RAG MVP

apps/experiments/tasks.py

@@ -3,15 +3,18 @@
 
 from celery.app import shared_task
 from langchain.schema import AIMessage, HumanMessage
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import PyMuPDFLoader, TextLoader
 from taskbadger.celery import Task as TaskbadgerTask
 
 from apps.channels.datamodels import WebMessage
 from apps.chat.bots import create_conversation
 from apps.chat.channels import WebChannel
-from apps.experiments.models import ExperimentSession, PromptBuilderHistory, SourceMaterial
+from apps.experiments.models import Experiment, ExperimentSession, PromptBuilderHistory, SourceMaterial
 from apps.service_providers.models import LlmProvider
 from apps.users.models import CustomUser
 from apps.utils.taskbadger import update_taskbadger_data
+from apps.vectordb.vectorstore import PGVector
 
 
 @shared_task(bind=True, base=TaskbadgerTask)
@@ -23,6 +26,44 @@ def get_response_for_webchat_task(self, experiment_session_id: int, message_text
     return message_handler.new_user_message(message)
 
 
+@shared_task(bind=True, base=TaskbadgerTask)
+def store_rag_embedding(self, experiment_id: int) -> None:
+    experiment = Experiment.objects.get(id=experiment_id)
+    file_path = experiment.files.all().last().file.path
+    splits = load_rag_file(file_path)
+    embeddings_model = experiment.get_llm_service().get_openai_embeddings()
+    PGVector.from_texts(splits, embeddings_model, None, experiment)
+
+
+def load_rag_file(file_path: str) -> list[str]:
+    """
+    Loads a text file of any supported type (PDF, TXT, HTML) into Langchain.
+
+    Args:
+        file_path (str): The path to the text file.
+
+    Returns:
+        str_splits: A list of strings from  Langchain Document objects
+        containing the loaded page_content.
+    """
+
+    # Automatically detect loader based on file extension if not provided
+    extension = file_path.split(".")[-1].lower()
+    if extension == "pdf":
+        loader = PyMuPDFLoader(file_path, extract_images=False)
+    elif extension in ("txt", "text"):
+        loader = TextLoader(file_path)
+    else:
+        raise ValueError(f"Unsupported file type: {extension}")
+
+    # Load the text file using the appropriate loader
+    documents = loader.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    splits = text_splitter.split_documents(documents)
+    str_splits = [s.page_content for s in splits[0:10]]
+    return str_splits
+
+
 @shared_task
 def get_prompt_builder_response_task(team_id: int, user_id, data_dict: dict) -> dict[str, str | int]:
     llm_service = LlmProvider.objects.get(id=data_dict["provider"]).get_llm_service()

apps/experiments/views/experiment.py

@@ -53,7 +53,7 @@
 from apps.experiments.helpers import get_real_user_or_none
 from apps.experiments.models import Experiment, ExperimentSession, Participant, SessionStatus, SyntheticVoice
 from apps.experiments.tables import ExperimentSessionsTable, ExperimentTable
-from apps.experiments.tasks import get_response_for_webchat_task
+from apps.experiments.tasks import get_response_for_webchat_task, store_rag_embedding
 from apps.experiments.views.prompt import PROMPT_DATA_SESSION_KEY
 from apps.files.forms import get_file_formset
 from apps.files.views import BaseAddFileHtmxView, BaseDeleteFileView
@@ -220,6 +220,10 @@ def _validate_prompt_variables(form_data):
     available_variables = set()
     if form_data.get("source_material"):
         available_variables.add("source_material")
+        #  available_variables below should be added by making a
+        #  db request to check if there are  any RAG files uploaded
+    available_variables.add("context")
+    available_variables.add("input")
     missing_vars = required_variables - available_variables
     known_vars = {"source_material"}
     if missing_vars:
@@ -361,6 +365,7 @@ def form_valid(self, form):
         experiment = get_object_or_404(Experiment, team=self.request.team, pk=self.kwargs["pk"])
         file = super().form_valid(form)
         experiment.files.add(file)
+        store_rag_embedding(experiment.id)
         return file
 
     def get_delete_url(self, file):

apps/service_providers/llm_service/main.py

@@ -7,6 +7,7 @@
 from langchain_community.chat_models import ChatAnthropic
 from langchain_core.callbacks import BaseCallbackHandler
 from langchain_core.language_models import BaseLanguageModel
+from langchain_openai import OpenAIEmbeddings
 from langchain_openai.chat_models import AzureChatOpenAI, ChatOpenAI
 from openai import OpenAI
 from openai._base_client import SyncAPIClient
@@ -42,6 +43,14 @@ class OpenAILlmService(LlmService):
     openai_api_base: str = None
     openai_organization: str = None
 
+    def get_openai_embeddings(self, model="text-embedding-3-small") -> OpenAIEmbeddings:
+        return OpenAIEmbeddings(
+            openai_api_key=self.openai_api_key,
+            openai_api_base=self.openai_api_base,
+            openai_organization=self.openai_organization,
+            model=model,
+        )
+
     def get_raw_client(self) -> OpenAI:
         return OpenAI(api_key=self.openai_api_key, organization=self.openai_organization, base_url=self.openai_api_base)
 

apps/service_providers/llm_service/runnables.py

@@ -29,6 +29,7 @@
 from apps.chat.conversation import compress_chat_history
 from apps.chat.models import ChatMessage, ChatMessageType
 from apps.experiments.models import Experiment, ExperimentSession
+from apps.vectordb.vectorstore import PGVector
 
 logger = logging.getLogger(__name__)
 
@@ -51,7 +52,8 @@ def create_experiment_runnable(experiment: Experiment, session: ExperimentSessio
     assert experiment.llm_provider, "Experiment must have an LLM provider"
     if experiment.tools_enabled:
         return AgentExperimentRunnable(experiment=experiment, session=session)
-
+    if experiment.files.exists():
+        return RagExperimentRunnable(experiment=experiment, session=session)
     return SimpleExperimentRunnable(experiment=experiment, session=session)
 
 
@@ -219,6 +221,25 @@ def _build_chain(self) -> Runnable[dict[str, Any], str]:
         )
 
 
+class RagExperimentRunnable(ExperimentRunnable):
+    def _build_chain(self) -> Runnable[dict[str, Any], str]:
+        def format_docs(docs):
+            return "\n\n".join(doc.page_content for doc in docs)
+
+        model = self.llm_service.get_chat_model(self.experiment.llm, self.experiment.temperature)
+        embeddings = self.experiment.get_llm_service().get_openai_embeddings()
+        retriever = PGVector(self.experiment, embeddings).as_retriever()
+        return (
+            {"context": retriever | format_docs, "input": RunnablePassthrough()}
+            | RunnablePassthrough.assign(
+                history=RunnableLambda(self.memory.load_memory_variables) | itemgetter("history")
+            )
+            | self.prompt
+            | model
+            | StrOutputParser()
+        )
+
+
 class AgentExperimentRunnable(ExperimentRunnable):
     def _parse_output(self, output):
         return output.get("output", "")

requirements/requirements.in

@@ -45,3 +45,5 @@ turn-python>=0.2.0
 jinja2
 django-taggit
 pgvector
+PyMuPDF
+langchainhub