stache-ai 0.1.1

Personal AI-powered knowledge base with RAG

stache-ai

A Python library for building AI-powered knowledge bases using Retrieval-Augmented Generation (RAG).

Overview

stache-ai provides a pluggable framework for ingesting documents, storing embeddings, and executing semantic search with optional reranking. It includes support for multiple vector databases, LLM providers, embedding models, and document formats.

Installation

Install the core package:

pip install stache-ai

Quick Start

from stache_ai.rag.pipeline import get_pipeline

# Get the pipeline (uses configured providers)
pipeline = get_pipeline()

# Ingest text
result = pipeline.ingest_text(
    text="Your knowledge base content here",
    metadata={"source": "example"}
)
print(f"Created {result['chunks_created']} chunks")

# Search
results = pipeline.query(
    question="What is this about?",
    top_k=5
)
for source in results['sources']:
    print(f"- {source['text'][:100]}...")

Provider Packages

stache-ai uses a provider pattern to support different backends. Install optional provider packages to enable specific functionality:

AWS Providers

pip install "stache-ai[aws]"

Includes:

• stache-ai-s3vectors - Amazon S3 Vectors for semantic search
• stache-ai-dynamodb - Amazon DynamoDB for namespace and document index storage
• stache-ai-bedrock - Amazon Bedrock for LLMs and embeddings

Ollama

pip install "stache-ai[ollama]"

Includes:

• stache-ai-ollama - Ollama for local LLM and embedding models

OpenAI

pip install "stache-ai[openai]"

Includes:

• stache-ai-openai - OpenAI for GPT models and embeddings

Configuration

Configure stache-ai via environment variables or a .env file:

# Vector Database
VECTORDB_PROVIDER=s3vectors
VECTORDB_S3_REGION=us-east-1
VECTORDB_S3_INDEX_NAME=stache

# Embeddings
EMBEDDING_PROVIDER=bedrock
EMBEDDING_MODEL=cohere.embed-english-v3

# Namespaces
NAMESPACE_PROVIDER=dynamodb
NAMESPACE_DYNAMODB_TABLE=stache-namespaces

# LLM
LLM_PROVIDER=bedrock
LLM_MODEL=anthropic.claude-3-5-sonnet-20241022-v2:0

# Optional features
ENABLE_DOCUMENT_INDEX=true
EMBEDDING_AUTO_SPLIT_ENABLED=true

See src/stache_ai/config.py for all available options.

Usage Examples

Document Chunking

from stache_ai.chunking import ChunkingStrategy

# Recursive character-level chunking
chunks = ChunkingStrategy.create(
    strategy="recursive",
    chunk_size=1024,
    chunk_overlap=100
).chunk("Your document text")

for chunk in chunks:
    print(chunk)

Filtering Results

# Search with metadata filter
results = pipeline.query(
    question="API documentation",
    filter={"source": "docs"}
)

Namespace Isolation

# Ingest to a specific namespace
pipeline.ingest_text(
    text="Project A data",
    namespace="project-a"
)

# Search within a namespace
results = pipeline.query(
    question="Find related content",
    namespace="project-a"
)

API Server

Run a FastAPI server for HTTP access:

pip install stache-ai[dev]
python -m stache_ai.api.main

Server exposes endpoints for:

• /api/query - Semantic search
• /api/capture - Text ingestion
• /api/namespaces - Manage namespaces
• /api/documents - List and retrieve documents
• /api/upload - Upload files (PDF, DOCX, etc.)

CLI Tools

Admin CLI (stache-admin)

# Import documents from a directory
stache-import /path/to/documents --namespace my-docs

# List namespaces
stache-admin namespace-list

# View vector statistics
stache-admin vectors stats

User CLI (stache-tools)

For search, ingest, and MCP server, install stache-tools:

pip install stache-tools

# Search
stache search "your query"

# Ingest text
stache ingest -t "your text" -n namespace

Testing

pip install stache-ai[dev]
pytest

Documentation

• GitHub Repository
• Architecture Guide

License

MIT