Solutions/Context Engineering

Context Engineering
on VeloDB

Store vectors, full-text, structured data, and JSON in one database. Retrieve with hybrid search and fusion re-ranking. Serve fresh context to LLMs and agents in milliseconds.

Keep context fresh with streaming ingest, real-time CDC and incremental index update in multimodel dataset

Store vectors, full-text, structured tables, and semi-structured JSON in one engine

Scale to billions of vectors with progressive filtering and IVPQ compression

Connect to AI applications through MCP Server, REST APIs, CLI and standard SQL

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Context that's
fresh
Retrieval that's accurate

The quality of every AI answer depends on the context behind it. VeloDB keeps that context fresh, stores it all in one place, and retrieves exactly what matters

Real-time context, not stale snapshots

Streaming ingest and native CDC keep your knowledge base current as source documents, policies, and transactional records change. Your LLM retrieves what is true now, not what was true when the index was last rebuilt.

Store all of context in one place

Your context lives in documents, metadata, embeddings, and event logs. VeloDB stores and queries all of them together. One SQL statement retrieves across vectors, full-text, structured columns, and JSON without stitching results from separate systems.

Retrieval accuracy without runaway infrastructure cost

VeloDB uses progressive filtering to keep retrieval accurate without scaling cost. Cheap SQL filters shrink the retrieval dimensions first, followed by keyword matching to prune out exact matches or negative matches, and finally vector similarity to the remaining data. Every stage reduces the work for the next one, so accuracy goes up while the computation time decreases.

Trusted in production

AI teams run on VeloDB

ByteDance searches 1 billion vectors for talent matching with 94% relevance, up from 58% on pure vector search, at 400ms latency.

94%

Relevance, from 58%

400ms

Latency at billion scale

384x

Compression with IVPQ

“Per-segment BM25 caused ranking instability on every segment merge. Global statistics in Doris 4.0 with progressive filtering fixed it. Relevance jumped from 58% to 94%. Latency dropped from 2.8 seconds to 400 milliseconds. Storage shrank from 10TB on 20 servers to 500GB on a single server.”

Engineering Team, ByteDance

Talent matching across global product portfolio

Read the full story

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Real-world tradeoffs

Challenges building
production RAG and agents

01·Stale context

Your knowledge base is already outdated by the time your AI retrieves from it

Source documents change, policies get updated, transactional records shift. But most RAG pipelines rebuild indexes on a schedule, daily or weekly.

In the gap between updates, your AI retrieves information that is no longer true. Users get confident, wrong answers.

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How VeloDB solves it

Context that stays current as the source data changes

VeloDB ingests updates through streaming load and native CDC so changes from upstream databases and document stores become searchable in seconds, not days. Only changed content is re-indexed, so there are no expensive full-corpus rebuilds. Your knowledge base reflects what is true now, not what was true at the last scheduled refresh.

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02·Fragmented context

Your AI needs context from four systems but can only query one at a time

Embeddings live in a vector database. Full-text lives in a search engine. Metadata lives in a relational database. JSON payloads live somewhere else.

Retrieving a complete answer means calling four APIs, merging results in application code, and hoping the data stays consistent across all of them.

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How VeloDB solves it

All context types stored and queried in one place

VeloDB stores vectors, full-text, structured columns, JSON, and bitmap labels together. A single SQL query searches across all modalities and ranks results using reciprocal rank fusion. No app-layer joins, no cross-system consistency gaps, and no extra infrastructure to operate.

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03·Retrieval cost

Retrieval gets more expensive as your knowledge base grows but accuracy does not improve

Vector search scans the full index on every query. As the corpus grows from thousands to millions of documents, compute cost grows linearly while relevance stays flat or gets worse.

Teams end up choosing between retrieval quality and infrastructure budget.

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How VeloDB solves it

Progressive filtering for accurate and cost-efficient retrieval

VeloDB uses progressive filtering to keep retrieval accuracy high without scaling cost. Each query passes through multiple retrieval steps, each step narrowing the search space before the next one runs. As your knowledge base grows, the cost per query stays flat because expensive operations only apply to the pre-filtered subset of the total data that needs to be processed.

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Architecture overview

VeloDB for generative AI

Ingest documents, transactional records, and event streams. Embed and chunk incrementally. Store vectors, text, structured metadata, JSON, and labels in one engine. Retrieve and rank via progressive filtering. Serve fresh context to LLMs and agents.

VeloDB context store

Context Sources

Documents

Confluence, PDF, S3 buckets

Transactional

MySQL, Postgres via CDC

Event streams

Kafka, Pulsar

External APIs

SaaS, webhooks

Ingest & Embed

CocoIndex