Metadata-Version: 2.4
Name: ezrag
Version: 0.1.0b1
Summary: Async-first RAG SDK. The fastest way to build RAG applications with Python.
Author-email: Manuel Armillas Hernández <manuelarmillas7@gmail.com>
License: MIT License
        
        Copyright (c) 2026 Manuel Armillas
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
License-File: LICENSE
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.12
Provides-Extra: cohere
Requires-Dist: cohere>=5.0.0; extra == 'cohere'
Provides-Extra: firestore
Requires-Dist: google-cloud-firestore>=2.16.0; extra == 'firestore'
Provides-Extra: ollama
Requires-Dist: httpx>=0.27.0; extra == 'ollama'
Provides-Extra: openai
Requires-Dist: openai>=1.0.0; extra == 'openai'
Provides-Extra: pgvector
Requires-Dist: asyncpg>=0.30.0; extra == 'pgvector'
Requires-Dist: pgvector>=0.3.0; extra == 'pgvector'
Description-Content-Type: text/markdown

# ezrag

A modular, hexagonal-architecture RAG SDK for Python.

## Install

```bash
pip install ezrag
# optional extras
pip install "ezrag[openai]"   # OpenAI embeddings + generation
pip install "ezrag[ollama]"   # Ollama (local LLM) support
pip install "ezrag[firestore]" # Firestore vector store
```

## Quickstart

```python
import asyncio
from ezrag import rag, Document
from ezrag.embeddings import OpenAIEmbedder
from ezrag.stores import InMemoryVectorStore
from ezrag.generators import OpenAIGenerator

async def main() -> None:
    sdk = rag(
        embedder=OpenAIEmbedder(api_key="..."),
        store=InMemoryVectorStore(),
        generator=OpenAIGenerator(api_key="..."),
    )
    await sdk.ingest([Document(content="RAG combines retrieval with generation.")])
    result = await sdk.query("What is RAG?")
    answer = await sdk.generate("Explain RAG simply")
    print(result.results)
    print(answer.answer)

asyncio.run(main())
```

## Documentation

- [docs/quickstart.md](docs/quickstart.md) — install and first local RAG flow
- [docs/extras.md](docs/extras.md) — optional dependencies matrix
- [docs/adapters.md](docs/adapters.md) — public adapter namespaces
- [docs/stores.md](docs/stores.md) — in-memory vs Firestore
- [docs/chunkers.md](docs/chunkers.md) — chunker selection guide
- [docs/operations.md](docs/operations.md) — ingest / query / generate / generate_stream
- [docs/environments.md](docs/environments.md) — local, OpenAI, Ollama, Firestore setup
- [docs/limitations.md](docs/limitations.md) — beta constraints

## Examples

Runnable scripts in [`examples/`](examples/):

- `examples/local_memory.py` — zero-service, local adapters
- `examples/openai_memory.py` — OpenAI embedder + in-memory store
- `examples/ollama_memory.py` — Ollama embedder + in-memory store
- `examples/openai_firestore.py` — OpenAI embedder + Firestore store
- `examples/openai_pgvector.py` — OpenAI embedder + PgVectorStore
