graph.py

from dotenv import load_dotenv
import os
import streamlit as st
from langchain_aws import BedrockEmbeddings
from langchain_core.vectorstores import InMemoryVectorStore
from langchain.chat_models import init_chat_model
from langchain_core.documents import Document
from typing_extensions import List, Dict

from langchain_text_splitters import RecursiveCharacterTextSplitter
from langgraph.graph import START, StateGraph, END

from langchain_community.document_loaders import DirectoryLoader, PyPDFLoader
from langgraph.graph import MessagesState
from langchain_core.tools import tool
from langchain_core.messages import SystemMessage
from langgraph.prebuilt import ToolNode, tools_condition
from langchain_milvus import Milvus
from utils import extract_text_from_content
from logging_config import setup_logger
from load_vector_db import init_vector_db
from logging_config import setup_logger
import time

logger = setup_logger(__name__)


def init_graph():
    """Initialize the app components and return them."""
    with st.spinner("Initializing PDF chat application..."):
        # Initialize LLM
        llm = init_chat_model(
            "anthropic.claude-3-5-sonnet-20240620-v1:0", 
            model_provider="bedrock_converse",
            temperature=0
        )

        # Initialize embeddings
        embeddings = BedrockEmbeddings(model_id="amazon.titan-embed-text-v1")

        vector_store, compression_retriever = init_vector_db(embeddings)

        class State(MessagesState):
            context: List[Document]

        # Create a retrieval tool that captures the vector_store
        @tool(response_format="content_and_artifact")
        def retrieve_tool(query: str):
            """Retrieve information related to a query."""
            start = time.time()
            # retrieved_docs = vector_store.similarity_search(query, k=50)
            retrieved_docs = compression_retriever.invoke(input = query,k=10)
            serialized = "\n\n".join(
                (f"Source: {doc.metadata}\n" f"Content: {doc.page_content}")
                for doc in retrieved_docs
            )
            end = time.time()
            logger.info(f"Time taken for vectordb retrieval: {end - start} seconds")
            # print(f"retrieved_docs : {retrieved_docs}")
            logger.info(f"retrieved_docs num: {len(retrieved_docs)}")
            logger.info(f"retrieved_docs : {retrieved_docs}")
            return serialized, retrieved_docs

        # Create the LLM tool-calling function with direct reference to llm
        def query_or_respond_fn(state: State):
            """Generate tool call for retrieval or respond."""
            # print(f"state['messages'] : {state["messages"]}")
            start = time.time()
            logger.info(f"state['messages'] : {state['messages']}")
            valid_messages = [
                    msg for msg in state["messages"] 
                    if msg.content
                ]
                
            if not valid_messages:
                return {"messages": []}
            llm_with_tools = llm.bind_tools([retrieve_tool])
            response = llm_with_tools.invoke(state["messages"])
            end = time.time()
            logger.info(f"Time taken for query_or_respond_fn LLM invocation: {end - start} seconds")
            # MessagesState appends messages to state instead of overwriting
            return {"messages": [response]}

        # Create the generate function with direct reference to llm
        def generate_fn(state: State):
            """Generate answer."""
            # Get generated ToolMessages
            start = time.time()
            recent_tool_messages = []
            for message in reversed(state["messages"]):
                if message.type == "tool":
                    recent_tool_messages.append(message)
                else:
                    break
            tool_messages = recent_tool_messages[::-1]

            # Format into prompt
            sources_text = ""
            # print(f"tool_messages { tool_messages}")
            # print(f"tool_messages { len(tool_messages)}")
            logger.info(f"tool_messages {tool_messages}")

            tool_messages_latest = tool_messages[0]
            for artifact in tool_messages_latest.artifact:
                # artifact = i.artifact
                page_label = artifact.metadata.get('page_label')
                page = artifact.metadata.get('page')
                source = artifact.metadata.get('source')

                sources_text += f"Source: {source}, Page: {page}, Page Label: {page_label}\n"

                # print(source, page, page_label)
            # print(f"sources_text { sources_text}")
            logger.info(f"sources_text {sources_text}")

            docs_content = "\n\n".join(doc.content for doc in tool_messages)
            system_message_content = (
                "You are an assistant for question-answering tasks."
                "Use the following pieces of retrieved context to answer the question."
                "This is your only source of knowledge."
                "If you don't know the answer, say that you don't know and STOP - do not provide related information."
                "You are not allowed to make up answers."
                "You are not allowed to use any external knowledge."
                "You are not allowed to make assumptions."
                "If the query is not clearly and directly addressed in the knowledge source, simply state that you don't have enough information and DO NOT elaborate with tangentially related content."
                "Keep your answers strictly limited to information that directly answers the user's specific question."
                "When information is insufficient, acknowledge this limitation in one sentence without expanding into related topics."
                "If the query is single word or phrase, ask the user to provide a complete question."
                "If the query is not clear, ask for clarification."
                "If the query is not a complete question, ask the user to provide a complete question and provide some sample questions."
                "If the query contains multiple questions, answer only the first question and ask the user to ask the next question."
                "If the query contains complex or compound questions, break them down into simpler parts and answer each part separately."
                "If the query is not related to the given knowledge source, mention that you can only answer from the knowledge base."
                "Keep your answers accurate and concise to the source content."
                "\n\n"
                f"{docs_content}"

            )
            conversation_messages = [
                message
                for message in state["messages"]
                if message.type in ("human", "system")
                or (message.type == "ai" and not message.tool_calls)
            ]
            prompt = [SystemMessage(system_message_content)] + conversation_messages

            # Run
            start_llm = time.time()
            response = llm.invoke(prompt)
            # return {"messages": [response]}
            context = []
            for tool_message in tool_messages:
                context.extend(tool_message.artifact)

            end = time.time()
            logger.info(f"Time taken for generate_fn : {end - start} seconds")
            logger.info(f"Time taken for generate_fn LLM invocation: {end - start_llm} seconds")
            

            return {"messages": [response], "context": context}

        # Execute the retrieval
        tools_node = ToolNode([retrieve_tool])

        # Build the graph
        graph_builder = StateGraph(MessagesState)
        graph_builder.add_node("query_or_respond", query_or_respond_fn)
        graph_builder.add_node("tools", tools_node)
        graph_builder.add_node("generate", generate_fn)
        graph_builder.set_entry_point("query_or_respond")
        graph_builder.add_conditional_edges(
            "query_or_respond",
            tools_condition,
            {END: END, "tools": "tools"},
        )
        graph_builder.add_edge("tools", "generate")
        graph_builder.add_edge("generate", END)
        graph = graph_builder.compile()
        
        st.success("Initialization complete!")
        return {"graph": graph}