Knowledge bases
A knowledge base in Meko is a RAG (Retrieval-Augmented Generation) pipeline that ingests your documents, breaks them into chunks, generates vector embeddings, and indexes them for semantic search. Agents can then query the knowledge base to find relevant information from your documents.
How it works
When you add a knowledge base to a datapack using the Meko UI, Meko's pg_dist_rag pipeline:
- Fetches documents from the source (S3, local filesystem, a web page, or an NFS mount).
- Preprocesses to extract text from PDFs, HTML, markdown files, text files, images, parquet, iceberg, JSON, and more.
- Chunks the text into segments (configurable chunk size).
- Embeds each chunk using the configured embedding model.
- Indexes the embeddings in pgvector for fast similarity search.
All of this happens within your datapack's database; there's no separate vector database to manage.
Supported document formats
Currently, Meko accepts PDF, TXT, TEXT, MD, CSV, JSON, XML, HTML, and HTM files up to 10 per batch. The per-file size cap depends on your plan: 5 MB on Free, 50 MB on Standard.
Free accounts are limited to 100 MB of total storage across all datapacks for knowledge-base files and artifacts combined, with individual files capped at 5 MB. Standard accounts get 5 GB of total storage and 50 MB per file. See Meko pricing for plan details.
Documents can be uploaded to your datapacks via the Meko portal.
Query knowledge
Once indexed, agents can query the knowledge base through the MCP server. The MCP tool for knowledge search handles embedding the query, performing similarity search, and returning relevant chunks.
Next steps
- Work with knowledge bases - How to build and query knowledge bases
- Learn about datapacks