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>
282 lines
8.9 KiB
Python
282 lines
8.9 KiB
Python
#!/usr/bin/env python3
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"""
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Step 3: Query Weaviate
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This script demonstrates various query patterns with Weaviate:
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1. Hybrid search (keyword + vector)
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2. Metadata filtering
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3. Limit and pagination
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Usage:
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# Local Docker
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python 3_query_example.py
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# Weaviate Cloud
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python 3_query_example.py --url https://your-cluster.weaviate.network --api-key YOUR_KEY
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"""
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import argparse
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import sys
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try:
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import weaviate
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from weaviate.auth import AuthApiKey
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from rich.console import Console
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from rich.table import Table
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from rich.panel import Panel
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except ImportError:
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print("❌ Missing dependencies!")
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print("Install with: pip install weaviate-client rich")
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sys.exit(1)
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console = Console()
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def connect_to_weaviate(url: str, api_key: str = None):
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"""Connect to Weaviate instance."""
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try:
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if api_key:
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auth_config = AuthApiKey(api_key)
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client = weaviate.Client(url=url, auth_client_secret=auth_config)
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else:
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client = weaviate.Client(url=url)
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if client.is_ready():
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return client
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else:
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console.print("[red]❌ Weaviate is not ready[/red]")
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sys.exit(1)
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except Exception as e:
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console.print(f"[red]❌ Connection failed: {e}[/red]")
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sys.exit(1)
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def hybrid_search_example(client, class_name: str = "React"):
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"""Example 1: Hybrid Search (keyword + vector)."""
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console.print("\n" + "=" * 60)
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console.print("[bold cyan]Example 1: Hybrid Search[/bold cyan]")
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console.print("=" * 60)
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query = "How do I use React hooks?"
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alpha = 0.5 # 50% keyword, 50% vector
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console.print(f"\n[yellow]Query:[/yellow] {query}")
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console.print(f"[yellow]Alpha:[/yellow] {alpha} (0=keyword only, 1=vector only)")
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try:
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result = (
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client.query.get(class_name, ["content", "source", "category", "file"])
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.with_hybrid(query=query, alpha=alpha)
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.with_limit(3)
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.do()
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)
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objects = result["data"]["Get"][class_name]
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if not objects:
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console.print("[red]No results found[/red]")
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return
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# Create results table
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table = Table(show_header=True, header_style="bold magenta")
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table.add_column("#", style="dim", width=3)
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table.add_column("Category", style="cyan")
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table.add_column("File", style="green")
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table.add_column("Content Preview", style="white")
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for i, obj in enumerate(objects, 1):
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content_preview = obj["content"][:100] + "..." if len(obj["content"]) > 100 else obj["content"]
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table.add_row(
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str(i),
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obj["category"],
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obj["file"],
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content_preview
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)
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console.print(table)
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except Exception as e:
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console.print(f"[red]Query failed: {e}[/red]")
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def keyword_only_search(client, class_name: str = "React"):
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"""Example 2: Keyword-Only Search (alpha=0)."""
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console.print("\n" + "=" * 60)
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console.print("[bold cyan]Example 2: Keyword-Only Search[/bold cyan]")
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console.print("=" * 60)
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query = "useState Hook"
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alpha = 0 # Pure keyword search
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console.print(f"\n[yellow]Query:[/yellow] {query}")
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console.print(f"[yellow]Alpha:[/yellow] {alpha} (pure keyword/BM25)")
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try:
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result = (
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client.query.get(class_name, ["content", "category", "file"])
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.with_hybrid(query=query, alpha=alpha)
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.with_limit(3)
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.do()
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)
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objects = result["data"]["Get"][class_name]
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for i, obj in enumerate(objects, 1):
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panel = Panel(
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f"[cyan]Category:[/cyan] {obj['category']}\n"
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f"[cyan]File:[/cyan] {obj['file']}\n\n"
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f"[white]{obj['content'][:200]}...[/white]",
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title=f"Result {i}",
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border_style="green"
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)
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console.print(panel)
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except Exception as e:
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console.print(f"[red]Query failed: {e}[/red]")
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def filtered_search(client, class_name: str = "React"):
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"""Example 3: Search with Metadata Filter."""
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console.print("\n" + "=" * 60)
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console.print("[bold cyan]Example 3: Filtered Search[/bold cyan]")
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console.print("=" * 60)
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query = "component"
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category_filter = "api"
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console.print(f"\n[yellow]Query:[/yellow] {query}")
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console.print(f"[yellow]Filter:[/yellow] category = '{category_filter}'")
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try:
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result = (
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client.query.get(class_name, ["content", "category", "file"])
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.with_hybrid(query=query, alpha=0.5)
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.with_where({
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"path": ["category"],
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"operator": "Equal",
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"valueText": category_filter
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})
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.with_limit(5)
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.do()
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)
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objects = result["data"]["Get"][class_name]
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if not objects:
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console.print("[red]No results found[/red]")
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return
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console.print(f"\n[green]Found {len(objects)} results in '{category_filter}' category:[/green]\n")
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for i, obj in enumerate(objects, 1):
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console.print(f"[bold]{i}. {obj['file']}[/bold]")
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console.print(f" {obj['content'][:150]}...\n")
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except Exception as e:
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console.print(f"[red]Query failed: {e}[/red]")
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def semantic_search(client, class_name: str = "React"):
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"""Example 4: Pure Semantic Search (alpha=1)."""
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console.print("\n" + "=" * 60)
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console.print("[bold cyan]Example 4: Semantic Search[/bold cyan]")
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console.print("=" * 60)
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query = "managing application state" # Conceptual query
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alpha = 1 # Pure vector/semantic search
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console.print(f"\n[yellow]Query:[/yellow] {query}")
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console.print(f"[yellow]Alpha:[/yellow] {alpha} (pure semantic/vector)")
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try:
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result = (
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client.query.get(class_name, ["content", "category", "file"])
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.with_hybrid(query=query, alpha=alpha)
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.with_limit(3)
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.do()
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)
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objects = result["data"]["Get"][class_name]
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for i, obj in enumerate(objects, 1):
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console.print(f"\n[bold green]Result {i}:[/bold green]")
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console.print(f"[cyan]Category:[/cyan] {obj['category']}")
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console.print(f"[cyan]File:[/cyan] {obj['file']}")
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console.print(f"[white]{obj['content'][:200]}...[/white]")
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except Exception as e:
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console.print(f"[red]Query failed: {e}[/red]")
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def get_statistics(client, class_name: str = "React"):
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"""Show database statistics."""
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console.print("\n" + "=" * 60)
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console.print("[bold cyan]Database Statistics[/bold cyan]")
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console.print("=" * 60)
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try:
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# Total count
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result = client.query.aggregate(class_name).with_meta_count().do()
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total_count = result["data"]["Aggregate"][class_name][0]["meta"]["count"]
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console.print(f"\n[green]Total objects:[/green] {total_count}")
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# Count by category
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result = (
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client.query.aggregate(class_name)
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.with_group_by_filter(["category"])
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.with_meta_count()
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.do()
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)
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groups = result["data"]["Aggregate"][class_name]
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console.print(f"\n[green]Objects by category:[/green]")
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for group in groups:
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category = group["groupedBy"]["value"]
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count = group["meta"]["count"]
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console.print(f" • {category}: {count}")
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except Exception as e:
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console.print(f"[red]Statistics failed: {e}[/red]")
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def main():
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parser = argparse.ArgumentParser(description="Query Weaviate examples")
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parser.add_argument(
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"--url",
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default="http://localhost:8080",
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help="Weaviate URL (default: http://localhost:8080)"
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)
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parser.add_argument(
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"--api-key",
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help="Weaviate API key (for cloud instances)"
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)
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parser.add_argument(
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"--class",
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dest="class_name",
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default="React",
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help="Class name to query (default: React)"
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)
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args = parser.parse_args()
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console.print("[bold green]Weaviate Query Examples[/bold green]")
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console.print(f"[dim]Connected to: {args.url}[/dim]")
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# Connect
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client = connect_to_weaviate(args.url, args.api_key)
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# Get statistics
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get_statistics(client, args.class_name)
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# Run examples
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hybrid_search_example(client, args.class_name)
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keyword_only_search(client, args.class_name)
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filtered_search(client, args.class_name)
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semantic_search(client, args.class_name)
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console.print("\n[bold green]✅ All examples completed![/bold green]")
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console.print("\n[cyan]💡 Tips:[/cyan]")
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console.print(" • Adjust 'alpha' to balance keyword vs semantic search")
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console.print(" • Use filters to narrow results by metadata")
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console.print(" • Combine multiple filters with 'And'/'Or' operators")
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console.print(" • See README.md for more customization options")
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if __name__ == "__main__":
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main()
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