Knowledge Retrieval (RAG)

Enable advanced RAG pipelines, letting you split, chunk, embed and retrieve knowledge from over 300 data sources

How does it work

Agent Cloud comes with a built in RAG pipeline which can chunk, embed and vector store over 300 data sources out of the box.

Get started with

300+ data sources (including confluence, google drive)

RAG strategies (semantic chunking)

Built in vector database (powered by qdrant)

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Explore other features

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The end to end RAG pipeline

Select your connector

Use our collection of data sources to sync data from other systems like confluence or upload your own pdf, docx, txt or csv file.
When selecting systems like databases (postgres, snowflake, bigquery) you can select tables and even columns to ingest.

Prep your data

For files you can provide instructions on how to split and chunk your data. Leverage Open AI latest text-embedding-3-small for embedding or select from open source models like BGE/base.

Vector store your data

Once data has been embedded the platform will store your data within a vector database. We also expose

Keep data fresh

Select what frequency you would like to sync data from the source. This can be manual, scheduled or a cron expression. This means users can query fresh data and know how recent the source was updated.

Start chatting with your data!

Now that data is synced, simply create an agent with your choice of LLM and start a session to talk to your data.

Knowledge Retrieval (RAG)

How does it work

Explore other features

Security

Open Source or Cloud LLMs

Tools

The end to end RAG pipeline

Select your connector

Prep your data

Vector store your data

Keep data fresh

Start chatting with your data!