docs: Add 4 comprehensive vector database examples (Weaviate, Chroma, FAISS, Qdrant)

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>
2026-02-07 22:38:15 +03:00
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+# Weaviate Vector Database Example
+
+This example demonstrates how to use Skill Seekers with Weaviate, a powerful vector database with hybrid search capabilities (keyword + semantic).
+
+## What You'll Learn
+
+- How to generate skills in Weaviate format
+- How to create a Weaviate schema and upload data
+- How to perform hybrid searches (keyword + vector)
+- How to filter by metadata categories
+
+## Prerequisites
+
+### 1. Weaviate Instance
+
+**Option A: Weaviate Cloud (Recommended for production)**
+- Sign up at https://console.weaviate.cloud/
+- Create a free sandbox cluster
+- Get your cluster URL and API key
+
+**Option B: Local Docker (Recommended for development)**
+```bash
+docker run -d \
+  --name weaviate \
+  -p 8080:8080 \
+  -e AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true \
+  -e PERSISTENCE_DATA_PATH=/var/lib/weaviate \
+  semitechnologies/weaviate:latest
+```
+
+### 2. Python Dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+## Step-by-Step Guide
+
+### Step 1: Generate Skill from Documentation
+
+First, we'll scrape React documentation and package it for Weaviate:
+
+```bash
+python 1_generate_skill.py
+```
+
+This script will:
+1. Scrape React docs (limited to 20 pages for demo)
+2. Package the skill in Weaviate format (JSON with schema + objects)
+3. Save to `sample_output/react-weaviate.json`
+
+**Expected Output:**
+```
+✅ Weaviate data packaged successfully!
+📦 Output: output/react-weaviate.json
+📊 Total objects: 21
+📂 Categories: overview (1), guides (8), api (12)
+```
+
+**What's in the JSON?**
+```json
+{
+  "schema": {
+    "class": "React",
+    "description": "React documentation skill",
+    "properties": [
+      {"name": "content", "dataType": ["text"]},
+      {"name": "source", "dataType": ["text"]},
+      {"name": "category", "dataType": ["text"]},
+      ...
+    ]
+  },
+  "objects": [
+    {
+      "id": "uuid-here",
+      "properties": {
+        "content": "React is a JavaScript library...",
+        "source": "react",
+        "category": "overview",
+        ...
+      }
+    }
+  ],
+  "class_name": "React"
+}
+```
+
+### Step 2: Upload to Weaviate
+
+Now we'll create the schema and upload all objects to Weaviate:
+
+```bash
+python 2_upload_to_weaviate.py
+```
+
+**For local Docker:**
+```bash
+python 2_upload_to_weaviate.py --url http://localhost:8080
+```
+
+**For Weaviate Cloud:**
+```bash
+python 2_upload_to_weaviate.py \
+  --url https://your-cluster.weaviate.network \
+  --api-key YOUR_API_KEY
+```
+
+This script will:
+1. Connect to your Weaviate instance
+2. Create the schema (class + properties)
+3. Batch upload all objects
+4. Verify the upload was successful
+
+**Expected Output:**
+```
+🔗 Connecting to Weaviate at http://localhost:8080...
+✅ Weaviate is ready!
+
+📊 Creating schema: React
+✅ Schema created successfully!
+
+📤 Uploading 21 objects in batches...
+✅ Batch 1/1 uploaded (21 objects)
+
+✅ Successfully uploaded 21 documents to Weaviate
+🔍 Class 'React' now contains 21 objects
+```
+
+### Step 3: Query and Search
+
+Now the fun part - querying your knowledge base!
+
+```bash
+python 3_query_example.py
+```
+
+**For local Docker:**
+```bash
+python 3_query_example.py --url http://localhost:8080
+```
+
+**For Weaviate Cloud:**
+```bash
+python 3_query_example.py \
+  --url https://your-cluster.weaviate.network \
+  --api-key YOUR_API_KEY
+```
+
+This script demonstrates:
+1. **Keyword Search**: Traditional text search
+2. **Hybrid Search**: Combines keyword + vector similarity
+3. **Metadata Filtering**: Filter by category
+4. **Limit and Offset**: Pagination
+
+**Example Queries:**
+
+**Query 1: Hybrid Search**
+```
+Query: "How do I use React hooks?"
+Alpha: 0.5 (50% keyword, 50% vector)
+
+Results:
+1. Category: api
+   Snippet: Hooks are functions that let you "hook into" React state and lifecycle...
+
+2. Category: guides
+   Snippet: To use a Hook, you need to call it at the top level of your component...
+```
+
+**Query 2: Filter by Category**
+```
+Query: API reference
+Category: api
+
+Results:
+1. useState Hook - Manage component state
+2. useEffect Hook - Perform side effects
+3. useContext Hook - Access context values
+```
+
+## Understanding Weaviate Features
+
+### Hybrid Search (`alpha` parameter)
+
+Weaviate's killer feature is hybrid search, which combines:
+- **Keyword Search (BM25)**: Traditional text matching
+- **Vector Search (ANN)**: Semantic similarity
+
+Control the balance with `alpha`:
+- `alpha=0`: Pure keyword search (BM25 only)
+- `alpha=0.5`: Balanced (default - recommended)
+- `alpha=1`: Pure vector search (semantic only)
+
+**When to use what:**
+- **Exact terms** (API names, error messages): `alpha=0` to `alpha=0.3`
+- **Concepts** (how to do X, why does Y): `alpha=0.7` to `alpha=1`
+- **General queries**: `alpha=0.5` (balanced)
+
+### Metadata Filtering
+
+Filter results by any property:
+```python
+.with_where({
+    "path": ["category"],
+    "operator": "Equal",
+    "valueText": "api"
+})
+```
+
+Supported operators:
+- `Equal`, `NotEqual`
+- `GreaterThan`, `LessThan`
+- `And`, `Or`, `Not`
+
+### Schema Design
+
+Our schema includes:
+- **content**: The actual documentation text (vectorized)
+- **source**: Skill name (e.g., "react")
+- **category**: Document category (e.g., "api", "guides")
+- **file**: Source file name
+- **type**: Document type ("overview" or "reference")
+- **version**: Skill version
+
+## Customization
+
+### Generate Your Own Skill
+
+Want to use a different documentation source? Easy:
+
+```python
+# 1_generate_skill.py (modify line 10)
+"--config", "configs/vue.json",  # Change to your config
+```
+
+Or scrape from scratch:
+```bash
+skill-seekers scrape --config configs/your_framework.json
+skill-seekers package output/your_framework --target weaviate
+```
+
+### Adjust Search Parameters
+
+In `3_query_example.py`, modify:
+```python
+# Adjust hybrid search balance
+alpha=0.7  # More semantic, less keyword
+
+# Adjust result count
+.with_limit(10)  # Get more results
+
+# Add more filters
+.with_where({
+    "operator": "And",
+    "operands": [
+        {"path": ["category"], "operator": "Equal", "valueText": "api"},
+        {"path": ["type"], "operator": "Equal", "valueText": "reference"}
+    ]
+})
+```
+
+## Troubleshooting
+
+### Connection Refused
+```
+Error: Connection refused to http://localhost:8080
+```
+
+**Solution:** Ensure Weaviate is running:
+```bash
+docker ps | grep weaviate
+# If not running, start it:
+docker start weaviate
+```
+
+### Schema Already Exists
+```
+Error: Class 'React' already exists
+```
+
+**Solution:** Delete the existing class:
+```bash
+# In Python or using Weaviate API
+client.schema.delete_class("React")
+```
+
+Or use the example's built-in reset:
+```bash
+python 2_upload_to_weaviate.py --reset
+```
+
+### Empty Results
+```
+Query returned 0 results
+```
+
+**Possible causes:**
+1. **No embeddings**: Weaviate needs a vectorizer configured (we use default)
+2. **Wrong class name**: Check the class name matches
+3. **Data not uploaded**: Verify with `client.query.aggregate("React").with_meta_count().do()`
+
+**Solution:** Check object count:
+```python
+result = client.query.aggregate("React").with_meta_count().do()
+print(result)  # Should show {"data": {"Aggregate": {"React": [{"meta": {"count": 21}}]}}}
+```
+
+## Next Steps
+
+1. **Try other skills**: Generate skills for your favorite frameworks
+2. **Production deployment**: Use Weaviate Cloud for scalability
+3. **Add custom vectorizers**: Use OpenAI, Cohere, or local models
+4. **Build RAG apps**: Integrate with LangChain or LlamaIndex
+
+## Resources
+
+- **Weaviate Docs**: https://weaviate.io/developers/weaviate
+- **Hybrid Search**: https://weaviate.io/developers/weaviate/search/hybrid
+- **Python Client**: https://weaviate.io/developers/weaviate/client-libraries/python
+- **Skill Seekers Docs**: https://github.com/yourusername/skill-seekers
+
+## File Structure
+
+```
+weaviate-example/
+├── README.md                      # This file
+├── requirements.txt               # Python dependencies
+├── 1_generate_skill.py            # Generate Weaviate-format skill
+├── 2_upload_to_weaviate.py        # Upload to Weaviate instance
+├── 3_query_example.py             # Query demonstrations
+└── sample_output/                 # Example outputs
+    ├── react-weaviate.json        # Generated skill (21 objects)
+    └── query_results.txt          # Sample query results
+```
+
+---
+
+**Last Updated:** February 2026
+**Tested With:** Weaviate v1.25.0, Python 3.10+, skill-seekers v2.10.0