data · open source
RAG & Knowledge Retrieval with Pinecone
RAG & Knowledge Retrieval built on Pinecone, chosen where it genuinely fits, and swapped where it does not.
- Category
- data
- Vendor
- Open source
- Alternatives we also use
- 9
Why Pinecone for this
Orqent Labs builds RAG systems where accuracy is measured against a labelled question set, so you know the number rather than trusting a vibe.
Pinecone is strongest at scales past where Postgres vector search starts to strain, with little operational work. For rag & knowledge retrieval that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: another system, another bill, and no joins to your relational data. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.
You own the code, the models where they are open-weight, and the documentation to run it without us.
The honest assessment
- What it is
- Managed vector database built for large-scale similarity search.
- Strongest at
- scales past where Postgres vector search starts to strain, with little operational work
- Trade-off
- another system, another bill, and no joins to your relational data
- Category
- data
We are not a reseller for Pinecone and hold no commission on this choice. Where a different option fits your workload better, the recommendation will say so. That is the entire value of asking us.
What is included
- Ingestion pipeline for your real document formats
- Chunking and embedding strategy tuned to your corpus
- Hybrid keyword plus vector retrieval with reranking
- Citations on every answer, traceable to the source page
- Permission-aware retrieval that respects existing access rules
- Retrieval quality benchmarked against a labelled question set
Questions
RAG or fine-tuning?
RAG for knowledge that changes and must be cited; fine-tuning for style, format and task behaviour. Most production systems use RAG for the facts and light fine-tuning or few-shot prompting for the form.
How accurate will it be?
We build a labelled question set from your domain and report retrieval precision and answer accuracy against it. That number is the deliverable. We do not ship a system whose quality nobody has measured.
Can it respect our existing permissions?
Yes. Retrieval is filtered by the user's actual entitlements, so the assistant can never surface a document the user could not already open.
Alternatives for rag & knowledge retrieval
Same capability, different stack. Each page states its own trade-off.
What else we build on Pinecone
Building with Pinecone?
Bring us the workload and we will tell you whether this is the right stack for it.
Or email bd@dtrasglobal.com · call +91 74118 77878