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from langchain_openai import ChatOpenAI |
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from langchain_core.output_parsers import StrOutputParser |
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder |
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from langgraph.graph import START, StateGraph |
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from langchain_core.messages import HumanMessage, AIMessage, BaseMessage, SystemMessage |
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from typing import List |
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from typing_extensions import List, TypedDict |
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from langchain_core.documents import Document |
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import os |
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from backend.pinecone_utilis import vectorstore |
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from dotenv import load_dotenv |
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load_dotenv() |
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OPENAI_API_KEY=os.getenv("OPENAI_API_KEY") |
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retriever = vectorstore.as_retriever(search_kwargs={"k": 4}) |
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llm = ChatOpenAI( |
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model='gpt-4.1', |
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api_key=OPENAI_API_KEY |
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) |
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output_parser = StrOutputParser() |
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contextualize_q_system_prompt = ( |
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"Given a chat history and the latest user question " |
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"which might reference context in the chat history, " |
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"formulate a standalone question which can be understood " |
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"without the chat history. Do NOT answer the question, " |
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"just reformulate it if needed and otherwise return it as is." |
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) |
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contextualize_q_prompt = ChatPromptTemplate.from_messages([ |
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("system", contextualize_q_system_prompt), |
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MessagesPlaceholder("chat_history"), |
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("human", "{input}"), |
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]) |
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qa_prompt = ChatPromptTemplate.from_messages([ |
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("system", "You are a helpful AI assistant. Use the following context to answer the user's question."), |
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("system", "Context: {context}"), |
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MessagesPlaceholder(variable_name="chat_history"), |
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("human", "{input}") |
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]) |
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class State(TypedDict): |
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messages: List[BaseMessage] |
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def retrieve(query: str): |
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retrieved_docs = vectorstore.similarity_search(query) |
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return retrieved_docs |
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def generate_response(query: str, state: State)->State: |
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retrieved_docs=retrieve(query=query) |
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docs_content = "\n\n".join(doc.page_content for doc in retrieved_docs) |
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system_message = SystemMessage( |
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content="You are a helpful AI assistant. Answer the user's question using ONLY the information provided below. " |
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"If the answer is not in the context, say 'I don't know.' Do not make up information. " |
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f"Context: {docs_content}" |
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) |
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state['messages'].append(system_message) |
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state['messages'].append(HumanMessage(content=query)) |
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response = llm.invoke(state["messages"]) |
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state['messages'].append(AIMessage(content=response.content)) |
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return state |
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