53d37e61dd
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>
73 lines
2.0 KiB
Python
73 lines
2.0 KiB
Python
#!/usr/bin/env python3
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"""Query FAISS index"""
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import json, sys, os
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import numpy as np
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try:
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import faiss
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from openai import OpenAI
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from rich.console import Console
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from rich.table import Table
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except ImportError:
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print("❌ Run: pip install -r requirements.txt")
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sys.exit(1)
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console = Console()
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# Load index and metadata
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console.print("📥 Loading FAISS index...")
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index = faiss.read_index("flask.index")
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with open("flask_metadata.json") as f:
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data = json.load(f)
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console.print(f"✅ Loaded {index.ntotal} vectors")
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# Initialize OpenAI
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client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
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def search(query_text: str, k: int = 5):
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"""Search FAISS index"""
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console.print(f"\n[yellow]Query:[/yellow] {query_text}")
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# Generate query embedding
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response = client.embeddings.create(
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model="text-embedding-ada-002",
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input=query_text
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)
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query_vector = np.array([response.data[0].embedding]).astype('float32')
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# Search
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distances, indices = index.search(query_vector, k)
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# Display results
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table = Table(show_header=True, header_style="bold magenta")
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table.add_column("#", width=3)
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table.add_column("Distance", width=10)
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table.add_column("Category", width=12)
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table.add_column("Content Preview")
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for i, (dist, idx) in enumerate(zip(distances[0], indices[0]), 1):
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doc = data["documents"][idx]
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meta = data["metadatas"][idx]
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preview = doc[:80] + "..." if len(doc) > 80 else doc
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table.add_row(
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str(i),
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f"{dist:.2f}",
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meta.get("category", "N/A"),
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preview
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)
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console.print(table)
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console.print("[dim]💡 Distance: Lower = more similar[/dim]")
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# Example queries
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console.print("[bold green]FAISS Query Examples[/bold green]\n")
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search("How do I create a Flask route?", k=3)
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search("database models and ORM", k=3)
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search("authentication and security", k=3)
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console.print("\n✅ All examples completed!")
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