thoth-vdbmanager 0.2.23

A vector database management module for ThothAI Project

Thoth Vector Database Manager v2.0

A high-performance, Haystack-based vector database manager with support for multiple backends and local embedding capabilities.

🤖 MCP Server Support

This project is configured with MCP (Model Context Protocol) servers for enhanced AI-assisted development:

• Context7: Enhanced context management
• Serena: IDE assistance and development support

See docs/MCP_SETUP.md for details.

🚀 Features

• Multi-backend support: Qdrant, Weaviate, Chroma, PostgreSQL pgvector, Milvus, Pinecone
• Haystack integration: Uses Haystack as an abstraction layer over vector stores
• Local embeddings: Uses open-source Sentence Transformers for local embedding generation
• Memory optimization: Lazy loading and efficient batch processing
• API compatibility: Maintains backward compatibility with existing ThothVectorStore API
• Type safety: Full type hints and Pydantic validation
• Flexible deployment: Multiple modes (memory, filesystem, server) for different use cases
• Production-ready: Comprehensive testing and robust error handling

📦 Installation

⚠️ IMPORTANT: Backend Compatibility

Weaviate and Milvus cannot be installed together due to conflicting gRPC requirements. Choose one of the following installation options:

Option 1: Milvus Configuration (Recommended)

# Basic installation with Milvus support
pip install thoth-vdbmanager[milvus]

# All backends except Weaviate (includes Milvus)
pip install thoth-vdbmanager[all]

Option 2: Weaviate Configuration

# Basic installation with Weaviate support
pip install thoth-vdbmanager[weaviate]

# All backends except Milvus (includes Weaviate)
pip install thoth-vdbmanager[all-with-weaviate]

Option 3: Safe Backends Only

# No gRPC conflicts (Qdrant, Chroma, pgvector, Pinecone)
pip install thoth-vdbmanager[all-safe]

# Individual backends
pip install thoth-vdbmanager[qdrant]
pip install thoth-vdbmanager[chroma]
pip install thoth-vdbmanager[pgvector]
pip install thoth-vdbmanager[pinecone]

📖 For detailed compatibility information, see Backend Compatibility Guide

🏗️ Architecture

The library is built on a clean architecture with:

• Core: Base interfaces and document types
• Adapters: Backend-specific implementations using Haystack
• Factory: Unified creation interface
• Compatibility: Legacy API support

🚀 Quick Start

New API (Recommended)

from vdbmanager import VectorStoreFactory, ColumnNameDocument, SqlDocument, HintDocument

# Create a vector store
store = VectorStoreFactory.create(
    backend="qdrant",
    collection="my_collection",
    host="localhost",
    port=6333
)

# Add documents
column_doc = ColumnNameDocument(
    table_name="users",
    column_name="email",
    original_column_name="user_email",
    column_description="User email address",
    value_description="Valid email format"
)

doc_id = store.add_column_description(column_doc)

# Search documents
results = store.search_similar(
    query="user email",
    doc_type="column_name",
    top_k=5
)

Legacy API (Backward Compatible)

from vdbmanager import ThothVectorStore

# Works exactly like before
store = ThothVectorStore(
    backend="qdrant",
    collection="my_collection",
    host="localhost",
    port=6333
)

# All existing methods work
doc_id = store.add_column_description(column_doc)
results = store.search_similar("user email", "column_name")

🔧 Configuration

Qdrant

store = VectorStoreFactory.create(
    backend="qdrant",
    collection="my_collection",
    host="localhost",
    port=6333,
    api_key="your-api-key",  # Optional
    embedding_dim=384,  # Optional
    hnsw_config={"m": 16, "ef_construct": 100}
)

Weaviate (Production-Ready with Docker)

Docker Setup (Recommended):

store = VectorStoreFactory.create(
    backend="weaviate",
    collection="MyCollection",
    url="http://localhost:8080",
    use_docker=True,
    docker_compose_file="docker-compose-weaviate.yml"
)

Manual Configuration:

store = VectorStoreFactory.create(
    backend="weaviate",
    collection="MyCollection",
    url="http://localhost:8080",
    timeout=30,
    skip_init_checks=False,  # Set to True if gRPC issues
    api_key="your-api-key"  # Optional
)

📖 See Weaviate Configuration Guide for detailed setup instructions

Chroma (Multiple Modes)

Memory Mode (Recommended for Testing):

store = VectorStoreFactory.create(
    backend="chroma",
    collection="my_collection",
    mode="memory"  # Fast, isolated, no persistence
)

Filesystem Mode:

store = VectorStoreFactory.create(
    backend="chroma",
    collection="my_collection",
    mode="filesystem",
    persist_path="./chroma_db"
)

Server Mode (Production):

store = VectorStoreFactory.create(
    backend="chroma",
    collection="my_collection",
    mode="server",
    host="localhost",
    port=8000
)

📖 See Chroma Configuration Guide for detailed setup instructions

PostgreSQL pgvector

store = VectorStoreFactory.create(
    backend="pgvector",
    collection="my_table",
    connection_string="postgresql://user:pass@localhost:5432/dbname"
)

Milvus (Multiple Modes)

Lite Mode (Recommended for Testing):

store = VectorStoreFactory.create(
    backend="milvus",
    collection="my_collection",
    mode="lite",
    connection_uri="./milvus.db"  # File-based storage
)

Server Mode (Production):

store = VectorStoreFactory.create(
    backend="milvus",
    collection="my_collection",
    mode="server",
    host="localhost",
    port=19530
)

📖 See Milvus Configuration Guide for detailed setup instructions

Pinecone

store = VectorStoreFactory.create(
    backend="pinecone",
    collection="my-index",
    api_key="your-api-key",
    environment="us-west1-gcp-free"
)

📊 Performance Optimizations

Memory Usage

• Lazy initialization: Embedders and connections are initialized on first use
• Singleton pattern: Same configuration reuses existing instances
• Batch processing: Efficient bulk operations

Performance Tuning

# Optimize for specific use cases
store = VectorStoreFactory.create(
    backend="qdrant",
    collection="optimized",
    embedding_model="sentence-transformers/all-MiniLM-L6-v2",  # 384-dim, fast
    hnsw_config={"m": 32, "ef_construct": 200}  # Better search quality
)

🧪 Testing

# Run all tests
pytest

# Run specific backend tests
pytest tests/test_qdrant.py -v

# Run with coverage
pytest --cov=vdbmanager tests/

📈 Migration Guide

From v1.x to v2.x

Simple Migration

# Old code (v1.x)
from vdbmanager import QdrantHaystackStore

store = QdrantHaystackStore(
    collection="my_docs",
    host="localhost",
    port=6333
)

# New code (v2.x) - fully compatible
from vdbmanager import QdrantHaystackStore  # Still works!

# Or use new API
from vdbmanager import VectorStoreFactory

store = VectorStoreFactory.create(
    backend="qdrant",
    collection="my_docs",
    host="localhost",
    port=6333
)

Advanced Migration

# Old code
from vdbmanager import ThothVectorStore

# New code - same interface, better internals
from vdbmanager import ThothVectorStore  # Still works with warnings

# Recommended new approach
from vdbmanager import QdrantAdapter

store = QdrantAdapter(
    collection="my_docs",
    host="localhost",
    port=6333
)

🔍 API Reference

Core Classes

VectorStoreFactory

# Create store
store = VectorStoreFactory.create(backend, collection, **kwargs)

# From config
config = {"backend": "qdrant", "params": {...}}
store = VectorStoreFactory.from_config(config)

# List backends
backends = VectorStoreFactory.list_backends()

Document Types

• ColumnNameDocument: Column metadata
• SqlDocument: SQL examples
• HintDocument: General hints

Methods

• add_column_description(doc): Add column metadata
• add_sql(doc): Add SQL example
• add_hint(doc): Add hint
• search_similar(query, doc_type, top_k=5, score_threshold=0.7): Semantic search
• get_document(doc_id): Retrieve by ID
• bulk_add_documents(docs): Batch insert
• get_collection_info(): Get stats

🐛 Troubleshooting

Common Issues

Connection Errors

# Check service availability
import requests
requests.get("http://localhost:6333")  # Qdrant

Memory Issues

# Use smaller embedding model
store = VectorStoreFactory.create(
    backend="qdrant",
    collection="my_collection",
    embedding_model="sentence-transformers/all-MiniLM-L6-v2"  # 384-dim
)

Performance Issues

# Tune HNSW parameters
store = VectorStoreFactory.create(
    backend="qdrant",
    collection="my_collection",
    hnsw_config={"m": 16, "ef_construct": 100}
)

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

📄 License

MIT License - see LICENSE file for details.

Directory structure

vdbmanager/ ├── core/ # Base interfaces and document types │ ├── base.py # Core document classes and interfaces │ └── init.py ├── adapters/ # Backend-specific implementations │ ├── haystack_adapter.py # Base Haystack adapter │ ├── qdrant_adapter.py # Qdrant implementation │ ├── weaviate_adapter.py # Weaviate implementation │ ├── chroma_adapter.py # Chroma implementation │ ├── pgvector_adapter.py # PostgreSQL pgvector │ ├── milvus_adapter.py # Milvus implementation │ └── pinecone_adapter.py # Pinecone implementation ├── factory.py # Unified creation interface ├── compat/ # Legacy compatibility layer │ ├── init.py │ └── thoth_vector_store.py └── init.py # Public API exports

NewAPI (reccomended)

from vdbmanager import VectorStoreFactory, ColumnNameDocument

Create any backend

store = VectorStoreFactory.create( backend="qdrant", collection="my_docs", host="localhost", port=6333 )

Use optimized methods

doc_id = store.add_column_description(column_doc) results = store.search_similar("user email", "column_name")

Old API (Fully compatible)

from vdbmanager import ThothVectorStore # Works with warnings

Existing code continues to work

store = ThothVectorStore( backend="qdrant", collection="my_docs", host="localhost", port=6333 )