RAG AI Agent in Python

Production-Ready Retrieval-Augmented Generation (RAG) AI Agent in Python This repository contains a production-oriented reference implementation of a Retrieval-Augmented Generation (RAG) AI agent built in Python. It's designed to serve as a practical starting point for building, deploying, and operating RAG-powered assistants that combine vector search over your knowledge sources with a large language model (LLM) for accurate, up-to-date responses.

Key goals:

• Modular, maintainable architecture suitable for production.
• Clear examples for ingesting data, building a vector store, and serving a queryable RAG agent.
• Best-practice considerations for configuration, logging, testing, and deployment.

Table of contents

• Features
• Architecture overview
• Quickstart
• Installation
• Configuration
• Typical workflows
  • Ingesting documents
  • Building/using a vector store
  • Querying the agent
• Deployment
• Testing & CI
• Observability & Monitoring
• Security & privacy
• Contributing
• License
• Acknowledgements & References

Features

• Document ingestion pipeline (file system, PDFs, text, HTML — extendable)
• Embeddings + vector store (FAISS / Chroma / Milvus / Weaviate friendly)
• LLM orchestration layer for RAG responses (plug any LLM: OpenAI, Hugging Face, local)
• Conversation memory/session management
• API server example (FastAPI) for production endpoints
• Dockerfile and container-friendly configuration
• Example scripts for common tasks (ingest, index, query)
• Tests and example CI workflow

Architecture overview

1. Data ingestion: preprocess raw docs -> text chunks -> metadata
2. Embeddings: convert text chunks to vectors via an embeddings model
3. Vector store: store and query embeddings (approximate nearest neighbors)
4. RAG agent: retrieve context, construct prompts, call LLM, optionally perform chain-of-thought / tool use
5. API & orchestration: expose endpoints, session handling, rate-limiting
6. Observability: logging, request tracing, metrics for latency / error rates

Quickstart (fast path) Prerequisites

• Python 3.10+
• Git
• Optional: Docker for containerized deployment
• LLM/embedding credentials (e.g., OPENAI_API_KEY) or local model runtime

Clone and install

git clone https://github.com/Parrot90/Production_Ready_RAG_AI_Agent_in_Python.git
cd Production_Ready_RAG_AI_Agent_in_Python
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Ingest your documents (example)

python scripts/ingest.py --source ./data/docs --output ./data/chunks

Build or update vector index

python scripts/index.py --chunks ./data/chunks --store ./data/vectorstore

Run the API server

uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload

Installation (detailed)

1. Create a virtual environment: python -m venv .venv source .venv/bin/activate
2. Install dependencies: pip install -r requirements.txt
3. Optional: install system packages required by some vector stores (e.g., FAISS).

Configuration The project uses environment variables and a small config loader (config.py). Typical config options:

• OPENAI_API_KEY or other provider credentials
• EMBEDDING_MODEL (provider/model for embeddings)
• LLM_MODEL (model for generation)
• VECTOR_STORE (faiss | chroma | milvus | weaviate)
• PERSIST_DIRECTORY (where to persist vector stores / metadata)
• MAX_CONTEXT_TOKENS, CHUNK_SIZE, CHUNK_OVERLAP, etc.

Typical workflows

1. Ingesting documents

• Purpose: convert raw documents into cleaned text chunks with metadata (filename, source, modified time).
• Steps:
  • Detect file type (txt, md, pdf, html, docx)
  • Extract text
  • Split into chunks (size + overlap tuned for your LLM context window)
  • Save chunk JSON/CSV ready for embedding

2. Building/using a vector store

• Create embeddings for chunks using your chosen embedder
• Save vectors & metadata to a vector index (FAISS/Chroma)
• For production, consider a database-backed vector store (Milvus/Weaviate) for persistence, scaling, and multi-node support

3. Querying the agent (RAG flow)

• Receive a user query via API
• Embed the query and retrieve top-N relevant chunks
• Construct a prompt combining retrieved context and a system/instruction template
• Call the LLM for generation
• Optionally store the conversational turn and any analytics/metrics

Example usage snippet (pseudo)

from rag_agent import RAGAgent

agent = RAGAgent(config)
response = agent.query("How do I rotate a vector in 3D?", session_id="user-123")
print(response.answer)

Deployment

• Containerization: Dockerfile provided. Build and push image to your registry.
• Orchestration: Kubernetes manifests are not included by default, but you can use a simple Deployment + HPA, Service, and Ingress.
• Consider autoscaling, GPU-equipped nodes for local LLMs, or managed hosting for third-party LLM APIs.
• Secrets management: use your cloud provider or HashiCorp Vault for keys.
• Persistent volumes: for vector store persistence (if using self-hosted FAISS/Chroma).

Testing & CI

• Unit tests: pytest
• Integration tests: small sample document set + local vector store + mocked LLM responses
• Example GitHub Actions workflow (ci.yml) may include:
  • linting (flake8/ruff)
  • unit tests
  • build and push Docker image on release

Observability & Monitoring

• Logging: structured JSON logs via Python logging; configurable level via LOG_LEVEL
• Metrics: expose Prometheus metrics (request latency, error count, embedding/LLM call latencies)
• Tracing: add OpenTelemetry for distributed tracing through ingestion -> retrieval -> LLM calls
• Alerts: setup alerts for high error rate, high latency, low recall (if you have test queries)

Security & privacy

• Do not log sensitive user data; sanitize before logging.
• If storing user conversations, provide retention policy and opt-out mechanisms.
• Secure APIs with authentication (API keys, OAuth).
• Network: use TLS in production; limit access to vector store endpoints.
• Rate limit LLM/API calls to avoid cost spikes.

Performance & scaling tips

• Cache embeddings for repeated queries or common documents.
• Use approximate nearest neighbor (ANN) index with HNSW or IVF for FAISS.
• Use batching for embedding requests to reduce API overhead.
• Use streaming responses (if your LLM supports) to reduce perceived latency.

Best practices for production

• Keep ingestion reproducible: store hashes & source metadata so you can rebuild the index deterministically.
• Version your vector store and model choices.
• Have a small set of evaluation queries for continuous monitoring of retrieval quality.
• Implement canary releases when changing LLMs or prompt templates.

Contributing Contributions welcome! Please follow these steps:

1. Fork the repo and create a branch: git checkout -b feat/my-feature
2. Write tests for any new behavior.
3. Run linting and tests: tox or pytest
4. Submit a pull request describing the change.

Code of conduct: Be respectful. See CODE_OF_CONDUCT.md (add if needed).

Recommended repository layout

• app/ # API server and agent orchestration
• scripts/ # Command-line ingestion / index / utils
• libs/ # core RAG logic: retriever, prompt templates, llm wrappers
• tests/ # unit and integration tests
• Dockerfile
• requirements.txt
• README.md

Troubleshooting

• If embeddings or LLM calls fail: check credentials and network access.
• If retrieval quality is poor: tune chunk size/overlap, increase top_k, experiment with different embeddings.
• If performance is slow: profile embedding calls and vector queries, consider batching and index tuning.

License This repository is provided under the MIT License. See LICENSE for details.

Acknowledgements & references

• LangChain: Patterns and chain design influenced by the LangChain ecosystem
• FAISS, Chroma, Milvus: popular vector stores
• OpenAI / Hugging Face: LLM and embeddings providers

Contact Maintainer: Parrot90 For questions and help, open an issue in this repository.

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If you’d like, I can:

• Generate example scripts (ingest.py, index.py, app/main.py).
• Create a Dockerfile, GitHub Actions CI, and a basic test suite.
• Tailor the README with exact commands and config after I inspect the repository contents.