Using Vector Databases

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)

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)

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

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"}}
        ]
    }
)

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' } }
    ]
  }
});

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)

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
}

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)