90 lines
2.3 KiB
Markdown
90 lines
2.3 KiB
Markdown
# RAG Agent with ChromaDB and Web Search
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This project implements a simple Retrieval-Augmented Generation (RAG) agent that uses **ChromaDB** for vector storage and **OpenAI** embeddings for text representation. The agent exposes two HTTP endpoints:
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- `POST /ingest` – ingest documents into the vector store.
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- `POST /query` – retrieve the most similar documents for a given query.
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## Features
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- **Vector Store**: ChromaDB collection named `rag_collection`.
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- **Embeddings**: OpenAI `text-embedding-ada-002` (configurable).
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- **API**: FastAPI based, can be run locally or in Docker.
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- **No Qdrant**: The implementation uses only ChromaDB as required.
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## Prerequisites
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- Python 3.11+
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- Docker (optional, for containerized deployment)
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- An OpenAI API key (set as `OPENAI_API_KEY` environment variable).
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## Setup
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### Local
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```bash
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# Clone the repository
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git clone https://git.brojs.ru/kuzakhmetovartur/ekzamen-rag-agent-s-chromadb-i-veb-poisk.git
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cd ekzamen-rag-agent-s-chromadb-i-veb-poisk
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# Create virtual environment
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python -m venv venv
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source venv/bin/activate
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# Install dependencies
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pip install -r requirements.txt
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# Set OpenAI API key
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export OPENAI_API_KEY="sk-..."
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# Run the server
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uvicorn src.main:app --reload
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```
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The API will be available at `http://127.0.0.1:8000`.
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### Docker
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```bash
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# Build the image
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docker build -t rag-agent .
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# Run the container
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docker run -d -p 8000:8000 --env OPENAI_API_KEY="sk-..." rag-agent
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```
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## API Usage
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### Ingest Documents
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```bash
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curl -X POST http://localhost:8000/ingest \
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-H "Content-Type: application/json" \
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-d '{
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"documents": [
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{"content": "The quick brown fox jumps over the lazy dog."},
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{"content": "Python is a versatile programming language."}
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]
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}'
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```
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### Query
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```bash
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curl -X POST http://localhost:8000/query \
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-H "Content-Type: application/json" \
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-d '{
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"query": "What is Python?",
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"k": 3
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}'
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```
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## Notes
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- The vector store is persisted in memory by default. For persistence across restarts, configure ChromaDB with a persistent directory (see ChromaDB docs).
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- The agent currently only returns the raw similarity search results. Integration with a language model for generation can be added later.
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- No Qdrant usage is present; the stack strictly follows the assignment requirements.
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## License
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MIT License |