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Created complete working examples for all 4 vector databases with RAG adaptors: Weaviate Example: - Comprehensive README with hybrid search guide - 3 Python scripts (generate, upload, query) - Sample outputs and query results - Covers hybrid search, filtering, schema design Chroma Example: - Simple, local-first approach - In-memory and persistent storage options - Semantic search and metadata filtering - Comparison with Weaviate FAISS Example: - Facebook AI Similarity Search integration - OpenAI embeddings generation - Index building and persistence - Performance-focused for scale Qdrant Example: - Advanced filtering capabilities - Production-ready features - Complex query patterns - Rust-based performance Each example includes: - Detailed README with setup and troubleshooting - requirements.txt with dependencies - 3 working Python scripts - Sample outputs directory Total files: 20 (4 examples × 5 files each) Documentation: 4 comprehensive READMEs (~800 lines total) Phase 2 of optional enhancements complete. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
FAISS Vector Database Example
Facebook AI Similarity Search (FAISS) is a library for efficient similarity search of dense vectors. Perfect for large-scale semantic search.
Quick Start
# 1. Install dependencies
pip install -r requirements.txt
# 2. Generate skill
python 1_generate_skill.py
# 3. Build FAISS index (requires OpenAI API key)
export OPENAI_API_KEY=sk-...
python 2_build_faiss_index.py
# 4. Query the index
python 3_query_example.py
What's Different About FAISS?
- No database server: Pure Python library
- Blazing fast: Optimized C++ implementation
- Scales to billions: Efficient for massive datasets
- Requires embeddings: You must generate vectors (we use OpenAI)
Key Features
Generate Embeddings
FAISS doesn't generate embeddings - you must provide them:
from openai import OpenAI
client = OpenAI()
# Generate embedding
response = client.embeddings.create(
model="text-embedding-ada-002",
input="Your text here"
)
embedding = response.data[0].embedding # 1536-dim vector
Build Index
import faiss
import numpy as np
# Create index (L2 distance)
dimension = 1536 # OpenAI ada-002
index = faiss.IndexFlatL2(dimension)
# Add vectors
vectors = np.array(embeddings).astype('float32')
index.add(vectors)
# Save to disk
faiss.write_index(index, "skill.index")
Search
# Load index
index = faiss.read_index("skill.index")
# Query (returns distances + indices)
distances, indices = index.search(query_vector, k=5)
Cost Estimate
OpenAI embeddings: ~$0.10 per 1M tokens
- 20 documents (~10K tokens): < $0.001
- 1000 documents (~500K tokens): ~$0.05
Files Structure
1_generate_skill.py- Package for FAISS2_build_faiss_index.py- Generate embeddings & build index3_query_example.py- Search queries
Resources
- FAISS GitHub: https://github.com/facebookresearch/faiss
- FAISS Wiki: https://github.com/facebookresearch/faiss/wiki
- OpenAI Embeddings: https://platform.openai.com/docs/guides/embeddings
Note: FAISS is best for advanced users who need maximum performance at scale. For simpler use cases, try ChromaDB or Weaviate.