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>
83 lines
2.3 KiB
Python
83 lines
2.3 KiB
Python
#!/usr/bin/env python3
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"""Query Qdrant (demonstrates filtering without vectors)"""
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import argparse
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try:
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from qdrant_client import QdrantClient
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from qdrant_client.models import Filter, FieldCondition, MatchValue
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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 qdrant-client rich")
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exit(1)
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console = Console()
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parser = argparse.ArgumentParser()
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parser.add_argument("--url", default="http://localhost:6333")
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args = parser.parse_args()
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console.print("[bold green]Qdrant Query Examples[/bold green]")
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console.print(f"[dim]Connected to: {args.url}[/dim]\n")
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# Connect
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client = QdrantClient(url=args.url)
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collection_name = "django"
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# Example 1: Scroll (get all) with filter
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console.print("[bold cyan]Example 1: Filter by Category[/bold cyan]\n")
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result = client.scroll(
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collection_name=collection_name,
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scroll_filter=Filter(
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must=[
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FieldCondition(
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key="category",
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match=MatchValue(value="api")
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)
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]
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),
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limit=5
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)
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points = result[0]
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table = Table(show_header=True, header_style="bold magenta")
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table.add_column("ID")
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table.add_column("Category")
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table.add_column("File")
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table.add_column("Content Preview")
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for point in points:
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preview = point.payload["content"][:60] + "..."
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table.add_row(
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str(point.id)[:8] + "...",
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point.payload["category"],
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point.payload["file"],
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preview
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)
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console.print(table)
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# Example 2: Complex filter (AND condition)
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console.print("\n[bold cyan]Example 2: Complex Filter (AND)[/bold cyan]\n")
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result = client.scroll(
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collection_name=collection_name,
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scroll_filter=Filter(
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must=[
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FieldCondition(key="category", match=MatchValue(value="guides")),
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FieldCondition(key="type", match=MatchValue(value="reference"))
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]
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),
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limit=3
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)
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console.print(f"[green]Found {len(result[0])} points matching both conditions:[/green]\n")
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for i, point in enumerate(result[0], 1):
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console.print(f"[bold]{i}. {point.payload['file']}[/bold]")
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console.print(f" {point.payload['content'][:100]}...\n")
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console.print("✅ Query examples completed!")
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console.print("\n[yellow]💡 Note:[/yellow] For vector search, add embeddings to points!")
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