Using Vector Databases

Vector Databases for AI Applications

When to Use This Skill

Use this skill when implementing:

• RAG (Retrieval-Augmented Generation) systems for AI chatbots
• Semantic search capabilities (meaning-based, not just keyword)
• Recommendation systems based on similarity
• Multi-modal AI (unified search across text, images, audio)
• Document similarity and deduplication
• Question answering over private knowledge bases

Quick Decision Framework

1. Vector Database Selection

START: Choosing a Vector Database

EXISTING INFRASTRUCTURE?
├─ Using PostgreSQL already?
│  └─ pgvector (<10M vectors, tight budget)
│      See: references/pgvector.md
│
└─ No existing vector database?
   │
   ├─ OPERATIONAL PREFERENCE?
   │  │
   │  ├─ Zero-ops managed only
   │  │  └─ Pinecone (fully managed, excellent DX)
   │  │      See: references/pinecone.md
   │  │
   │  └─ Flexible (self-hosted or managed)
   │     │
   │     ├─ SCALE: <100M vectors + complex filtering ⭐
   │     │  └─ Qdrant (RECOMMENDED)
   │     │      • Best metadata filtering
   │     │      • Built-in hybrid search (BM25 + Vector)
   │     │      • Self-host: Docker/K8s
   │     │      • Managed: Qdrant Cloud
   │     │      See: references/qdrant.md
   │     │
   │     ├─ SCALE: >100M vectors + GPU acceleration
   │     │  └─ Milvus / Zilliz Cloud
   │     │      See: references/milvus.md
   │     │
   │     ├─ Embedded / No server
   │     │  └─ LanceDB (serverless, edge deployment)
   │     │
   │     └─ Local prototyping
   │        └─ Chroma (simple API, in-memory)

2. Embedding Model Selection

REQUIREMENTS?

├─ Best quality (cost no object)
│  └─ Voyage AI voyage-3 (1024d)
│      • 9.74% better than OpenAI on MTEB
│      • ~$0.12/1M tokens
│      See: references/embedding-strategies.md
│
├─ Enterprise reliability
│  └─ OpenAI text-embedding-3-large (3072d)
│      • Industry standard
│      • ~$0.13/1M tokens
│      • Maturity shortening: reduce to 256/512/1024d
│
├─ Cost-optimized
│  └─ OpenAI text-embedding-3-small (1536d)
│      • ~$0.02/1M tokens (6x cheaper)
│      • 90-95% of large model performance
│
├─ Multilingual (100+ languages)
│  └─ Cohere embed-v3 (1024d)
│      • ~$0.10/1M tokens
│
└─ Self-hosted / Privacy-critical
   ├─ English: nomic-embed-text-v1.5 (768d, Apache 2.0)
   ├─ Multilingual: BAAI/bge-m3 (1024d, MIT)
   └─ Long docs: jina-embeddings-v2 (768d, 8K context)

Core Concepts

Document Chunking Strategy

Recommended defaults for most RAG systems:

• Chunk size: 512 tokens (not characters)
• Overlap: 50 tokens (10% overlap)

Why these numbers?

• 512 tokens balances context vs. precision
  • Too small (128-256): Fragments concepts, loses context
  • Too large (1024-2048): Dilutes relevance, wastes LLM tokens
• 50 token overlap ensures sentences aren't split mid-context

See references/chunking-patterns.md for advanced strategies by content type.

Hybrid Search (Vector + Keyword)

Hybrid Search = Vector Similarity + BM25 Keyword Matching

User Query: "OAuth refresh token implementation"
           │
    ┌──────┴──────┐
    │             │
Vector Search   Keyword Search
(Semantic)      (BM25)
    │             │
Top 20 docs   Top 20 docs
    │             │
    └──────┬──────┘
           │
   Reciprocal Rank Fusion
   (Merge + Re-rank)
           │
    Final Top 5 Results

Why hybrid matters:

• Vector captures semantic meaning ("OAuth refresh" ≈ "token renewal")
• Keyword ensures exact matches ("refresh_token" literal)
• Combined provides best retrieval quality

See references/hybrid-search.md for implementation details.

Getting Started

Python + Qdrant Example

from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct

# 1. Initialize client
client = QdrantClient("localhost", port=6333)

# 2. Create collection
client.create_collection(
    collection_name="documents",
    vectors_config=VectorParams(size=1024, distance=Distance.COSINE)
)

# 3. Insert documents with embeddings
points = [
    PointStruct(
        id=idx,
        vector=embedding,  # From OpenAI/Voyage/etc
        payload={
            "text": chunk_text,
            "source": "docs/api.md",
            "section": "Authentication"
        }
    )
    for idx, (embedding, chunk_text) in enumerate(chunks)
]
client.upsert(collection_name="documents", points=points)

# 4. Search with metadata filtering
results = client.search(
    collection_name="documents",
    query_vector=query_embedding,
    limit=5,
    query_filter={
        "must": [
            {"key": "section", "match": {"value": "Authentication"}}
        ]
    }
)

For complete examples, see examples/qdrant-python/.

TypeScript + Qdrant Example

import { QdrantClient } from '@qdrant/js-client-rest';

const client = new QdrantClient({ url: 'http://localhost:6333' });

// Create collection
await client.createCollection('documents', {
  vectors: { size: 1024, distance: 'Cosine' }
});

// Insert documents
await client.upsert('documents', {
  points: chunks.map((chunk, idx) => ({
    id: idx,
    vector: chunk.embedding,
    payload: {
      text: chunk.text,
      source: chunk.source
    }
  }))
});

// Search
const results = await client.search('documents', {
  vector: queryEmbedding,
  limit: 5,
  filter: {
    must: [
      { key: 'source', match: { value: 'docs/api.md' } }
    ]
  }
});

For complete examples, see examples/typescript-rag/.

RAG Pipeline Architecture

Complete Pipeline Components

1. INGESTION
   ├─ Document Loading (PDF, web, code, Office)
   ├─ Text Extraction & Cleaning
   ├─ Chunking (semantic, recursive, code-aware)
   └─ Embedding Generation (batch, rate-limited)

2. INDEXING
   ├─ Vector Store Insertion (batch upsert)
   ├─ Index Configuration (HNSW, distance metric)
   └─ Keyword Index (BM25 for hybrid search)

3. RETRIEVAL (Query Time)
   ├─ Query Processing (expansion, embedding)
   ├─ Hybrid Search (vector + keyword)
   ├─ Filtering & Post-Processing (metadata, MMR)
   └─ Re-Ranking (cross-encoder, LLM-based)

4. GENERATION
   ├─ Context Construction (format chunks, citations)
   ├─ Prompt Engineering (system + context + query)
   ├─ LLM Inference (streaming, temperature tuning)
   └─ Response Post-Processing (citations, validation)

5. EVALUATION (Production Critical)
   ├─ Retrieval Metrics (precision, recall, relevancy)
   ├─ Generation Metrics (faithfulness, correctness)
   └─ System Metrics (latency, cost, satisfaction)

Essential Metadata for Production RAG

Critical for filtering and relevance:

metadata = {
    # SOURCE TRACKING
    "source": "docs/api-reference.md",
    "source_type": "documentation",  # code, docs, logs, chat
    "last_updated": "2025-12-01T12:00:00Z",

    # HIERARCHICAL CONTEXT
    "section": "Authentication",
    "subsection": "OAuth 2.1",
    "heading_hierarchy": ["API Reference", "Authentication", "OAuth 2.1"],

    # CONTENT CLASSIFICATION
    "content_type": "code_example",  # prose, code, table, list
    "programming_language": "python",

    # FILTERING DIMENSIONS
    "product_version": "v2.0",
    "audience": "enterprise",  # free, pro, enterprise

    # RETRIEVAL HINTS
    "chunk_index": 3,
    "total_chunks": 12,
    "has_code": True
}

Why metadata matters:

• Enables filtering BEFORE vector search (reduces search space)
• Improves relevance through targeted retrieval
• Supports multi-tenant systems (filter by user/org)
• Enables versioned documentation (filter by product version)

Evaluation with RAGAS

Use scripts/evaluate_rag.py for automated evaluation:

from ragas import evaluate
from ragas.metrics import (
    faithfulness,       # Answer grounded in context
    answer_relevancy,   # Answer addresses query
    context_recall,     # Retrieved docs cover ground truth
    context_precision   # Retrieved docs are relevant
)

# Test dataset
test_data = {
    "question": ["How do I refresh OAuth tokens?"],
    "answer": ["Use /token with refresh_token grant..."],
    "contexts": [["OAuth refresh documentation..."]],
    "ground_truth": ["POST to /token with grant_type=refresh_token"]
}

# Evaluate
results = evaluate(test_data, metrics=[
    faithfulness,
    answer_relevancy,
    context_recall,
    context_precision
])

# Production targets:
# faithfulness: >0.90 (minimal hallucination)
# answer_relevancy: >0.85 (addresses user query)
# context_recall: >0.80 (sufficient context retrieved)
# context_precision: >0.75 (minimal noise)

Performance Optimization

Embedding Generation

• Batch processing: 100-500 chunks per batch
• Caching: Cache embeddings by content hash
• Rate limiting: Respect API provider limits (exponential backoff)

Vector Search

• Index type: HNSW (Hierarchical Navigable Small World) for most cases
• Distance metric: Cosine for normalized embeddings
• Pre-filtering: Apply metadata filters before vector search
• Result diversity: Use MMR (Maximal Marginal Relevance) to reduce redundancy

Cost Optimization

• Embedding model: Consider text-embedding-3-small for budget constraints
• Dimension reduction: Use maturity shortening (3072d → 1024d)
• Caching: Implement semantic caching for repeated queries
• Batch operations: Group insertions/updates for efficiency

Common Workflows

1. Building a RAG Chatbot

• Vector database: Qdrant (self-hosted or cloud)
• Embeddings: OpenAI text-embedding-3-large
• Chunking: 512 tokens, 50 overlap, semantic splitter
• Search: Hybrid (vector + BM25)
• Integration: Frontend with ai-chat skill

See examples/qdrant-python/ for complete implementation.

2. Semantic Search Engine

• Vector database: Qdrant or Pinecone
• Embeddings: Voyage AI voyage-3 (best quality)
• Chunking: Content-type specific (see chunking-patterns.md)
• Search: Hybrid with re-ranking
• Filtering: Pre-filter by metadata (date, category, etc.)

3. Code Search

• Vector database: Qdrant
• Embeddings: OpenAI text-embedding-3-large
• Chunking: AST-based (function/class boundaries)
• Metadata: Language, file path, imports
• Search: Hybrid with language filtering

See examples/qdrant-python/ for code-specific implementation.

Integration with Other Skills

Frontend Skills

• ai-chat: Vector DB powers RAG pipeline behind chat interface
• search-filter: Replace keyword search with semantic search
• data-viz: Visualize embedding spaces, similarity scores

Backend Skills

• databases-relational: Hybrid approach using pgvector extension
• api-patterns: Expose semantic search via REST/GraphQL
• observability: Monitor embedding quality and retrieval metrics

Multi-Language Support

Python (Primary)

• Client: qdrant-client
• Framework: LangChain, LlamaIndex
• See: examples/qdrant-python/

Rust

• Client: qdrant-client (1,549 code snippets in Context7)
• Framework: Raw Rust for performance-critical systems
• See: examples/rust-axum-vector/

TypeScript

• Client: @qdrant/js-client-rest
• Framework: LangChain.js, integration with Next.js
• See: examples/typescript-rag/

Go

• Client: qdrant-go
• Use case: High-performance microservices

Troubleshooting

Poor Retrieval Quality

1. Check chunking strategy (too large/small?)
2. Verify metadata filtering (too restrictive?)
3. Try hybrid search instead of vector-only
4. Implement re-ranking stage
5. Evaluate with RAGAS metrics

Slow Performance

1. Use HNSW index (not Flat)
2. Pre-filter with metadata before vector search
3. Reduce vector dimensions (maturity shortening)
4. Batch operations (insertions, searches)
5. Consider GPU acceleration (Milvus)

High Costs

1. Switch to text-embedding-3-small
2. Implement semantic caching
3. Reduce chunk overlap
4. Use self-hosted embeddings (nomic, bge-m3)
5. Batch embedding generation

Qdrant Context7 Documentation

Primary resource: /llmstxt/qdrant_tech_llms-full_txt

• Trust score: High
• Code snippets: 10,154
• Quality score: 83.1

Access via Context7:

resolve-library-id({ libraryName: "Qdrant" })
get-library-docs({
  context7CompatibleLibraryID: "/llmstxt/qdrant_tech_llms-full_txt",
  topic: "hybrid search collections python",
  mode: "code"
})

Additional Resources

Reference Documentation

• references/qdrant.md - Comprehensive Qdrant guide
• references/pgvector.md - PostgreSQL pgvector extension
• references/milvus.md - Milvus/Zilliz for billion-scale
• references/embedding-strategies.md - Embedding model comparison
• references/chunking-patterns.md - Advanced chunking techniques

Code Examples

• examples/qdrant-python/ - FastAPI + Qdrant RAG pipeline
• examples/pgvector-prisma/ - PostgreSQL + Prisma integration
• examples/typescript-rag/ - TypeScript RAG with Hono

Automation Scripts

• scripts/generate_embeddings.py - Batch embedding generation
• scripts/benchmark_similarity.py - Performance benchmarking
• scripts/evaluate_rag.py - RAGAS-based evaluation

Next Steps:

1. Choose vector database based on scale and infrastructure
2. Select embedding model based on quality vs. cost trade-off
3. Implement chunking strategy for the content type
4. Set up hybrid search for production quality
5. Evaluate with RAGAS metrics
6. Optimize for performance and cost