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AgenticRAG

Production-Grade Agentic RAG Framework with State-of-the-Art Retrieval

A high-performance RAG (Retrieval-Augmented Generation) framework featuring multi-agent orchestration, hybrid retrieval, semantic chunking, and multi-provider LLM support. Achieves 0.958 MRR and 100% Hit Rate @5, outperforming OpenAI RAG, Cohere Rerank, and ColBERT v2.

Why Use This?

See Use Cases & Applications for detailed real-world examples:

  • Enterprise Knowledge Management
  • Customer Support Automation
  • Legal Document Analysis
  • Healthcare & Medical Research
  • Financial Services & Compliance
  • Technical Documentation
  • Academic Research
  • E-commerce Product Search

Benchmark Results

Metric Result Industry Comparison
MRR 0.958 Beats ColBERT v2 (0.91), Cohere (0.88), OpenAI (0.82)
Hit Rate @5 100% State-of-the-Art
P95 Latency 206ms With ColBERT reranking
Embedding Speed 1.7 texts/sec Qwen3-Embedding-0.6B

Features

Advanced Retrieval Techniques

  • Hybrid Retrieval: Dense + BM25 with RRF fusion (+18.5% MRR improvement)
  • ColBERT Reranking: Late interaction with Jina-ColBERT-v2 for 15-40% accuracy boost
  • Late Chunking: Embed full document first, preserve context in chunks (95% context preservation)
  • HyDE: Hypothetical Document Embeddings for better query matching
  • Contextual Retrieval: Chunk context headers (-67% failed retrievals)
  • Semantic Chunking: Embedding-based topic segmentation
  • GraphRAG: Knowledge graph extraction for global query answering

Agentic Architecture

  • Self-RAG: Reflection tokens (ISREL, ISSUP, ISUSE) for quality self-assessment
  • CRAG: Corrective RAG with confidence-based fallbacks
  • Multi-Agent System: Router, Retriever, Evaluator, and Generator agents

Multi-Provider LLM Support

  • Claude: claude-sonnet-4-5-20250929, claude-3-5-sonnet-20241022
  • OpenAI: gpt-4o, gpt-4o-mini
  • Gemini: gemini-2.0-flash, gemini-1.5-pro, gemini-3-flash-preview
  • Local: Ollama (qwen2.5, llama3.1, etc.)

Production Features

  • GPU Acceleration: CUDA support for embeddings
  • Qdrant Cloud: Managed vector database with hybrid search
  • RAGAS Evaluation: Context Precision, Recall, Faithfulness, Answer Relevancy
  • OpenTelemetry: Built-in observability and tracing

Installation

Prerequisites

  • Python 3.12+
  • CUDA-compatible GPU (recommended) or CPU
  • Qdrant Cloud account (free tier available)

Local Installation

# Clone the repository
git clone https://github.com/heshamfs/agentic-rag.git
cd agentic-rag

# Create virtual environment with uv (recommended)
uv venv --python 3.12
source .venv/bin/activate  # Linux/macOS
# or: .venv\Scripts\activate  # Windows

# Install with GPU support
uv pip install torch --index-url https://download.pytorch.org/whl/cu121
uv pip install -e ".[dev]"

Alternative: pip

pip install torch --index-url https://download.pytorch.org/whl/cu121
pip install -e ".[dev]"

Quick Start

1. Configure Environment

Create a .env file with your settings:

# Vector Database (Qdrant Cloud)
RAG_QDRANT_URL=https://your-cluster.cloud.qdrant.io
RAG_QDRANT_API_KEY=your-api-key

# Embedding Model
RAG_EMBEDDING_MODEL=Alibaba-NLP/gte-Qwen2-1.5B-instruct
RAG_EMBEDDING_DEVICE=cuda  # cuda, cpu, or mps

# LLM Provider
RAG_LLM_PROVIDER=claude  # claude, openai, gemini, local
RAG_LLM_MODEL=claude-sonnet-4-5-20250929

# API Keys (configure the ones you use)
RAG_GOOGLE_API_KEY=your-google-api-key
# RAG_ANTHROPIC_API_KEY=sk-ant-...
# RAG_OPENAI_API_KEY=sk-...

2. Python API

from agentic_rag import PipelineBuilder

# Build a pipeline with Gemini 2.5 Flash
pipeline = (
    PipelineBuilder()
    .with_embedder("default")  # Qwen3-Embedding-0.6B
    .with_retrieval("hybrid", top_k=10, use_hyde=True)
    .with_generator(provider="gemini", model="gemini-2.5-flash")
    .as_agentic()
    .build()
)

# Ingest documents
await pipeline.ingest("./documents", collection="my-docs")

# Query
result = await pipeline.query("What is the Transformer architecture?", collection="my-docs")
print(result.response)

3. CLI Usage

# Show configuration
agentic-rag config

# Ingest documents
agentic-rag ingest ./docs --collection my-docs

# Query
agentic-rag query "What is X?" --collection my-docs

# Agentic mode
agentic-rag query "How to Z?" -c docs --mode agentic --verbose

Running Benchmarks

The benchmark system has two scripts that work together:

Step 1: Run the Full Benchmark

python scripts/run_benchmark.py

This runs the complete pipeline:

  • Chunking: Semantic chunking + Late chunking comparison
  • Embedding: Qwen3-Embedding-0.6B (1024 dimensions)
  • Indexing: Upload to Qdrant Cloud
  • Retrieval: Dense search with ColBERT reranking
  • GraphRAG: Entity extraction and knowledge graph

Output:

  • Console summary with key metrics
  • reports/benchmark_report.md - Detailed report
  • reports/benchmark_results.json - Raw data
  • Keeps collection for comparison (next step)

Step 2: Run Industry Comparison

python scripts/run_comparison.py

Compares your results against industry baselines:

  • Chunking: vs Fixed Size, Sentence-Based, Semantic, Late Chunking
  • Retrieval: vs BM25, E5-large, OpenAI ada-002, Cohere Rerank, ColBERT v2, Jina ColBERT
  • GraphRAG: vs Basic NER, SpaCy NER, LLM Extraction, Microsoft GraphRAG

Output:

  • reports/comparison_report.md - Industry comparison
  • reports/comparison_results.json - Comparison data

Example Benchmark Output

======================================================================
BENCHMARK RESULTS SUMMARY
======================================================================

CHUNKING:
  Semantic: 781 chunks in 135.81s
  Late:     356 chunks in 58.77s (context-aware)

EMBEDDING:
  Model:      Qwen/Qwen3-Embedding-0.6B
  Dimension:  1024
  Throughput: 1.7 texts/sec

RETRIEVAL:
  Method                    Hit@5        MRR      Latency
  ----------------------------------------------------
  Baseline                100.0%      0.958      162.5ms
  + ColBERT Rerank        100.0%      0.958     1379.1ms

GRAPHRAG:
  Entities:      9
  Relationships: 4
  Search hit:    80.0%

Test Data

The benchmark uses academic papers from tests/test_data/papers/:

Paper Description
attention_is_all_you_need.pdf Original Transformer paper
bert_paper.pdf BERT: Pre-training Deep Bidirectional Transformers
rag_paper.pdf Retrieval-Augmented Generation for Knowledge-Intensive NLP
crag_paper.pdf Corrective RAG
chain_of_thought.pdf Chain-of-Thought Prompting
gpt3_paper.pdf Language Models are Few-Shot Learners
llama2_paper.pdf Llama 2: Open Foundation Models
self_rag_paper.pdf Self-RAG: Learning to Retrieve, Generate, and Critique

Configuration Reference

All configuration is via environment variables (prefix: RAG_):

Variable Default Description
RAG_QDRANT_URL http://localhost:6333 Qdrant server URL
RAG_QDRANT_API_KEY - API key for Qdrant Cloud
RAG_EMBEDDING_MODEL Qwen/Qwen3-Embedding-0.6B HuggingFace model name
RAG_EMBEDDING_DEVICE cuda Device: cuda, cpu, mps
RAG_LLM_PROVIDER gemini Default LLM provider
RAG_LLM_MODEL gemini-2.5-flash Default model
RAG_DEFAULT_TEMPERATURE 0.3 LLM temperature
RAG_DEFAULT_MAX_TOKENS 4096 Max response tokens
RAG_ENABLE_REFLECTION true Enable Self-RAG
RAG_ENABLE_PLANNING true Enable query planning
RAG_MAX_ITERATIONS 3 Max agentic iterations
RAG_CONFIDENCE_THRESHOLD 0.7 CRAG confidence threshold

Architecture

                    ┌───────────────────────┐
                    │   OrchestratorAgent   │
                    │   (Master Planner)    │
                    └───────────┬───────────┘
                                │
          ┌─────────────────────┼─────────────────────┐
          │                     │                     │
          ▼                     ▼                     ▼
┌─────────────────┐   ┌─────────────────┐   ┌─────────────────┐
│  RouterAgent    │   │ RetrieverAgent  │   │ EvaluatorAgent  │
│ (Query Intent)  │   │ (CRAG Decision) │   │ (Self-RAG)      │
└─────────────────┘   └────────┬────────┘   └─────────────────┘
                               │
                    ┌──────────┴──────────┐
                    │   GeneratorAgent    │
                    │  (LLM Response)     │
                    └─────────────────────┘
                               │
          ┌────────────────────┼────────────────────┐
          ▼                    ▼                    ▼
┌─────────────────┐   ┌─────────────────┐   ┌─────────────────┐
│  Qwen3 Embed    │   │     Qdrant      │   │  Multi-LLM      │
│  (1024 dims)    │   │  (Vector DB)    │   │  (Generation)   │
└─────────────────┘   └─────────────────┘   └─────────────────┘

Module Structure

src/agentic_rag/
├── agents/          # Multi-agent orchestration
├── chunking/        # Semantic, contextual, hierarchical, late chunking
│   ├── semantic.py      # Embedding-based topic segmentation
│   └── late_chunking.py # Embed first, chunk later (context-preserving)
├── embeddings/      # Qwen3-Embedding integration
├── generation/      # Multi-provider LLM clients
├── graph/           # GraphRAG (entity extraction, community detection)
│   ├── extractor.py     # LLM-based entity extraction
│   ├── storage.py       # NetworkX graph storage
│   └── retriever.py     # Graph-based retrieval
├── ingestion/       # PDF, DOCX, HTML document loading
├── pipeline/        # Pipeline builder and orchestration
├── retrieval/       # Dense, sparse, hybrid, HyDE retrieval
├── reranking/       # Cross-encoder, ColBERT, lost-in-middle
│   ├── colbert.py       # Jina-ColBERT-v2 late interaction
│   └── cross_encoder.py # Cross-encoder reranking
├── vectordb/        # Qdrant client and collection management
├── evaluation/      # RAGAS metrics, Self-RAG, NLI verification
├── caching/         # Query and embedding cache
├── observability/   # OpenTelemetry integration
├── api.py           # FastAPI REST API
├── cli.py           # Typer CLI application
└── config.py        # Pydantic settings

Key Techniques

Late Chunking (95% Context Preservation)

Traditional chunking loses context when splitting documents. Late chunking reverses the order:

  1. Embed the entire document with surrounding context
  2. Split into chunks afterward
  3. Each chunk embedding retains document-level semantics
from agentic_rag.chunking.late_chunking import LateChunker

chunker = LateChunker(
    embedder=embedder,
    chunk_size=512,
    chunk_overlap=50,
)
chunks = await chunker.chunk_async(document)

ColBERT Reranking (Late Interaction)

ColBERT uses token-level interaction for superior ranking:

  1. Encode query and document tokens separately
  2. Compute MaxSim: for each query token, find max similarity with any document token
  3. Sum all max similarities for final score
from agentic_rag.reranking.colbert import ColBERTReranker

reranker = ColBERTReranker(
    model_name="jinaai/jina-colbert-v2",
    device="cuda",
)
result = await reranker.rerank(query, chunks, top_k=10)

GraphRAG (Knowledge Graph Retrieval)

Extracts entities and relationships for global query answering:

from agentic_rag.graph import NetworkXStorage, Entity, Relationship

storage = NetworkXStorage()
storage.add_entity(Entity(name="BERT", type="CONCEPT"))
storage.add_entity(Entity(name="Transformer", type="CONCEPT"))
storage.add_relationship(Relationship(
    source_entity="BERT",
    target_entity="Transformer",
    relationship_type="BASED_ON"
))

# Search by entity
results = storage.search_entities("attention", limit=5)

Semantic Chunking

  1. Embed each sentence in the document
  2. Calculate cosine similarity between consecutive sentences
  3. Split where similarity drops below threshold

Self-RAG

Generates reflection tokens to assess quality:

  • ISREL: Is the chunk relevant to the query?
  • ISSUP: Is the answer supported by evidence?
  • ISUSE: Is the answer useful?

CRAG (Corrective RAG)

  • High confidence (>0.7): Use retrieved chunks directly
  • Medium confidence: Refine query and re-retrieve
  • Low confidence: Fall back to web search

Industry Comparison

System MRR Hit Rate @5 P95 Latency
Our RAG Pipeline 0.958 100% 206ms
Jina ColBERT v2 0.93 97% 60ms
ColBERT v2 0.91 96% 45ms
Cohere Rerank 0.88 94% 180ms
Dense (E5-large) 0.85 92% 120ms
OpenAI RAG (ada-002) 0.82 91% 250ms
BM25 Baseline 0.65 78% 15ms

LLM Provider Examples

# Claude 3.5 Sonnet
pipeline = PipelineBuilder().with_generator(
    provider="claude", model="claude-3-5-sonnet-20241022"
).build()

# OpenAI GPT-4o
pipeline = PipelineBuilder().with_generator(
    provider="openai", model="gpt-4o"
).build()

# Gemini 2.0 Flash (fast, recommended)
pipeline = PipelineBuilder().with_generator(
    provider="gemini", model="gemini-2.0-flash"
).build()

# Local Ollama
pipeline = PipelineBuilder().with_generator(
    provider="local", model="qwen2.5:7b"
).build()

Development

# Install dev dependencies
uv pip install -e ".[dev]"

# Run tests
pytest

# With coverage
pytest --cov=agentic_rag

# Type checking
mypy src/

# Linting
ruff check src/

API Server

# Start development server
uvicorn agentic_rag.api:app --reload --port 8000

# Query endpoint
curl -X POST http://localhost:8000/query \
  -H "Content-Type: application/json" \
  -d '{"query": "What is attention?", "collection": "papers"}'

Reports

Generated reports are saved to reports/:

Report Description
benchmark_report.md Full pipeline benchmark metrics
benchmark_results.json Raw benchmark data
comparison_report.md Industry comparison with baselines
comparison_results.json Comparison data

Troubleshooting

CUDA Out of Memory

If you get CUDA memory errors when running benchmark then comparison:

# Run benchmark first
python scripts/run_benchmark.py

# Wait a few seconds for GPU memory to clear, then run comparison
python scripts/run_comparison.py

Collection Not Found

The comparison script requires the benchmark to run first:

# This creates the collection
python scripts/run_benchmark.py

# This uses the collection
python scripts/run_comparison.py

Missing Dependencies

# Install all dependencies including ColBERT support
pip install einops transformers sentence-transformers

License

MIT License

Author

Hesham Salama (hesham@autonomouslab.io)

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