data · open source
RAG & Knowledge Retrieval with pgvector
RAG & Knowledge Retrieval built on pgvector, chosen where it genuinely fits, and swapped where it does not.
- Category
- data
- Vendor
- Open source
- Alternatives we also use
- 9
Why pgvector for this
Your documents do not arrive as clean markdown. They are scanned PDFs, merged cells, ten-year-old templates. The ingestion pipeline is most of the work, and we build it for the corpus you actually have.
pgvector is strongest at one database for relational and vector data, with transactions across both. For rag & knowledge retrieval that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: at very large vector volumes a dedicated index outperforms it. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. Every engagement opens with a measurement: the cycle time, the cost per transaction, or the error rate we are being asked to move.
Six weeks to something running in production, not six quarters to a strategy document.
The honest assessment
- What it is
- Vector search inside Postgres, no separate vector database to operate.
- Strongest at
- one database for relational and vector data, with transactions across both
- Trade-off
- at very large vector volumes a dedicated index outperforms it
- Category
- data
We are not a reseller for pgvector 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 pgvector
Building with pgvector?
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