recalldb 0.1.0

A multi-user vector database for efficient document storage and retrieval

RecallDB: Multi-User Vector Database

RecallDB is a multi-user vector database that allows independent document access for each user. It supports vector embeddings for semantic search and efficient document retrieval.

Features

• Multi-user support: Each user has their own isolated document space
• Collection-based organization: Group related documents into collections
• Vector search: Use semantic search to find relevant documents
• Metadata filtering: Search and filter using document metadata
• PyArrow storage: Efficient data storage using PyArrow's Arrow IPC format
• Embedding models: Support for various embedding models
• Parallel processing: Asynchronous/batched embeddings for faster processing
• Chunking support: Break large documents into manageable chunks
• Flexible API: Simple interface for document management and retrieval

Installation

pip install recalldb

Quick Start

from recall_db import RecallDB

# Create embedding function
def embedding_function(text):
    # Your embedding logic here
    # Should return a normalized numpy array
    pass

# Initialize RecallDB
db = RecallDB(
    embedding_function=embedding_function,
    storage_path="./recalldb_data"
)

# Add documents for a user
user_id = "user123"
db.add_document(
    user_id=user_id,
    text="This is a sample document about artificial intelligence.",
    collection="ai_docs",
    metadata={"topic": "AI", "type": "introduction"}
)

# Search for documents
results = db.search(
    user_id=user_id,
    query="artificial intelligence concepts",
    collections=["ai_docs"],
    top_k=5
)

# Print results
for result in results:
    print(f"Document ID: {result['id']}")
    print(f"Content: {result['content'][:100]}...")
    print(f"Score: {result['score']}")
    print(f"Metadata: {result['metadata']}")
    print("---")

Core Concepts

Users and Data Isolation

RecallDB maintains strict data isolation between users. Each user has their own separate database instance, ensuring that:

• User A cannot access documents from User B
• Search queries are scoped to the requesting user's documents only
• Each user's data can be independently managed and persisted

Collections

Collections in RecallDB are similar to tables in traditional databases:

• Documents are organized into named collections (e.g., "Articles", "Products", "Notes")
• Collections help organize documents logically
• Search can be restricted to specific collections

Documents and Metadata

Each document in RecallDB consists of:

• Text content that is semantically embedded
• Optional metadata for filtering and organization
• A unique document ID (auto-generated or user-provided)

Performance

In benchmarks using the GIST-1M dataset, RecallDB demonstrated excellent performance:

• Recall@1: 0.99 with ~6ms latency
• Consistent sub-7ms query times
• See benchmark results for details

License

MIT