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Merge pull request #59 from Madhuvod/rag-doc-router

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Added new demo
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Shubham Saboo 2024-12-26 10:51:42 -06:00 committed by GitHub
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ai_agent_tutorials/ai_recruitment_agent_team/README.md
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@ -35,8 +35,8 @@ A Streamlit application that simulates a full-service recruitment team using mul
1. **Setup Environment**
```bash
# Clone the repository
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd ai_agent_tutorials/ai_recruitment_agent_team
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd ai_agent_tutorials/ai_recruitment_agent_team
# Install dependencies
pip install -r requirements.txt

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ai_agent_tutorials/multimodal_design_agent_team/requirements.txt
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google-generativeai==0.8.3
streamlit==1.30.0
streamlit==1.41.1
phidata==2.7.2
Pillow==11.0.0
duckduckgo-search==6.3.7

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rag_tutorials/rag_database_routing/README.md Normal file
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# RAG Agent with Database Routing
This project showcases the RAG with database routing capabilities - which is a very efficient way to retrieve information from a large set of documents. The application allows users to:
1. Upload documents to three different databases:
- Product Information
- Customer Support & FAQ
- Financial Information
2. Query information using natural language, with automatic routing to the most relevant database.
## Features
- **Document Upload**: Users can upload multiple PDF documents related to a particular company. These documents are processed and stored in one of the three databases: Product Information, Customer Support & FAQ, or Financial Information.
- **Natural Language Querying**: Users can ask questions in natural language. The system automatically routes the query to the most relevant database using a phidata agent as the router.
- **RAG Orchestration**: Utilizes Langchain for orchestrating the retrieval augmented generation process, ensuring that the most relevant information is retrieved and presented to the user.
- **Fallback Mechanism**: If no relevant documents are found in the databases, a LangGraph agent with a DuckDuckGo search tool is used to perform web research and provide an answer.
- **User Interface**: Built with Streamlit, providing an intuitive and interactive user experience.
## How to Run?
1. **Clone the Repository**:
```bash
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd rag_tutorials/rag_database_routing
```
2. **Install Dependencies**:
```bash
pip install -r requirements.txt
```
3. **Run the Application**:
```bash
streamlit run rag_database_routing.py
```
4. **Configure API Key**: Obtain an OpenAI API key and set it in the application. This is required for initializing the language models used in the application.
5. **Upload Documents**: Use the document upload section to add PDF documents to the desired database.
6. **Ask Questions**: Enter your questions in the query section. The application will route your question to the appropriate database and provide an answer.
## Technologies Used
- **Langchain**: For RAG orchestration, ensuring efficient retrieval and generation of information.
- **Phidata Agent**: Used as the router agent to determine the most relevant database for a given query.
- **LangGraph Agent**: Acts as a fallback mechanism, utilizing DuckDuckGo for web research when necessary.
- **Streamlit**: Provides a user-friendly interface for document upload and querying.
- **ChromaDB**: Used for managing the databases, storing and retrieving document embeddings efficiently.
This application is designed to streamline the process of retrieving information from large sets of documents, making it easier for users to find the answers they need quickly and efficiently.

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rag_tutorials/rag_database_routing/rag_database_routing.py Normal file
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import os
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 Qdrant
from langchain_openai import OpenAIEmbeddings
from langchain_openai import ChatOpenAI
import tempfile
from phi.agent import Agent
from phi.model.openai import OpenAIChat
from langchain.schema import HumanMessage
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain.chains import create_retrieval_chain
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
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:
st.session_state.llm = None
if 'databases' not in st.session_state:
st.session_state.databases = {}
init_session_state()
DatabaseType = Literal["products", "support", "finance"]
PERSIST_DIRECTORY = "db_storage"
@dataclass
class CollectionConfig:
name: str
description: str
collection_name: str # This will be used as Qdrant collection name
# Collection configurations
COLLECTIONS: Dict[DatabaseType, CollectionConfig] = {
"products": CollectionConfig(
name="Product Information",
description="Product details, specifications, and features",
collection_name="products_collection"
),
"support": CollectionConfig(
name="Customer Support & FAQ",
description="Customer support information, frequently asked questions, and guides",
collection_name="support_collection"
),
"finance": CollectionConfig(
name="Financial Information",
description="Financial data, revenue, costs, and liabilities",
collection_name="finance_collection"
)
}
def initialize_models():
"""Initialize OpenAI models and Qdrant client"""
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-small")
st.session_state.llm = ChatOpenAI(temperature=0)
try:
# Initialize Qdrant client with session state credentials
client = QdrantClient(
url=st.session_state.qdrant_url,
api_key=st.session_state.qdrant_api_key
)
# 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]:
"""Process uploaded PDF document"""
try:
with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as tmp_file:
tmp_file.write(file.getvalue())
tmp_path = tmp_file.name
loader = PyPDFLoader(tmp_path)
documents = loader.load()
# Clean up temporary file
os.unlink(tmp_path)
text_splitter = RecursiveCharacterTextSplitter(
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
),
tools=[],
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=[
"Follow these rules strictly:",
"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",
"5. If you're not confident about the routing, return an empty response"
],
markdown=False,
show_tool_calls=False
)
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('', '', '`\'"')))
if db_type in COLLECTIONS:
st.success(f"Using LLM routing decision: {db_type}")
return db_type
st.warning("No suitable database found, will use web search fallback")
return None
except Exception as e:
st.error(f"Routing error: {str(e)}")
return None
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)
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]
agent = create_react_agent(model=chat_model,
tools=tools,
debug=False)
return agent
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",
search_kwargs={"k": 4}
)
relevant_docs = retriever.get_relevant_documents(question)
if relevant_docs:
# 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
raise ValueError("No relevant documents found in database")
except Exception as e:
st.error(f"Error: {str(e)}")
return "I encountered an error. Please try rephrasing your question.", []
def _handle_web_fallback(question: str) -> tuple[str, list]:
st.info("No relevant documents found. Searching web...")
fallback_agent = create_fallback_agent(st.session_state.llm)
with st.spinner('Researching...'):
agent_input = {
"messages": [
HumanMessage(content=f"Research and provide a detailed answer for: '{question}'")
],
"is_last_step": False
}
try:
response = fallback_agent.invoke(agent_input, config={"recursion_limit": 100})
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 keys and configuration
with st.sidebar:
st.header("Configuration")
# OpenAI API Key
api_key = st.text_input(
"Enter OpenAI API Key:",
type="password",
value=st.session_state.openai_api_key,
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("Connected to OpenAI and Qdrant successfully!")
else:
st.error("Failed to initialize. Please check your credentials.")
else:
st.warning("Please enter all required credentials to continue")
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([collection_config.name for collection_config in COLLECTIONS.values()])
for (collection_type, collection_config), tab in zip(COLLECTIONS.items(), tabs):
with tab:
st.write(collection_config.description)
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...'):
all_texts = []
for uploaded_file in uploaded_files:
texts = process_document(uploaded_file)
all_texts.extend(texts)
if all_texts:
db = st.session_state.databases[collection_type]
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)
if collection_type is None:
# Use web search fallback directly
answer, relevant_docs = _handle_web_fallback(question)
st.write("### Answer (from web search)")
st.write(answer)
else:
# 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()

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rag_tutorials/rag_database_routing/requirements.txt Normal file
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langchain==0.3.12
langchain-community==0.3.12
langchain-core==0.3.28
qdrant-client==1.12.1
streamlit>=1.29.0
pypdf>=4.0.0
sentence-transformers>=2.2.2
phidata==2.7.3
langchain-openai==0.2.14
langgraph==0.2.53
duckduckgo-search==6.4.1