added qdrant as db

rag_tutorials/rag_database_routing/rag_database_routing.py

@@ -1,11 +1,11 @@
 import os
-from typing import List, Dict, Any, Literal
+from typing import List, Dict, Any, Literal, Optional
 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_community.vectorstores import Qdrant
 from langchain_openai import OpenAIEmbeddings
 from langchain_openai import ChatOpenAI
 import tempfile
@@ -19,11 +19,17 @@ 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
+from qdrant_client import QdrantClient
+from qdrant_client.models import Distance, VectorParams
 
 def init_session_state():
     """Initialize session state variables"""
     if 'openai_api_key' not in st.session_state:
         st.session_state.openai_api_key = ""
+    if 'qdrant_url' not in st.session_state:
+        st.session_state.qdrant_url = ""
+    if 'qdrant_api_key' not in st.session_state:
+        st.session_state.qdrant_api_key = ""
     if 'embeddings' not in st.session_state:
         st.session_state.embeddings = None
     if 'llm' not in st.session_state:
@@ -40,61 +46,68 @@ PERSIST_DIRECTORY = "db_storage"
 class CollectionConfig:
     name: str
     description: str
-    collection_name: str
+    collection_name: str  # This will be used as Qdrant collection name
-    persist_directory: str
 
 # Collection configurations
 COLLECTIONS: Dict[DatabaseType, CollectionConfig] = {
     "products": CollectionConfig(
         name="Product Information",
         description="Product details, specifications, and features",
-        collection_name="products_collection",
+        collection_name="products_collection"
-        persist_directory=f"{PERSIST_DIRECTORY}/products"
     ),
     "support": CollectionConfig(
         name="Customer Support & FAQ",
         description="Customer support information, frequently asked questions, and guides",
-        collection_name="support_collection",
+        collection_name="support_collection"
-        persist_directory=f"{PERSIST_DIRECTORY}/support"
     ),
     "finance": CollectionConfig(
         name="Financial Information",
         description="Financial data, revenue, costs, and liabilities",
-        collection_name="finance_collection",
+        collection_name="finance_collection"
-        persist_directory=f"{PERSIST_DIRECTORY}/finance"
     )
 }
 
 def initialize_models():
-    """Initialize OpenAI models with API key"""
+    """Initialize OpenAI models and Qdrant client"""
-    if st.session_state.openai_api_key:
+    if (st.session_state.openai_api_key and 
+        st.session_state.qdrant_url and 
+        st.session_state.qdrant_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.embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
         st.session_state.llm = ChatOpenAI(temperature=0)
         
-        # Ensure directories exist
+        try:
-        for collection_config in COLLECTIONS.values():
+            # Initialize Qdrant client with session state credentials
-            os.makedirs(collection_config.persist_directory, exist_ok=True)
+            client = QdrantClient(
-        
+                url=st.session_state.qdrant_url,
-        # Initialize Chroma collections
+                api_key=st.session_state.qdrant_api_key
-        st.session_state.databases = {
-            "products": Chroma(
-                collection_name=COLLECTIONS["products"].collection_name,
-                embedding_function=st.session_state.embeddings,
-                persist_directory=COLLECTIONS["products"].persist_directory
-            ),
-            "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
+            # Test connection
+            client.get_collections()
+            vector_size = 1536  
+            st.session_state.databases = {}
+            for db_type, config in COLLECTIONS.items():
+                try:
+                    client.get_collection(config.collection_name)
+                except Exception:
+                    # Create collection if it doesn't exist
+                    client.create_collection(
+                        collection_name=config.collection_name,
+                        vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE)
+                    )
+                
+                st.session_state.databases[db_type] = Qdrant(
+                    client=client,
+                    collection_name=config.collection_name,
+                    embeddings=st.session_state.embeddings
+                )
+            
+            return True
+        except Exception as e:
+            st.error(f"Failed to connect to Qdrant: {str(e)}")
+            return False
     return False
 
 def process_document(file) -> List[Document]:
@@ -136,33 +149,62 @@ def create_routing_agent() -> Agent:
             "1. For questions about products, features, specifications, or item details, or product manuals → return 'products'",
             "2. For questions about help, guidance, troubleshooting, or customer service, FAQ, or guides → return 'support'",
             "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"
+            "4. Return ONLY the database name, no other text or explanation",
+            "5. If you're not confident about the routing, return an empty response"
         ],
         markdown=False,
         show_tool_calls=False
     )
 
-def route_query(question: str) -> DatabaseType:
+def route_query(question: str) -> Optional[DatabaseType]:
+    """Route query by searching all databases and comparing relevance scores.
+    Returns None if no suitable database is found."""
     try:
+        best_score = -1
+        best_db_type = None
+        all_scores = {}  # Store all scores for debugging
+        
+        # Search each database and compare relevance scores
+        for db_type, db in st.session_state.databases.items():
+            results = db.similarity_search_with_score(
+                question,
+                k=3
+            )
+            
+            if results:
+                avg_score = sum(score for _, score in results) / len(results)
+                all_scores[db_type] = avg_score
+                
+                if avg_score > best_score:
+                    best_score = avg_score
+                    best_db_type = db_type
+        
+        confidence_threshold = 0.5
+        if best_score >= confidence_threshold and best_db_type:
+            st.success(f"Using vector similarity routing: {best_db_type} (confidence: {best_score:.3f})")
+            return best_db_type
+            
+        st.warning(f"Low confidence scores (below {confidence_threshold}), falling back to LLM routing")
+        
+        # Fallback to LLM routing
         routing_agent = create_routing_agent()
         response = routing_agent.run(question)
         
         db_type = (response.content
                   .strip()
                   .lower()
-                  .translate(str.maketrans('', '', '`\'"')))  # More elegant string cleaning
+                  .translate(str.maketrans('', '', '`\'"')))
         
-        # Validate database type
+        if db_type in COLLECTIONS:
-        if db_type not in COLLECTIONS:
+            st.success(f"Using LLM routing decision: {db_type}")
-            st.warning(f"Invalid database type: {db_type}, defaulting to products")
+            return db_type
-            return "products"
+            
-        
+        st.warning("No suitable database found, will use web search fallback")
-        st.info(f"Routing question to {db_type} database")
+        return None
-        return db_type
         
     except Exception as e:
         st.error(f"Routing error: {str(e)}")
-        return "products"
+        return None
 
 def create_fallback_agent(chat_model: BaseLanguageModel):
     """Create a LangGraph agent for web research."""
@@ -184,11 +226,12 @@ def create_fallback_agent(chat_model: BaseLanguageModel):
     
     return agent
 
-def query_database(db: Chroma, question: str) -> tuple[str, list]:
+def query_database(db: Qdrant, question: str) -> tuple[str, list]:
+    """Query the database and return answer and relevant documents"""
     try:
         retriever = db.as_retriever(
-            search_type="similarity_score_threshold",
+            search_type="similarity",
-            search_kwargs={"k": 4, "score_threshold": 0.3}
+            search_kwargs={"k": 4}
         )
 
         relevant_docs = retriever.get_relevant_documents(question)
@@ -210,7 +253,8 @@ def query_database(db: Chroma, question: str) -> tuple[str, list]:
             
             response = retrieval_chain.invoke({"input": question})
             return response['answer'], relevant_docs
-        return _handle_web_fallback(question)
+        
+        raise ValueError("No relevant documents found in database")
 
     except Exception as e:
         st.error(f"Error: {str(e)}")
@@ -244,9 +288,11 @@ def main():
     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
+    # Sidebar for API keys and configuration
     with st.sidebar:
         st.header("Configuration")
+        
+        # OpenAI API Key
         api_key = st.text_input(
             "Enter OpenAI API Key:",
             type="password",
@@ -254,15 +300,37 @@ def main():
             key="api_key_input"
         )
         
+        # Qdrant Configuration
+        qdrant_url = st.text_input(
+            "Enter Qdrant URL:",
+            value=st.session_state.qdrant_url,
+            help="Example: https://your-cluster.qdrant.tech"
+        )
+        
+        qdrant_api_key = st.text_input(
+            "Enter Qdrant API Key:",
+            type="password",
+            value=st.session_state.qdrant_api_key
+        )
+        
+        # Update session state
         if api_key:
             st.session_state.openai_api_key = api_key
+        if qdrant_url:
+            st.session_state.qdrant_url = qdrant_url
+        if qdrant_api_key:
+            st.session_state.qdrant_api_key = qdrant_api_key
+            
+        # Initialize models if all credentials are provided
+        if (st.session_state.openai_api_key and 
+            st.session_state.qdrant_url and 
+            st.session_state.qdrant_api_key):
             if initialize_models():
-                st.success("API Key set successfully!")
+                st.success("Connected to OpenAI and Qdrant successfully!")
             else:
-                st.error("Invalid API Key")
+                st.error("Failed to initialize. Please check your credentials.")
-        
+        else:
-        if not st.session_state.openai_api_key:
+            st.warning("Please enter all required credentials to continue")
-            st.warning("Please enter your OpenAI API key to continue")
             st.stop()
 
         st.markdown("---")
@@ -302,15 +370,19 @@ def main():
         with st.spinner('Finding answer...'):
             # Route the question
             collection_type = route_query(question)
-            db = st.session_state.databases[collection_type]
             
-            # Display routing information
+            if collection_type is None:
-            st.info(f"Routing question to: {COLLECTIONS[collection_type].name}")
+                # Use web search fallback directly
-            
+                answer, relevant_docs = _handle_web_fallback(question)
-            # Get and display answer
+                st.write("### Answer (from web search)")
-            answer, relevant_docs = query_database(db, question)
+                st.write(answer)
-            st.write("### Answer")
+            else:
-            st.write(answer)
+                # Display routing information and query the database
+                st.info(f"Routing question to: {COLLECTIONS[collection_type].name}")
+                db = st.session_state.databases[collection_type]
+                answer, relevant_docs = query_database(db, question)
+                st.write("### Answer")
+                st.write(answer)
 
 if __name__ == "__main__":
     main()

rag_tutorials/rag_database_routing/requirements.txt

@@ -1,7 +1,7 @@
 langchain==0.3.12
 langchain-community==0.3.12
 langchain-core==0.3.28
-chromadb==0.5.20
+qdrant-client==1.12.1
 streamlit>=1.29.0
 pypdf>=4.0.0
 sentence-transformers>=2.2.2