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import uuid
from typing import Any, Callable, Dict, List
from langchain.chains import create_history_aware_retriever, create_retrieval_chain
from langchain.chains.base import Chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_openai import ChatOpenAI
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
from app.internal.export_report import extract_pdf_references
from app.internal.template_prompt import summary_system_prompt
from app.settings import settings
def get_chat_llm() -> BaseChatModel:
"""
Initializes and returns a ChatOpenAI instance configured with the provided settings.
Returns:
ChatOpenAI: An instance of ChatOpenAI configured to use the specified model, API endpoint, and API key.
Raises:
ValueError: If any of the required settings (endpoint, API key, or model name) is missing.
"""
try:
if settings.provider == "hf_local":
pass
if settings.provider == "hf_api":
if not settings.hf_token:
raise ValueError("The HugginFace APIs token is not set.")
llm = HuggingFaceEndpoint(
repo_id=settings.llm_model_name,
task="text-generation",
max_new_tokens=settings.max_length,
do_sample=False,
repetition_penalty=1.03,
temperature=settings.temperature,
# huggingfacehub_api_token=settings.hf_token,
)
return ChatHuggingFace(llm=llm)
if settings.provider == "openai":
if not settings.scw_generative_apis_endpoint:
raise ValueError("The SCW Generative APIs endpoint is not set.")
if not settings.scw_api_key:
raise ValueError("The SCW API key is not set.")
if not settings.llm_model_name:
raise ValueError("The LLM model name is not set.")
return ChatOpenAI(
base_url=settings.scw_generative_apis_endpoint,
api_key=settings.scw_api_key,
model=settings.llm_model_name,
temperature=settings.temperature,
)
except Exception as e:
raise RuntimeError(f"Failed to initialize ChatOpenAI: {e}")
def get_history_retriever(llm, retriever, contextualize_q_prompt) -> object:
"""
Creates a history-aware retriever using the provided LLM, retriever, and contextualization prompt.
Args:
llm: The language model used for generating context-aware queries.
retriever: The retriever instance for querying a vector store or similar.
contextualize_q_prompt: A prompt template for contextualizing queries.
Returns:
object: A history-aware retriever instance.
Raises:
ValueError: If any of the required inputs are None or invalid.
"""
if not llm or not retriever or not contextualize_q_prompt:
raise ValueError(
"LLM, retriever, and contextualize_q_prompt must all be provided."
)
try:
return create_history_aware_retriever(llm, retriever, contextualize_q_prompt)
except Exception as e:
raise RuntimeError(f"Failed to create history-aware retriever: {e}")
def get_system_prompt_chain(llm, qa_prompt) -> object:
"""
Creates a prompt chain for processing system-level instructions with a question-answering prompt.
Args:
llm: The language model used for processing the system prompt.
qa_prompt: The prompt template for question-answering tasks.
Returns:
object: A chain instance for system prompt processing.
Raises:
ValueError: If either `llm` or `qa_prompt` is None.
"""
if not llm or not qa_prompt:
raise ValueError("LLM and qa_prompt must both be provided.")
try:
return create_stuff_documents_chain(llm, qa_prompt)
except Exception as e:
raise RuntimeError(f"Failed to create system prompt chain: {e}")
def get_rag_chain(history_aware_retriever, question_answer_chain) -> object:
"""
Creates a Retrieval-Augmented Generation (RAG) chain using a history-aware retriever and a Q&A chain.
Args:
history_aware_retriever: A retriever configured to incorporate conversation history into queries.
question_answer_chain: A chain for handling question-answering tasks.
Returns:
object: A RAG chain instance.
Raises:
ValueError: If either `history_aware_retriever` or `question_answer_chain` is None.
"""
if not history_aware_retriever or not question_answer_chain:
raise ValueError(
"Both history_aware_retriever and question_answer_chain must be provided."
)
try:
return create_retrieval_chain(history_aware_retriever, question_answer_chain)
except Exception as e:
raise RuntimeError(f"Failed to create RAG chain: {e}")
def get_session_history(session_id: str, history_store: dict) -> BaseChatMessageHistory:
"""
Retrieves or initializes the chat history for a given session ID.
Args:
session_id (str): The unique identifier for the session.
history_store (dict): A dictionary to store session histories.
Returns:
BaseChatMessageHistory: The chat message history for the session.
Raises:
ValueError: If `session_id` is not provided.
"""
if not session_id:
raise ValueError("A valid session_id must be provided.")
if session_id not in history_store:
history_store[session_id] = ChatMessageHistory()
return history_store[session_id]
def get_conversational_rag_chain(
rag_chain: Chain,
session_history_func: Callable[[str], BaseChatMessageHistory],
) -> RunnableWithMessageHistory:
"""
Creates a conversational Retrieval-Augmented Generation (RAG) chain with session history.
Args:
rag_chain (Chain): The RAG chain for handling retrieval and generation tasks.
session_history_func (Callable): A function to retrieve or initialize session history.
Returns:
RunnableWithMessageHistory: A chain that maintains message history and processes input/output.
Raises:
ValueError: If `rag_chain` or `session_history_func` is not provided.
"""
if not rag_chain:
raise ValueError("A valid rag_chain must be provided.")
if not session_history_func:
raise ValueError("A valid session history function must be provided.")
return RunnableWithMessageHistory(
rag_chain,
session_history_func,
input_messages_key="input",
history_messages_key="chat_history",
output_messages_key="answer",
)
def question_to_conversational_rag_chain(
user_query: str, conversational_rag_chain: Any, session_id: str = None
) -> Dict[str, Any]:
"""
Sends a user query to a conversational RAG chain and retrieves the response.
Args:
user_query (str): The query from the user.
conversational_rag_chain (Any): The conversational RAG chain instance.
session_id (str, optional): A unique identifier for the session. If not provided, a new session_id is generated.
Returns:
Dict[str, Any]: The response from the conversational RAG chain.
Raises:
ValueError: If the user query is empty or the RAG chain is not provided.
RuntimeError: If an error occurs during the invocation of the RAG chain.
"""
if not user_query:
raise ValueError("The user query must be a non-empty string.")
if not conversational_rag_chain:
raise ValueError("A valid conversational RAG chain must be provided.")
# Generate a session_id if none is provided
if not session_id:
session_id = str(uuid.uuid4())
try:
# Invoke the conversational RAG chain
return conversational_rag_chain.invoke(
{"input": user_query}, config={"configurable": {"session_id": session_id}}
)
except Exception as e:
raise RuntimeError(f"Failed to process the query with the RAG chain: {e}")
def get_documents_retrieve(output: Dict[str, Any], max_docs: int = 3) -> List[str]:
"""
Retrieves the titles of the documents from the output context.
Args:
output (Dict[str, Any]): The output containing context and metadata.
max_docs (int): The maximum number of document titles to retrieve. Default is 3.
Returns:
List[str]: A list of document titles.
Raises:
ValueError: If the 'context' key is missing or empty in the output.
"""
if "context" not in output:
return None
return [
output["context"][i].metadata.get("Title", "Untitled Document")
for i in range(min(len(output["context"]), max_docs))
] # TODO add filtre sur le type de documents à retourner
def get_llm_answer(output: Dict[str, Any]) -> str:
"""
Extracts the answer generated by the LLM from the output.
Args:
output (Dict[str, Any]): The output containing the answer.
Returns:
str: The LLM-generated answer.
Raises:
ValueError: If the 'answer' key is missing or empty in the output.
"""
if "answer" not in output or not output["answer"]:
raise ValueError("The output does not contain a valid 'answer'.")
return output["answer"]
def get_format_output(answer: str, context: List[str]) -> str:
"""
Formats the LLM answer with a list of related document titles.
Args:
answer (str): The LLM-generated answer.
context (List[str]): A list of document titles related to the answer.
Returns:
str: A formatted string containing the answer and document references.
Raises:
ValueError: If the answer is empty or None.
"""
if not answer:
raise ValueError("The 'answer' must be a non-empty string.")
formatted_output = f"{answer}"
if context:
uniques_doc = set(context)
formatted_output += (
"\n\nConsultez les documents suivants pour plus d'information:\n\n"
)
formatted_output += "\n\n".join(uniques_doc)
return formatted_output
def clean_output(answer): # TODO add clean process for output
pass
def generate_summary(llm, conversation: List[dict]) -> str:
"""
Generate a summary of the conversation with LangChain and append PDF references at the end.
Args:
conversation (List[dict]): List of dictionaries representing the conversation.
Each dictionary contains 'role' ('user' or 'assistant')
and 'content' (message string).
llm (str): OpenAI model to use.
Returns:
str: The generated summary with PDF references appended.
"""
# Extract unique PDF references
pdf_references = extract_pdf_references(conversation)
# Prepare the messages
messages = summary_system_prompt
for message in conversation:
if message["role"] == "user":
messages.append(HumanMessage(content=message["content"]))
elif message["role"] == "assistant":
messages.append(AIMessage(content=message["content"]))
# Generate the summary
summary_prompt = ChatPromptTemplate.from_messages(messages).format()
summary = llm.invoke(summary_prompt)
# Append the PDF references
summary_text = summary.content
if pdf_references:
summary_text += (
"\n\nDocuments pdf à consulter pour plus d'information:"
+ "\n".join(sorted(pdf_references))
)
return summary_text
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