agentic rag

langchain-streamlit-demo/app.py

@@ -442,6 +442,7 @@ if st.session_state.llm:
  MEMORY,
  chat_prompt,
  prompt,
+ STMEMORY,
  )
 
  # --- LLM call ---

langchain-streamlit-demo/llm_resources.py

@@ -1,8 +1,15 @@
+import uuid
 from tempfile import NamedTemporaryFile
 from typing import Tuple, List, Optional, Dict
 
+from langchain.agents import AgentExecutor
+from langchain.agents.agent_toolkits import create_retriever_tool
+from langchain.agents.openai_functions_agent.agent_token_buffer_memory import (
+ AgentTokenBufferMemory,
+)
+from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
 from langchain.callbacks.base import BaseCallbackHandler
-from langchain.chains import RetrievalQA, LLMChain
+from langchain.chains import LLMChain
 from langchain.chat_models import (
  AzureChatOpenAI,
  ChatOpenAI,
@@ -11,15 +18,16 @@ from langchain.chat_models import (
 )
 from langchain.document_loaders import PyPDFLoader
 from langchain.embeddings import AzureOpenAIEmbeddings, OpenAIEmbeddings
+from langchain.prompts import MessagesPlaceholder
 from langchain.retrievers import EnsembleRetriever
-from langchain.schema import Document, BaseRetriever
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import FAISS
-
 from langchain.retrievers.multi_query import MultiQueryRetriever
 from langchain.retrievers.multi_vector import MultiVectorRetriever
+from langchain.schema import Document, BaseRetriever
+from langchain.schema.runnable import RunnablePassthrough
 from langchain.storage import InMemoryStore
-import uuid
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain_core.messages import SystemMessage
 
 from defaults import DEFAULT_CHUNK_SIZE, DEFAULT_CHUNK_OVERLAP, DEFAULT_RETRIEVER_K
 from qagen import get_rag_qa_gen_chain
@@ -34,6 +42,7 @@ def get_runnable(
  memory,
  chat_prompt,
  summarization_prompt,
+ chat_history,
 ):
  if not use_document_chat:
  return LLMChain(
@@ -54,13 +63,44 @@ def get_runnable(
  llm,
  )
  else:
- return RetrievalQA.from_chain_type(
+ tool = create_retriever_tool(
+ retriever,
+ "search_user_document",
+ "Retrieves custom context provided by the user for this conversation. Use this if you cannot answer immediately and confidently.",
+ )
+ tools = [tool]
+ memory_key = "agent_history"
+ system_message = SystemMessage(
+ content=(
+ "Do your best to answer the questions. "
+ "Feel free to use any tools available to look up "
+ "relevant information, only if necessary"
+ ),
+ )
+ prompt = OpenAIFunctionsAgent.create_prompt(
+ system_message=system_message,
+ extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)],
+ )
+ agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
+
+ agent_memory = AgentTokenBufferMemory(
+ chat_memory=chat_history,
+ memory_key=memory_key,
  llm=llm,
- chain_type=document_chat_chain_type,
- retriever=retriever,
- memory=memory,
- output_key="output_text",
- ) | (lambda output: output["output_text"])
+ )
+
+ agent_executor = AgentExecutor(
+ agent=agent,
+ tools=tools,
+ memory=agent_memory,
+ verbose=True,
+ return_intermediate_steps=True,
+ )
+ return (
+ {"input": RunnablePassthrough()}
+ | agent_executor
+ | (lambda output: output["output"])
+ )
 
 
 def get_llm(