Metadata-Version: 2.4
Name: animuz-core
Version: 0.1.0
Summary: Core shared utilities for Animuz RAG system - LLM clients, pipelines, vector DB, and document ingestion
Author-email: Animuz Team <dev@animuz.com>
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pydantic>=2.0.0
Requires-Dist: python-dotenv>=1.0.0
Requires-Dist: httpx>=0.25.0
Requires-Dist: aiohttp>=3.9.0
Requires-Dist: requests>=2.31.0
Requires-Dist: langchain-text-splitters>=0.0.1
Provides-Extra: openai
Requires-Dist: openai>=1.0.0; extra == "openai"
Provides-Extra: anthropic
Requires-Dist: anthropic>=0.39.0; extra == "anthropic"
Provides-Extra: ollama
Requires-Dist: ollama>=0.1.0; extra == "ollama"
Provides-Extra: qdrant
Requires-Dist: qdrant-client>=1.7.0; extra == "qdrant"
Provides-Extra: aws
Requires-Dist: boto3>=1.28.0; extra == "aws"
Requires-Dist: aiobotocore>=2.7.0; extra == "aws"
Requires-Dist: watchtower>=3.0.0; extra == "aws"
Requires-Dist: sagemaker>=2.200.0; extra == "aws"
Provides-Extra: azure
Requires-Dist: azure-ai-documentintelligence>=1.0.0b2; extra == "azure"
Provides-Extra: ingest
Requires-Dist: unstructured-client>=0.11.0; extra == "ingest"
Requires-Dist: PyMuPDF>=1.23.0; extra == "ingest"
Provides-Extra: fastapi
Requires-Dist: fastapi>=0.104.0; extra == "fastapi"
Provides-Extra: all
Requires-Dist: animuz-core[anthropic,aws,azure,fastapi,ingest,ollama,openai,qdrant]; extra == "all"
Provides-Extra: dev
Requires-Dist: animuz-core[all]; extra == "dev"
Requires-Dist: pytest>=7.4.0; extra == "dev"
Requires-Dist: pytest-asyncio>=0.21.0; extra == "dev"
Requires-Dist: black>=23.0.0; extra == "dev"
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Dynamic: license-file

# animuz-core

Core shared utilities for Animuz RAG (Retrieval-Augmented Generation) system.

## Features

- **LLM Clients**: OpenAI, Anthropic Claude, Ollama
- **RAG Pipelines**: Simple and Agentic RAG implementations
- **Vector Database**: Qdrant integration with hybrid search
- **Embedding Clients**: Multiple embedding providers (Modal, S3, SageMaker)
- **Document Ingestion**: Azure Document Intelligence, Unstructured, custom parsers
- **CloudWatch Logging**: Structured logging with watchtower

## Installation

```bash
pip install animuz-core
```

Or with uv (faster):
```bash
uv pip install animuz-core
```

## Usage

```python
from animuz_core.genai import OpenAIAgentClient
from animuz_core.pipelines import AgenticRAG
from animuz_core.vectordb import QdrantDBClient

# Initialize LLM client
agent = OpenAIAgentClient(tools={})

# Use RAG pipeline
pipeline = AgenticRAG(...)
result = await pipeline.run(query="What is RAG?")
```

## Development

Install in editable mode:
```bash
pip install -e .
```

Run tests:
```bash
pytest tests/
```

## License

MIT
