🤖 Agentic RAG System

An intelligent question-answering system powered by advanced hybrid search, re-ranking, and multi-tool integration.

📋 Table of Contents

• Features
• System Architecture
• Installation
• Usage
• Configuration
• Tools
• Example Queries

✨ Features

RAG (Retrieval-Augmented Generation)

• Hybrid Search: Combination of BM25 and vector-based search
• Cross-Encoder Re-ranking: Results re-ranked using ms-marco-TinyBERT-L-2 model
• Multi-language Support: Turkish and English query support
• ChromaDB Integration: Efficient vector storage and retrieval

Agent Capabilities

• Multi-Tool Support: 10+ different tool integrations
• ReAct Framework: Think-and-act reasoning loop
• Automatic Tool Selection: Selects the most appropriate tool based on query
• Error Management: Robust error handling and recovery mechanisms

Supported Tools

• 🌦️ Weather information
• 🔍 Web search (DuckDuckGo)
• 📚 Wikipedia search
• 🎓 Academic paper search (arXiv)
• 🖼️ Image analysis (Vision AI)
• 🎥 YouTube video transcription
• 🎤 Audio file transcription (Whisper)
• 🐍 Python code execution
• 📖 RAG-based knowledge retrieval

🏗️ System Architecture

graph TB
    User[👤 User Query] --> Agent[🤖 Agent Executor<br/>ReAct Framework]
    
    Agent --> ToolRouter{Tool Router}
    
    ToolRouter --> RAG[📖 RAG Pipeline]
    ToolRouter --> Weather[🌦️ Weather Tool]
    ToolRouter --> WebSearch[🔍 Web Search]
    ToolRouter --> Wiki[📚 Wikipedia]
    ToolRouter --> Arxiv[🎓 ArXiv]
    ToolRouter --> Vision[🖼️ Vision AI]
    ToolRouter --> Audio[🎤 Audio/YouTube]
    ToolRouter --> Python[🐍 Python REPL]
    
    RAG --> QueryProc[1. Query Processing]
    QueryProc --> HybridSearch[2. Hybrid Retrieval]
    
    HybridSearch --> BM25[BM25 Search]
    HybridSearch --> VectorSearch[Vector Search]
    
    BM25 --> Ensemble[Ensemble Retriever]
    VectorSearch --> Ensemble
    
    Ensemble --> Rerank[3. Cross-Encoder<br/>Re-ranking]
    Rerank --> LLM[4. LLM Generation<br/>Gemma 3 27B]
    
    LLM --> Response[✅ Final Answer]
    
    VectorSearch -.-> ChromaDB[(🗄️ ChromaDB<br/>Vector Store)]
    BM25 -.-> ChromaDB
    
    style Agent fill:#e1f5ff
    style RAG fill:#fff4e6
    style ChromaDB fill:#f3e5f5
    style LLM fill:#e8f5e9
    style Response fill:#c8e6c9

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🚀 Installation

Requirements

• Python 3.11+
• Docker (for ChromaDB)
• Ollama (LLM server)
• CUDA (optional, for GPU support)

1. Install Dependencies

pip install -r requirements.txt

2. Start ChromaDB Server

docker run -p 8000:8000 chromadb/chroma

3. Install Ollama and Download Model

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Download Gemma 3 model
ollama pull gemma3:27b

4. Setup Cloudflare Tunnel (Optional)

If running Ollama on a remote server:

cloudflared tunnel --url http://localhost:11434

📊 Database Setup

1. Prepare Data

Place your data as JSON files in SQuAD format in the ./database/ directory:

{
  "data": [
    {
      "title": "Title",
      "paragraphs": [
        {
          "context": "Text content...",
          "qas": []
        }
      ]
    }
  ]
}

2. Update Database

from croma_db_update import db_update

vectorstore = db_update()

💻 Usage

Basic RAG Usage

from hybrid_reranking_rag import reranked_rag_query, create_reranked_rag_chain
import chromadb

# ChromaDB connection
client = chromadb.HttpClient(host="localhost", port=8000)

# Prepare RAG components
llm, prompt = create_reranked_rag_chain(vectorstore)

# Run query
response, elapsed_time = reranked_rag_query(
    llm,
    prompt,
    ensemble_retriever,
    query="What language did the Normans speak?"
)

print(f"Answer: {response}")
print(f"Time: {elapsed_time:.2f} seconds")

Agent Usage

from agent import build_agent

# Build agent
agent = build_agent()

# Ask question
result = agent.invoke({
    "input": "What's the weather in Tokyo?",
    "chat_history": []
})

print(result["output"])

Run Test Suite

from agent_tester import tool_test_loop, TEST_CASES

agent = build_agent()
tool_test_loop(agent, TEST_CASES)

⚙️ Configuration

Important Parameters

RAG Settings (hybrid_reranking_rag.py):

TOP_K_RETRIEVAL = 20  # Number of documents to retrieve in first pass
TOP_K_RERANK = 5      # Number of documents to send to LLM after re-ranking

ChromaDB Settings (croma_db_update.py):

CHROMA_HOST = "localhost"
CHROMA_PORT = 8000
COLLECTION_NAME = "rag_test_data"
MAX_BATCH_SIZE = 5000

LLM Settings:

OLLAMA_MODEL_ID = "gemma3:27b"
CLOUDFLARE_TUNNEL_URL = "https://your-tunnel-url.trycloudflare.com/"

Chunking Parameters:

chunk_size = 500
chunk_overlap = 50

🛠️ Tools

1. RAG Tool

@tool
def rag_tool(question: str) -> str:
    """Question-answering from encyclopedic knowledge base"""
    # Automatically finds most relevant documents and generates answer

2. Weather Tool

@tool
def WeatherInfoTool(location: str) -> str:
    """Weather information for specified location"""

3. Image Caption Tool

@tool
def caption_image_func(raw_input: str) -> str:
    """Image analysis and caption generation"""
    # Usage: image_path='path/to/image.png', prompt='What is this?'

4. YouTube Transcript Tool

@tool
def youtube_transcript_func(url: str) -> str:
    """Automatic transcript extraction from YouTube videos"""

5. Web Search Tool

• DuckDuckGo Search: General web search for current information

6. Wikipedia Tool

• Wikipedia Search: Encyclopedic information lookup

7. ArXiv Tool

• Academic Search: Search scientific papers and research articles

8. Python REPL Tool

• Code Execution: Run Python code snippets dynamically

🎯 Example Queries

# Turkish factual question
"Rollo'nun Vikinglerinin torunları hangi dili ve dini benimsedi?"
# Expected: Uses RAG tool to find historical information

# English multi-hop question
"What is the metric term less used than the Newton?"
# Expected: Uses RAG tool for physics knowledge

# Image analysis
"image_path='chess.png', prompt='What is the best move?'"
# Expected: Uses Vision AI tool

# Weather query
"What's the weather in Istanbul?"
# Expected: Uses Weather tool

# Academic search
"Latest AI research papers published in 2024"
# Expected: Uses ArXiv tool

# Web search
"Current air pollution status in Istanbul"
# Expected: Uses DuckDuckGo search tool

# Python execution
"Calculate the product of 174.5 and 93.2"
# Expected: Uses Python REPL tool

# YouTube transcript
"Extract transcript from https://www.youtube.com/watch?v=dQw4w9WgXcQ"
# Expected: Uses YouTube transcript tool

🧠 Models Used

Embedding Model

• Model: paraphrase-multilingual-mpnet-base-v2
• Dimensions: 768
• Language Support: 50+ languages

Re-ranker Model

• Model: cross-encoder/ms-marco-TinyBERT-L-2
• Purpose: Semantic similarity scoring

LLM

• Model: gemma3:27b
• Provider: Ollama
• Context Window: 8K tokens

📊 Test Results

The system was tested with 10 different scenarios covering all tool functionalities. Here are the results:

#	Test Scenario	Expected Tool	Status	Time (s)	Accuracy
1	Chess move analysis from image	caption_image_func	✅ Pass	43.79	⭐⭐⭐⭐⭐ Excellent
2	Istanbul air pollution search	general_web_search	✅ Pass	21.62	⭐⭐⭐⭐⭐ Excellent
3	2024 biological AI papers	academic_search	✅ Pass	38.08	⭐⭐⭐⭐⭐ Excellent
4	European Union history	wikipedia_search	✅ Pass	46.04	⭐⭐⭐⭐ Good
5	Tokyo weather	WeatherInfoTool	✅ Pass	7.69	⭐⭐⭐⭐⭐ Excellent
6	Multiplication: 174.5 × 93.2	python_repl_tool	✅ Pass	6.85	⭐⭐⭐⭐⭐ Excellent
7	Division and addition: 5000÷125+17	python_repl_tool	✅ Pass	6.34	⭐⭐⭐⭐⭐ Excellent
8	Python list length calculation	python_repl_tool	✅ Pass	7.36	⭐⭐⭐⭐⭐ Excellent
9	YouTube video transcription	youtube_transcript_func	✅ Pass	55.81	⭐⭐⭐⭐⭐ Excellent
10	Physics: Non-conservative forces	rag_tool	✅ Pass	26.41	⭐⭐⭐⭐⭐ Excellent

Test Summary

• Total Tests: 10
• Passed: 10 (100%)
• Failed: 0 (0%)
• Average Response Time: 26.00 seconds
• Fastest Response: 6.34s (Python calculation)
• Slowest Response: 55.81s (YouTube transcription with audio processing)

Accuracy Ratings

• ⭐⭐⭐⭐⭐ Excellent (9/10): Tool selected correctly, answer highly accurate and complete
• ⭐⭐⭐⭐ Good (1/10): Tool selected correctly, answer accurate but used RAG instead of Wikipedia

Key Observations

1. Tool Selection: The agent demonstrated excellent tool selection capabilities with 100% accuracy in choosing appropriate tools for each task.

2. Multi-modal Capabilities: Successfully handled diverse input types including images, URLs, mathematical operations, and natural language queries.

3. Language Flexibility: Effectively processed both Turkish and English queries with accurate responses.

4. RAG Performance: The RAG tool correctly answered complex factual questions by retrieving and synthesizing information from the knowledge base.

5. Response Times:

   • Simple calculations: 6-8 seconds
   • Web/Wikipedia searches: 20-46 seconds
   • Complex tasks (image analysis, video transcription): 40-56 seconds

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Made with ❤️ using LangChain, ChromaDB, and Ollama