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Vector Database Toolkit

$39

Setup guides for Pinecone, Weaviate, ChromaDB, and pgvector with indexing strategies, hybrid search, and benchmarking scripts.

📁 31 files
MarkdownPythonDockerPostgreSQLLLM

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📁 File Structure 31 files

vector-database-toolkit/ ├── LICENSE ├── README.md ├── benchmarks/ │ ├── __pycache__/ │ │ └── harness.cpython-312.pyc │ └── harness.py ├── free-sample.zip ├── guide/ │ └── 01-indexing-strategies.md ├── guides/ │ └── indexing-strategies.md ├── index.html ├── requirements.txt ├── src/ │ ├── __init__.py │ ├── __pycache__/ │ │ ├── __init__.cpython-312.pyc │ │ ├── interface.cpython-312.pyc │ │ └── migration.cpython-312.pyc │ ├── adapters/ │ │ ├── __init__.py │ │ ├── __pycache__/ │ │ │ ├── __init__.cpython-312.pyc │ │ │ ├── chroma_adapter.cpython-312.pyc │ │ │ ├── memory_adapter.cpython-312.pyc │ │ │ ├── pgvector_adapter.cpython-312.pyc │ │ │ ├── pinecone_adapter.cpython-312.pyc │ │ │ └── weaviate_adapter.cpython-312.pyc │ │ ├── chroma_adapter.py │ │ ├── memory_adapter.py │ │ ├── pgvector_adapter.py │ │ ├── pinecone_adapter.py │ │ └── weaviate_adapter.py │ ├── interface.py │ └── migration.py └── tests/ ├── __pycache__/ │ ├── conftest.cpython-312.pyc │ └── test_adapters.cpython-312.pyc ├── conftest.py └── test_adapters.py

📖 Documentation Preview README excerpt

Vector Database Toolkit

Unified interface, per-backend adapters, benchmarking harness, and migration tools for Pinecone, Weaviate, ChromaDB, and pgvector.

Stop rewriting vector database code when you switch backends. One interface, four adapters, a benchmark suite that runs without external services, and migration scripts to move data between any two backends.

What You Get

  • Common VectorStore interface — write backend-agnostic code that works with any vector database
  • Four production adapters — Pinecone (serverless + pods), Weaviate (native hybrid search), ChromaDB (zero-config), pgvector (PostgreSQL)
  • In-memory reference adapter — full brute-force + BM25 implementation for testing and benchmarks, zero external deps
  • Benchmarking harness — measure insert throughput, search latency at multiple top-k values, and recall@10 against brute-force ground truth
  • Migration tool — move vectors between any two backends with progress tracking and error handling
  • Indexing strategy guide — HNSW vs IVFFlat parameter tuning with per-backend configuration examples

File Tree


vector-database-toolkit/
├── README.md
├── LICENSE
├── requirements.txt
├── src/
│   ├── __init__.py
│   ├── interface.py                      # Common VectorStore abstract interface
│   ├── migration.py                      # Cross-backend migration tool
│   └── adapters/
│       ├── __init__.py
│       ├── memory_adapter.py             # In-memory adapter (BM25 + cosine, no deps)
│       ├── pinecone_adapter.py           # Pinecone serverless/pod adapter
│       ├── weaviate_adapter.py           # Weaviate adapter (native hybrid search)
│       ├── chroma_adapter.py             # ChromaDB adapter
│       └── pgvector_adapter.py           # PostgreSQL + pgvector adapter
├── benchmarks/
│   └── harness.py                        # Latency/recall benchmarking harness
├── tests/
│   ├── conftest.py
│   └── test_adapters.py                  # CRUD, search, filter, migration tests
└── guides/
    └── indexing-strategies.md            # HNSW/IVF tuning guide

Quick Start

1. Install


pip install -r requirements.txt
# Or install only what you need:
pip install pinecone-client    # for Pinecone
pip install chromadb           # for ChromaDB
pip install weaviate-client    # for Weaviate
pip install psycopg2-binary   # for pgvector

2. Use the Common Interface



*... continues with setup instructions, usage examples, and more.*

📄 Code Sample .py preview

src/interface.py """ Common VectorStore Interface — Vector Database Toolkit ======================================================= Defines the abstract contract that all vector database adapters implement. Use this interface to write backend-agnostic code that can swap between Pinecone, Weaviate, ChromaDB, pgvector, or the included in-memory implementation. By Datanest Digital — support@datanest.dev """ from __future__ import annotations import logging from abc import ABC, abstractmethod from dataclasses import dataclass, field from typing import Any, Dict, List, Optional logger = logging.getLogger(__name__) @dataclass class VectorRecord: """A record stored in the vector database. Attributes: id: Unique identifier for this vector. vector: The dense embedding vector. metadata: Arbitrary key-value metadata attached to the vector. Use this for filtering, source tracking, and hybrid search. text: Optional original text that was embedded. Stored alongside the vector for retrieval convenience. """ id: str = "" vector: List[float] = field(default_factory=list) metadata: Dict[str, Any] = field(default_factory=dict) text: str = "" @dataclass class SearchResult: """A single search hit from a vector database query. Attributes: record: The matched vector record. score: Relevance score (higher is better). The scale depends on the backend and distance metric — cosine similarity returns [0, 1], L2 distance returns non-negative values where lower is better (inverted before returning). distance_metric: Which metric produced this score. # ... 141 more lines ...
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