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The Agent Knowledge Retrieval node is in beta.
Use Agent Knowledge Retrieval to find relevant content in agent knowledge bases for later nodes to use—for example, as context for an LLM’s answer.
For classic knowledge bases, use the Classic Knowledge Retrieval node instead.

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Set the Query

Search with text, images, or both:
  • Query text: Select a text variable. Text can contain up to 16,000 characters.
    • In a Chatflow, use the built-in userinput.query variable for the current user message.
    • In a Workflow, use a User Input variable or text prepared by an earlier node.
  • Query Images: Select a variable containing one image or an array of images. A query can include up to four GIF, JPEG, PNG, or WebP images, up to 10 MB each and 32 MB total. Images over these limits, or a file that isn’t an image, make the node fail. In a Chatflow, set File Upload to accept images only, up to four per message. For supported model combinations, see Multimodal Retrieval.
    For image-only searches, make sure every selected knowledge base uses a vision-capable embedding or reasoning model. Otherwise, the node fails and returns no results.

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Select Agent Knowledge Bases

Select up to 10 knowledge bases. For each query, the node searches all selected knowledge bases and combines the results. If you cannot find a knowledge base in the picker, ask whoever can manage its permissions to grant you access.
If retrieval fails for any selected knowledge base, the node returns no partial results and the current run stops.

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Adjust Retrieval

Keep each knowledge base’s configured retrieval depth, or apply the same depth to all knowledge bases in this node.
Unlike in retrieval tests, Research returns only matching chunks without generating an answer.
The rerank model, Top K, and score threshold apply to the combined results from all selected knowledge bases. For image-only searches, the node does not rerank the results. See Retrieval Settings for details on each setting.

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Limit the Search to Specific Documents

Retrieval normally searches every document in a knowledge base. With metadata, you can narrow a search to only the documents that match a condition. A typical case is one knowledge base that serves several apps, each of which should answer from only part of it. Say an internal knowledge base of company policies holds regional guidelines for different countries, and each region has an assistant app for its employees built on it. Without a filter, the EU assistant can answer a parental-leave question from the US guideline. To prevent this, label each document with facts about it, then filter searches by those labels:
  1. On the knowledge base’s Document page, click Meta Data and add a field for each fact you’ll filter on. For the policy knowledge base, that’s one region field of type string. Pick the type with the filter in mind, since it can’t be changed later: number for values you’ll compare, time for dates you’ll filter before or after.
  2. Open each document, click Start labeling, pick the field, and enter its value. The EU guidelines get eu, the US ones us. Labels survive source syncs that update a document’s content.
  3. In this node, set Metadata Filtering to Manual and add the condition: region is eu for the EU assistant, whose answers now come from EU guidelines alone. You can also choose Automatic and let a model work out the conditions from each question. Searches run unfiltered when the LLM can’t determine one, so use Manual wherever the filter must hold. If the node searches several knowledge bases at once, give shared fields the same name and type in each. Filters can only use fields that exist in all of them.

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Generate an Answer from Retrieved Content

In a later LLM node on the same execution path:
1

Pass the retrieved content

Select this node’s result variable in Context, then insert the Context variable into the prompt.
2

Pass the query

Insert the same query variable or variables into the LLM node’s User message. For image queries, choose a vision-capable model.
3

Guide the answer

In the prompt, instruct the LLM to answer the query using the retrieved content.
Images found in the knowledge base reach later nodes only as text: their caption, alt text, or the text read from them. The image files themselves aren’t passed on.

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Handle No Matches

The node still succeeds when no content matches, returning an empty result. If the LLM should not answer without retrieved content, add an IF/ELSE node after this node and before the LLM, then check whether result is empty.
Last modified on October 28, 2026