Update processing.py

processing.py

@@ -5,7 +5,8 @@ from langchain_community.vectorstores import FAISS
 from llm_loader import load_model
 from config import openai_api_key
 from langchain.chains import RetrievalQA, LLMChain
-from langchain.retrievers import MultiQueryRetriever
+from langchain.retrievers import MultiQueryRetriever, MultiVectorRetriever
+from langchain.schema import Document
 from langchain.prompts import PromptTemplate
 import os
 import json
@@ -42,8 +43,22 @@ text_retriever = text_faiss_index.as_retriever()
 attachments_retriever = attachments_faiss_index.as_retriever()
 personalities_retriever = personalities_faiss_index.as_retriever()
 
-# Create a combined retriever
-combined_retriever = text_retriever.add_retrievers([attachments_retriever, personalities_retriever])
+# Create a list of all retrievers
+all_retrievers = [text_retriever, attachments_retriever, personalities_retriever]
+
+# Create a function to combine results from all retrievers
+def combined_retriever_function(query):
+    combined_docs = []
+    for retriever in all_retrievers:
+        docs = retriever.get_relevant_documents(query)
+        combined_docs.extend(docs)
+    return combined_docs
+
+# Create a MultiVectorRetriever
+combined_retriever = MultiVectorRetriever(
+    retrievers=all_retrievers,
+    retriever_query=combined_retriever_function
+)
 
 # Create prompt template for query generation
 prompt_template = PromptTemplate(
@@ -61,7 +76,7 @@ multi_query_retriever = MultiQueryRetriever(
     parser_key="lines"  # Assuming the LLM outputs queries line by line
 )
 
-# Create QA chain with multi-query retriever
+# Use the multi-query retriever in the QA chain
 qa_chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=multi_query_retriever)
 
 def load_text(file_path: str) -> str: