From f1a12d2700016d01de3ab8b39ef79b7cbf3f6802 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Sun, 22 Dec 2024 17:42:40 -0600 Subject: [PATCH] WIP: separate frontend from backend so we can host the frontend (#242) * fix: prompt engineer agent for map decomposition * feat: host application * chore: update azure * chore: update azure * chore: update azure * fix: adding default ops in init * chore: refactoring fastapi models * feat: allowing frontend to use a separate server for backend * feat: allowing frontend to use a separate server for backend * feat: allowing frontend to use a separate server for backend * feat: allowing frontend to use a separate server for backend * feat: allowing frontend to use a separate server for backend * feat: allowing frontend to use a separate server for backend --- docetl/config_wrapper.py | 18 +- docetl/operations/__init__.py | 39 +- docetl/operations/utils/api.py | 2 +- docetl/operations/utils/llm.py | 6 +- .../map_optimizer/config_generators.py | 4 +- .../map_optimizer/plan_generators.py | 2 +- docetl/utils.py | 30 + docs/playground/index.md | 31 +- poetry.lock | 1747 ++++++++++------- pyproject.toml | 3 +- server/app/main.py | 10 +- server/app/models.py | 33 + server/app/routes/convert.py | 7 +- server/app/routes/filesystem.py | 168 ++ server/app/routes/pipeline.py | 38 +- website/package-lock.json | 17 + website/package.json | 2 + website/src/app/api/chat/route.ts | 42 +- website/src/app/api/checkNamespace/route.ts | 31 +- website/src/app/api/convertDocuments/route.ts | 28 +- website/src/app/api/getInputOutput/route.ts | 51 +- website/src/app/api/readFile/route.ts | 28 +- website/src/app/api/readFilePage/route.ts | 36 +- website/src/app/api/saveDocuments/route.ts | 56 +- .../app/api/serveDocument/[...path]/route.ts | 45 +- website/src/app/api/shouldOptimize/route.ts | 6 +- website/src/app/api/uploadFile/route.ts | 36 +- website/src/app/api/utils.ts | 68 +- .../src/app/api/writePipelineConfig/route.ts | 57 +- website/src/app/playground/page.tsx | 95 +- website/src/app/types.ts | 5 + website/src/components/AIChatPanel.tsx | 83 +- website/src/components/APIKeysDialog.tsx | 237 +++ website/src/components/ColumnDialog.tsx | 289 +-- website/src/components/DatasetView.tsx | 190 +- website/src/components/FileExplorer.tsx | 24 + website/src/components/NamespaceDialog.tsx | 64 +- website/src/components/OperationCard.tsx | 662 +++++-- .../src/components/OperationHelpButton.tsx | 229 +++ website/src/components/Output.tsx | 204 +- website/src/components/PipelineGui.tsx | 989 +++++----- .../components/PromptImprovementDialog.tsx | 58 +- website/src/components/ResizableDataTable.tsx | 16 +- website/src/components/operations/args.tsx | 4 +- website/src/components/ui/alert.tsx | 59 + website/src/contexts/PipelineContext.tsx | 32 +- website/src/contexts/WebSocketContext.tsx | 20 +- 47 files changed, 3906 insertions(+), 1995 deletions(-) create mode 100644 server/app/routes/filesystem.py create mode 100644 website/src/components/APIKeysDialog.tsx create mode 100644 website/src/components/OperationHelpButton.tsx create mode 100644 website/src/components/ui/alert.tsx diff --git a/docetl/config_wrapper.py b/docetl/config_wrapper.py index c650c8b5..b9d20ccc 100644 --- a/docetl/config_wrapper.py +++ b/docetl/config_wrapper.py @@ -1,7 +1,7 @@ import datetime import os from docetl.console import get_console -from docetl.utils import load_config +from docetl.utils import decrypt, load_config from typing import Any, Dict, List, Optional, Tuple, Union from docetl.operations.utils import APIWrapper import pyrate_limiter @@ -71,6 +71,19 @@ def __init__( self.console = DOCETL_CONSOLE self.max_threads = max_threads or (os.cpu_count() or 1) * 4 self.status = None + encrypted_llm_api_keys = self.config.get("llm_api_keys", {}) + if encrypted_llm_api_keys: + self.llm_api_keys = { + key: decrypt(value, os.environ.get("DOCETL_ENCRYPTION_KEY", "")) + for key, value in encrypted_llm_api_keys.items() + } + else: + self.llm_api_keys = {} + + # Temporarily set environment variables for API keys + self._original_env = os.environ.copy() + for key, value in self.llm_api_keys.items(): + os.environ[key] = value buckets = { param: pyrate_limiter.InMemoryBucket( @@ -95,3 +108,6 @@ def __init__( self.rate_limiter = pyrate_limiter.Limiter(bucket_factory, max_delay=math.inf) self.api = APIWrapper(self) + + def reset_env(self): + os.environ = self._original_env diff --git a/docetl/operations/__init__.py b/docetl/operations/__init__.py index 43d567b1..badcc9af 100644 --- a/docetl/operations/__init__.py +++ b/docetl/operations/__init__.py @@ -1,19 +1,50 @@ import importlib.metadata +from docetl.operations.cluster import ClusterOperation +from docetl.operations.code_operations import CodeFilterOperation, CodeMapOperation, CodeReduceOperation +from docetl.operations.equijoin import EquijoinOperation +from docetl.operations.filter import FilterOperation +from docetl.operations.gather import GatherOperation +from docetl.operations.map import MapOperation +from docetl.operations.reduce import ReduceOperation +from docetl.operations.resolve import ResolveOperation +from docetl.operations.split import SplitOperation +from docetl.operations.sample import SampleOperation +from docetl.operations.unnest import UnnestOperation +mapping = { + "cluster": ClusterOperation, + "code_filter": CodeFilterOperation, + "code_map": CodeMapOperation, + "code_reduce": CodeReduceOperation, + "equijoin": EquijoinOperation, + "filter": FilterOperation, + "gather": GatherOperation, + "map": MapOperation, + "reduce": ReduceOperation, + "resolve": ResolveOperation, + "split": SplitOperation, + "sample": SampleOperation, + "unnest": UnnestOperation, +} + def get_operation(operation_type: str): """Loads a single operation by name""" try: entrypoint = importlib.metadata.entry_points(group="docetl.operation")[ operation_type ] - except KeyError as e: + return entrypoint.load() + except KeyError: + if operation_type in mapping: + return mapping[operation_type] raise KeyError(f"Unrecognized operation {operation_type}") - return entrypoint.load() def get_operations(): """Load all available operations and return them as a dictionary""" - return { + operations = mapping.copy() + operations.update({ op.name: op.load() for op in importlib.metadata.entry_points(group="docetl.operation") - } + }) + return operations diff --git a/docetl/operations/utils/api.py b/docetl/operations/utils/api.py index af64ab3d..387b42d0 100644 --- a/docetl/operations/utils/api.py +++ b/docetl/operations/utils/api.py @@ -446,7 +446,7 @@ def _call_llm_with_cache( dataset_description = self.runner.config.get("system_prompt", {}).get("dataset_description", "a collection of unstructured documents") parethetical_op_instructions = "many inputs:one output" if op_type == "reduce" else "one input:one output" - system_prompt = f"You are a {persona}, intelligently transforming data. The dataset description is: {dataset_description}. You will be performing a {op_type} operation ({parethetical_op_instructions}). You will perform the specified task on the provided data, as accurately, precisely, and exhaustively as possible. The result should be a structured output that you will send back to the user." + system_prompt = f"You are a {persona}, helping the user make sense of their data. The dataset description is: {dataset_description}. You will be performing a {op_type} operation ({parethetical_op_instructions}). You will perform the specified task on the provided data, as precisely and exhaustively (i.e., high recall) as possible. The result should be a structured output that you will send back to the user, with the `send_output` function. Do not influence your answers too much based on the `send_output` function parameter names; just use them to send the result back to the user." if scratchpad: system_prompt += f""" diff --git a/docetl/operations/utils/llm.py b/docetl/operations/utils/llm.py index f98413fa..e1fcd3fd 100644 --- a/docetl/operations/utils/llm.py +++ b/docetl/operations/utils/llm.py @@ -5,7 +5,7 @@ from typing import Any, Dict, List, Optional import tiktoken from jinja2 import Template -from litellm import completion, RateLimitError +from litellm import model_cost from pydantic import BaseModel from rich import print as rprint @@ -69,7 +69,9 @@ def truncate_messages( from_agent: bool = False ) -> List[Dict[str, str]]: """Truncate messages to fit within model's context length.""" - model_input_context_length = 8192 # Default + model_input_context_length = model_cost.get(model.split("/")[-1], {}).get( + "max_input_tokens", 8192 + ) total_tokens = sum(count_tokens(json.dumps(msg), model) for msg in messages) if total_tokens <= model_input_context_length - 100: diff --git a/docetl/optimizers/map_optimizer/config_generators.py b/docetl/optimizers/map_optimizer/config_generators.py index 5d7008e0..18a523b8 100644 --- a/docetl/optimizers/map_optimizer/config_generators.py +++ b/docetl/optimizers/map_optimizer/config_generators.py @@ -84,7 +84,7 @@ def _get_split_config( Determine the split key and subprompt for processing chunks of the input data. The split key should be a key in the input data that contains a string to be split. - The subprompt should be designed to process individual chunks of the split data. + The subprompt should be designed to process individual chunks of the split data, and only process the main chunk in within chunk delimiters if they are present. Note that the subprompt's output schema might be different from the original operation's output schema, since you may want to extract more information or make the information less structured/more free text. The original output schema will be preserved when combining the chunks' processed results. Important: @@ -148,6 +148,8 @@ def _get_split_config( result["subprompt_output_schema"].update(op_config["output"]["schema"]) + result["subprompt"] = result["subprompt"] + " Only process the main chunk in --- Begin Main Chunk --- and --- End Main Chunk --- delimiters if they are present." + self.console.log( f"[yellow]Breaking down operation {op_config['name']}[/yellow]" ) diff --git a/docetl/optimizers/map_optimizer/plan_generators.py b/docetl/optimizers/map_optimizer/plan_generators.py index fd8c0d83..665d0e30 100644 --- a/docetl/optimizers/map_optimizer/plan_generators.py +++ b/docetl/optimizers/map_optimizer/plan_generators.py @@ -218,7 +218,7 @@ def determine_metadata_with_retry(): map_op = self.operation_creator.create_map_operation( op_config, subprompt_output_schema, - split_result["subprompt"] + " Only process the main chunk.", + split_result["subprompt"] , ) # unnest_ops = self.operation_creator.create_unnest_operations(op_config) diff --git a/docetl/utils.py b/docetl/utils.py index d190ecb5..740eb886 100644 --- a/docetl/utils.py +++ b/docetl/utils.py @@ -7,6 +7,36 @@ from jinja2 import Environment, meta from litellm import completion_cost as lcc +from lzstring import LZString + +class Decryptor: + def __init__(self, secret_key: str): + self.key = secret_key + self.lz = LZString() + + def decrypt(self, encrypted_data: str) -> str: + try: + # First decompress the data + compressed = self.lz.decompressFromBase64(encrypted_data) + if not compressed: + raise ValueError("Invalid compressed data") + + # Then decode using the key + result = '' + for i in range(len(compressed)): + char_code = ord(compressed[i]) - ord(self.key[i % len(self.key)]) + result += chr(char_code) + + return result + + except Exception as e: + print(f"Decryption failed: {str(e)}") + return None + +def decrypt(encrypted_data: str, secret_key: str) -> str: + if not secret_key: + return encrypted_data + return Decryptor(secret_key).decrypt(encrypted_data) class StageType(Enum): SAMPLE_RUN = "sample_run" diff --git a/docs/playground/index.md b/docs/playground/index.md index ad1a29a9..1b8047f0 100644 --- a/docs/playground/index.md +++ b/docs/playground/index.md @@ -12,6 +12,14 @@ Building complex LLM pipelines for your data often requires experimentation and - ✨ Refine operations based on sample outputs - 🔄 Build complex pipelines step-by-step +## Public Playground + +You can access our hosted playground at [docetl.org/playground](https://docetl.org/playground). You'll need to provide your own LLM API keys to use the service. The chatbot and prompt engineering assistants are powered by OpenAI models, so you'll need to provide an OpenAI API key. + +!!! note "Data Storage Notice" + + As this is a research project, we cache results and store data on our servers to improve the system. While we will never sell or release your data, if you have privacy concerns, we recommend running the playground locally using the installation instructions below. + ## Installation There are two ways to run the playground: @@ -24,7 +32,9 @@ The easiest way to get started is using Docker: Create `.env` in the root directory (for the FastAPI backend): ```bash -OPENAI_API_KEY=your_api_key_here # Or your LLM provider's API key +# Required: API key for your preferred LLM provider (OpenAI, Anthropic, etc) +# The key format will depend on your chosen provider (sk-..., anthro-...) +OPENAI_API_KEY=your_api_key_here BACKEND_ALLOW_ORIGINS= BACKEND_HOST=localhost BACKEND_PORT=8000 @@ -35,9 +45,11 @@ FRONTEND_PORT=3000 Create `.env.local` in the `website` directory (for the frontend) **note that this must be in the `website` directory**: ```bash -OPENAI_API_KEY=sk-xxx # For the AI assistant in the interface -OPENAI_API_BASE=https://api.openai.com/v1 # For the AI assistant in the interface -MODEL_NAME=gpt-4o-mini # For the AI assistant in the interface +# Optional: These are only needed if you want to use the AI assistant chatbot +# and prompt engineering tools. Must be OpenAI API keys specifically. +OPENAI_API_KEY=sk-xxx +OPENAI_API_BASE=https://api.openai.com/v1 +MODEL_NAME=gpt-4o-mini NEXT_PUBLIC_BACKEND_HOST=localhost NEXT_PUBLIC_BACKEND_PORT=8000 @@ -72,7 +84,7 @@ cd docetl 2. Set up environment variables in `.env` in the root directory: ```bash -OPENAI_API_KEY=your_api_key_here +LLM_API_KEY=your_api_key_here BACKEND_ALLOW_ORIGINS= BACKEND_HOST=localhost BACKEND_PORT=8000 @@ -113,6 +125,15 @@ The UI offers an optional chat-based assistant that can help you iteratively dev To use the assistant, you need to set your OpenAI API key in the `.env.local` file in the website directory. You can get an API key [here](https://platform.openai.com/api-keys). The API key should be in the following format: `sk-proj-...`. We only support the openai models for the assistant. +!!! tip "Self-hosting with UI API key management" + + If you want to host your own version of DocETL for your organization while allowing users to set their API keys through the UI, you'll need to set up encryption. Add the following to both `.env` and `website/.env.local`: + ```bash + DOCETL_ENCRYPTION_KEY=your_secret_key_here + ``` + This shared encryption key allows API keys to be securely encrypted when sent to your server. Make sure to use the same value in both files. + + ## Complex Tutorial See this [YouTube video](https://www.youtube.com/watch?v=IlgueVqtHGo) for a more in depth tutorial on how to use the playground. \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index eb0d586c..cb64b1bd 100644 --- a/poetry.lock +++ b/poetry.lock @@ -13,87 +13,87 @@ files = [ [[package]] name = "aiohttp" -version = "3.11.9" +version = "3.11.11" description = "Async http client/server framework (asyncio)" optional = false python-versions = ">=3.9" files = [ - {file = "aiohttp-3.11.9-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:0411777249f25d11bd2964a230b3ffafcbed6cd65d0f2b132bc2b8f5b8c347c7"}, - {file = "aiohttp-3.11.9-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:499368eb904566fbdf1a3836a1532000ef1308f34a1bcbf36e6351904cced771"}, - {file = "aiohttp-3.11.9-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0b5a5009b0159a8f707879dc102b139466d8ec6db05103ec1520394fdd8ea02c"}, - {file = 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(>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.21,<0.22)", "urllib3 (<2.0.0)"] +dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "libcst", "librosa", "nltk (<=3.8.1)", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"] +flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"] +flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] +ftfy = ["ftfy"] +integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"] +ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"] +modelcreation = ["cookiecutter (==1.7.3)"] +natten = ["natten (>=0.14.6,<0.15.0)"] +onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"] +onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"] +optuna = ["optuna"] +quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "libcst", "rich", "ruff (==0.5.1)", "urllib3 (<2.0.0)"] +ray = ["ray[tune] (>=2.7.0)"] +retrieval = ["datasets (!=2.5.0)", "faiss-cpu"] +ruff = ["ruff (==0.5.1)"] +sagemaker = ["sagemaker (>=2.31.0)"] +sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"] +serving = ["fastapi", "pydantic", "starlette", "uvicorn"] +sigopt = ["sigopt"] +sklearn = ["scikit-learn"] +speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] +testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"] +tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] +tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"] +tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] +tiktoken = ["blobfile", "tiktoken"] +timm = ["timm (<=1.0.11)"] +tokenizers = ["tokenizers (>=0.21,<0.22)"] +torch = ["accelerate (>=0.26.0)", "torch"] +torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] +torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"] +torchhub = ["filelock", "huggingface-hub (>=0.24.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.21,<0.22)", "torch", "tqdm (>=4.27)"] +video = ["av (==9.2.0)"] +vision = ["Pillow (>=10.0.1,<=15.0)"] + [[package]] name = "triton" version = "3.1.0" @@ -5103,4 +5428,4 @@ server = ["azure-ai-documentintelligence", "azure-ai-formrecognizer", "docling", [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "82e66f32512cdbead2ceb993eb58233578bff394d592d348a9864ed779d5b539" +content-hash = "a7ff2aeefcd400baf5f7204c1a0875b9c4aa010014b3c8a0c73300db984387d6" diff --git a/pyproject.toml b/pyproject.toml index 709e74cf..8503b68a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,7 +29,6 @@ openpyxl = { version = "^3.1.5", optional = true } python-docx = { version = "^1.1.2", optional = true } pydub = { version = "^0.25.1", optional = true } python-pptx = { version = "^1.0.2", optional = true } -azure-ai-documentintelligence = { version = "^1.0.0b4", optional = true } paddlepaddle = { version = "^2.6.2", optional = true } pymupdf = { version = "^1.24.10", optional = true } jsonschema = "^4.23.0" @@ -39,6 +38,8 @@ uvicorn = { version = "^0.31.0", optional = true } websockets = "^13.1" docling = { version = "^2.5.2", optional = true } azure-ai-formrecognizer = { version = "^3.3.3", optional = true } +lzstring = "^1.0.4" +azure-ai-documentintelligence = { version = "^1.0.0b4", optional = true } [tool.poetry.extras] parsing = ["python-docx", "openpyxl", "pydub", "python-pptx", "azure-ai-documentintelligence", "paddlepaddle", "pymupdf"] diff --git a/server/app/main.py b/server/app/main.py index 23b6f406..b3c55147 100644 --- a/server/app/main.py +++ b/server/app/main.py @@ -1,7 +1,7 @@ import os from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware -from server.app.routes import pipeline, convert +from server.app.routes import pipeline, convert, filesystem from dotenv import load_dotenv load_dotenv() @@ -17,26 +17,24 @@ app = FastAPI() os.environ["USE_FRONTEND"] = "true" - # Add CORS middleware app.add_middleware( CORSMiddleware, - allow_origins=allow_origins, # Adjust this to your Next.js app's URL + allow_origins=allow_origins, allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) +# Include all routers app.include_router(pipeline.router) app.include_router(convert.router) - +app.include_router(filesystem.router, prefix="/fs") @app.get("/") async def root(): return {"message": "DocETL API is running"} - if __name__ == "__main__": import uvicorn - uvicorn.run("server.app.main:app", host=host, port=port, reload=reload) diff --git a/server/app/models.py b/server/app/models.py index c21db8c8..85ef103b 100644 --- a/server/app/models.py +++ b/server/app/models.py @@ -1,5 +1,38 @@ from pydantic import BaseModel +from typing import List, Dict, Any, Optional +from datetime import datetime +from enum import Enum class PipelineRequest(BaseModel): yaml_config: str + +class PipelineConfigRequest(BaseModel): + namespace: str + name: str + config: str + input_path: str + output_path: str + +class TaskStatus(str, Enum): + PENDING = "pending" + PROCESSING = "processing" + COMPLETED = "completed" + FAILED = "failed" + CANCELLED = "cancelled" + +class OptimizeResult(BaseModel): + task_id: str + status: TaskStatus + should_optimize: Optional[str] = None + input_data: Optional[List[Dict[str, Any]]] = None + output_data: Optional[List[Dict[str, Any]]] = None + cost: Optional[float] = None + error: Optional[str] = None + created_at: datetime + completed_at: Optional[datetime] = None + +class OptimizeRequest(BaseModel): + yaml_config: str + step_name: str + op_name: str \ No newline at end of file diff --git a/server/app/routes/convert.py b/server/app/routes/convert.py index 7f18daa3..173a27d5 100644 --- a/server/app/routes/convert.py +++ b/server/app/routes/convert.py @@ -4,7 +4,7 @@ import os import aiohttp from pathlib import Path -from azure.ai.documentintelligence.models import AnalyzeDocumentRequest, ContentFormat, AnalyzeResult +from azure.ai.documentintelligence.models import AnalyzeDocumentRequest, AnalyzeResult, DocumentContentFormat from azure.ai.documentintelligence import DocumentIntelligenceClient from azure.core.credentials import AzureKeyCredential import asyncio @@ -37,7 +37,7 @@ def process_document_with_azure(file_path: str, endpoint: str, key: str) -> str: with open(file_path, "rb") as f: poller = document_analysis_client.begin_analyze_document( - "prebuilt-layout", AnalyzeDocumentRequest(bytes_source=f.read()), output_content_format=ContentFormat.MARKDOWN, + "prebuilt-layout", AnalyzeDocumentRequest(bytes_source=f.read()), output_content_format=DocumentContentFormat.MARKDOWN, ) result = poller.result() @@ -47,7 +47,8 @@ def process_document_with_azure(file_path: str, endpoint: str, key: str) -> str: return f"Error processing document: {str(e)}" @router.post("/api/convert-documents") -async def convert_documents(files: List[UploadFile] = File(...), use_docetl_server: bool = False): +async def convert_documents(files: List[UploadFile] = File(...), use_docetl_server: str = "false"): + use_docetl_server = use_docetl_server.lower() == "true" # TODO: make this a boolean # Only try Modal endpoint if use_docetl_server is true and there are no txt files all_txt_files = all(file.filename.lower().endswith('.txt') or file.filename.lower().endswith('.md') for file in files) if use_docetl_server and not all_txt_files: diff --git a/server/app/routes/filesystem.py b/server/app/routes/filesystem.py new file mode 100644 index 00000000..e33a1709 --- /dev/null +++ b/server/app/routes/filesystem.py @@ -0,0 +1,168 @@ +from fastapi import APIRouter, UploadFile, File, Form, HTTPException +from fastapi.responses import FileResponse, JSONResponse +from typing import List, Optional +import os +import yaml +import shutil +from pathlib import Path +from server.app.models import PipelineConfigRequest + +router = APIRouter() + + +def get_home_dir() -> str: + """Get the home directory from env var or user home""" + return os.getenv("DOCETL_HOME_DIR", os.path.expanduser("~")) + +def get_namespace_dir(namespace: str) -> Path: + """Get the namespace directory path""" + home_dir = get_home_dir() + return Path(home_dir) / ".docetl" / namespace + +@router.post("/check-namespace") +async def check_namespace(namespace: str): + """Check if namespace exists and create if it doesn't""" + try: + namespace_dir = get_namespace_dir(namespace) + exists = namespace_dir.exists() + + if not exists: + namespace_dir.mkdir(parents=True, exist_ok=True) + + return {"exists": exists} + except Exception as e: + raise HTTPException(status_code=500, detail=f"Failed to check/create namespace: {str(e)}") + +@router.post("/upload-file") +async def upload_file(file: UploadFile = File(...), namespace: str = Form(...)): + """Upload a single file to the namespace files directory""" + try: + upload_dir = get_namespace_dir(namespace) / "files" + upload_dir.mkdir(parents=True, exist_ok=True) + + file_path = upload_dir / file.filename + with file_path.open("wb") as f: + shutil.copyfileobj(file.file, f) + + return {"path": str(file_path)} + except Exception as e: + raise HTTPException(status_code=500, detail=f"Failed to upload file: {str(e)}") + +@router.post("/save-documents") +async def save_documents(files: List[UploadFile] = File(...), namespace: str = Form(...)): + """Save multiple documents to the namespace documents directory""" + try: + uploads_dir = get_namespace_dir(namespace) / "documents" + uploads_dir.mkdir(parents=True, exist_ok=True) + + saved_files = [] + for file in files: + # Create safe filename + safe_name = "".join(c if c.isalnum() or c in ".-" else "_" for c in file.filename) + file_path = uploads_dir / safe_name + + with file_path.open("wb") as f: + shutil.copyfileobj(file.file, f) + + saved_files.append({ + "name": file.filename, + "path": str(file_path) + }) + + return {"files": saved_files} + except Exception as e: + raise HTTPException(status_code=500, detail=f"Failed to save documents: {str(e)}") + +@router.post("/write-pipeline-config") +async def write_pipeline_config(request: PipelineConfigRequest): + """Write pipeline configuration YAML file""" + try: + home_dir = get_home_dir() + pipeline_dir = Path(home_dir) / ".docetl" / request.namespace / "pipelines" + config_dir = pipeline_dir / "configs" + name_dir = pipeline_dir / request.name / "intermediates" + + config_dir.mkdir(parents=True, exist_ok=True) + name_dir.mkdir(parents=True, exist_ok=True) + + file_path = config_dir / f"{request.name}.yaml" + with file_path.open("w") as f: + f.write(request.config) + + return { + "filePath": str(file_path), + "inputPath": request.input_path, + "outputPath": request.output_path + } + except Exception as e: + raise HTTPException(status_code=500, detail=f"Failed to write pipeline configuration: {str(e)}") + +@router.get("/read-file") +async def read_file(path: str): + """Read file contents""" + try: + if path.startswith(("http://", "https://")): + # For HTTP URLs, we'll need to implement request handling + raise HTTPException(status_code=400, detail="HTTP URLs not supported in this endpoint") + + file_path = Path(path) + if not file_path.exists(): + raise HTTPException(status_code=404, detail="File not found") + + return FileResponse(path) + except Exception as e: + raise HTTPException(status_code=500, detail=f"Failed to read file: {str(e)}") + +@router.get("/read-file-page") +async def read_file_page(path: str, page: int = 0, chunk_size: int = 500000): + """Read file contents by page""" + try: + file_path = Path(path) + if not file_path.exists(): + raise HTTPException(status_code=404, detail="File not found") + + file_size = file_path.stat().st_size + start = page * chunk_size + + with file_path.open("rb") as f: + f.seek(start) + content = f.read(chunk_size).decode("utf-8") + + return { + "content": content, + "totalSize": file_size, + "page": page, + "hasMore": start + len(content) < file_size + } + except Exception as e: + raise HTTPException(status_code=500, detail=f"Failed to read file: {str(e)}") + +@router.get("/serve-document/{path:path}") +async def serve_document(path: str): + """Serve document files""" + try: + # Security check for path traversal + if ".." in path: + raise HTTPException(status_code=400, detail="Invalid file path") + + file_path = Path(path) + if not file_path.exists(): + raise HTTPException(status_code=404, detail="File not found") + + return FileResponse( + path=file_path, + filename=file_path.name, + headers={"Cache-Control": "public, max-age=3600"} + ) + except Exception as e: + raise HTTPException(status_code=500, detail=f"Failed to serve file: {str(e)}") + +@router.get("/check-file") +async def check_file(path: str): + """Check if a file exists without reading it""" + try: + file_path = Path(path) + exists = file_path.exists() + return {"exists": exists} + except Exception as e: + raise HTTPException(status_code=500, detail=f"Failed to check file: {str(e)}") diff --git a/server/app/routes/pipeline.py b/server/app/routes/pipeline.py index 92af480c..6ddc91cd 100644 --- a/server/app/routes/pipeline.py +++ b/server/app/routes/pipeline.py @@ -1,15 +1,14 @@ from typing import Any, Dict, List, Optional import uuid from fastapi import APIRouter, HTTPException, WebSocket, WebSocketDisconnect -from server.app.models import PipelineRequest from docetl.runner import DSLRunner import asyncio from asyncio import Task from rich.logging import RichHandler import logging -from pydantic import BaseModel from datetime import datetime, timedelta from enum import Enum +from server.app.models import OptimizeResult, TaskStatus, OptimizeRequest, PipelineRequest # Setup logging FORMAT = "%(message)s" @@ -19,29 +18,6 @@ router = APIRouter() -class TaskStatus(str, Enum): - PENDING = "pending" - PROCESSING = "processing" - COMPLETED = "completed" - FAILED = "failed" - CANCELLED = "cancelled" - -class OptimizeResult(BaseModel): - task_id: str - status: TaskStatus - should_optimize: Optional[str] = None - input_data: Optional[List[Dict[str, Any]]] = None - output_data: Optional[List[Dict[str, Any]]] = None - cost: Optional[float] = None - error: Optional[str] = None - created_at: datetime - completed_at: Optional[datetime] = None - -class OptimizeRequest(BaseModel): - yaml_config: str - step_name: str - op_name: str - # Task storage tasks: Dict[str, OptimizeResult] = {} asyncio_tasks: Dict[str, Task] = {} @@ -108,6 +84,7 @@ async def run_optimization(task_id: str, yaml_config: str, step_name: str, op_na finally: if task_id in asyncio_tasks: del asyncio_tasks[task_id] + runner.reset_env() @router.on_event("startup") async def startup_event(): @@ -181,6 +158,7 @@ def run_pipeline(request: PipelineRequest) -> Dict[str, Any]: try: runner = DSLRunner.from_yaml(request.yaml_config) cost = runner.load_run_save() + runner.reset_env() return {"cost": cost, "message": "Pipeline executed successfully"} except Exception as e: import traceback @@ -188,9 +166,10 @@ def run_pipeline(request: PipelineRequest) -> Dict[str, Any]: print(f"Error occurred:\n{e}\n{error_traceback}") raise HTTPException(status_code=500, detail=str(e) + "\n" + error_traceback) -@router.websocket("/ws/run_pipeline") -async def websocket_run_pipeline(websocket: WebSocket): +@router.websocket("/ws/run_pipeline/{client_id}") +async def websocket_run_pipeline(websocket: WebSocket, client_id: str): await websocket.accept() + runner = None try: config = await websocket.receive_json() runner = DSLRunner.from_yaml(config["yaml_config"]) @@ -205,7 +184,6 @@ async def run_pipeline(): return await asyncio.to_thread(runner.optimize, return_pipeline=False, model=config.get("optimizer_model", "gpt-4o")) else: - async def run_pipeline(): return await asyncio.to_thread(runner.load_run_save) @@ -294,6 +272,8 @@ async def run_pipeline(): } ) except WebSocketDisconnect: + if runner is not None: + runner.reset_env() print("Client disconnected") except Exception as e: import traceback @@ -302,4 +282,6 @@ async def run_pipeline(): print(f"Error occurred:\n{error_traceback}") await websocket.send_json({"type": "error", "data": str(e), "traceback": error_traceback}) finally: + if runner is not None: + runner.reset_env() await websocket.close() diff --git a/website/package-lock.json b/website/package-lock.json index 7d6d8e32..4a108b45 100644 --- a/website/package-lock.json +++ b/website/package-lock.json @@ -39,6 +39,7 @@ "@tanstack/react-table": "^8.20.5", "@tanstack/react-virtual": "^3.10.9", "@types/diff": "^6.0.0", + "@types/lz-string": "^1.3.34", "@types/mime-types": "^2.1.4", "@types/react-resizable": "^3.0.8", "ai": "^3.4.29", @@ -57,6 +58,7 @@ "lodash": "^4.17.21", "lodash.throttle": "^4.1.1", "lucide-react": "^0.441.0", + "lz-string": "^1.5.0", "next": "14.2.11", "re-resizable": "^6.10.0", "react": "^18", @@ -4067,6 +4069,12 @@ "integrity": "sha512-YpS0zzoduEhuOWjAotS6A5AVCva7X4lVlYLF0FYHAY9sdraBfnatttHItlWeZdGhuEkf+OzMNg2ZYAx8t+52uQ==", "dev": true }, + "node_modules/@types/lz-string": { + "version": "1.3.34", + "resolved": "https://registry.npmjs.org/@types/lz-string/-/lz-string-1.3.34.tgz", + "integrity": "sha512-j6G1e8DULJx3ONf6NdR5JiR2ZY3K3PaaqiEuKYkLQO0Czfi1AzrtjfnfCROyWGeDd5IVMKCwsgSmMip9OWijow==", + "license": "MIT" + }, "node_modules/@types/mdast": { "version": "4.0.4", "resolved": "https://registry.npmjs.org/@types/mdast/-/mdast-4.0.4.tgz", @@ -9193,6 +9201,15 @@ "react": "^16.5.1 || ^17.0.0 || ^18.0.0 || ^19.0.0-rc" } }, + "node_modules/lz-string": { + "version": "1.5.0", + "resolved": "https://registry.npmjs.org/lz-string/-/lz-string-1.5.0.tgz", + "integrity": "sha512-h5bgJWpxJNswbU7qCrV0tIKQCaS3blPDrqKWx+QxzuzL1zGUzij9XCWLrSLsJPu5t+eWA/ycetzYAO5IOMcWAQ==", + "license": "MIT", + "bin": { + "lz-string": "bin/bin.js" + } + }, "node_modules/magic-string": { "version": "0.30.12", "resolved": "https://registry.npmjs.org/magic-string/-/magic-string-0.30.12.tgz", diff --git a/website/package.json b/website/package.json index 5b222600..bef41552 100644 --- a/website/package.json +++ b/website/package.json @@ -40,6 +40,7 @@ "@tanstack/react-table": "^8.20.5", "@tanstack/react-virtual": "^3.10.9", "@types/diff": "^6.0.0", + "@types/lz-string": "^1.3.34", "@types/mime-types": "^2.1.4", "@types/react-resizable": "^3.0.8", "ai": "^3.4.29", @@ -58,6 +59,7 @@ "lodash": "^4.17.21", "lodash.throttle": "^4.1.1", "lucide-react": "^0.441.0", + "lz-string": "^1.5.0", "next": "14.2.11", "re-resizable": "^6.10.0", "react": "^18", diff --git a/website/src/app/api/chat/route.ts b/website/src/app/api/chat/route.ts index cac7e49b..5f1fbadf 100644 --- a/website/src/app/api/chat/route.ts +++ b/website/src/app/api/chat/route.ts @@ -3,20 +3,38 @@ import { streamText } from "ai"; // Allow streaming responses up to 60 seconds export const maxDuration = 60; -const openai = createOpenAI({ - apiKey: process.env.OPENAI_API_KEY, - baseURL: process.env.OPENAI_API_BASE, - compatibility: "strict", // strict mode, enable when using the OpenAI API -}); export async function POST(req: Request) { - const { messages } = await req.json(); + try { + const { messages } = await req.json(); + const apiKey = + req.headers.get("x-openai-key") || process.env.OPENAI_API_KEY; - const result = await streamText({ - model: openai(process.env.MODEL_NAME), - system: "You are a helpful assistant.", - messages, - }); + if (!apiKey) { + return new Response( + JSON.stringify({ error: "OpenAI API key is required" }), + { status: 400 } + ); + } - return result.toDataStreamResponse(); + const openai = createOpenAI({ + apiKey, + baseURL: process.env.OPENAI_API_BASE, + compatibility: "strict", + }); + + const result = await streamText({ + model: openai(process.env.MODEL_NAME), + system: "You are a helpful assistant.", + messages, + }); + + return result.toDataStreamResponse(); + } catch (error) { + console.error("Chat API error:", error); + return new Response( + error instanceof Error ? error.message : "An error occurred", + { status: 500 } + ); + } } diff --git a/website/src/app/api/checkNamespace/route.ts b/website/src/app/api/checkNamespace/route.ts index b88e4d23..b8a4e0c2 100644 --- a/website/src/app/api/checkNamespace/route.ts +++ b/website/src/app/api/checkNamespace/route.ts @@ -1,12 +1,14 @@ import { NextResponse } from "next/server"; -import fs from "fs"; -import { getNamespaceDir } from "@/app/api/utils"; -import os from "os"; + +const FASTAPI_URL = `${ + process.env.NEXT_PUBLIC_BACKEND_HTTPS ? "https" : "http" +}://${process.env.NEXT_PUBLIC_BACKEND_HOST}:${ + process.env.NEXT_PUBLIC_BACKEND_PORT +}`; export async function POST(request: Request) { try { const { namespace } = await request.json(); - const homeDir = process.env.DOCETL_HOME_DIR || os.homedir(); if (!namespace) { return NextResponse.json( @@ -15,18 +17,25 @@ export async function POST(request: Request) { ); } - const namespaceDir = getNamespaceDir(homeDir, namespace); - const exists = fs.existsSync(namespaceDir); + console.log(FASTAPI_URL); + const response = await fetch( + `${FASTAPI_URL}/fs/check-namespace?namespace=${namespace}`, + { + method: "POST", + } + ); - if (!exists) { - fs.mkdirSync(namespaceDir, { recursive: true }); + if (!response.ok) { + const error = await response.json(); + throw new Error(error.detail || "Failed to check namespace"); } - return NextResponse.json({ exists }); + const data = await response.json(); + return NextResponse.json(data); } catch (error) { - console.error("Error checking/creating namespace:", error); + console.error("Error checking namespace:", error); return NextResponse.json( - { error: "Failed to check/create namespace" }, + { error: "Failed to check namespace" }, { status: 500 } ); } diff --git a/website/src/app/api/convertDocuments/route.ts b/website/src/app/api/convertDocuments/route.ts index cd1f9985..bce02769 100644 --- a/website/src/app/api/convertDocuments/route.ts +++ b/website/src/app/api/convertDocuments/route.ts @@ -1,6 +1,10 @@ import { NextRequest, NextResponse } from "next/server"; -export const runtime = "nodejs"; +const FASTAPI_URL = `${ + process.env.NEXT_PUBLIC_BACKEND_HTTPS ? "https" : "http" +}://${process.env.NEXT_PUBLIC_BACKEND_HOST}:${ + process.env.NEXT_PUBLIC_BACKEND_PORT +}`; export async function POST(request: NextRequest) { try { @@ -12,18 +16,6 @@ export async function POST(request: NextRequest) { return NextResponse.json({ error: "No files provided" }, { status: 400 }); } - // Create a new FormData to forward to the Python backend - const backendFormData = new FormData(); - files.forEach((file) => { - backendFormData.append("files", file); - }); - - // Add conversion method to form data - backendFormData.append( - "use_docetl_server", - conversionMethod === "docetl" ? "true" : "false" - ); - // Get Azure credentials from headers if they exist const azureEndpoint = request.headers.get("azure-endpoint"); const azureKey = request.headers.get("azure-key"); @@ -41,9 +33,17 @@ export async function POST(request: NextRequest) { headers["azure-key"] = azureKey; } + // Create FormData since FastAPI expects multipart/form-data + const backendFormData = new FormData(); + for (const file of files) { + backendFormData.append("files", file); + } + // Forward the request to the Python backend const response = await fetch( - `http://${process.env.NEXT_PUBLIC_BACKEND_HOST}:${process.env.NEXT_PUBLIC_BACKEND_PORT}${endpoint}`, + `${FASTAPI_URL}${endpoint}?use_docetl_server=${ + conversionMethod === "docetl" ? "true" : "false" + }`, { method: "POST", body: backendFormData, diff --git a/website/src/app/api/getInputOutput/route.ts b/website/src/app/api/getInputOutput/route.ts index d4c193b2..777e2081 100644 --- a/website/src/app/api/getInputOutput/route.ts +++ b/website/src/app/api/getInputOutput/route.ts @@ -1,7 +1,13 @@ import { NextResponse } from "next/server"; import { generatePipelineConfig } from "@/app/api/utils"; -import fs from "fs/promises"; import os from "os"; + +const FASTAPI_URL = `${ + process.env.NEXT_PUBLIC_BACKEND_HTTPS ? "https" : "http" +}://${process.env.NEXT_PUBLIC_BACKEND_HOST}:${ + process.env.NEXT_PUBLIC_BACKEND_PORT +}`; + export async function POST(request: Request) { try { const { @@ -40,10 +46,24 @@ export async function POST(request: Request) { sample_size ); - // Check if inputPath exists - try { - await fs.access(inputPath); - } catch (error) { + // Check if files exist using FastAPI endpoints + const checkInputResponse = await fetch( + `${FASTAPI_URL}/fs/check-file?path=${encodeURIComponent(inputPath)}`, + { + method: "GET", + } + ); + + if (!checkInputResponse.ok) { + console.error(`Failed to check input path: ${inputPath}`); + return NextResponse.json( + { error: "Failed to check input path" }, + { status: 500 } + ); + } + + const inputResult = await checkInputResponse.json(); + if (!inputResult.exists) { console.error(`Input path does not exist: ${inputPath}`); return NextResponse.json( { error: "Input path does not exist" }, @@ -51,10 +71,23 @@ export async function POST(request: Request) { ); } - // Check if outputPath exists - try { - await fs.access(outputPath); - } catch (error) { + const checkOutputResponse = await fetch( + `${FASTAPI_URL}/fs/check-file?path=${encodeURIComponent(outputPath)}`, + { + method: "GET", + } + ); + + if (!checkOutputResponse.ok) { + console.error(`Failed to check output path: ${outputPath}`); + return NextResponse.json( + { error: "Failed to check output path" }, + { status: 500 } + ); + } + + const outputResult = await checkOutputResponse.json(); + if (!outputResult.exists) { console.error(`Output path does not exist: ${outputPath}`); return NextResponse.json( { error: "Output path does not exist" }, diff --git a/website/src/app/api/readFile/route.ts b/website/src/app/api/readFile/route.ts index d61ed88a..d262b8f6 100644 --- a/website/src/app/api/readFile/route.ts +++ b/website/src/app/api/readFile/route.ts @@ -1,6 +1,10 @@ import { NextRequest, NextResponse } from "next/server"; -import fs from "fs/promises"; -import axios from "axios"; + +const FASTAPI_URL = `${ + process.env.NEXT_PUBLIC_BACKEND_HTTPS ? "https" : "http" +}://${process.env.NEXT_PUBLIC_BACKEND_HOST}:${ + process.env.NEXT_PUBLIC_BACKEND_PORT +}`; export async function GET(req: NextRequest) { const filePath = req.nextUrl.searchParams.get("path"); @@ -10,14 +14,20 @@ export async function GET(req: NextRequest) { } try { - let fileContent; - if (filePath.startsWith("http")) { - const response = await axios.get(filePath); - fileContent = response.data; - } else { - fileContent = await fs.readFile(filePath, "utf-8"); + const response = await fetch( + `${FASTAPI_URL}/fs/read-file?path=${encodeURIComponent(filePath)}` + ); + + if (!response.ok) { + const error = await response.json(); + return NextResponse.json( + { error: error.detail || "Failed to read file" }, + { status: response.status } + ); } - return new NextResponse(fileContent, { status: 200 }); + + const content = await response.text(); + return new NextResponse(content, { status: 200 }); } catch (error) { console.error("Error reading file:", error); return NextResponse.json({ error: "Failed to read file" }, { status: 500 }); diff --git a/website/src/app/api/readFilePage/route.ts b/website/src/app/api/readFilePage/route.ts index 865f89bb..7ec80e12 100644 --- a/website/src/app/api/readFilePage/route.ts +++ b/website/src/app/api/readFilePage/route.ts @@ -1,7 +1,11 @@ import { NextRequest, NextResponse } from "next/server"; -import fs from "fs/promises"; -const CHUNK_SIZE = 500000; // Number of characters to read at a time +const FASTAPI_URL = `${ + process.env.NEXT_PUBLIC_BACKEND_HTTPS ? "https" : "http" +}://${process.env.NEXT_PUBLIC_BACKEND_HOST}:${ + process.env.NEXT_PUBLIC_BACKEND_PORT +}`; +const CHUNK_SIZE = 1000000; // Number of characters to read at a time (roughly 1MB of text) export async function GET(req: NextRequest) { const filePath = req.nextUrl.searchParams.get("path"); @@ -12,27 +16,19 @@ export async function GET(req: NextRequest) { } try { - const fileHandle = await fs.open(filePath, "r"); - const stats = await fileHandle.stat(); - const fileSize = stats.size; - - const start = page * CHUNK_SIZE; - const buffer = Buffer.alloc(CHUNK_SIZE); - const { bytesRead } = await fileHandle.read(buffer, 0, CHUNK_SIZE, start); + const response = await fetch( + `${FASTAPI_URL}/fs/read-file-page?path=${encodeURIComponent( + filePath + )}&page=${page}&chunk_size=${CHUNK_SIZE}` + ); - await fileHandle.close(); + if (!response.ok) { + throw new Error(`HTTP error! status: ${response.status}`); + } - const content = buffer.toString("utf-8", 0, bytesRead); + const data = await response.json(); - return NextResponse.json( - { - content, - totalSize: fileSize, - page, - hasMore: start + bytesRead < fileSize, - }, - { status: 200 } - ); + return NextResponse.json(data, { status: 200 }); } catch (error) { console.error("Error reading file:", error); return NextResponse.json({ error: "Failed to read file" }, { status: 500 }); diff --git a/website/src/app/api/saveDocuments/route.ts b/website/src/app/api/saveDocuments/route.ts index 920ffe35..31d6b83c 100644 --- a/website/src/app/api/saveDocuments/route.ts +++ b/website/src/app/api/saveDocuments/route.ts @@ -1,13 +1,10 @@ import { NextRequest, NextResponse } from "next/server"; -import { writeFile } from "fs/promises"; -import path from "path"; -import { mkdir } from "fs/promises"; -import os from "os"; -interface SavedFile { - name: string; - path: string; -} +const FASTAPI_URL = `${ + process.env.NEXT_PUBLIC_BACKEND_HTTPS ? "https" : "http" +}://${process.env.NEXT_PUBLIC_BACKEND_HOST}:${ + process.env.NEXT_PUBLIC_BACKEND_PORT +}`; export async function POST(request: NextRequest) { try { @@ -19,32 +16,25 @@ export async function POST(request: NextRequest) { return NextResponse.json({ error: "No files provided" }, { status: 400 }); } - const homeDir = process.env.DOCETL_HOME_DIR || os.homedir(); - - // Create uploads directory in user's home directory if it doesn't exist - const uploadsDir = path.join(homeDir, ".docetl", namespace, "documents"); - await mkdir(uploadsDir, { recursive: true }); - - const savedFiles = await Promise.all( - files.map(async (file) => { - const bytes = await file.arrayBuffer(); - const buffer = Buffer.from(bytes); - - // Create a safe filename - const fileName = file.name.replace(/[^a-zA-Z0-9.-]/g, "_"); - const filePath = path.join(uploadsDir, fileName); - - // Save the file - await writeFile(filePath, buffer); - - return { - name: file.name, - path: filePath, - } as SavedFile; - }) - ); + // Create a new FormData object to send to the backend + const backendFormData = new FormData(); + files.forEach((file) => { + backendFormData.append("files", file); + }); + backendFormData.append("namespace", namespace); + + // Send to FastAPI backend + const response = await fetch(`${FASTAPI_URL}/fs/save-documents`, { + method: "POST", + body: backendFormData, + }); + + if (!response.ok) { + throw new Error(`Backend responded with status ${response.status}`); + } - return NextResponse.json({ files: savedFiles }, { status: 200 }); + const result = await response.json(); + return NextResponse.json(result, { status: 200 }); } catch (error) { console.error("Error saving documents:", error); return NextResponse.json( diff --git a/website/src/app/api/serveDocument/[...path]/route.ts b/website/src/app/api/serveDocument/[...path]/route.ts index 6582e274..c7f2c2bd 100644 --- a/website/src/app/api/serveDocument/[...path]/route.ts +++ b/website/src/app/api/serveDocument/[...path]/route.ts @@ -1,10 +1,11 @@ // app/api/documents/[...path]/route.ts import { NextRequest, NextResponse } from "next/server"; -import { readFile } from "fs/promises"; -import path from "path"; -import { lookup } from "mime-types"; -export const dynamic = "force-dynamic"; +const FASTAPI_URL = `${ + process.env.NEXT_PUBLIC_BACKEND_HTTPS ? "https" : "http" +}://${process.env.NEXT_PUBLIC_BACKEND_HOST}:${ + process.env.NEXT_PUBLIC_BACKEND_PORT +}`; export async function GET( request: NextRequest, @@ -14,22 +15,32 @@ export async function GET( // Join the path segments and decode any URL encoding const filePath = decodeURIComponent(params.path.join("/")); - // Basic security check to prevent directory traversal - const normalizedPath = path.normalize(filePath); - if (normalizedPath.includes("..")) { - return NextResponse.json({ error: "Invalid file path" }, { status: 400 }); - } + // Forward the request to FastAPI's serve-document endpoint + const response = await fetch( + `${FASTAPI_URL}/fs/serve-document/${filePath}`, + { + method: "GET", + } + ); - const fileBuffer = await readFile(normalizedPath); - const mimeType = lookup(normalizedPath) || "application/octet-stream"; + if (!response.ok) { + const error = await response.json(); + return NextResponse.json( + { error: error.detail }, + { status: response.status } + ); + } - return new NextResponse(fileBuffer, { + // Stream the response from FastAPI + const data = await response.blob(); + return new NextResponse(data, { headers: { - "Content-Type": mimeType, - "Content-Disposition": `inline; filename="${path.basename( - normalizedPath - )}"`, - "Cache-Control": "public, max-age=3600", + "Content-Type": + response.headers.get("Content-Type") || "application/octet-stream", + "Content-Disposition": + response.headers.get("Content-Disposition") || "inline", + "Cache-Control": + response.headers.get("Cache-Control") || "public, max-age=3600", }, }); } catch (error) { diff --git a/website/src/app/api/shouldOptimize/route.ts b/website/src/app/api/shouldOptimize/route.ts index 14ee3d25..f22e841d 100644 --- a/website/src/app/api/shouldOptimize/route.ts +++ b/website/src/app/api/shouldOptimize/route.ts @@ -1,6 +1,10 @@ import { NextRequest, NextResponse } from "next/server"; -const FASTAPI_URL = `http://${process.env.NEXT_PUBLIC_BACKEND_HOST}:${process.env.NEXT_PUBLIC_BACKEND_PORT}`; +const FASTAPI_URL = `${ + process.env.NEXT_PUBLIC_BACKEND_HTTPS ? "https" : "http" +}://${process.env.NEXT_PUBLIC_BACKEND_HOST}:${ + process.env.NEXT_PUBLIC_BACKEND_PORT +}`; // Helper to handle errors consistently const handleError = (error: unknown, status = 500) => { diff --git a/website/src/app/api/uploadFile/route.ts b/website/src/app/api/uploadFile/route.ts index 4e79b375..a8974eac 100644 --- a/website/src/app/api/uploadFile/route.ts +++ b/website/src/app/api/uploadFile/route.ts @@ -1,8 +1,4 @@ import { NextRequest, NextResponse } from "next/server"; -import { writeFile } from "fs/promises"; -import path from "path"; -import { mkdir } from "fs/promises"; -import os from "os"; export async function POST(request: NextRequest) { try { @@ -13,23 +9,27 @@ export async function POST(request: NextRequest) { return NextResponse.json({ error: "No file uploaded" }, { status: 400 }); } - // Convert the file to buffer - const bytes = await file.arrayBuffer(); - const buffer = Buffer.from(bytes); + // Construct FastAPI URL from environment variables + const FASTAPI_URL = `${ + process.env.NEXT_PUBLIC_BACKEND_HTTPS ? "https" : "http" + }://${process.env.NEXT_PUBLIC_BACKEND_HOST}:${ + process.env.NEXT_PUBLIC_BACKEND_PORT + }`; + const apiFormData = new FormData(); + apiFormData.append("file", file); + apiFormData.append("namespace", namespace); - // Create uploads directory in user's home directory if it doesn't exist - const homeDir = process.env.DOCETL_HOME_DIR || os.homedir(); - const uploadDir = path.join(homeDir, ".docetl", namespace, "files"); - await mkdir(uploadDir, { recursive: true }); + const response = await fetch(`${FASTAPI_URL}/fs/upload-file`, { + method: "POST", + body: apiFormData, + }); - // Create full file path - const filePath = path.join(uploadDir, file.name); - - // Write the file - await writeFile(filePath, buffer); + if (!response.ok) { + throw new Error(`API responded with status ${response.status}`); + } - // Return the absolute path - return NextResponse.json({ path: filePath }); + const data = await response.json(); + return NextResponse.json({ path: data.path }); } catch (error) { console.error("Error uploading file:", error); return NextResponse.json( diff --git a/website/src/app/api/utils.ts b/website/src/app/api/utils.ts index 95d28df2..8d1df00e 100644 --- a/website/src/app/api/utils.ts +++ b/website/src/app/api/utils.ts @@ -1,6 +1,33 @@ import yaml from "js-yaml"; import path from "path"; -import { Operation, SchemaItem } from "@/app/types"; +import { Operation, SchemaItem, APIKey } from "@/app/types"; +import * as LZString from "lz-string"; + +class Encryptor { + private key: string; + + constructor(secretKey: string) { + this.key = secretKey; + } + + encrypt(text: string): string { + // First, encode the text using the key + let encoded = ""; + for (let i = 0; i < text.length; i++) { + const charCode = + text.charCodeAt(i) + this.key.charCodeAt(i % this.key.length); + encoded += String.fromCharCode(charCode); + } + + // Then compress the encoded text + return LZString.compressToBase64(encoded); + } +} + +function encrypt(value: string, key: string): string { + const encryptor = new Encryptor(key); + return encryptor.encrypt(value); +} export function getNamespaceDir(homeDir: string, namespace: string) { return path.join(homeDir, ".docetl", namespace); @@ -20,7 +47,9 @@ export function generatePipelineConfig( system_prompt: { datasetDescription: string | null; persona: string | null; - } | null = null + } | null = null, + apiKeys: APIKey[] = [], + docetl_encryption_key: string = "" ) { const datasets = { input: { @@ -162,7 +191,23 @@ export function generatePipelineConfig( operations.findIndex((op: Operation) => op.id === operation_id) + 1 ); - const pipelineConfig = { + // Fix type errors by asserting the pipeline config type + const pipelineConfig: { + datasets: any; + default_model: string; + optimizer_config: any; + operations: any[]; + pipeline: { + steps: any[]; + output: { + type: string; + path: string; + intermediate_dir: string; + }; + }; + system_prompt: Record; + llm_api_keys?: Record; + } = { datasets, default_model, optimizer_config: { @@ -173,7 +218,7 @@ export function generatePipelineConfig( steps: [ { name: "data_processing", - input: Object.keys(datasets)[0], // Assuming the first dataset is the input + input: Object.keys(datasets)[0], operations: operationsToRun.map((op) => op.name), }, ], @@ -200,6 +245,21 @@ export function generatePipelineConfig( system_prompt: {}, }; + if (apiKeys.length > 0) { + if (docetl_encryption_key) { + pipelineConfig.llm_api_keys = apiKeys.reduce((acc, key) => { + // Encrypt the API key value using the encryption key + const encryptedValue = encrypt(key.value, docetl_encryption_key); + return { ...acc, [key.name]: encryptedValue }; + }, {}); + } else { + pipelineConfig.llm_api_keys = apiKeys.reduce( + (acc, key) => ({ ...acc, [key.name]: key.value }), + {} + ); + } + } + if (system_prompt) { if (system_prompt.datasetDescription) { // @ts-ignore diff --git a/website/src/app/api/writePipelineConfig/route.ts b/website/src/app/api/writePipelineConfig/route.ts index e06c289e..be97decd 100644 --- a/website/src/app/api/writePipelineConfig/route.ts +++ b/website/src/app/api/writePipelineConfig/route.ts @@ -1,8 +1,12 @@ import { NextResponse } from "next/server"; -import fs from "fs/promises"; -import path from "path"; -import os from "os"; import { generatePipelineConfig } from "@/app/api/utils"; +import os from "os"; + +const FASTAPI_URL = `${ + process.env.NEXT_PUBLIC_BACKEND_HTTPS ? "https" : "http" +}://${process.env.NEXT_PUBLIC_BACKEND_HOST}:${ + process.env.NEXT_PUBLIC_BACKEND_PORT +}`; export async function POST(request: Request) { try { @@ -17,6 +21,7 @@ export async function POST(request: Request) { clear_intermediate = false, system_prompt, namespace, + apiKeys, } = await request.json(); if (!name) { @@ -34,6 +39,7 @@ export async function POST(request: Request) { } const homeDir = process.env.DOCETL_HOME_DIR || os.homedir(); + const docetl_encryption_key = process.env.DOCETL_ENCRYPTION_KEY || ""; const { yamlString, inputPath, outputPath } = generatePipelineConfig( namespace, @@ -46,28 +52,37 @@ export async function POST(request: Request) { sample_size, optimize, clear_intermediate, - system_prompt + system_prompt, + apiKeys, + docetl_encryption_key ); - // Save the YAML file in the user's home directory - const pipelineDir = path.join(homeDir, ".docetl", namespace, "pipelines"); - const configDir = path.join(pipelineDir, "configs"); - const nameDir = path.join(pipelineDir, name, "intermediates"); + // Use the FastAPI endpoint to write the pipeline config + const response = await fetch(`${FASTAPI_URL}/fs/write-pipeline-config`, { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify({ + namespace, + name, + config: yamlString, + input_path: inputPath, + output_path: outputPath, + }), + }); - try { - await fs.mkdir(configDir, { recursive: true }); - await fs.mkdir(nameDir, { recursive: true }); - const filePath = path.join(configDir, `${name}.yaml`); - await fs.writeFile(filePath, yamlString, "utf8"); - - return NextResponse.json({ filePath, inputPath, outputPath }); - } catch (fsError) { - console.error("File system error:", fsError); - return NextResponse.json( - `Failed to write pipeline configuration: ${fsError.message}`, - { status: 500 } - ); + if (!response.ok) { + const error = await response.text(); + throw new Error(`Failed to write pipeline configuration: ${error}`); } + + const result = await response.json(); + return NextResponse.json({ + filePath: result.filePath, + inputPath: result.inputPath, + outputPath: result.outputPath, + }); } catch (error) { console.error("Pipeline configuration error:", error); return NextResponse.json( diff --git a/website/src/app/playground/page.tsx b/website/src/app/playground/page.tsx index afdfcb0a..342efe5c 100644 --- a/website/src/app/playground/page.tsx +++ b/website/src/app/playground/page.tsx @@ -98,6 +98,7 @@ const NamespaceDialog = dynamic( } ); import { ThemeProvider, useTheme, Theme } from "@/contexts/ThemeContext"; +import { APIKeysDialog } from "@/components/APIKeysDialog"; const LeftPanelIcon: React.FC<{ isActive: boolean }> = ({ isActive }) => ( { const [showDatasetView, setShowDatasetView] = useState(false); const [showChat, setShowChat] = useState(false); const [showNamespaceDialog, setShowNamespaceDialog] = useState(false); + const [showAPIKeysDialog, setShowAPIKeysDialog] = useState(false); const { theme, setTheme } = useTheme(); useEffect(() => { @@ -340,6 +342,9 @@ const CodeEditorPipelineApp: React.FC = () => { setShowNamespaceDialog(true)}> Change Namespace + setShowAPIKeysDialog(true)}> + Edit API Keys + Change Theme @@ -575,7 +580,7 @@ const CodeEditorPipelineApp: React.FC = () => { - {showDatasetView && currentFile && ( + {showDatasetView && ( <> { saveProgress(); }} /> + ); }; -const WrappedCodeEditorPipelineApp: React.FC = () => { - const [isLocalhost, setIsLocalhost] = useState(true); - - useEffect(() => { - setIsLocalhost( - window.location.hostname === "localhost" || - window.location.hostname === "127.0.0.1" - ); - }, []); +const WebSocketWrapper: React.FC<{ children: React.ReactNode }> = ({ + children, +}) => { + const { namespace } = usePipelineContext(); - if (!isLocalhost) { - return ( -
-
-

- DocETL Playground -

-

- The DocETL playground is designed to run locally. To use it, please - follow these steps: -

-
    -
  1. - Clone the GitHub repo:{" "} - - https://github.com/ucbepic/docetl - -
    2. -
  2. - Set up the project by running: - 
    -                make install
-
    - 
    -                make install-ui
-
    -
    3. -
  3. - Start the application: - 
    -                make run-ui-prod
-
    -
    4. -
  4. - Navigate to{" "} - - http://localhost:3000/playground - -
    5. -
-

- Once you've completed these steps, you'll be able to use - the DocETL playground locally. -

-
-
- ); - } + return ( + + {children} + + ); +}; +const WrappedCodeEditorPipelineApp: React.FC = () => { return ( - - + + - - + + ); }; diff --git a/website/src/app/types.ts b/website/src/app/types.ts index 9d86ade0..b7f85f2c 100644 --- a/website/src/app/types.ts +++ b/website/src/app/types.ts @@ -104,3 +104,8 @@ export interface OptimizeResult { created_at: string; completed_at?: string; } + +export interface APIKey { + name: string; + value: string; +} diff --git a/website/src/components/AIChatPanel.tsx b/website/src/components/AIChatPanel.tsx index 3108395d..b1faa34c 100644 --- a/website/src/components/AIChatPanel.tsx +++ b/website/src/components/AIChatPanel.tsx @@ -8,7 +8,7 @@ import React, { useCallback, } from "react"; import { ResizableBox } from "react-resizable"; -import { RefreshCw, X, Copy } from "lucide-react"; +import { RefreshCw, X, Copy, AlertCircle } from "lucide-react"; import { Button } from "@/components/ui/button"; import { Input } from "@/components/ui/input"; import { ScrollArea } from "@/components/ui/scroll-area"; @@ -26,6 +26,7 @@ import { } from "@/components/ui/popover"; import { Textarea } from "@/components/ui/textarea"; import { debounce } from "lodash"; +import { toast } from "@/hooks/use-toast"; interface AIChatPanelProps { onClose: () => void; @@ -39,6 +40,8 @@ const DEFAULT_SUGGESTIONS = [ ]; const AIChatPanel: React.FC = ({ onClose }) => { + const { serializeState, highLevelGoal, setHighLevelGoal, apiKeys } = + usePipelineContext(); const [position, setPosition] = useState({ x: window.innerWidth - 400, y: 80, @@ -46,6 +49,8 @@ const AIChatPanel: React.FC = ({ onClose }) => { const isDragging = useRef(false); const dragOffset = useRef({ x: 0, y: 0 }); const scrollAreaRef = useRef(null); + const [error, setError] = useState(null); + const { messages, setMessages, @@ -53,14 +58,33 @@ const AIChatPanel: React.FC = ({ onClose }) => { handleInputChange, handleSubmit, isLoading, + error: chatError, } = useChat({ api: "/api/chat", initialMessages: [], id: "persistent-chat", + headers: useMemo(() => { + const openAiKey = apiKeys.find( + (key) => key.name === "OPENAI_API_KEY" + )?.value; + return openAiKey ? { "x-openai-key": openAiKey } : {}; + }, [apiKeys]), + onError: (error) => { + setError(error.message); + toast({ + title: "Error", + description: error.message, + variant: "destructive", + }); + }, }); - const { serializeState, highLevelGoal, setHighLevelGoal } = - usePipelineContext(); + const [localGoal, setLocalGoal] = useState(highLevelGoal); + const [isLocalMode, setIsLocalMode] = useState(false); + + const hasOpenAIKey = useMemo(() => { + return apiKeys.some((key) => key.name === "OPENAI_API_KEY"); + }, [apiKeys]); const handleMouseDown = (e: React.MouseEvent) => { if ((e.target as HTMLElement).classList.contains("drag-handle")) { @@ -110,6 +134,17 @@ const AIChatPanel: React.FC = ({ onClose }) => { e.preventDefault(); if (!input.trim() || isLoading) return; + setError(null); + + if (!hasOpenAIKey && !isLocalMode) { + toast({ + title: "OpenAI API Key Required", + description: "Please add your OpenAI API key in Edit > Edit API Keys", + variant: "destructive", + }); + return; + } + const pipelineState = await serializeState(); setMessages([ @@ -292,9 +327,40 @@ Remember, all the output fields have been converted to strings, even if they wer
+ {error && ( +
+
+ +
+

Error

+

{error}

+
+
+
+ )} {messages.filter((message) => message.role !== "system").length === 0 ? (
+ {!hasOpenAIKey && !isLocalMode && ( +
+
+ +
+

OpenAI API Key Required

+

+ To use the AI assistant, please add your OpenAI API + key in Edit {">"} Edit API Keys. +

+ +
+
+
+ )} {DEFAULT_SUGGESTIONS.map((suggestion, index) => ( +
+ +
+ +
+ {localApiKeys.map((key, index) => ( +
+
+ + + handleInputChange(index, "name", e.target.value) + } + placeholder="e.g., OPENAI_API_KEY" + className="font-mono text-sm h-8 mt-1" + /> +
+
+ + + handleInputChange(index, "value", e.target.value) + } + placeholder="Enter key value" + className="font-mono text-sm h-8 mt-1" + /> +
+ {index >= 8 && ( + + )} +
+ ))} +
+ +
+
+
+ +
+
+ + + + + + + ); +} diff --git a/website/src/components/ColumnDialog.tsx b/website/src/components/ColumnDialog.tsx index 5ef6be77..ca4e4082 100644 --- a/website/src/components/ColumnDialog.tsx +++ b/website/src/components/ColumnDialog.tsx @@ -172,8 +172,8 @@ const ValueStats = React.memo( : null; return ( -
-
+
+
{percentile !== null && (
@@ -184,9 +184,12 @@ const ValueStats = React.memo(
)} -
+
{columnStats.isLowCardinality ? ( - + ) : ( ({ @@ -208,6 +211,7 @@ const ValueStats = React.memo( : "" }`, }))} + height={120} /> )}
@@ -341,7 +345,11 @@ export function ColumnDialog>({ return () => window.removeEventListener("keydown", handleKeyDown); }, [handleKeyDown]); - const renderRowContent = (row: T | null, value: unknown) => { + const renderRowContent = ( + row: T | null, + value: unknown, + isCompareView: boolean = false + ) => { if (!row) return null; const { addBookmark, getNotesForRowAndColumn, removeBookmark } = useBookmarkContext(); @@ -440,7 +448,7 @@ export function ColumnDialog>({ - +
@@ -488,139 +496,148 @@ export function ColumnDialog>({
- - -
-
-

Add Notes

-

- Your notes will help improve prompts via the{" "} - {" "} - Improve Prompt feature in operation settings -

-
-
- {existingNotes.length > 0 && ( -
- - {showPreviousNotes && ( -
- {existingNotes.map((note) => ( -
-
- “{note.note}” -
- + {!isCompareView && ( + <> + + +
+
+

+ Add Notes +

+

+ Your notes will help improve prompts via the{" "} + {" "} + Improve Prompt feature in operation settings +

+
+ +
+ {existingNotes.length > 0 && ( +
+ + {showPreviousNotes && ( +
+ {existingNotes.map((note) => ( +
+
+ “{note.note}” +
+ +
+ ))}
- ))} + )}
)} -
- )} - -
-