Results for “pgvector”
15 skills
antigravity Mesh Memory
Provides persistent, self-hosted semantic memory for AI agents via MCP, storing worklogs, decisions, and notes in PostgreSQL with pgvector for meaning-based retrieval across sessions.
42.4k
goldentrii Agent Recall
Provides persistent, compounding memory for AI agents across sessions using local markdown files, with optional Supabase-backed semantic search.
365 · bundle
joshuashepherd Book Chunk
Chunks a book into canonical retrieval units with heading-aware structure splitting, recursive token targets, and contextual prefixes for downstream RAG ingestion.
1
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nimoqup046-collab LLM Ops
Guides production AI systems: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and caching.
2
jorcan LLM Ops
Guides production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and AI architectures.
0 · bundle
oyi77 Ruvector
Generates and manages vector embeddings for semantic search and RAG retrieval across knowledge bases, with self-learning capabilities.
10
mukul975 Assessing Vector And Embedding Weaknesses
Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.
24.6k · bundle
lord1egypt Qdrant Vector Search
Builds production RAG and semantic search systems with Qdrant, covering collection setup, vector indexing, filtered and hybrid search, and integration with LangChain and LlamaIndex.
2
orchestra-research Pinecone
Provides code examples and best practices for using Pinecone, a managed vector database for production RAG, recommendation, and semantic search applications.
10.4k · bundle
joshuashepherd Build RAG
Builds or modifies a RAG pipeline with intent-based routing, vector store search, citation rendering, and book fidelity enforcement.
1
tools-only 014 API F0515c8f
Reference for configuring and using LangChain4j vector stores, covering setup, search, filtering, and ingestion.
7 · bundle
joshuashepherd Agent RAG
Build or modify a RAG retrieval pipeline with vector store search, corpus routing, citation rendering, and book fidelity enforcement.
1
lucaspmarie-a11y LLM Ops
Provides guidance on production AI operations including RAG pipelines, vector databases, embeddings, fine-tuning, prompt engineering, cost estimation, and quality evaluation.
5
phoroth LLM Ops
Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.
3
antigravity LLM Ops
Provides guidance and code for production AI workflows including RAG pipelines, vector databases, embedding indexing, prompt engineering, cost estimation, semantic caching, and quality evaluation.
42.4k