File size: 8,377 Bytes
3cfb95f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | 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}
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