Prismy / Service SpecificationSpec Sheet
Enterprise RAG & Knowledge Engines
Transform internal company documents, product catalogs, and databases into ultra-fast semantic search — powered by Pinecone, Pgvector, and OpenAI embeddings.
Sec 0106 Items
Knowledge Infrastructure Stack
T.01
Pinecone
Managed vector database for production-scale similarity search
T.02
Pgvector
Vector search inside PostgreSQL, keeping data close to your existing stack
T.03
OpenAI Embeddings
The embedding layer behind semantic retrieval
T.04
LangChain
Retrieval chains, reranking, and tool integration
T.05
Ingestion Pipelines
PDF, SQL, Notion, and website connectors that keep indexes fresh
T.06
Python & FastAPI
Async services for embedding and retrieval at scale
Sec 0202 Blocks
Methodology
M.01
Document Ingestion Pipeline
Getting messy internal data clean, chunked, and indexed — and keeping it fresh when files change
Key Benefits:
- Clean, deduplicated data
- Incremental sync as documents change
- Permission-aware indexing
Tools & Frameworks:
PDF ParsersNotion APISQL ConnectorsCron Sync
M.02
Retrieval Quality
Search that returns the right answer fast, not just relevant paragraphs
Key Benefits:
- Accurate, grounded answers
- Low-latency retrieval
- Fewer hallucinations
Design Patterns:
Hybrid Semantic + Keyword SearchRerankingChunking Strategy
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