AmazonkendraRetriver

[vinayburam]

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Example Code

from langchain_community.retrievers import AmazonKendraRetriever
import orjson
import boto3
from langchain.chains import RetrievalQA ,ConversationalRetrievalChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import OpenAI
from langchain_openai import AzureChatOpenAI
import json
from openai import AzureOpenAI
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.messages import HumanMessage
from langchain.chains import create_history_aware_retriever
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory
import os
from new_promts import prompt1

store = {}
  
  
def lambda_handler(event, context):
  region = os.environ["REGION"]
  kendra_index_id = os.environ["KENDRA_INDEX"]
  print("Event: ",event)
  #llm = OpenAI(batch_size=5, temperature=0, max_tokens=300)
  # The base URL for your Azure OpenAI resource. e.g. "https://<your resource name>.openai.azure.com"
  #azure_endpoint="https://api.geneai.thermofisher.com/dev"
  #gpt 4 config
  azure_endpoint =  os.environ["azure_endpoint"]
  # API version e.g. "2023-07-01-preview"
  #openai_api_version="2024-03-01-preview"
  openai_api_version="2024-03-01-preview"
  
  # The name of your Azure OpenAI deployment chat model. e.g. "gpt-35-turbo-0613"
  #deployment_name="gpt-35-turbo"
  deployment_name="gpt-4o"
  # The API key for your Azure OpenAI resource.
  
  openai_api_key = os.environ["openai_api_key"]
  
  # This is set to `azure`
  openai_api_type="azure"
  
  
  llm = AzureChatOpenAI(
      azure_endpoint=azure_endpoint,
      openai_api_version=openai_api_version,
      deployment_name=deployment_name,
      openai_api_key=openai_api_key,
      openai_api_type=openai_api_type,
  )
      
  #retriever = AmazonKendraRetriever(index_id=kendra_index_id, region_name=region)
  data_source_id = '1bf6a354-7aef-471e-b5b9-9ce733e9d085'
  retriever = AmazonKendraRetriever(index_id=kendra_index_id, top_k=10, 
    attribute_filter = {'AndAllFilters': 
            [ 
                {"EqualsTo": {"Key": "_language_code","Value": {"StringValue": "en"}}},
                {"EqualsTo": {"Key": "_data_source_id","Value": {"StringValue": data_source_id}}}
                
            ]
            }
    
    
    ) 

  contextualize_q_system_prompt = """Given a chat history and the latest user question \
    which might reference context in the chat history, formulate a standalone question \
    which can be understood without the chat history. Do NOT answer the question, \
    just reformulate it if needed and otherwise return it as is."""
  #prompt_template = prompt_template1
  #prompt_template= prompmt3
  contextualize_q_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", contextualize_q_system_prompt),
        MessagesPlaceholder("chat_history"),
        ("human", "{input}"),
    ]
  )
    
  
  history_aware_retriever = create_history_aware_retriever(llm, retriever, contextualize_q_prompt )
  
  '''qa_system_prompt = """You are an assistant for question-answering tasks. \
    Use the following pieces of retrieved context to answer the question. \
    If you don't know the answer, just say that you don't know. \
    Use three sentences maximum and keep the answer concise.\
    
    {context}"""
  '''
  print("*****Context*****: ",context)
  qa_system_prompt =  prompt1
  qa_prompt = ChatPromptTemplate.from_messages(
        [
            ("system", qa_system_prompt),
            MessagesPlaceholder("chat_history"),
            ("human", "{input}"),
        ]
    )

  question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
  rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
  
    ### Statefully manage chat history ###
  query = event.get("query", "SELECT 1")
  sessionId = event.get("sessionId", "SELECT 1")
  #sessionId
  question = query
  
  conversational_rag_chain = RunnableWithMessageHistory(
      rag_chain,
      get_session_history,
      input_messages_key="input",
      history_messages_key="chat_history",
      output_messages_key="answer",
  )
  
  ai_msg_1 = conversational_rag_chain.invoke(
    {"input": question},
    config={
        "configurable": {"session_id": sessionId}
    },
  )["answer"]
  
  print("ai_msg_1 :",ai_msg_1)
  #print("ai_msg_2 :",ai_msg_2)
  print("********",store[sessionId].messages)
  # Extract retrieved documents from the retriever
  print(retriever)
  retrieved_docs = retriever.invoke(question)
  print("Fetched Documents:")
  for i, doc in enumerate(retrieved_docs):
        print(f"Document {i + 1}:")
        print(f"Page Content: {doc.page_content}")
        print(f"Metadata: {doc.metadata}")
  return ai_msg_1
  
  '''
  chat_history = []
  query = event.get("query", "SELECT 1")
  question = query
  ai_msg_1 = rag_chain.invoke({"input": question, "chat_history": chat_history})
  chat_history.extend([HumanMessage(content=question), ai_msg_1["answer"]])
  print("ai_msg_1: ",ai_msg_1["answer"])
  for document in ai_msg_1["context"]:
    print("Retrived document: ",document)
    print()


  print(chat_history)
  '''
  #return "success"
  
def get_session_history(session_id: str) -> BaseChatMessageHistory:
      if session_id not in store:
          store[session_id] = ChatMessageHistory()
      return store[session_id]

Description

I am getting top 10 results from kendra but my only top 1 or 2 documents are used by the llm to summarize the question.
can you please help me to pass all the documents

System Info

na

[vinayburam]

@Dosubot