final changes

rag_tutorials/rag_database_routing/rag_database_routing.py

@@ -1,12 +1,13 @@
 import os
-from typing import List, Dict, Literal
+from typing import List, Dict, Any, Literal
 from dataclasses import dataclass
 import streamlit as st
 from langchain_core.documents import Document
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.document_loaders import PyPDFLoader
 from langchain_community.vectorstores import Chroma
-from langchain_openai import OpenAIEmbeddings, ChatOpenAI
+from langchain_openai import OpenAIEmbeddings
+from langchain_openai import ChatOpenAI
 import tempfile
 from phi.agent import Agent
 from phi.model.openai import OpenAIChat
@@ -17,8 +18,10 @@ from langchain import hub
 from langgraph.prebuilt import create_react_agent
 from langchain_community.tools import DuckDuckGoSearchRun
 from langchain_core.language_models import BaseLanguageModel
+from langchain.prompts import ChatPromptTemplate
 
 def init_session_state():
+    """Initialize session state variables"""
     if 'openai_api_key' not in st.session_state:
         st.session_state.openai_api_key = ""
     if 'embeddings' not in st.session_state:
@@ -40,6 +43,7 @@ class CollectionConfig:
     collection_name: str
     persist_directory: str
 
+# Collection configurations
 COLLECTIONS: Dict[DatabaseType, CollectionConfig] = {
     "products": CollectionConfig(
         name="Product Information",
@@ -62,25 +66,39 @@ COLLECTIONS: Dict[DatabaseType, CollectionConfig] = {
 }
 
 def initialize_models():
+    """Initialize OpenAI models with API key"""
     if st.session_state.openai_api_key:
         os.environ["OPENAI_API_KEY"] = st.session_state.openai_api_key
         st.session_state.embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
         st.session_state.llm = ChatOpenAI(temperature=0)
         
-        for config in COLLECTIONS.values():
+        # Ensure directories exist
-            os.makedirs(config.persist_directory, exist_ok=True)
+        for collection_config in COLLECTIONS.values():
+            os.makedirs(collection_config.persist_directory, exist_ok=True)
         
+        # Initialize Chroma collections
         st.session_state.databases = {
-            db_type: Chroma(
+            "products": Chroma(
-                collection_name=config.collection_name,
+                collection_name=COLLECTIONS["products"].collection_name,
                 embedding_function=st.session_state.embeddings,
-                persist_directory=config.persist_directory
+                persist_directory=COLLECTIONS["products"].persist_directory
-            ) for db_type, config in COLLECTIONS.items()
+            ),
+            "support": Chroma(
+                collection_name=COLLECTIONS["support"].collection_name,
+                embedding_function=st.session_state.embeddings,
+                persist_directory=COLLECTIONS["support"].persist_directory
+            ),
+            "finance": Chroma(
+                collection_name=COLLECTIONS["finance"].collection_name,
+                embedding_function=st.session_state.embeddings,
+                persist_directory=COLLECTIONS["finance"].persist_directory
+            )
         }
         return True
     return False
 
 def process_document(file) -> List[Document]:
+    """Process uploaded PDF document"""
     try:
         with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as tmp_file:
             tmp_file.write(file.getvalue())
@@ -88,24 +106,37 @@ def process_document(file) -> List[Document]:
             
         loader = PyPDFLoader(tmp_path)
         documents = loader.load()
+        
+        # Clean up temporary file
         os.unlink(tmp_path)
         
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=300)
+        text_splitter = RecursiveCharacterTextSplitter(
-        return text_splitter.split_documents(documents)
+            chunk_size=1000,
+            chunk_overlap=200
+        )
+        texts = text_splitter.split_documents(documents)
+        
+        return texts
     except Exception as e:
         st.error(f"Error processing document: {e}")
         return []
 
 def create_routing_agent() -> Agent:
+    """Creates a routing agent using phidata framework"""
     return Agent(
-        model=OpenAIChat(id="gpt-4o", api_key=st.session_state.openai_api_key),
+        model=OpenAIChat(
+            id="gpt-4o",
+            api_key=st.session_state.openai_api_key
+        ),
         tools=[],
-        description="You are a query routing expert. Your only job is to analyze questions and determine which database they should be routed to.",
+        description="""You are a query routing expert. Your only job is to analyze questions and determine which database they should be routed to.
+        You must respond with exactly one of these three options: 'products', 'support', or 'finance'. The user's question is: {question}""",
         instructions=[
-            "1. For questions about products, return 'products'",
+            "Follow these rules strictly:",
-            "2. For questions about support, return 'support'",
+            "1. For questions about products, features, specifications, or item details, or product manuals → return 'products'",
-            "3. For questions about finance, return 'finance'",
+            "2. For questions about help, guidance, troubleshooting, or customer service, FAQ, or guides → return 'support'",
-            "4. Return ONLY the database name"
+            "3. For questions about costs, revenue, pricing, or financial data, or financial reports and investments → return 'finance'",
+            "4. Return ONLY the database name, no other text or explanation"
         ],
         markdown=False,
         show_tool_calls=False
@@ -115,42 +146,72 @@ def route_query(question: str) -> DatabaseType:
     try:
         routing_agent = create_routing_agent()
         response = routing_agent.run(question)
-        db_type = response.content.strip().lower().translate(str.maketrans('', '', '`\'"'))
         
+        db_type = (response.content
+                  .strip()
+                  .lower()
+                  .translate(str.maketrans('', '', '`\'"')))  # More elegant string cleaning
+        
+        # Validate database type
         if db_type not in COLLECTIONS:
             st.warning(f"Invalid database type: {db_type}, defaulting to products")
             return "products"
         
         st.info(f"Routing question to {db_type} database")
         return db_type
+        
     except Exception as e:
         st.error(f"Routing error: {str(e)}")
         return "products"
 
 def create_fallback_agent(chat_model: BaseLanguageModel):
+    """Create a LangGraph agent for web research."""
+    
     def web_research(query: str) -> str:
+        """Web search with result formatting."""
         try:
             search = DuckDuckGoSearchRun(num_results=5)
-            return search.run(query)
+            results = search.run(query)
+            return results
         except Exception as e:
             return f"Search failed: {str(e)}. Providing answer based on general knowledge."
 
     tools = [web_research]
-    return create_react_agent(model=chat_model, tools=tools, debug=False)
+    
+    agent = create_react_agent(model=chat_model,
+                             tools=tools,
+                             debug=False)
+    
+    return agent
 
 def query_database(db: Chroma, question: str) -> tuple[str, list]:
     try:
-        retriever = db.as_retriever(search_type="similarity_score_threshold", search_kwargs={"k": 4, "score_threshold": 0.4})
+        retriever = db.as_retriever(
+            search_type="similarity_score_threshold",
+            search_kwargs={"k": 4, "score_threshold": 0.3}
+        )
+
         relevant_docs = retriever.get_relevant_documents(question)
 
         if relevant_docs:
-            retrieval_qa_prompt = hub.pull("langchain-ai/retrieval-qa-chat")
+            # Use simpler chain creation with hub prompt
+            retrieval_qa_prompt = ChatPromptTemplate.from_messages([
+                ("system", """You are a helpful AI assistant that answers questions based on provided context.
+                             Always be direct and concise in your responses.
+                             If the context doesn't contain enough information to fully answer the question, acknowledge this limitation.
+                             Base your answers strictly on the provided context and avoid making assumptions."""),
+                ("human", "Here is the context:\n{context}"),
+                ("human", "Question: {input}"),
+                ("assistant", "I'll help answer your question based on the context provided."),
+                ("human", "Please provide your answer:"),
+            ])
             combine_docs_chain = create_stuff_documents_chain(st.session_state.llm, retrieval_qa_prompt)
             retrieval_chain = create_retrieval_chain(retriever, combine_docs_chain)
+            
             response = retrieval_chain.invoke({"input": question})
             return response['answer'], relevant_docs
-
         return _handle_web_fallback(question)
+
     except Exception as e:
         st.error(f"Error: {str(e)}")
         return "I encountered an error. Please try rephrasing your question.", []
@@ -161,7 +222,9 @@ def _handle_web_fallback(question: str) -> tuple[str, list]:
     
     with st.spinner('Researching...'):
         agent_input = {
-            "messages": [HumanMessage(content=f"Research and provide a detailed answer for: '{question}'")],
+            "messages": [
+                HumanMessage(content=f"Research and provide a detailed answer for: '{question}'")
+            ],
             "is_last_step": False
         }
         
@@ -170,17 +233,26 @@ def _handle_web_fallback(question: str) -> tuple[str, list]:
             if isinstance(response, dict) and "messages" in response:
                 answer = response["messages"][-1].content
                 return f"Web Search Result:\n{answer}", []
+                
         except Exception:
+            # Fallback to general LLM response
             fallback_response = st.session_state.llm.invoke(question).content
             return f"Web search unavailable. General response: {fallback_response}", []
 
 def main():
+    """Main application function."""
     st.set_page_config(page_title="RAG Agent with Database Routing", page_icon="📚")
     st.title("📚 RAG Agent with Database Routing")
     
+    # Sidebar for API key and database management
     with st.sidebar:
         st.header("Configuration")
-        api_key = st.text_input("Enter OpenAI API Key:", type="password", value=st.session_state.openai_api_key, key="api_key_input")
+        api_key = st.text_input(
+            "Enter OpenAI API Key:",
+            type="password",
+            value=st.session_state.openai_api_key,
+            key="api_key_input"
+        )
         
         if api_key:
             st.session_state.openai_api_key = api_key
@@ -194,15 +266,20 @@ def main():
             st.stop()
 
         st.markdown("---")
-    
+
     st.header("Document Upload")
     st.info("Upload documents to populate the databases. Each tab corresponds to a different database.")
-    tabs = st.tabs([config.name for config in COLLECTIONS.values()])
+    tabs = st.tabs([collection_config.name for collection_config in COLLECTIONS.values()])
     
-    for (collection_type, config), tab in zip(COLLECTIONS.items(), tabs):
+    for (collection_type, collection_config), tab in zip(COLLECTIONS.items(), tabs):
         with tab:
-            st.write(config.description)
+            st.write(collection_config.description)
-            uploaded_files = st.file_uploader(f"Upload PDF documents to {config.name}", type="pdf", key=f"upload_{collection_type}", accept_multiple_files=True)
+            uploaded_files = st.file_uploader(
+                f"Upload PDF documents to {collection_config.name}",
+                type="pdf",
+                key=f"upload_{collection_type}",
+                accept_multiple_files=True  
+            )
             
             if uploaded_files:
                 with st.spinner('Processing documents...'):
@@ -216,15 +293,21 @@ def main():
                         db.add_documents(all_texts)
                         st.success("Documents processed and added to the database!")
     
+    # Query section
     st.header("Ask Questions")
     st.info("Enter your question below to find answers from the relevant database.")
     question = st.text_input("Enter your question:")
     
     if question:
         with st.spinner('Finding answer...'):
+            # Route the question
             collection_type = route_query(question)
             db = st.session_state.databases[collection_type]
+            
+            # Display routing information
             st.info(f"Routing question to: {COLLECTIONS[collection_type].name}")
+            
+            # Get and display answer
             answer, relevant_docs = query_database(db, question)
             st.write("### Answer")
             st.write(answer)