Adding AzureSearch AI as vector store

app/rag_chain.py

@@ -1,5 +1,5 @@
 from llm.ollama import load_llm
-from vectordb.vector_store import retrieve
+from vectordb.azure_search import retrieve
 from langchain.prompts import PromptTemplate
 from langchain_core.output_parsers import StrOutputParser
 
@@ -16,24 +16,29 @@ prompt = PromptTemplate(
     input_variables=["question", "documents"],
 )
 
-def get_rag_response(query):
-	print("⌄⌄⌄⌄ Retrieving ⌄⌄⌄⌄")
-	retrieved_docs, metadata = retrieve(query, 10)
-	print("Query Found %d documents." % len(retrieved_docs[0]))
-	for meta in metadata[0]:
-		print("Metadata: ", meta)
-	print("⌃⌃⌃⌃ Retrieving ⌃⌃⌃⌃ " )
 
-	print("⌄⌄⌄⌄ Augmented Prompt ⌄⌄⌄⌄")
-	llm = load_llm()
-	# Create a chain combining the prompt template and LLM
-	rag_chain = prompt | llm | StrOutputParser()
-	context = " ".join(retrieved_docs[0]) if retrieved_docs else "No relevant documents found."
-	print("⌃⌃⌃⌃ Augmented Prompt ⌃⌃⌃⌃")
-	
-	print("⌄⌄⌄⌄ Generation ⌄⌄⌄⌄")
-	response = rag_chain.invoke({"question": query, "context": context});
-	print(response)
-	print("⌃⌃⌃⌃ Generation ⌃⌃⌃⌃")
- 
-	return response
+def get_rag_response(query):
+    print("⌄⌄⌄⌄ Retrieving ⌄⌄⌄⌄")
+    retrieved_docs = retrieve(query, 10)
+    print("Query Found %d documents." % len(retrieved_docs))
+
+    print("⌃⌃⌃⌃ Retrieving ⌃⌃⌃⌃ ")
+
+    print("⌄⌄⌄⌄ Augmented Prompt ⌄⌄⌄⌄")
+    llm = load_llm()
+    # Create a chain combining the prompt template and LLM
+    rag_chain = prompt | llm | StrOutputParser()
+    context = (
+        (" ".join(doc.page_content) for doc in retrieved_docs)
+        if retrieved_docs
+        else "No relevant documents found."
+    )
+
+    print("⌃⌃⌃⌃ Augmented Prompt ⌃⌃⌃⌃")
+
+    print("⌄⌄⌄⌄ Generation ⌄⌄⌄⌄")
+    response = rag_chain.invoke({"question": query, "context": context})
+    print(response)
+    print("⌃⌃⌃⌃ Generation ⌃⌃⌃⌃")
+
+    return response

app/streamlit_app.py

@@ -4,6 +4,6 @@ from app.rag_chain import get_rag_response
 st.title("RAG System")
 query = st.text_input("Ask a question:")
 if query:
-	response = get_rag_response(query)
-	st.write("### Response:")
-	st.write(response)
+    response = get_rag_response(query)
+    st.write("### Response:")
+    st.write(response)