$ cat services.json

Vector Store Implementation

Set up and optimize vector databases for your AI applications.

Deliverables:

Database selection guidance
Schema design for embeddings
Index optimization
Hybrid search setup
Performance tuning

Embedding Pipeline Development

Build pipelines to generate and store embeddings efficiently.

Deliverables:

Embedding model selection
Batch processing pipelines
Incremental updates
Metadata management
Cost optimization

Semantic Search Systems

Implement production-grade semantic search.

Deliverables:

Query understanding
Re-ranking implementation
Filtering and facets
Result scoring
Analytics and monitoring

$ man vector-databases

Vector Database Comparison

PGVector - PostgreSQL extension

Best for: Existing PostgreSQL infrastructure
Pros: ACID compliance, familiar SQL, low ops overhead
Cons: Limited scale without sharding

Pinecone - Managed vector database

Best for: Production scale, minimal ops
Pros: Fully managed, fast, filtering support
Cons: Vendor lock-in, cost at scale

Chroma - Open source, developer-friendly

Best for: Prototyping, local development
Pros: Simple API, good LangChain integration
Cons: Less mature for production

Qdrant - Performance-focused

Best for: Complex filtering, high performance
Pros: Excellent filtering, payload storage
Cons: Smaller community

Embedding Model Selection

I help you choose the right embedding model:

OpenAI text-embedding-3-large: Best overall quality
Cohere embed-v3: Good for multilingual
BGE/E5: Open source, self-hosted
Sentence Transformers: Custom fine-tuning

$ cat README.md

Vector Database Architecture

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from langchain_community.vectorstores import PGVector
from langchain_openai import OpenAIEmbeddings

# Production PGVector Setup
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")

vectorstore = PGVector.from_connection_string(
    connection_string=DATABASE_URL,
    embedding=embeddings,
    collection_name="documents",
    pre_delete_collection=False
)

# Hybrid search with metadata filtering
results = vectorstore.similarity_search_with_score(
    query="patent infringement claims",
    k=10,
    filter={"category": "patents", "year": {"$gte": 2020}}
)

When to Use Each Vector Database

Use Case	Recommended	Reason
PostgreSQL shop	PGVector	Unified infrastructure
Scale + low ops	Pinecone	Fully managed
Complex filters	Qdrant	Advanced filtering
Prototyping	Chroma	Simple setup
Self-hosted	Qdrant/Milvus	Full control

Experience:

AI Backend Lead at Anaqua - Built PGVector-based RAG

Case Studies: Enterprise RAG for Legal Documents | Agentic AI Knowledge Systems

Related Technologies: RAG Systems, LangChain, PostgreSQL, OpenAI

$ ls -la projects/

Legal Document Search

@ Anaqua (RightHub)

Challenge:

Search millions of patent documents with semantic understanding.

Solution:

PGVector with custom chunking, hybrid search combining semantic + BM25, and citation-aware retrieval.

Result:

50% faster search, lawyers trusted the system for production work.

Knowledge Base Q&A

@ Sparrow Intelligence

Challenge:

Enable natural language queries over proprietary documentation.

Solution:

Pinecone for scale with metadata filtering, custom embedding pipeline, and re-ranking.

Result:

Accurate answers with source citations in milliseconds.

🔮 Vector Databases

$ cat services.json

Vector Store Implementation

Embedding Pipeline Development

Semantic Search Systems

$ man vector-databases

Vector Database Comparison

Embedding Model Selection

$ cat README.md

Vector Database Architecture

When to Use Each Vector Database

$ ls -la projects/

Legal Document Search

Knowledge Base Q&A

$ diff me competitors/

Build Your Vector Search