Feature Service
RAG Pipeline with Vector Search
We create a RAG foundation with ingestion, chunking, embeddings, vector search, prompt grounding, source references, and quality checks for selected content.
OpenAIPinecone/pgvectorLangChainPython/Node
Fixed starting scope
Each feature starts with a clear base package, then grows only if your app needs more integrations, screens, or edge cases.
Integrated into your code
We adapt implementation to your stack instead of dropping a generic snippet that still needs hours of cleanup.
Handoff included
You get implementation notes, environment variables, testing notes, and what to watch before pushing to production.
Included
What you get
Document ingestion workflow.
Chunking and embedding setup.
Vector database integration.
Grounded answer endpoint.
Source references and initial evaluation set.
Approach
How we implement it
1
Collect and normalize source documents.
2
Choose embedding model and vector store.
3
Build ingestion and reindexing workflow.
4
Retrieve relevant chunks for each question.
5
Evaluate answers against real sample questions.