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| # Adapted from https://github.com/langchain-ai/langgraph/blob/main/examples/rag/langgraph_crag.ipynb?ref=blog.langchain.dev | |
| from typing import List | |
| from typing_extensions import TypedDict | |
| from langgraph.graph import END, StateGraph, START | |
| from modules.chat.base import BaseRAG | |
| from langchain.memory import ChatMessageHistory | |
| from langchain_core.prompts import ChatPromptTemplate | |
| from langchain_core.pydantic_v1 import BaseModel, Field | |
| from langchain_openai import ChatOpenAI | |
| from langchain_core.output_parsers import StrOutputParser | |
| from langchain_core.prompts import ChatPromptTemplate | |
| class GradeDocuments(BaseModel): | |
| """Binary score for relevance check on retrieved documents.""" | |
| binary_score: str = Field( | |
| description="Documents are relevant to the question, 'yes' or 'no'" | |
| ) | |
| class GraphState(TypedDict): | |
| """ | |
| Represents the state of our graph. | |
| Attributes: | |
| question: question | |
| generation: LLM generation | |
| documents: list of documents | |
| """ | |
| question: str | |
| generation: str | |
| documents: List[str] | |
| class Langgraph_RAG(BaseRAG): | |
| def __init__(self, llm, memory, retriever, qa_prompt: str, rephrase_prompt: str): | |
| """ | |
| Initialize the Langgraph_RAG class. | |
| Args: | |
| llm (LanguageModelLike): The language model instance. | |
| memory (BaseChatMessageHistory): The chat message history instance. | |
| retriever (BaseRetriever): The retriever instance. | |
| qa_prompt (str): The QA prompt string. | |
| rephrase_prompt (str): The rephrase prompt string. | |
| """ | |
| self.llm = llm | |
| self.structured_llm_grader = llm.with_structured_output(GradeDocuments) | |
| self.memory = self.add_history_from_list(memory) | |
| self.retriever = retriever | |
| self.qa_prompt = ( | |
| "You are an AI Tutor for the course DS598, taught by Prof. Thomas Gardos. Answer the user's question using the provided context. Only use the context if it is relevant. The context is ordered by relevance. " | |
| "If you don't know the answer, do your best without making things up. Keep the conversation flowing naturally. " | |
| "Speak in a friendly and engaging manner, like talking to a friend. Avoid sounding repetitive or robotic.\n\n" | |
| "Context:\n{context}\n\n" | |
| "Answer the student's question below in a friendly, concise, and engaging manner. Use the context and history only if relevant, otherwise, engage in a free-flowing conversation.\n" | |
| "Student: {question}\n" | |
| "AI Tutor:" | |
| ) | |
| self.rephrase_prompt = rephrase_prompt | |
| self.store = {} | |
| ## Fix below ## | |
| system = """You are a grader assessing relevance of a retrieved document to a user question. \n | |
| If the document contains keyword(s) or semantic meaning related to the question, grade it as relevant. \n | |
| Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.""" | |
| grade_prompt = ChatPromptTemplate.from_messages( | |
| [ | |
| ("system", system), | |
| ( | |
| "human", | |
| "Retrieved document: \n\n {document} \n\n User question: {question}", | |
| ), | |
| ] | |
| ) | |
| self.retrieval_grader = grade_prompt | self.structured_llm_grader | |
| system = """You a question re-writer that converts an input question to a better version that is optimized \n | |
| for web search. Look at the input and try to reason about the underlying semantic intent / meaning.""" | |
| re_write_prompt = ChatPromptTemplate.from_messages( | |
| [ | |
| ("system", system), | |
| ( | |
| "human", | |
| "Here is the initial question: \n\n {question} \n Formulate an improved question.", | |
| ), | |
| ] | |
| ) | |
| self.question_rewriter = re_write_prompt | self.llm | StrOutputParser() | |
| # Generate | |
| self.qa_prompt_template = ChatPromptTemplate.from_template(self.qa_prompt) | |
| self.rag_chain = self.qa_prompt_template | self.llm | StrOutputParser() | |
| ### | |
| # build the agentic graph | |
| self.app = self.create_agentic_graph() | |
| def retrieve(self, state): | |
| """ | |
| Retrieve documents | |
| Args: | |
| state (dict): The current graph state | |
| Returns: | |
| state (dict): New key added to state, documents, that contains retrieved documents | |
| """ | |
| print("---RETRIEVE---") | |
| question = state["question"] | |
| # Retrieval | |
| documents = self.retriever.get_relevant_documents(question) | |
| return {"documents": documents, "question": question} | |
| def generate(self, state): | |
| """ | |
| Generate answer | |
| Args: | |
| state (dict): The current graph state | |
| Returns: | |
| state (dict): New key added to state, generation, that contains LLM generation | |
| """ | |
| print("---GENERATE---") | |
| question = state["question"] | |
| documents = state["documents"] | |
| # RAG generation | |
| generation = self.rag_chain.invoke({"context": documents, "question": question}) | |
| return {"documents": documents, "question": question, "generation": generation} | |
| def transform_query(self, state): | |
| """ | |
| Transform the query to produce a better question. | |
| Args: | |
| state (dict): The current graph state | |
| Returns: | |
| state (dict): Updates question key with a re-phrased question | |
| """ | |
| print("---TRANSFORM QUERY---") | |
| question = state["question"] | |
| documents = state["documents"] | |
| # Re-write question | |
| better_question = self.question_rewriter.invoke({"question": question}) | |
| return {"documents": documents, "question": better_question} | |
| def grade_documents(self, state): | |
| """ | |
| Determines whether the retrieved documents are relevant to the question. | |
| Args: | |
| state (dict): The current graph state | |
| Returns: | |
| state (dict): Updates documents key with only filtered relevant documents | |
| """ | |
| print("---CHECK DOCUMENT RELEVANCE TO QUESTION---") | |
| question = state["question"] | |
| documents = state["documents"] | |
| # Score each doc | |
| filtered_docs = [] | |
| web_search = "No" | |
| for d in documents: | |
| score = self.retrieval_grader.invoke( | |
| {"question": question, "document": d.page_content} | |
| ) | |
| grade = score.binary_score | |
| if grade == "yes": | |
| print("---GRADE: DOCUMENT RELEVANT---") | |
| filtered_docs.append(d) | |
| else: | |
| print("---GRADE: DOCUMENT NOT RELEVANT---") | |
| web_search = "Yes" | |
| continue | |
| return { | |
| "documents": filtered_docs, | |
| "question": question, | |
| "web_search": web_search, | |
| } | |
| def decide_to_generate(self, state): | |
| """ | |
| Determines whether to generate an answer, or re-generate a question. | |
| Args: | |
| state (dict): The current graph state | |
| Returns: | |
| str: Binary decision for next node to call | |
| """ | |
| print("---ASSESS GRADED DOCUMENTS---") | |
| state["question"] | |
| web_search = state["web_search"] | |
| state["documents"] | |
| if web_search == "Yes": | |
| # All documents have been filtered check_relevance | |
| # We will re-generate a new query | |
| print( | |
| "---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---" | |
| ) | |
| return "transform_query" | |
| else: | |
| # We have relevant documents, so generate answer | |
| print("---DECISION: GENERATE---") | |
| return "generate" | |
| def create_agentic_graph(self): | |
| """ | |
| Create an agentic graph to answer questions. | |
| Returns: | |
| dict: Agentic graph | |
| """ | |
| self.workflow = StateGraph(GraphState) | |
| self.workflow.add_node("retrieve", self.retrieve) | |
| self.workflow.add_node( | |
| "grade_documents", self.grade_documents | |
| ) # grade documents | |
| self.workflow.add_node("generate", self.generate) # generatae | |
| self.workflow.add_node( | |
| "transform_query", self.transform_query | |
| ) # transform_query | |
| # build the graph | |
| self.workflow.add_edge(START, "retrieve") | |
| self.workflow.add_edge("retrieve", "grade_documents") | |
| self.workflow.add_conditional_edges( | |
| "grade_documents", | |
| self.decide_to_generate, | |
| { | |
| "transform_query": "transform_query", | |
| "generate": "generate", | |
| }, | |
| ) | |
| self.workflow.add_edge("transform_query", "generate") | |
| self.workflow.add_edge("generate", END) | |
| # Compile | |
| app = self.workflow.compile() | |
| return app | |
| def invoke(self, user_query, config): | |
| """ | |
| Invoke the chain. | |
| Args: | |
| kwargs: The input variables. | |
| Returns: | |
| dict: The output variables. | |
| """ | |
| inputs = { | |
| "question": user_query["input"], | |
| } | |
| for output in self.app.stream(inputs): | |
| for key, value in output.items(): | |
| # Node | |
| print(f"Node {key} returned: {value}") | |
| print("\n\n") | |
| print(value["generation"]) | |
| # rename generation to answer | |
| value["answer"] = value.pop("generation") | |
| value["context"] = value.pop("documents") | |
| return value | |
| def add_history_from_list(self, history_list): | |
| """ | |
| Add messages from a list to the chat history. | |
| Args: | |
| messages (list): The list of messages to add. | |
| """ | |
| history = ChatMessageHistory() | |
| for idx, message_pairs in enumerate(history_list): | |
| history.add_user_message(message_pairs[0]) | |
| history.add_ai_message(message_pairs[1]) | |
| return history | |