]\n deployment_answer = inquirer.prompt(questions)\n if deployment_answer and deployment_answer[\"deployment\"]:\n config[\"embeddings-deployment\"] = deployment_answer[\"deployment\"]\n\n if config[\"llm-host\"] == LlmHost.AZURE_OPENAI.value:\n questions = [\n inquirer.Text(\n \"deployment\",\n message=\"Please enter the Azure OpenAI model deployment name\",\n default=\"\",\n ),\n ]\n deployment_answer = inquirer.prompt(questions)\n if deployment_answer and deployment_answer[\"deployment\"]:\n config[\"model-deployment\"] = deployment_answer[\"deployment\"]\n config[\"chat-model\"] = deployment_answer[\"deployment\"]\n\n elif config[\"llm-host\"] == LlmHost.LLAMACPP.value:\n questions = [\n inquirer.Text(\n \"chat-model\",\n message=\"Please enter the path to the LLM\",\n default=\"\",\n ),\n ]\n\n elif config[\"llm-host\"] == LlmHost.OLLAMA.value:\n questions = [\n inquirer.List(\n \"chat-model\",\n message=\"Which model do you want to use with Ollama?\",\n choices=[\n \"llama2:7b\",\n \"llama2:13b\",\n \"llama3.2:1b\",\n \"llama3.2:3b\",\n \"llama3.1:8b\",\n \"codellama:7b\",\n \"codellama:13b\",\n \"gemma2:9b\",\n \"gemma2:2b\",\n \"deepseek-r1:1.5b\",\n \"deepseek-r1:7b\",\n \"deepseek-r1:8b\",\n \"qwen2.5-coder:7b\",\n \"qwen2.5-coder:3b\",\n ],\n default=\"llama2:13b\",\n ),\n ]\n\n elif config[\"llm-host\"] == LlmHost.OPENAI.value:\n questions = [\n inquirer.List(\n \"chat-model\",\n message=\"Which OpenAI model do you want to use?\",\n choices=[\n \"gpt-3.5-turbo\",\n \"gpt-4\",\n \"gpt-4-turbo\",\n \"gpt-4o\",\n \"gpt-4o-mini\",\n \"o1\",\n \"o1-mini\",\n \"o3-mini\",\n ],\n default=\"gpt-3.5-turbo\",\n ),\n ]\n\n elif config[\"llm-host\"] == LlmHost.ANTHROPIC.value:\n questions = [\n inquirer.List(\n \"chat-model\",\n message=\"Which Anthropic model do you want to use?\",\n choices=[\n \"claude-3-opus-latest\",\n \"claude-3-5-sonnet-latest\",\n \"claude-3-5-haiku-latest\",\n ],\n default=\"claude-3-opus-latest\",\n ),\n ]\n\n # Check if \"chat-model\" is already present in the case of Azure_OpenAI\n if \"chat-model\" not in config:\n answersChatmodel = inquirer.prompt(questions)\n if answersChatmodel and answersChatmodel[\"chat-model\"]:\n config[\"chat-model\"] = answersChatmodel[\"chat-model\"]\n\n save_config(config)\n\n return config\n\n return {}"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def create_config():\n \n os.makedirs(os.path.dirname(get_config_path()), exist_ok=True)\n\n questions = [\n inquirer.Confirm(\n \"confirm\",\n message=\"Do you want to use local embedding models?\",\n default=False,\n ),\n ]\n\n confirm = inquirer.prompt(questions)\n\n if confirm and confirm[\"confirm\"]:\n questions = [\n inquirer.List(\n \"embeddings\",\n message=\"Which local embeddings model do you want to use?\",\n choices=[\n EmbeddingsModel.INSTRUCTOR_LARGE.value,\n EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MPNET_BASE_V2.value,\n EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MINILM_L6_V2.value,\n ],\n default=EmbeddingsModel.INSTRUCTOR_LARGE.value,\n ),\n ]\n else:\n questions = [\n inquirer.List(\n \"embeddings\",\n message=\"Which remote embeddings do you want to use?\",\n choices=[\n EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002.value,\n EmbeddingsModel.AZURE_OPENAI.value,\n ],\n default=EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002.value,\n ),\n ]\n\n answersEmbedding = inquirer.prompt(questions)\n\n questions = [\n inquirer.Confirm(\n \"confirm\", message=\"Do you want to use local chat models?\", default=False\n ),\n ]\n\n confirm = inquirer.prompt(questions)\n\n if confirm and confirm[\"confirm\"]:\n questions = [\n inquirer.List(\n \"llm-host\",\n message=\"Which local LLM host do you want to use?\",\n choices=[\n LlmHost.LLAMACPP.value,\n LlmHost.OLLAMA.value,\n ],\n default=LlmHost.LLAMACPP.value,\n ),\n ]\n else:\n questions = [\n inquirer.List(\n \"llm-host\",\n message=\"Which remote LLM provider do you want to use?\",\n choices=[\n LlmHost.OPENAI.value,\n LlmHost.AZURE_OPENAI.value,\n LlmHost.ANTHROPIC.value,\n ],\n default=LlmHost.OPENAI.value,\n ),\n ]\n\n answersLlm = inquirer.prompt(questions)\n\n if confirm and answersEmbedding and answersLlm:\n config = {\n \"embeddings\": answersEmbedding[\"embeddings\"],\n \"llm-host\": answersLlm[\"llm-host\"],\n }\n\n if config[\"embeddings\"] == EmbeddingsModel.AZURE_OPENAI.value:\n questions = [\n inquirer.Text(\n \"deployment\",\n message=\"Please enter the Azure OpenAI embeddings deployment name.\",\n default=\"\",\n ),\n ]\n deployment_answer = inquirer.prompt(questions)\n if deployment_answer and deployment_answer[\"deployment\"]:\n config[\"embeddings-deployment\"] = deployment_answer[\"deployment\"]\n\n if config[\"llm-host\"] == LlmHost.AZURE_OPENAI.value:\n questions = [\n inquirer.Text(\n \"deployment\",\n message=\"Please enter the Azure OpenAI model deployment name\",\n default=\"\",\n ),\n ]\n deployment_answer = inquirer.prompt(questions)\n if deployment_answer and deployment_answer[\"deployment\"]:\n config[\"model-deployment\"] = deployment_answer[\"deployment\"]\n config[\"chat-model\"] = deployment_answer[\"deployment\"]\n\n elif config[\"llm-host\"] == LlmHost.LLAMACPP.value:\n questions = [\n inquirer.Text(\n \"chat-model\",\n message=\"Please enter the path to the LLM\",\n default=\"\",\n ),\n ]\n\n elif config[\"llm-host\"] == LlmHost.OLLAMA.value:\n questions = [\n inquirer.List(\n \"chat-model\",\n message=\"Which model do you want to use with Ollama?\",\n choices=[\n \"llama2:7b\",\n \"llama2:13b\",\n \"llama3.2:1b\",\n \"llama3.2:3b\",\n \"llama3.1:8b\",\n \"codellama:7b\",\n \"codellama:13b\",\n \"gemma2:9b\",\n \"gemma2:2b\",\n \"deepseek-r1:1.5b\",\n \"deepseek-r1:7b\",\n \"deepseek-r1:8b\",\n \"qwen2.5-coder:7b\",\n \"qwen2.5-coder:3b\",\n ],\n default=\"llama2:13b\",\n ),\n ]\n\n elif config[\"llm-host\"] == LlmHost.OPENAI.value:\n questions = [\n inquirer.List(\n \"chat-model\",\n message=\"Which OpenAI model do you want to use?\",\n choices=[\n \"gpt-3.5-turbo\",\n \"gpt-4\",\n \"gpt-4-turbo\",\n \"gpt-4o\",\n \"gpt-4o-mini\",\n \"o1\",\n \"o1-mini\",\n \"o3-mini\",\n ],\n default=\"gpt-3.5-turbo\",\n ),\n ]\n\n elif config[\"llm-host\"] == LlmHost.ANTHROPIC.value:\n questions = [\n inquirer.List(\n \"chat-model\",\n message=\"Which Anthropic model do you want to use?\",\n choices=[\n \"claude-3-opus-latest\",\n \"claude-3-5-sonnet-latest\",\n \"claude-3-5-haiku-latest\",\n ],\n default=\"claude-3-opus-latest\",\n ),\n ]\n\n # Check if \"chat-model\" is already present in the case of Azure_OpenAI\n if \"chat-model\" not in config:\n answersChatmodel = inquirer.prompt(questions)\n if answersChatmodel and answersChatmodel[\"chat-model\"]:\n config[\"chat-model\"] = answersChatmodel[\"chat-model\"]\n\n save_config(config)\n\n return config\n\n return {}"}, {"role": "assistant", "content": "\"\"\"\n Creates a new configuration interactively by prompting the user for input.\n\n This function prompts the user with a series of questions to configure the embeddings model and LLM host.\n Based on the user's responses, it constructs a configuration dictionary and saves it to the configuration file.\n\n Returns:\n dict: The configuration dictionary created based on user input.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Loads documents into the vector store.\n\n This method reads the serialized FAISS index from a file, deserializes it, and loads it into the FAISS database.\n It also loads the vector cache from a JSON file and initializes the retriever with the specified search parameters.\n \"\"\""}, {"role": "assistant", "content": "def load_documents(self):\n \n with open(\n os.path.join(get_cache_path(), f\"{self.name}.faiss.bytes\"), \"rb\"\n ) as file:\n index = file.read()\n\n self.db = FAISS.deserialize_from_bytes(\n embeddings=self.embeddings, serialized=index\n )\n self.vector_cache = load_vector_cache(f\"{self.name}.json\")\n self.retriever = self.db.as_retriever(search_type=\"mmr\", search_kwargs={\"k\": 8})"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def load_documents(self):\n \n with open(\n os.path.join(get_cache_path(), f\"{self.name}.faiss.bytes\"), \"rb\"\n ) as file:\n index = file.read()\n\n self.db = FAISS.deserialize_from_bytes(\n embeddings=self.embeddings, serialized=index\n )\n self.vector_cache = load_vector_cache(f\"{self.name}.json\")\n self.retriever = self.db.as_retriever(search_type=\"mmr\", search_kwargs={\"k\": 8})"}, {"role": "assistant", "content": "\"\"\"\n Loads documents into the vector store.\n\n This method reads the serialized FAISS index from a file, deserializes it, and loads it into the FAISS database.\n It also loads the vector cache from a JSON file and initializes the retriever with the specified search parameters.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Indexes the given documents and stores them in the vector store.\n\n This method creates a FAISS index from the provided documents and serializes it to a file.\n It also creates a vector cache for quick lookup of document vectors and initializes the retriever.\n\n Args:\n documents (list[Document]): A list of Document objects to be indexed.\n \"\"\""}, {"role": "assistant", "content": "def index_documents(self, documents: list[Document]):\n \n self.vector_cache = {}\n self.db = FAISS.from_documents(documents, self.embeddings)\n index = self.db.serialize_to_bytes()\n with open(\n os.path.join(get_cache_path(), f\"{self.name}.faiss.bytes\"), \"wb\"\n ) as binary_file:\n binary_file.write(index)\n # Create vector cache\n index_to_docstore_id = self.db.index_to_docstore_id\n for i in range(len(documents)):\n document = self.db.docstore.search(index_to_docstore_id[i])\n if document and document is type(Document):\n # Check if the document is already present in the vector cache\n # if yes, then add the vector id to the vector cache entry\n if self.vector_cache.get(document.metadata[\"filename\"]):\n self.vector_cache[document.metadata[\"filename\"]].vector_ids.append(\n index_to_docstore_id[i]\n )\n # if no, then create a new entry in the vector cache\n else:\n self.vector_cache[document.metadata[\"filename\"]] = VectorCache(\n document.metadata[\"filename\"],\n [index_to_docstore_id[i]],\n document.metadata[\"commit_hash\"],\n )\n\n self.retriever = self.db.as_retriever(search_type=\"mmr\", search_kwargs={\"k\": 8})"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def index_documents(self, documents: list[Document]):\n \n self.vector_cache = {}\n self.db = FAISS.from_documents(documents, self.embeddings)\n index = self.db.serialize_to_bytes()\n with open(\n os.path.join(get_cache_path(), f\"{self.name}.faiss.bytes\"), \"wb\"\n ) as binary_file:\n binary_file.write(index)\n # Create vector cache\n index_to_docstore_id = self.db.index_to_docstore_id\n for i in range(len(documents)):\n document = self.db.docstore.search(index_to_docstore_id[i])\n if document and document is type(Document):\n # Check if the document is already present in the vector cache\n # if yes, then add the vector id to the vector cache entry\n if self.vector_cache.get(document.metadata[\"filename\"]):\n self.vector_cache[document.metadata[\"filename\"]].vector_ids.append(\n index_to_docstore_id[i]\n )\n # if no, then create a new entry in the vector cache\n else:\n self.vector_cache[document.metadata[\"filename\"]] = VectorCache(\n document.metadata[\"filename\"],\n [index_to_docstore_id[i]],\n document.metadata[\"commit_hash\"],\n )\n\n self.retriever = self.db.as_retriever(search_type=\"mmr\", search_kwargs={\"k\": 8})"}, {"role": "assistant", "content": "\"\"\"\n Indexes the given documents and stores them in the vector store.\n\n This method creates a FAISS index from the provided documents and serializes it to a file.\n It also creates a vector cache for quick lookup of document vectors and initializes the retriever.\n\n Args:\n documents (list[Document]): A list of Document objects to be indexed.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Synchronizes the documents in the vector store with the provided files.\n\n This method checks if the documents in the vector store are up-to-date with the provided files.\n If a document has been modified, it deletes the old vectors and adds new vectors.\n If a document is new, it adds the document to the vector store.\n It also removes old documents that are no longer present in the provided files.\n\n Args:\n files (list[str]): List of file paths to synchronize with the vector store.\n \"\"\""}, {"role": "assistant", "content": "def sync_documents(self, files):\n \n new_filenames = set()\n for file in files:\n filename = os.path.basename(file)\n new_filenames.add(filename)\n commit_hash = get_commit_hash(file)\n # Check if the document is already present in the vector cache\n # if yes, then check if the document has been modified or not\n if filename in self.vector_cache:\n # Check if the document has been modified, if yes delete all old vectors and add new vector\n if self.vector_cache[filename].commit_hash != commit_hash:\n # This will delete all the vectors associated with the document\n # incluing db.index_to_docstore_id, db.docstore and db.index\n try:\n self.db.delete(self.vector_cache[filename].vector_ids)\n except Exception as e:\n print(f\"Error deleting vectors for file {filename}: {e}\")\n\n # Add the new document to the vector store and recreate the vector cache entry\n self.vector_cache[filename] = VectorCache(\n filename,\n [],\n commit_hash,\n )\n documents = parse_code_files_for_db([file])\n for document in documents:\n self.db.add_documents([document])\n self.vector_cache[filename].vector_ids.append(\n self.db.index_to_docstore_id[\n len(self.db.index_to_docstore_id) - 1\n ]\n )\n\n # if no, then create a new entry in the vector cache and add the document to the vector store\n else:\n self.vector_cache[filename] = VectorCache(\n filename,\n [],\n commit_hash,\n )\n documents = parse_code_files_for_db([file])\n for document in documents:\n self.db.add_documents([document])\n self.vector_cache[filename].vector_ids.append(\n self.db.index_to_docstore_id[\n len(self.db.index_to_docstore_id) - 1\n ]\n )\n\n # Remove old documents from the vector store\n deleted_files = []\n for cache_item in self.vector_cache.values():\n if cache_item.filename not in new_filenames:\n try:\n self.db.delete(cache_item.vector_ids)\n except Exception as e:\n print(f\"Error deleting vectors for file {cache_item.filename}: {e}\")\n deleted_files.append(cache_item.filename)\n\n # Remove old filenames from the vector cache\n for deleted_file in deleted_files:\n self.vector_cache.pop(deleted_file)\n\n index = self.db.serialize_to_bytes()\n with open(\n os.path.join(get_cache_path(), f\"{self.name}.faiss.bytes\"), \"wb\"\n ) as binary_file:\n binary_file.write(index)"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def sync_documents(self, files):\n \n new_filenames = set()\n for file in files:\n filename = os.path.basename(file)\n new_filenames.add(filename)\n commit_hash = get_commit_hash(file)\n # Check if the document is already present in the vector cache\n # if yes, then check if the document has been modified or not\n if filename in self.vector_cache:\n # Check if the document has been modified, if yes delete all old vectors and add new vector\n if self.vector_cache[filename].commit_hash != commit_hash:\n # This will delete all the vectors associated with the document\n # incluing db.index_to_docstore_id, db.docstore and db.index\n try:\n self.db.delete(self.vector_cache[filename].vector_ids)\n except Exception as e:\n print(f\"Error deleting vectors for file {filename}: {e}\")\n\n # Add the new document to the vector store and recreate the vector cache entry\n self.vector_cache[filename] = VectorCache(\n filename,\n [],\n commit_hash,\n )\n documents = parse_code_files_for_db([file])\n for document in documents:\n self.db.add_documents([document])\n self.vector_cache[filename].vector_ids.append(\n self.db.index_to_docstore_id[\n len(self.db.index_to_docstore_id) - 1\n ]\n )\n\n # if no, then create a new entry in the vector cache and add the document to the vector store\n else:\n self.vector_cache[filename] = VectorCache(\n filename,\n [],\n commit_hash,\n )\n documents = parse_code_files_for_db([file])\n for document in documents:\n self.db.add_documents([document])\n self.vector_cache[filename].vector_ids.append(\n self.db.index_to_docstore_id[\n len(self.db.index_to_docstore_id) - 1\n ]\n )\n\n # Remove old documents from the vector store\n deleted_files = []\n for cache_item in self.vector_cache.values():\n if cache_item.filename not in new_filenames:\n try:\n self.db.delete(cache_item.vector_ids)\n except Exception as e:\n print(f\"Error deleting vectors for file {cache_item.filename}: {e}\")\n deleted_files.append(cache_item.filename)\n\n # Remove old filenames from the vector cache\n for deleted_file in deleted_files:\n self.vector_cache.pop(deleted_file)\n\n index = self.db.serialize_to_bytes()\n with open(\n os.path.join(get_cache_path(), f\"{self.name}.faiss.bytes\"), \"wb\"\n ) as binary_file:\n binary_file.write(index)"}, {"role": "assistant", "content": "\"\"\"\n Synchronizes the documents in the vector store with the provided files.\n\n This method checks if the documents in the vector store are up-to-date with the provided files.\n If a document has been modified, it deletes the old vectors and adds new vectors.\n If a document is new, it adds the document to the vector store.\n It also removes old documents that are no longer present in the provided files.\n\n Args:\n files (list[str]): List of file paths to synchronize with the vector store.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Exports the dataset based on the specified format.\n\n This method checks the format of the dataset and calls the appropriate export method.\n It also prints messages indicating the progress and completion of the export process.\n\n Supported formats:\n - CONVERSATIONAL: Exports to conversational_dataset.json\n - ALPACA: Exports to alpaca_dataset.json\n - INSTRUCTION: Exports to instruction_dataset.json\n \"\"\""}, {"role": "assistant", "content": "def export(self):\n \n print(\"Exporting dataset...\")\n if self.format == DatasetFormat.CONVERSATIONAL.value:\n self.export_conversational()\n print(\"Dataset exported to conversational_dataset.json\")\n elif self.format == DatasetFormat.ALPACA.value:\n self.export_alpaca()\n print(\"Dataset exported to alpaca_dataset.json\")\n elif self.format == DatasetFormat.INSTRUCTION.value:\n self.export_instruction()\n print(\"Dataset exported to instruction_dataset.json\")"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def export(self):\n \n print(\"Exporting dataset...\")\n if self.format == DatasetFormat.CONVERSATIONAL.value:\n self.export_conversational()\n print(\"Dataset exported to conversational_dataset.json\")\n elif self.format == DatasetFormat.ALPACA.value:\n self.export_alpaca()\n print(\"Dataset exported to alpaca_dataset.json\")\n elif self.format == DatasetFormat.INSTRUCTION.value:\n self.export_instruction()\n print(\"Dataset exported to instruction_dataset.json\")"}, {"role": "assistant", "content": "\"\"\"\n Exports the dataset based on the specified format.\n\n This method checks the format of the dataset and calls the appropriate export method.\n It also prints messages indicating the progress and completion of the export process.\n\n Supported formats:\n - CONVERSATIONAL: Exports to conversational_dataset.json\n - ALPACA: Exports to alpaca_dataset.json\n - INSTRUCTION: Exports to instruction_dataset.json\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Exports the code snippets in a conversational format.\n\n This method processes each code snippet in the dataset and creates conversational messages\n for both implementation and explanation tasks. The messages are then saved to a JSON file.\n \"\"\""}, {"role": "assistant", "content": "def export_conversational(self):\n \n messages_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if (\n self.distillation_mode == DistillationMode.DOCUMENTATION\n or self.distillation_mode == DistillationMode.FULL\n ):\n docstring = self.distill_docstring(code_snippet)\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n if (\n self.distillation_mode == DistillationMode.CODE\n or self.distillation_mode == DistillationMode.FULL\n ):\n pass\n\n message = {\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write an implementation for the following description.\",\n },\n {\"role\": \"user\", \"content\": docstring},\n {\"role\": \"assistant\", \"content\": code_snippet.get(\"code\")},\n ]\n }\n\n messages_list.append(message)\n message = {\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Explain the following code.\",\n },\n {\"role\": \"user\", \"content\": code_snippet.get(\"code\")},\n {\n \"role\": \"assistant\",\n \"content\": docstring,\n },\n ]\n }\n\n messages_list.append(message)\n\n with open(\"conversational_dataset.json\", \"w\") as f:\n for messages in messages_list:\n json.dump(messages, f)\n f.write(\"\\n\")"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def export_conversational(self):\n \n messages_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if (\n self.distillation_mode == DistillationMode.DOCUMENTATION\n or self.distillation_mode == DistillationMode.FULL\n ):\n docstring = self.distill_docstring(code_snippet)\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n if (\n self.distillation_mode == DistillationMode.CODE\n or self.distillation_mode == DistillationMode.FULL\n ):\n pass\n\n message = {\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write an implementation for the following description.\",\n },\n {\"role\": \"user\", \"content\": docstring},\n {\"role\": \"assistant\", \"content\": code_snippet.get(\"code\")},\n ]\n }\n\n messages_list.append(message)\n message = {\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Explain the following code.\",\n },\n {\"role\": \"user\", \"content\": code_snippet.get(\"code\")},\n {\n \"role\": \"assistant\",\n \"content\": docstring,\n },\n ]\n }\n\n messages_list.append(message)\n\n with open(\"conversational_dataset.json\", \"w\") as f:\n for messages in messages_list:\n json.dump(messages, f)\n f.write(\"\\n\")"}, {"role": "assistant", "content": "\"\"\"\n Exports the code snippets in a conversational format.\n\n This method processes each code snippet in the dataset and creates conversational messages\n for both implementation and explanation tasks. The messages are then saved to a JSON file.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Exports the code snippets in an Alpaca format.\n\n This method processes each code snippet in the dataset and creates Alpaca entries\n for both implementation and explanation tasks. The entries are then saved to a JSON file.\n \"\"\""}, {"role": "assistant", "content": "def export_alpaca(self):\n \n alpaca_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if (\n self.distillation_mode == DistillationMode.DOCUMENTATION\n or self.distillation_mode == DistillationMode.FULL\n ):\n docstring = self.distill_docstring(code_snippet)\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n if (\n self.distillation_mode == DistillationMode.CODE\n or self.distillation_mode == DistillationMode.FULL\n ):\n pass\n\n alpaca_entry = {\n \"instruction\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write an implementation for the following description.\",\n \"input\": docstring,\n \"output\": code_snippet.get(\"code\"),\n }\n alpaca_list.append(alpaca_entry)\n alpaca_entry = {\n \"instruction\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Explain the following code.\",\n \"input\": code_snippet.get(\"code\"),\n \"output\": docstring,\n }\n alpaca_list.append(alpaca_entry)\n\n with open(\"alpaca_dataset.json\", \"w\") as f:\n json.dump(alpaca_list, f, indent=4)"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def export_alpaca(self):\n \n alpaca_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if (\n self.distillation_mode == DistillationMode.DOCUMENTATION\n or self.distillation_mode == DistillationMode.FULL\n ):\n docstring = self.distill_docstring(code_snippet)\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n if (\n self.distillation_mode == DistillationMode.CODE\n or self.distillation_mode == DistillationMode.FULL\n ):\n pass\n\n alpaca_entry = {\n \"instruction\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write an implementation for the following description.\",\n \"input\": docstring,\n \"output\": code_snippet.get(\"code\"),\n }\n alpaca_list.append(alpaca_entry)\n alpaca_entry = {\n \"instruction\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Explain the following code.\",\n \"input\": code_snippet.get(\"code\"),\n \"output\": docstring,\n }\n alpaca_list.append(alpaca_entry)\n\n with open(\"alpaca_dataset.json\", \"w\") as f:\n json.dump(alpaca_list, f, indent=4)"}, {"role": "assistant", "content": "\"\"\"\n Exports the code snippets in an Alpaca format.\n\n This method processes each code snippet in the dataset and creates Alpaca entries\n for both implementation and explanation tasks. The entries are then saved to a JSON file.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Exports the code snippets in an instruction format.\n\n This method processes each code snippet in the dataset and creates instruction entries\n for both implementation and explanation tasks. The entries are then saved to a JSON file.\n \"\"\""}, {"role": "assistant", "content": "def export_instruction(self):\n \n instructions_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if (\n self.distillation_mode == DistillationMode.DOCUMENTATION\n or self.distillation_mode == DistillationMode.FULL\n ):\n result = self.distill_docstring(code_snippet)\n if type(result) is str:\n docstring = result\n else:\n continue\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n if (\n self.distillation_mode == DistillationMode.CODE\n or self.distillation_mode == DistillationMode.FULL\n ):\n pass\n\n instruction = {\n \"prompt\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write an implementation for the following description:\\n\"\n + (docstring or \"\"),\n \"completion\": code_snippet.get(\"code\"),\n }\n instructions_list.append(instruction)\n instruction = {\n \"prompt\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Explain the following code:\\n\"\n + (code_snippet.get(\"code\") or \"\"),\n \"completion\": docstring,\n }\n instructions_list.append(instruction)\n\n with open(\"instruction_dataset.json\", \"w\") as f:\n json.dump(instructions_list, f, indent=4)"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def export_instruction(self):\n \n instructions_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if (\n self.distillation_mode == DistillationMode.DOCUMENTATION\n or self.distillation_mode == DistillationMode.FULL\n ):\n result = self.distill_docstring(code_snippet)\n if type(result) is str:\n docstring = result\n else:\n continue\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n if (\n self.distillation_mode == DistillationMode.CODE\n or self.distillation_mode == DistillationMode.FULL\n ):\n pass\n\n instruction = {\n \"prompt\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write an implementation for the following description:\\n\"\n + (docstring or \"\"),\n \"completion\": code_snippet.get(\"code\"),\n }\n instructions_list.append(instruction)\n instruction = {\n \"prompt\": \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Explain the following code:\\n\"\n + (code_snippet.get(\"code\") or \"\"),\n \"completion\": docstring,\n }\n instructions_list.append(instruction)\n\n with open(\"instruction_dataset.json\", \"w\") as f:\n json.dump(instructions_list, f, indent=4)"}, {"role": "assistant", "content": "\"\"\"\n Exports the code snippets in an instruction format.\n\n This method processes each code snippet in the dataset and creates instruction entries\n for both implementation and explanation tasks. The entries are then saved to a JSON file.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Exports the code snippets in a completion format.\n\n This method processes each code snippet in the dataset and creates completion entries.\n The entries are then saved to a JSON file.\n \"\"\""}, {"role": "assistant", "content": "def export_completion(self):\n \n completions_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if (\n self.distillation_mode == DistillationMode.DOCUMENTATION\n or self.distillation_mode == DistillationMode.FULL\n ):\n docstring = self.distill_docstring(code_snippet)\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n if (\n self.distillation_mode == DistillationMode.CODE\n or self.distillation_mode == DistillationMode.FULL\n ):\n pass\n\n completion = {\n \"input\": docstring,\n \"output\": code_snippet.get(\"code\"),\n }\n completions_list.append(completion)\n\n with open(\"completion_dataset.json\", \"w\") as f:\n json.dump(completions_list, f, indent=4)"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def export_completion(self):\n \n completions_list = []\n for code_snippet in self.code_snippets:\n if code_snippet.get(\"description\") is None:\n if (\n self.distillation_mode == DistillationMode.DOCUMENTATION\n or self.distillation_mode == DistillationMode.FULL\n ):\n docstring = self.distill_docstring(code_snippet)\n else:\n continue\n else:\n docstring = code_snippet.get(\"description\")\n\n if (\n self.distillation_mode == DistillationMode.CODE\n or self.distillation_mode == DistillationMode.FULL\n ):\n pass\n\n completion = {\n \"input\": docstring,\n \"output\": code_snippet.get(\"code\"),\n }\n completions_list.append(completion)\n\n with open(\"completion_dataset.json\", \"w\") as f:\n json.dump(completions_list, f, indent=4)"}, {"role": "assistant", "content": "\"\"\"\n Exports the code snippets in a completion format.\n\n This method processes each code snippet in the dataset and creates completion entries.\n The entries are then saved to a JSON file.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Distills a concise description from the given code snippet.\n\n Args:\n code_snippet (dict): A dictionary containing details about the code snippet, such as method name, programming language, and the actual code.\n\n Returns:\n str: A concise description of the code snippet.\n \"\"\""}, {"role": "assistant", "content": "def distill_docstring(self, code_snippet):\n \n spinner = yaspin(\n text=f\"Distilling {code_snippet.get('method_name')}...\",\n color=\"green\",\n )\n spinner.start()\n prompt = (\n \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write a short and concise description for the following code. Return only the description.\"\n )\n docstring = self.llm.chat_model.invoke(\n [\n (\"system\", prompt),\n (\"human\", code_snippet.get(\"code\") or \"\"),\n ]\n )\n spinner.stop()\n return docstring.content"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def distill_docstring(self, code_snippet):\n \n spinner = yaspin(\n text=f\"Distilling {code_snippet.get('method_name')}...\",\n color=\"green\",\n )\n spinner.start()\n prompt = (\n \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Write a short and concise description for the following code. Return only the description.\"\n )\n docstring = self.llm.chat_model.invoke(\n [\n (\"system\", prompt),\n (\"human\", code_snippet.get(\"code\") or \"\"),\n ]\n )\n spinner.stop()\n return docstring.content"}, {"role": "assistant", "content": "\"\"\"\n Distills a concise description from the given code snippet.\n\n Args:\n code_snippet (dict): A dictionary containing details about the code snippet, such as method name, programming language, and the actual code.\n\n Returns:\n str: A concise description of the code snippet.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Distills a given code snippet into smaller chunks and provides explanations for each chunk.\n\n Args:\n code_snippet (dict): A dictionary containing details about the code snippet, such as method name, programming language, and the actual code.\n\n Returns:\n dict: A dictionary containing the distilled code chunks and their explanations.\n \"\"\""}, {"role": "assistant", "content": "def distill_code(self, code_snippet):\n \n spinner = yaspin(\n text=f\"Distilling {code_snippet.get('method_name')}...\",\n color=\"green\",\n )\n spinner.start()\n prompt = (\n \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Split the following code into reasonable chunks and explain each chunk. \"\n + \"Return a JSON object with a list of objects containing the code chunk with key 'code' and the explanation with key 'explanation'.\"\n )\n code = self.llm.chat_model.invoke(\n [\n (\"system\", prompt),\n (\"human\", code_snippet.get(\"code\") or \"\"),\n ]\n )\n spinner.stop()\n\n try:\n # Ensure the content is a string before parsing\n if isinstance(code.content, str):\n # Parse the output to a JSON object\n code_json = json.loads(code.content)\n else:\n raise ValueError(\"Content is not a valid JSON string\")\n except (json.JSONDecodeError, ValueError) as e:\n # Handle JSON parsing error\n print(f\"Error parsing response JSON: {e}\")\n return {}\n\n return code_json"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def distill_code(self, code_snippet):\n \n spinner = yaspin(\n text=f\"Distilling {code_snippet.get('method_name')}...\",\n color=\"green\",\n )\n spinner.start()\n prompt = (\n \"You are a \"\n + (code_snippet.get(\"language\") or \"programming\")\n + \" expert. Split the following code into reasonable chunks and explain each chunk. \"\n + \"Return a JSON object with a list of objects containing the code chunk with key 'code' and the explanation with key 'explanation'.\"\n )\n code = self.llm.chat_model.invoke(\n [\n (\"system\", prompt),\n (\"human\", code_snippet.get(\"code\") or \"\"),\n ]\n )\n spinner.stop()\n\n try:\n # Ensure the content is a string before parsing\n if isinstance(code.content, str):\n # Parse the output to a JSON object\n code_json = json.loads(code.content)\n else:\n raise ValueError(\"Content is not a valid JSON string\")\n except (json.JSONDecodeError, ValueError) as e:\n # Handle JSON parsing error\n print(f\"Error parsing response JSON: {e}\")\n return {}\n\n return code_json"}, {"role": "assistant", "content": "\"\"\"\n Distills a given code snippet into smaller chunks and provides explanations for each chunk.\n\n Args:\n code_snippet (dict): A dictionary containing details about the code snippet, such as method name, programming language, and the actual code.\n\n Returns:\n dict: A dictionary containing the distilled code chunks and their explanations.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Loads a vector cache from a JSON file.\n\n Args:\n filename (str): The name of the file containing the vector cache.\n\n Returns:\n Dict[str, VectorCache]: A dictionary where the keys are strings and the values are VectorCache objects.\n \"\"\""}, {"role": "assistant", "content": "def load_vector_cache(filename) -> Dict[str, VectorCache]:\n \n with open(\n get_cache_path() + \"/\" + filename, \"r\", encoding=\"utf-8\"\n ) as vector_cache_file:\n vector_cache_json = json.load(vector_cache_file)\n vector_cache = {}\n for key, value in vector_cache_json.items():\n vector_cache[key] = VectorCache.from_json(value)\n return vector_cache"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def load_vector_cache(filename) -> Dict[str, VectorCache]:\n \n with open(\n get_cache_path() + \"/\" + filename, \"r\", encoding=\"utf-8\"\n ) as vector_cache_file:\n vector_cache_json = json.load(vector_cache_file)\n vector_cache = {}\n for key, value in vector_cache_json.items():\n vector_cache[key] = VectorCache.from_json(value)\n return vector_cache"}, {"role": "assistant", "content": "\"\"\"\n Loads a vector cache from a JSON file.\n\n Args:\n filename (str): The name of the file containing the vector cache.\n\n Returns:\n Dict[str, VectorCache]: A dictionary where the keys are strings and the values are VectorCache objects.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Saves a vector cache to a JSON file.\n\n Args:\n vector_cache (Dict[str, VectorCache]): A dictionary where the keys are strings and the values are VectorCache objects.\n filename (str): The name of the file to save the vector cache to.\n \"\"\""}, {"role": "assistant", "content": "def save_vector_cache(vector_cache, filename):\n \n with open(\n get_cache_path() + \"/\" + filename, \"w\", encoding=\"utf-8\"\n ) as vector_cache_file:\n json.dump(vector_cache, default=VectorCache.to_json, fp=vector_cache_file)"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def save_vector_cache(vector_cache, filename):\n \n with open(\n get_cache_path() + \"/\" + filename, \"w\", encoding=\"utf-8\"\n ) as vector_cache_file:\n json.dump(vector_cache, default=VectorCache.to_json, fp=vector_cache_file)"}, {"role": "assistant", "content": "\"\"\"\n Saves a vector cache to a JSON file.\n\n Args:\n vector_cache (Dict[str, VectorCache]): A dictionary where the keys are strings and the values are VectorCache objects.\n filename (str): The name of the file to save the vector cache to.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Returns the cache directory path based on the operating system.\n\n Returns:\n str: The path to the cache directory.\n\n Raises:\n NotImplementedError: If the operating system is not supported.\n \"\"\""}, {"role": "assistant", "content": "def get_cache_path():\n \n system = platform.system()\n\n if system == \"Linux\" or system == \"Darwin\":\n user_home = os.path.expanduser(\"~\")\n cache_dir = os.path.join(user_home, \".cache\", \"codeqai\")\n elif system == \"Windows\":\n user_home = os.path.expanduser(\"~\")\n cache_dir = os.path.join(user_home, \"AppData\", \"Local\", \"codeqai\")\n else:\n raise NotImplementedError(f\"Unsupported platform: {system}\")\n\n return cache_dir"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def get_cache_path():\n \n system = platform.system()\n\n if system == \"Linux\" or system == \"Darwin\":\n user_home = os.path.expanduser(\"~\")\n cache_dir = os.path.join(user_home, \".cache\", \"codeqai\")\n elif system == \"Windows\":\n user_home = os.path.expanduser(\"~\")\n cache_dir = os.path.join(user_home, \"AppData\", \"Local\", \"codeqai\")\n else:\n raise NotImplementedError(f\"Unsupported platform: {system}\")\n\n return cache_dir"}, {"role": "assistant", "content": "\"\"\"\n Returns the cache directory path based on the operating system.\n\n Returns:\n str: The path to the cache directory.\n\n Raises:\n NotImplementedError: If the operating system is not supported.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Creates the cache directory if it does not already exist.\n\n This function checks if the cache directory exists at the path returned by get_cache_path().\n If the directory does not exist, it creates the directory and any necessary parent directories.\n \"\"\""}, {"role": "assistant", "content": "def create_cache_dir():\n \n if not os.path.exists(get_cache_path()):\n path = Path(get_cache_path())\n path.mkdir(parents=True, exist_ok=True)"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def create_cache_dir():\n \n if not os.path.exists(get_cache_path()):\n path = Path(get_cache_path())\n path.mkdir(parents=True, exist_ok=True)"}, {"role": "assistant", "content": "\"\"\"\n Creates the cache directory if it does not already exist.\n\n This function checks if the cache directory exists at the path returned by get_cache_path().\n If the directory does not exist, it creates the directory and any necessary parent directories.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Initializes the LLM class with the specified parameters.\n\n Args:\n llm_host (LlmHost): The host for the language model (e.g., OPENAI, AZURE_OPENAI, ANTHROPIC, LLAMACPP, OLLAMA).\n chat_model (str): The chat model to use.\n max_tokens (int, optional): The maximum number of tokens for the model. Defaults to 2048.\n deployment (str, optional): The deployment name for Azure OpenAI. Defaults to None.\n\n Raises:\n ValueError: If the required environment variable for Azure OpenAI is not set.\n \"\"\""}, {"role": "assistant", "content": "def __init__(\n self, llm_host: LlmHost, chat_model: str, max_tokens=2048, deployment=None\n ):\n \n if llm_host == LlmHost.OPENAI:\n self.chat_model = ChatOpenAI(\n temperature=0.9, max_tokens=max_tokens, model=chat_model\n )\n elif llm_host == LlmHost.AZURE_OPENAI and deployment:\n azure_openai_endpoint = os.getenv(\"AZURE_OPENAI_ENDPOINT\")\n if azure_openai_endpoint:\n self.chat_model = AzureChatOpenAI(\n azure_endpoint=azure_openai_endpoint,\n temperature=0.9,\n max_tokens=max_tokens,\n model=chat_model,\n )\n else:\n raise ValueError(\n \"Azure OpenAI requires environment variable AZURE_OPENAI_ENDPOINT to be set.\"\n )\n elif llm_host == LlmHost.ANTHROPIC:\n self.chat_model = ChatAnthropic(\n temperature=0.9,\n max_tokens_to_sample=max_tokens,\n model_name=chat_model,\n timeout=30,\n api_key=None, # API key is set to environment variable ANTHROPIC_API_KEY\n )\n elif llm_host == LlmHost.LLAMACPP:\n self.install_llama_cpp()\n self.chat_model = LlamaCpp(\n model_path=chat_model,\n temperature=0.9,\n max_tokens=max_tokens,\n verbose=False,\n )\n elif llm_host == LlmHost.OLLAMA:\n self.chat_model = Ollama(\n base_url=\"http://localhost:11434\",\n model=chat_model,\n callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n )"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def __init__(\n self, llm_host: LlmHost, chat_model: str, max_tokens=2048, deployment=None\n ):\n \n if llm_host == LlmHost.OPENAI:\n self.chat_model = ChatOpenAI(\n temperature=0.9, max_tokens=max_tokens, model=chat_model\n )\n elif llm_host == LlmHost.AZURE_OPENAI and deployment:\n azure_openai_endpoint = os.getenv(\"AZURE_OPENAI_ENDPOINT\")\n if azure_openai_endpoint:\n self.chat_model = AzureChatOpenAI(\n azure_endpoint=azure_openai_endpoint,\n temperature=0.9,\n max_tokens=max_tokens,\n model=chat_model,\n )\n else:\n raise ValueError(\n \"Azure OpenAI requires environment variable AZURE_OPENAI_ENDPOINT to be set.\"\n )\n elif llm_host == LlmHost.ANTHROPIC:\n self.chat_model = ChatAnthropic(\n temperature=0.9,\n max_tokens_to_sample=max_tokens,\n model_name=chat_model,\n timeout=30,\n api_key=None, # API key is set to environment variable ANTHROPIC_API_KEY\n )\n elif llm_host == LlmHost.LLAMACPP:\n self.install_llama_cpp()\n self.chat_model = LlamaCpp(\n model_path=chat_model,\n temperature=0.9,\n max_tokens=max_tokens,\n verbose=False,\n )\n elif llm_host == LlmHost.OLLAMA:\n self.chat_model = Ollama(\n base_url=\"http://localhost:11434\",\n model=chat_model,\n callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n )"}, {"role": "assistant", "content": "\"\"\"\n Initializes the LLM class with the specified parameters.\n\n Args:\n llm_host (LlmHost): The host for the language model (e.g., OPENAI, AZURE_OPENAI, ANTHROPIC, LLAMACPP, OLLAMA).\n chat_model (str): The chat model to use.\n max_tokens (int, optional): The maximum number of tokens for the model. Defaults to 2048.\n deployment (str, optional): The deployment name for Azure OpenAI. Defaults to None.\n\n Raises:\n ValueError: If the required environment variable for Azure OpenAI is not set.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Initializes the Embeddings class with the specified model and deployment.\n\n Args:\n model (EmbeddingsModel): The embeddings model to use. Defaults to OPENAI_TEXT_EMBEDDING_ADA_002.\n deployment (str, optional): The deployment name for Azure OpenAI embeddings. Defaults to None.\n \"\"\""}, {"role": "assistant", "content": "def __init__(\n self,\n model=EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002,\n deployment=None,\n ):\n \n if model == EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002:\n self.embeddings = OpenAIEmbeddings(\n client=None, model=\"text-embedding-ada-002\"\n )\n elif model == EmbeddingsModel.AZURE_OPENAI and deployment:\n self.embeddings = AzureOpenAIEmbeddings(\n client=None, azure_deployment=deployment\n )\n else:\n try:\n import sentence_transformers # noqa: F401\n except ImportError:\n self._install_sentence_transformers()\n\n if model == EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MPNET_BASE_V2:\n self.embeddings = HuggingFaceEmbeddings()\n elif model == EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MINILM_L6_V2:\n self.embeddings = HuggingFaceEmbeddings(\n model_name=EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MINILM_L6_V2.value.replace(\n \"SentenceTransformers-\", \"\"\n )\n )\n elif model == EmbeddingsModel.INSTRUCTOR_LARGE:\n try:\n from InstructorEmbedding import INSTRUCTOR # noqa: F401\n except ImportError:\n self._install_instructor_embedding()\n\n self.embeddings = HuggingFaceEmbeddings(\n model_name=\"hkunlp/instructor-xl\"\n )"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def __init__(\n self,\n model=EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002,\n deployment=None,\n ):\n \n if model == EmbeddingsModel.OPENAI_TEXT_EMBEDDING_ADA_002:\n self.embeddings = OpenAIEmbeddings(\n client=None, model=\"text-embedding-ada-002\"\n )\n elif model == EmbeddingsModel.AZURE_OPENAI and deployment:\n self.embeddings = AzureOpenAIEmbeddings(\n client=None, azure_deployment=deployment\n )\n else:\n try:\n import sentence_transformers # noqa: F401\n except ImportError:\n self._install_sentence_transformers()\n\n if model == EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MPNET_BASE_V2:\n self.embeddings = HuggingFaceEmbeddings()\n elif model == EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MINILM_L6_V2:\n self.embeddings = HuggingFaceEmbeddings(\n model_name=EmbeddingsModel.SENTENCETRANSFORMERS_ALL_MINILM_L6_V2.value.replace(\n \"SentenceTransformers-\", \"\"\n )\n )\n elif model == EmbeddingsModel.INSTRUCTOR_LARGE:\n try:\n from InstructorEmbedding import INSTRUCTOR # noqa: F401\n except ImportError:\n self._install_instructor_embedding()\n\n self.embeddings = HuggingFaceEmbeddings(\n model_name=\"hkunlp/instructor-xl\"\n )"}, {"role": "assistant", "content": "\"\"\"\n Initializes the Embeddings class with the specified model and deployment.\n\n Args:\n model (EmbeddingsModel): The embeddings model to use. Defaults to OPENAI_TEXT_EMBEDDING_ADA_002.\n deployment (str, optional): The deployment name for Azure OpenAI embeddings. Defaults to None.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Returns the programming language based on the provided file extension.\n\n Args:\n file_extension (str): The file extension to determine the programming language of.\n\n Returns:\n Language: The programming language corresponding to the file extension. If the file extension is not found\n in the language mapping, returns Language.UNKNOWN.\n \"\"\""}, {"role": "assistant", "content": "def get_programming_language(file_extension: str) -> Language:\n \n language_mapping = {\n \".py\": Language.PYTHON,\n \".js\": Language.JAVASCRIPT,\n \".jsx\": Language.JAVASCRIPT,\n \".mjs\": Language.JAVASCRIPT,\n \".cjs\": Language.JAVASCRIPT,\n \".ts\": Language.TYPESCRIPT,\n \".tsx\": Language.TYPESCRIPT,\n \".java\": Language.JAVA,\n \".kt\": Language.KOTLIN,\n \".rs\": Language.RUST,\n \".go\": Language.GO,\n \".cpp\": Language.CPP,\n \".c\": Language.C,\n \".cs\": Language.C_SHARP,\n \".hs\": Language.HASKELL,\n \".rb\": Language.RUBY,\n }\n return language_mapping.get(file_extension, Language.UNKNOWN)"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def get_programming_language(file_extension: str) -> Language:\n \n language_mapping = {\n \".py\": Language.PYTHON,\n \".js\": Language.JAVASCRIPT,\n \".jsx\": Language.JAVASCRIPT,\n \".mjs\": Language.JAVASCRIPT,\n \".cjs\": Language.JAVASCRIPT,\n \".ts\": Language.TYPESCRIPT,\n \".tsx\": Language.TYPESCRIPT,\n \".java\": Language.JAVA,\n \".kt\": Language.KOTLIN,\n \".rs\": Language.RUST,\n \".go\": Language.GO,\n \".cpp\": Language.CPP,\n \".c\": Language.C,\n \".cs\": Language.C_SHARP,\n \".hs\": Language.HASKELL,\n \".rb\": Language.RUBY,\n }\n return language_mapping.get(file_extension, Language.UNKNOWN)"}, {"role": "assistant", "content": "\"\"\"\n Returns the programming language based on the provided file extension.\n\n Args:\n file_extension (str): The file extension to determine the programming language of.\n\n Returns:\n Language: The programming language corresponding to the file extension. If the file extension is not found\n in the language mapping, returns Language.UNKNOWN.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Returns the extension of a file from its given name.\n\n Parameters:\n file_name (str): The name of the file.\n\n Returns:\n str: The extension of the file.\n\n \"\"\""}, {"role": "assistant", "content": "def get_file_extension(file_name: str) -> str:\n \n return os.path.splitext(file_name)[-1]"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def get_file_extension(file_name: str) -> str:\n \n return os.path.splitext(file_name)[-1]"}, {"role": "assistant", "content": "\"\"\"\n Returns the extension of a file from its given name.\n\n Parameters:\n file_name (str): The name of the file.\n\n Returns:\n str: The extension of the file.\n\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Returns the given text formatted in bold.\n\n Args:\n text (str): The text to be formatted.\n\n Returns:\n str: The text formatted in bold using ANSI escape codes.\n \"\"\""}, {"role": "assistant", "content": "def get_bold_text(text):\n \n return f\"\\033[01m{text}\\033[0m\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def get_bold_text(text):\n \n return f\"\\033[01m{text}\\033[0m\""}, {"role": "assistant", "content": "\"\"\"\n Returns the given text formatted in bold.\n\n Args:\n text (str): The text to be formatted.\n\n Returns:\n str: The text formatted in bold using ANSI escape codes.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Finds the starting line number and indentation level of a code snippet within a file.\n\n Args:\n filename (str): The name of the file to search within.\n code_snippet (str): The code snippet to find in the file.\n\n Returns:\n tuple: A tuple containing the starting line number (int) and the indentation level (str) of the code snippet.\n If the file is not found or the code snippet is not found, returns (1, \"\").\n \"\"\""}, {"role": "assistant", "content": "def find_starting_line_and_indent(filename, code_snippet):\n \n file_path = find_file_in_git_repo(filename)\n if file_path is not None:\n with open(file_path, \"r\") as file:\n file_content = file.read()\n start_pos = file_content.find(code_snippet)\n return (\n file_content.count(\"\\n\", 0, start_pos) + 1,\n file_content[:start_pos].split(\"\\n\")[-1],\n )\n return 1, \"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def find_starting_line_and_indent(filename, code_snippet):\n \n file_path = find_file_in_git_repo(filename)\n if file_path is not None:\n with open(file_path, \"r\") as file:\n file_content = file.read()\n start_pos = file_content.find(code_snippet)\n return (\n file_content.count(\"\\n\", 0, start_pos) + 1,\n file_content[:start_pos].split(\"\\n\")[-1],\n )\n return 1, \"\""}, {"role": "assistant", "content": "\"\"\"\n Finds the starting line number and indentation level of a code snippet within a file.\n\n Args:\n filename (str): The name of the file to search within.\n code_snippet (str): The code snippet to find in the file.\n\n Returns:\n tuple: A tuple containing the starting line number (int) and the indentation level (str) of the code snippet.\n If the file is not found or the code snippet is not found, returns (1, \"\").\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Counts the number of tokens in the given text using the specified model's tokenizer.\n\n Args:\n text (str): The text to be tokenized and counted.\n model (str, optional): The model to use for tokenization. Defaults to \"gpt-4\".\n\n Returns:\n int: The number of tokens in the text.\n \"\"\""}, {"role": "assistant", "content": "def count_tokens(text, model=\"gpt-4\"):\n \n enc = tiktoken.encoding_for_model(model)\n return len(enc.encode(text))"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def count_tokens(text, model=\"gpt-4\"):\n \n enc = tiktoken.encoding_for_model(model)\n return len(enc.encode(text))"}, {"role": "assistant", "content": "\"\"\"\n Counts the number of tokens in the given text using the specified model's tokenizer.\n\n Args:\n text (str): The text to be tokenized and counted.\n model (str, optional): The model to use for tokenization. Defaults to \"gpt-4\".\n\n Returns:\n int: The number of tokens in the text.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Retrieves the name of the current Git repository.\n\n This function gets the root directory of the current Git repository based on the current working directory,\n and extracts the repository name from the root directory path.\n\n Returns:\n str: The name of the current Git repository.\n \"\"\""}, {"role": "assistant", "content": "def repo_name():\n \n return get_git_root(os.getcwd()).split(\"/\")[-1]"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def repo_name():\n \n return get_git_root(os.getcwd()).split(\"/\")[-1]"}, {"role": "assistant", "content": "\"\"\"\n Retrieves the name of the current Git repository.\n\n This function gets the root directory of the current Git repository based on the current working directory,\n and extracts the repository name from the root directory path.\n\n Returns:\n str: The name of the current Git repository.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Retrieves the root directory of the Git repository for the given path.\n\n Args:\n path (str): The path to a directory within the Git repository.\n\n Returns:\n str: The root directory of the Git repository.\n \"\"\""}, {"role": "assistant", "content": "def get_git_root(path):\n \n git_repo = Repo(path, search_parent_directories=True)\n git_root = git_repo.git.rev_parse(\"--show-toplevel\")\n return git_root"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def get_git_root(path):\n \n git_repo = Repo(path, search_parent_directories=True)\n git_root = git_repo.git.rev_parse(\"--show-toplevel\")\n return git_root"}, {"role": "assistant", "content": "\"\"\"\n Retrieves the root directory of the Git repository for the given path.\n\n Args:\n path (str): The path to a directory within the Git repository.\n\n Returns:\n str: The root directory of the Git repository.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Searches for a file with the given name in the current Git repository.\n\n Args:\n file_name (str): The name of the file to search for.\n\n Returns:\n str or None: The full path to the file if found, otherwise None.\n \"\"\""}, {"role": "assistant", "content": "def find_file_in_git_repo(file_name):\n \n git_root = get_git_root(os.getcwd())\n\n for root, dirs, files in os.walk(git_root):\n if any(blacklist in root for blacklist in BLACKLIST_DIR):\n continue\n for file in files:\n if file == file_name:\n return os.path.join(root, file)"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def find_file_in_git_repo(file_name):\n \n git_root = get_git_root(os.getcwd())\n\n for root, dirs, files in os.walk(git_root):\n if any(blacklist in root for blacklist in BLACKLIST_DIR):\n continue\n for file in files:\n if file == file_name:\n return os.path.join(root, file)"}, {"role": "assistant", "content": "\"\"\"\n Searches for a file with the given name in the current Git repository.\n\n Args:\n file_name (str): The name of the file to search for.\n\n Returns:\n str or None: The full path to the file if found, otherwise None.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Loads files from the current Git repository based on whitelist and blacklist criteria.\n\n This function walks through the directory structure of the Git repository,\n and collects files that match the whitelist extensions and are not in the blacklist directories or files.\n\n Returns:\n list: A list of file paths that meet the criteria.\n \"\"\""}, {"role": "assistant", "content": "def load_files():\n \n git_root = get_git_root(os.getcwd())\n file_list = []\n\n for root, dirs, files in os.walk(git_root):\n if any(blacklist in root for blacklist in BLACKLIST_DIR):\n continue\n for file in files:\n file_ext = os.path.splitext(file)[1]\n if any(whitelist == file_ext for whitelist in WHITELIST_FILES):\n if file not in BLACKLIST_FILES:\n file_list.append(os.path.join(root, file))\n\n return file_list"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def load_files():\n \n git_root = get_git_root(os.getcwd())\n file_list = []\n\n for root, dirs, files in os.walk(git_root):\n if any(blacklist in root for blacklist in BLACKLIST_DIR):\n continue\n for file in files:\n file_ext = os.path.splitext(file)[1]\n if any(whitelist == file_ext for whitelist in WHITELIST_FILES):\n if file not in BLACKLIST_FILES:\n file_list.append(os.path.join(root, file))\n\n return file_list"}, {"role": "assistant", "content": "\"\"\"\n Loads files from the current Git repository based on whitelist and blacklist criteria.\n\n This function walks through the directory structure of the Git repository,\n and collects files that match the whitelist extensions and are not in the blacklist directories or files.\n\n Returns:\n list: A list of file paths that meet the criteria.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Retrieves the latest commit hash for the specified file.\n\n Args:\n file_path (str): The path to the file for which to retrieve the commit hash.\n\n Returns:\n str or None: The latest commit hash if found, otherwise None.\n \"\"\""}, {"role": "assistant", "content": "def get_commit_hash(file_path):\n \n try:\n # Run the git log command\n result = subprocess.run(\n [\"git\", \"log\", \"-n\", \"1\", \"--pretty=format:%H\", \"--\", file_path],\n stdout=subprocess.PIPE,\n text=True,\n check=True,\n )\n\n # Extract the commit hash from the command output\n commit_hash = result.stdout.strip()\n return commit_hash\n\n except subprocess.CalledProcessError as e:\n print(f\"Error executing git command: {e}\")\n return None"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def get_commit_hash(file_path):\n \n try:\n # Run the git log command\n result = subprocess.run(\n [\"git\", \"log\", \"-n\", \"1\", \"--pretty=format:%H\", \"--\", file_path],\n stdout=subprocess.PIPE,\n text=True,\n check=True,\n )\n\n # Extract the commit hash from the command output\n commit_hash = result.stdout.strip()\n return commit_hash\n\n except subprocess.CalledProcessError as e:\n print(f\"Error executing git command: {e}\")\n return None"}, {"role": "assistant", "content": "\"\"\"\n Retrieves the latest commit hash for the specified file.\n\n Args:\n file_path (str): The path to the file for which to retrieve the commit hash.\n\n Returns:\n str or None: The latest commit hash if found, otherwise None.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Args :\n env_path = source path of .env file.\n required_keys = [\"OPENAI_KEY\"] #change this according to need\n\n #running/calling the function.\n configs = env_loader('.env', required_keys)\n \"\"\""}, {"role": "assistant", "content": "def env_loader(env_path, required_keys=None):\n \n\n # create env file if does not exists\n # parse required keys in the file if it's not None\n if not os.path.exists(env_path) or os.path.getsize(env_path) == 0:\n with open(env_path, \"w\") as env_f:\n if required_keys:\n for key in required_keys:\n env_f.write(f'{key}=\"\"\\n')\n else:\n pass\n\n configs = dotenv_values(env_path)\n changed = False\n for key, value in configs.items():\n env_key = os.getenv(key)\n if not value and not env_key:\n value = input(\n f\"[+] Key {utils.get_bold_text(key)} is required. Please enter it's value: \"\n )\n configs[key] = value\n changed = True\n elif not value and env_key:\n value = env_key\n configs[key] = value\n changed = True\n\n # update the .env file if config is changed/taken from user\n if changed:\n with open(env_path, \"w\") as env_f:\n for key, value in configs.items():\n env_f.write(f'{key}=\"{value}\"\\n')\n\n load_dotenv(env_path, override=True)"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def env_loader(env_path, required_keys=None):\n \n\n # create env file if does not exists\n # parse required keys in the file if it's not None\n if not os.path.exists(env_path) or os.path.getsize(env_path) == 0:\n with open(env_path, \"w\") as env_f:\n if required_keys:\n for key in required_keys:\n env_f.write(f'{key}=\"\"\\n')\n else:\n pass\n\n configs = dotenv_values(env_path)\n changed = False\n for key, value in configs.items():\n env_key = os.getenv(key)\n if not value and not env_key:\n value = input(\n f\"[+] Key {utils.get_bold_text(key)} is required. Please enter it's value: \"\n )\n configs[key] = value\n changed = True\n elif not value and env_key:\n value = env_key\n configs[key] = value\n changed = True\n\n # update the .env file if config is changed/taken from user\n if changed:\n with open(env_path, \"w\") as env_f:\n for key, value in configs.items():\n env_f.write(f'{key}=\"{value}\"\\n')\n\n load_dotenv(env_path, override=True)"}, {"role": "assistant", "content": "\"\"\"\n Args :\n env_path = source path of .env file.\n required_keys = [\"OPENAI_KEY\"] #change this according to need\n\n #running/calling the function.\n configs = env_loader('.env', required_keys)\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Parses a list of code files and returns a list of Document objects for database storage.\n\n Args:\n code_files (list[str]): List of paths to code files to be parsed.\n\n Returns:\n list[Document]: List of Document objects containing parsed code information.\n \"\"\""}, {"role": "assistant", "content": "def parse_code_files_for_db(code_files: list[str]) -> list[Document]:\n \n documents = []\n code_splitter = None\n for code_file in code_files:\n with open(code_file, \"r\", encoding=\"utf-8\") as file:\n file_bytes = file.read().encode()\n commit_hash = repo.get_commit_hash(code_file)\n\n file_extension = utils.get_file_extension(code_file)\n programming_language = utils.get_programming_language(file_extension)\n if programming_language == Language.UNKNOWN:\n continue\n\n langchain_language = utils.get_langchain_language(programming_language)\n\n if langchain_language:\n code_splitter = RecursiveCharacterTextSplitter.from_language(\n language=langchain_language,\n chunk_size=512,\n chunk_overlap=128,\n )\n\n treesitter_parser = Treesitter.create_treesitter(programming_language)\n treesitterNodes: list[TreesitterMethodNode] = treesitter_parser.parse(\n file_bytes\n )\n for node in treesitterNodes:\n method_source_code = node.method_source_code\n filename = os.path.basename(code_file)\n\n if node.doc_comment and programming_language != Language.PYTHON:\n method_source_code = node.doc_comment + \"\\n\" + method_source_code\n\n splitted_documents = [method_source_code]\n if code_splitter:\n splitted_documents = code_splitter.split_text(method_source_code)\n\n for splitted_document in splitted_documents:\n document = Document(\n page_content=splitted_document,\n metadata={\n \"filename\": filename,\n \"method_name\": node.name,\n \"commit_hash\": commit_hash,\n },\n )\n documents.append(document)\n\n return documents"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def parse_code_files_for_db(code_files: list[str]) -> list[Document]:\n \n documents = []\n code_splitter = None\n for code_file in code_files:\n with open(code_file, \"r\", encoding=\"utf-8\") as file:\n file_bytes = file.read().encode()\n commit_hash = repo.get_commit_hash(code_file)\n\n file_extension = utils.get_file_extension(code_file)\n programming_language = utils.get_programming_language(file_extension)\n if programming_language == Language.UNKNOWN:\n continue\n\n langchain_language = utils.get_langchain_language(programming_language)\n\n if langchain_language:\n code_splitter = RecursiveCharacterTextSplitter.from_language(\n language=langchain_language,\n chunk_size=512,\n chunk_overlap=128,\n )\n\n treesitter_parser = Treesitter.create_treesitter(programming_language)\n treesitterNodes: list[TreesitterMethodNode] = treesitter_parser.parse(\n file_bytes\n )\n for node in treesitterNodes:\n method_source_code = node.method_source_code\n filename = os.path.basename(code_file)\n\n if node.doc_comment and programming_language != Language.PYTHON:\n method_source_code = node.doc_comment + \"\\n\" + method_source_code\n\n splitted_documents = [method_source_code]\n if code_splitter:\n splitted_documents = code_splitter.split_text(method_source_code)\n\n for splitted_document in splitted_documents:\n document = Document(\n page_content=splitted_document,\n metadata={\n \"filename\": filename,\n \"method_name\": node.name,\n \"commit_hash\": commit_hash,\n },\n )\n documents.append(document)\n\n return documents"}, {"role": "assistant", "content": "\"\"\"\n Parses a list of code files and returns a list of Document objects for database storage.\n\n Args:\n code_files (list[str]): List of paths to code files to be parsed.\n\n Returns:\n list[Document]: List of Document objects containing parsed code information.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Parses a list of code files for fine-tuning and returns a list of dictionaries containing method information.\n\n Args:\n code_files (list[str]): List of paths to code files to be parsed.\n max_tokens (int): Maximum number of tokens allowed for output.\n\n Returns:\n list[dict]: List of dictionaries containing method information, including method name, code, description, and language.\n \"\"\""}, {"role": "assistant", "content": "def parse_code_files_for_finetuning(\n code_files: list[str], max_tokens, spinner\n) -> list[dict]:\n \n input_tokens = 0\n output_tokens = 0\n documents = []\n for code_file in code_files:\n with open(code_file, \"r\", encoding=\"utf-8\") as file:\n file_bytes = file.read().encode()\n\n file_extension = utils.get_file_extension(code_file)\n programming_language = utils.get_programming_language(file_extension)\n if programming_language == Language.UNKNOWN:\n continue\n\n treesitter_parser = Treesitter.create_treesitter(programming_language)\n treesitterNodes: list[TreesitterMethodNode] = treesitter_parser.parse(\n file_bytes\n )\n for node in treesitterNodes:\n method_source_code = node.method_source_code\n\n if node.doc_comment and programming_language == Language.PYTHON:\n method_source_code = method_source_code.replace(\n node.doc_comment, \"\"\n )\n\n document = {\n \"method_name\": node.name,\n \"code\": method_source_code,\n \"description\": node.doc_comment,\n \"language\": programming_language.value,\n }\n documents.append(document)\n\n if node.doc_comment is not None:\n input_tokens += utils.count_tokens(node.doc_comment)\n output_tokens += max_tokens\n\n spinner.stop()\n\n print(f\"Estimated input tokens for distillation needed: {input_tokens}.\")\n print(f\"Maximum output tokens for distillation nedeed: {output_tokens}.\")\n questions = [\n inquirer.Confirm(\n \"confirm\",\n message=\"Proceed?\",\n default=True,\n ),\n ]\n\n confirm = inquirer.prompt(questions)\n\n if confirm and confirm[\"confirm\"]:\n pass\n else:\n exit()\n\n return documents"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def parse_code_files_for_finetuning(\n code_files: list[str], max_tokens, spinner\n) -> list[dict]:\n \n input_tokens = 0\n output_tokens = 0\n documents = []\n for code_file in code_files:\n with open(code_file, \"r\", encoding=\"utf-8\") as file:\n file_bytes = file.read().encode()\n\n file_extension = utils.get_file_extension(code_file)\n programming_language = utils.get_programming_language(file_extension)\n if programming_language == Language.UNKNOWN:\n continue\n\n treesitter_parser = Treesitter.create_treesitter(programming_language)\n treesitterNodes: list[TreesitterMethodNode] = treesitter_parser.parse(\n file_bytes\n )\n for node in treesitterNodes:\n method_source_code = node.method_source_code\n\n if node.doc_comment and programming_language == Language.PYTHON:\n method_source_code = method_source_code.replace(\n node.doc_comment, \"\"\n )\n\n document = {\n \"method_name\": node.name,\n \"code\": method_source_code,\n \"description\": node.doc_comment,\n \"language\": programming_language.value,\n }\n documents.append(document)\n\n if node.doc_comment is not None:\n input_tokens += utils.count_tokens(node.doc_comment)\n output_tokens += max_tokens\n\n spinner.stop()\n\n print(f\"Estimated input tokens for distillation needed: {input_tokens}.\")\n print(f\"Maximum output tokens for distillation nedeed: {output_tokens}.\")\n questions = [\n inquirer.Confirm(\n \"confirm\",\n message=\"Proceed?\",\n default=True,\n ),\n ]\n\n confirm = inquirer.prompt(questions)\n\n if confirm and confirm[\"confirm\"]:\n pass\n else:\n exit()\n\n return documents"}, {"role": "assistant", "content": "\"\"\"\n Parses a list of code files for fine-tuning and returns a list of dictionaries containing method information.\n\n Args:\n code_files (list[str]): List of paths to code files to be parsed.\n max_tokens (int): Maximum number of tokens allowed for output.\n\n Returns:\n list[dict]: List of dictionaries containing method information, including method name, code, description, and language.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Parses the given file bytes and extracts method nodes.\n\n Args:\n file_bytes (bytes): The content of the file to be parsed.\n\n Returns:\n list[TreesitterMethodNode]: A list of TreesitterMethodNode objects representing the methods in the file.\n \"\"\""}, {"role": "assistant", "content": "def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]:\n \n self.tree = self.parser.parse(file_bytes)\n result = []\n methods = self._query_all_methods(self.tree.root_node)\n for method in methods:\n method_name = self._query_method_name(method)\n doc_comment = self._query_doc_comment(method)\n result.append(TreesitterMethodNode(method_name, doc_comment, None, method))\n return result"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]:\n \n self.tree = self.parser.parse(file_bytes)\n result = []\n methods = self._query_all_methods(self.tree.root_node)\n for method in methods:\n method_name = self._query_method_name(method)\n doc_comment = self._query_doc_comment(method)\n result.append(TreesitterMethodNode(method_name, doc_comment, None, method))\n return result"}, {"role": "assistant", "content": "\"\"\"\n Parses the given file bytes and extracts method nodes.\n\n Args:\n file_bytes (bytes): The content of the file to be parsed.\n\n Returns:\n list[TreesitterMethodNode]: A list of TreesitterMethodNode objects representing the methods in the file.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\""}, {"role": "assistant", "content": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None"}, {"role": "assistant", "content": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Queries all method nodes within the given syntax tree node, including those within class definitions.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of method nodes found within the given node.\n \"\"\""}, {"role": "assistant", "content": "def _query_all_methods(self, node: tree_sitter.Node):\n \n methods = []\n for child in node.children:\n if child.type == self.method_declaration_identifier:\n methods.append(child)\n if child.type == \"class_definition\":\n class_body = child.children[-1]\n for child_node in class_body.children:\n if child_node.type == self.method_declaration_identifier:\n methods.append(child_node)\n return methods"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def _query_all_methods(self, node: tree_sitter.Node):\n \n methods = []\n for child in node.children:\n if child.type == self.method_declaration_identifier:\n methods.append(child)\n if child.type == \"class_definition\":\n class_body = child.children[-1]\n for child_node in class_body.children:\n if child_node.type == self.method_declaration_identifier:\n methods.append(child_node)\n return methods"}, {"role": "assistant", "content": "\"\"\"\n Queries all method nodes within the given syntax tree node, including those within class definitions.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of method nodes found within the given node.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Queries the documentation comment for the given function definition node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node representing a function definition.\n\n Returns:\n str or None: The documentation comment string if found, otherwise None.\n \"\"\""}, {"role": "assistant", "content": "def _query_doc_comment(self, node: tree_sitter.Node):\n \n query_code = \"\"\"\n (function_definition\n body: (block . (expression_statement (string)) @function_doc_str))\n \"\"\"\n doc_str_query = self.language.query(query_code)\n doc_strs = doc_str_query.captures(node)\n\n if doc_strs:\n return doc_strs[0][0].text.decode()\n else:\n return None"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def _query_doc_comment(self, node: tree_sitter.Node):\n \n query_code = \"\"\"\n (function_definition\n body: (block . (expression_statement (string)) @function_doc_str))\n \"\"\"\n doc_str_query = self.language.query(query_code)\n doc_strs = doc_str_query.captures(node)\n\n if doc_strs:\n return doc_strs[0][0].text.decode()\n else:\n return None"}, {"role": "assistant", "content": "\"\"\"\n Queries the documentation comment for the given function definition node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node representing a function definition.\n\n Returns:\n str or None: The documentation comment string if found, otherwise None.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\""}, {"role": "assistant", "content": "def _query_method_name(self, node: tree_sitter.Node):\n \n first_match = None\n if node.type == self.method_declaration_identifier:\n for child in node.children:\n # if the return type is an object type, then the method name\n # is the second match\n if child.type == self.method_name_identifier and not first_match:\n first_match = child.text.decode()\n elif child.type == self.method_name_identifier and first_match:\n return child.text.decode()\n return first_match"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def _query_method_name(self, node: tree_sitter.Node):\n \n first_match = None\n if node.type == self.method_declaration_identifier:\n for child in node.children:\n # if the return type is an object type, then the method name\n # is the second match\n if child.type == self.method_name_identifier and not first_match:\n first_match = child.text.decode()\n elif child.type == self.method_name_identifier and first_match:\n return child.text.decode()\n return first_match"}, {"role": "assistant", "content": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\""}, {"role": "assistant", "content": "def _query_all_methods(self, node: tree_sitter.Node):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_nodes = []\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n current_doc_comment_node = node.prev_named_sibling\n while (\n current_doc_comment_node\n and current_doc_comment_node.type == self.doc_comment_identifier\n ):\n doc_comment_nodes.append(current_doc_comment_node.text.decode())\n if current_doc_comment_node.prev_named_sibling:\n current_doc_comment_node = (\n current_doc_comment_node.prev_named_sibling\n )\n else:\n current_doc_comment_node = None\n\n doc_comment_str = \"\"\n doc_comment_nodes.reverse()\n for doc_comment_node in doc_comment_nodes:\n doc_comment_str += doc_comment_node + \"\\n\"\n if doc_comment_str.strip() != \"\":\n methods.append({\"method\": node, \"doc_comment\": doc_comment_str.strip()})\n else:\n methods.append({\"method\": node, \"doc_comment\": None})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def _query_all_methods(self, node: tree_sitter.Node):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_nodes = []\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n current_doc_comment_node = node.prev_named_sibling\n while (\n current_doc_comment_node\n and current_doc_comment_node.type == self.doc_comment_identifier\n ):\n doc_comment_nodes.append(current_doc_comment_node.text.decode())\n if current_doc_comment_node.prev_named_sibling:\n current_doc_comment_node = (\n current_doc_comment_node.prev_named_sibling\n )\n else:\n current_doc_comment_node = None\n\n doc_comment_str = \"\"\n doc_comment_nodes.reverse()\n for doc_comment_node in doc_comment_nodes:\n doc_comment_str += doc_comment_node + \"\\n\"\n if doc_comment_str.strip() != \"\":\n methods.append({\"method\": node, \"doc_comment\": doc_comment_str.strip()})\n else:\n methods.append({\"method\": node, \"doc_comment\": None})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods"}, {"role": "assistant", "content": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\""}, {"role": "assistant", "content": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n # if method returns pointer, skip pointer declarator\n if child.type == \"pointer_declarator\":\n child = child.children[1]\n if child.type == \"function_declarator\":\n for child in child.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n # if method returns pointer, skip pointer declarator\n if child.type == \"pointer_declarator\":\n child = child.children[1]\n if child.type == \"function_declarator\":\n for child in child.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None"}, {"role": "assistant", "content": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\""}, {"role": "assistant", "content": "def _query_all_methods(self, node: tree_sitter.Node):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_nodes = []\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n current_doc_comment_node = node.prev_named_sibling\n while (\n current_doc_comment_node\n and current_doc_comment_node.type == self.doc_comment_identifier\n ):\n doc_comment_nodes.append(current_doc_comment_node.text.decode())\n if current_doc_comment_node.prev_named_sibling:\n current_doc_comment_node = (\n current_doc_comment_node.prev_named_sibling\n )\n else:\n current_doc_comment_node = None\n\n doc_comment_str = \"\"\n doc_comment_nodes.reverse()\n for doc_comment_node in doc_comment_nodes:\n doc_comment_str += doc_comment_node + \"\\n\"\n if doc_comment_str.strip() != \"\":\n methods.append({\"method\": node, \"doc_comment\": doc_comment_str.strip()})\n else:\n methods.append({\"method\": node, \"doc_comment\": None})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def _query_all_methods(self, node: tree_sitter.Node):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_nodes = []\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n current_doc_comment_node = node.prev_named_sibling\n while (\n current_doc_comment_node\n and current_doc_comment_node.type == self.doc_comment_identifier\n ):\n doc_comment_nodes.append(current_doc_comment_node.text.decode())\n if current_doc_comment_node.prev_named_sibling:\n current_doc_comment_node = (\n current_doc_comment_node.prev_named_sibling\n )\n else:\n current_doc_comment_node = None\n\n doc_comment_str = \"\"\n doc_comment_nodes.reverse()\n for doc_comment_node in doc_comment_nodes:\n doc_comment_str += doc_comment_node + \"\\n\"\n if doc_comment_str.strip() != \"\":\n methods.append({\"method\": node, \"doc_comment\": doc_comment_str.strip()})\n else:\n methods.append({\"method\": node, \"doc_comment\": None})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods"}, {"role": "assistant", "content": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\""}, {"role": "assistant", "content": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n # if method returns pointer, skip pointer declarator\n if child.type == \"pointer_declarator\":\n child = child.children[1]\n if child.type == \"function_declarator\":\n for child in child.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n # if method returns pointer, skip pointer declarator\n if child.type == \"pointer_declarator\":\n child = child.children[1]\n if child.type == \"function_declarator\":\n for child in child.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None"}, {"role": "assistant", "content": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Parses the given file bytes and extracts method nodes.\n\n Args:\n file_bytes (bytes): The content of the file to be parsed.\n\n Returns:\n list[TreesitterMethodNode]: A list of TreesitterMethodNode objects representing the methods in the file.\n \"\"\""}, {"role": "assistant", "content": "def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]:\n \n self.tree = self.parser.parse(file_bytes)\n result = []\n methods = self._query_all_methods(self.tree.root_node)\n for method in methods:\n method_name = self._query_method_name(method[\"method\"])\n doc_comment = method[\"doc_comment\"]\n source_code = None\n if method[\"method\"].type == \"signature\":\n sc = map(\n lambda x: \"\\n\" + x.text.decode() if x.type == \"function\" else \"\",\n method[\"method\"].children,\n )\n source_code = method[\"method\"].text.decode() + \"\".join(sc)\n result.append(\n TreesitterMethodNode(\n method_name, doc_comment, source_code, method[\"method\"]\n )\n )\n return result"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]:\n \n self.tree = self.parser.parse(file_bytes)\n result = []\n methods = self._query_all_methods(self.tree.root_node)\n for method in methods:\n method_name = self._query_method_name(method[\"method\"])\n doc_comment = method[\"doc_comment\"]\n source_code = None\n if method[\"method\"].type == \"signature\":\n sc = map(\n lambda x: \"\\n\" + x.text.decode() if x.type == \"function\" else \"\",\n method[\"method\"].children,\n )\n source_code = method[\"method\"].text.decode() + \"\".join(sc)\n result.append(\n TreesitterMethodNode(\n method_name, doc_comment, source_code, method[\"method\"]\n )\n )\n return result"}, {"role": "assistant", "content": "\"\"\"\n Parses the given file bytes and extracts method nodes.\n\n Args:\n file_bytes (bytes): The content of the file to be parsed.\n\n Returns:\n list[TreesitterMethodNode]: A list of TreesitterMethodNode objects representing the methods in the file.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\""}, {"role": "assistant", "content": "def _query_all_methods(\n self,\n node: tree_sitter.Node,\n ):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_node = None\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n doc_comment_node = node.prev_named_sibling.text.decode()\n else:\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == \"signature\"\n ):\n prev_node = node.prev_named_sibling\n if (\n prev_node.prev_named_sibling\n and prev_node.prev_named_sibling.type\n == self.doc_comment_identifier\n ):\n doc_comment_node = prev_node.prev_named_sibling.text.decode()\n prev_node.children.append(node)\n node = prev_node\n methods.append({\"method\": node, \"doc_comment\": doc_comment_node})\n else:\n for child in node.children:\n current = self._query_all_methods(child)\n if methods and current:\n previous = methods[-1]\n if self._query_method_name(\n previous[\"method\"]\n ) == self._query_method_name(current[0][\"method\"]):\n previous[\"method\"].children.extend(\n map(lambda x: x[\"method\"], current)\n )\n methods = methods[:-1]\n methods.append(previous)\n else:\n methods.extend(current)\n else:\n methods.extend(current)\n return methods"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def _query_all_methods(\n self,\n node: tree_sitter.Node,\n ):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_node = None\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n doc_comment_node = node.prev_named_sibling.text.decode()\n else:\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == \"signature\"\n ):\n prev_node = node.prev_named_sibling\n if (\n prev_node.prev_named_sibling\n and prev_node.prev_named_sibling.type\n == self.doc_comment_identifier\n ):\n doc_comment_node = prev_node.prev_named_sibling.text.decode()\n prev_node.children.append(node)\n node = prev_node\n methods.append({\"method\": node, \"doc_comment\": doc_comment_node})\n else:\n for child in node.children:\n current = self._query_all_methods(child)\n if methods and current:\n previous = methods[-1]\n if self._query_method_name(\n previous[\"method\"]\n ) == self._query_method_name(current[0][\"method\"]):\n previous[\"method\"].children.extend(\n map(lambda x: x[\"method\"], current)\n )\n methods = methods[:-1]\n methods.append(previous)\n else:\n methods.extend(current)\n else:\n methods.extend(current)\n return methods"}, {"role": "assistant", "content": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\""}, {"role": "assistant", "content": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == \"signature\" or node.type == self.method_declaration_identifier:\n for child in node.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == \"signature\" or node.type == self.method_declaration_identifier:\n for child in node.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None"}, {"role": "assistant", "content": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Parses the given file bytes and extracts method nodes.\n\n Args:\n file_bytes (bytes): The content of the file to be parsed.\n\n Returns:\n list[TreesitterMethodNode]: A list of TreesitterMethodNode objects representing the methods in the file.\n \"\"\""}, {"role": "assistant", "content": "def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]:\n \n self.tree = self.parser.parse(file_bytes)\n result = []\n methods = self._query_all_methods(self.tree.root_node)\n for method in methods:\n method_name = self._query_method_name(method[\"method\"])\n doc_comment = method[\"doc_comment\"]\n result.append(\n TreesitterMethodNode(method_name, doc_comment, None, method[\"method\"])\n )\n return result"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def parse(self, file_bytes: bytes) -> list[TreesitterMethodNode]:\n \n self.tree = self.parser.parse(file_bytes)\n result = []\n methods = self._query_all_methods(self.tree.root_node)\n for method in methods:\n method_name = self._query_method_name(method[\"method\"])\n doc_comment = method[\"doc_comment\"]\n result.append(\n TreesitterMethodNode(method_name, doc_comment, None, method[\"method\"])\n )\n return result"}, {"role": "assistant", "content": "\"\"\"\n Parses the given file bytes and extracts method nodes.\n\n Args:\n file_bytes (bytes): The content of the file to be parsed.\n\n Returns:\n list[TreesitterMethodNode]: A list of TreesitterMethodNode objects representing the methods in the file.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\""}, {"role": "assistant", "content": "def _query_all_methods(\n self,\n node: tree_sitter.Node,\n ):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_node = None\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n doc_comment_node = node.prev_named_sibling.text.decode()\n methods.append({\"method\": node, \"doc_comment\": doc_comment_node})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def _query_all_methods(\n self,\n node: tree_sitter.Node,\n ):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment_node = None\n if (\n node.prev_named_sibling\n and node.prev_named_sibling.type == self.doc_comment_identifier\n ):\n doc_comment_node = node.prev_named_sibling.text.decode()\n methods.append({\"method\": node, \"doc_comment\": doc_comment_node})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods"}, {"role": "assistant", "content": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\""}, {"role": "assistant", "content": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def _query_method_name(self, node: tree_sitter.Node):\n \n if node.type == self.method_declaration_identifier:\n for child in node.children:\n if child.type == self.method_name_identifier:\n return child.text.decode()\n return None"}, {"role": "assistant", "content": "\"\"\"\n Queries the method name from the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The syntax tree node to query.\n\n Returns:\n str or None: The method name if found, otherwise None.\n \"\"\""}]} +{"messages": [{"role": "system", "content": "You are a python expert. Write an implementation for the following description."}, {"role": "user", "content": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\""}, {"role": "assistant", "content": "def _query_all_methods(\n self,\n node: tree_sitter.Node,\n ):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment = []\n doc_comment_node = node\n while (\n doc_comment_node.prev_named_sibling\n and doc_comment_node.prev_named_sibling.type\n == self.doc_comment_identifier\n ):\n doc_comment_node = doc_comment_node.prev_named_sibling\n doc_comment.insert(0, doc_comment_node.text.decode())\n methods.append({\"method\": node, \"doc_comment\": \"\\n\".join(doc_comment)})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods"}]} +{"messages": [{"role": "system", "content": "You are a python expert. Explain the following code."}, {"role": "user", "content": "def _query_all_methods(\n self,\n node: tree_sitter.Node,\n ):\n \n methods = []\n if node.type == self.method_declaration_identifier:\n doc_comment = []\n doc_comment_node = node\n while (\n doc_comment_node.prev_named_sibling\n and doc_comment_node.prev_named_sibling.type\n == self.doc_comment_identifier\n ):\n doc_comment_node = doc_comment_node.prev_named_sibling\n doc_comment.insert(0, doc_comment_node.text.decode())\n methods.append({\"method\": node, \"doc_comment\": \"\\n\".join(doc_comment)})\n else:\n for child in node.children:\n methods.extend(self._query_all_methods(child))\n return methods"}, {"role": "assistant", "content": "\"\"\"\n Recursively queries all method nodes in the given syntax tree node.\n\n Args:\n node (tree_sitter.Node): The root node to start the query from.\n\n Returns:\n list: A list of dictionaries, each containing a method node and its associated doc comment (if any).\n \"\"\""}]} diff --git a/notebooks/alpaca_fine_tuning_qwen_2_5_coder_3B.ipynb b/notebooks/alpaca_fine_tuning_qwen_2_5_coder_3B.ipynb new file mode 100644 index 0000000..516cb8b --- /dev/null +++ b/notebooks/alpaca_fine_tuning_qwen_2_5_coder_3B.ipynb @@ -0,0 +1,184 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Installation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "# Normally using pip install unsloth is enough\n", + "# Temporarily as of Jan 31st 2025, Colab has some issues with Pytorch\n", + "# Using pip install unsloth will take 3 minutes, whilst the below takes <1 minute:\n", + "!pip install --no-deps bitsandbytes accelerate xformers==0.0.29 peft trl triton\n", + "!pip install --no-deps cut_cross_entropy unsloth_zoo\n", + "!pip install sentencepiece protobuf datasets huggingface_hub hf_transfer\n", + "!pip install --no-deps unsloth" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Unsloth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from unsloth import FastLanguageModel\n", + "import torch\n", + "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n", + "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", + "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n", + "\n", + "model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"unsloth/Qwen2.5-Coder-3B-bnb-4bit\", # or choose \"unsloth/Llama-3.2-1B-Instruct\"\n", + " max_seq_length = max_seq_length,\n", + " dtype = dtype,\n", + " load_in_4bit = load_in_4bit,\n", + " # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define Alpaca Prompt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "### Instruction:\n", + "{}\n", + "### Input:\n", + "{}\n", + "### Response:\n", + "{}\"\"\"\n", + "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n", + "def formatting_prompts_func(examples):\n", + " instructions = examples[\"instruction\"]\n", + " inputs = examples[\"input\"]\n", + " outputs = examples[\"output\"]\n", + " texts = []\n", + " for instruction, input, output in zip(instructions, inputs, outputs):\n", + " text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n", + " texts.append(text)\n", + " return { \"text\" : texts, }\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load local dataset in Alpaca format" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "dataset = load_dataset(\"json\", data_files=\"alpaca_fine_tuning_qwen_2_5_coder_3B.json\")\n", + "dataset = dataset.map(formatting_prompts_func, batched = True,)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define training configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from trl import SFTTrainer\n", + "from transformers import TrainingArguments\n", + "from unsloth import is_bfloat16_supported\n", + "trainer = SFTTrainer(\n", + " model = model,\n", + " tokenizer = tokenizer,\n", + " train_dataset = dataset,\n", + " dataset_text_field = \"text\",\n", + " max_seq_length = max_seq_length,\n", + " dataset_num_proc = 2,\n", + " args = TrainingArguments(\n", + " per_device_train_batch_size = 2,\n", + " gradient_accumulation_steps = 8,\n", + " # Use num_train_epochs = 1, warmup_ratio for full training runs!\n", + " warmup_steps = 20,\n", + " max_steps = 120,\n", + " learning_rate = 5e-5,\n", + " fp16 = not is_bfloat16_supported(),\n", + " bf16 = is_bfloat16_supported(),\n", + " logging_steps = 1,\n", + " optim = \"adamw_8bit\",\n", + " weight_decay = 0.01,\n", + " lr_scheduler_type = \"linear\",\n", + " seed = 3407,\n", + " output_dir = \"outputs\",\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train 🚀" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainer_stats = trainer.train()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/poetry.lock b/poetry.lock index b254bc1..2910d2d 100644 --- a/poetry.lock +++ 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a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "codeqai" -version = "0.0.19" +version = "0.0.20" description = "" authors = ["fynnfluegge "] readme = "README.md" @@ -9,7 +9,7 @@ include = ["./streamlit/*"] [tool.poetry.dependencies] python = ">=3.9,<3.9.7 || >3.9.7,<3.12" -tiktoken = "^0.5.2" +tiktoken = "^0.7.0" yaspin = "^3.0.0" inquirer = "^3.1.3" gitpython = "^3.1.36" @@ -21,11 +21,12 @@ tree-sitter-languages = "^1.7.0" rich = "^13.5.3" python-dotenv = "^1.0.0" streamlit = "^1.31.0" -langchain-community = "^0.0.32" -langchain-openai = "^0.0.5" +langchain-community = "^0.0.38" +langchain-openai = "^0.1.0" click = "^8.1.7" langchain-core = "^0.1.42" langchain-anthropic = "^0.1.8" +langchain-huggingface = "^0.0.3" [tool.poetry.scripts] codeqai = "codeqai.__main__:main" diff --git a/tests/vector_store_test.py b/tests/vector_store_test.py index 8f1ec33..0a21470 100644 --- a/tests/vector_store_test.py +++ b/tests/vector_store_test.py @@ -1,8 +1,8 @@ from pathlib import Path import pytest -from langchain.embeddings import FakeEmbeddings from langchain.schema import Document +from langchain_community.embeddings import FakeEmbeddings from codeqai.cache import get_cache_path from codeqai.vector_store import VectorStore