...

1. The original raw query is transformed into a clean query. For the default knowledge base configuration, this means taking an ambiguous sentence, like location of food and turning it into the format of a question, such as Where is the food located? so that the text matches the format of the questions that were generated by the ingestion engine

2. We lookup the embedding vector for the transformed query

3. We use the embedding vector to perform a query on the knowledge base and find the top K knowledge chunks whose matching text (a.k.a the generated question) is the closest to the query text

4. We use a reranking algorithm to rerank the matched knowledge chunks against the query

5. We discard all except the top 5 matching knowledge chunks

6. The matching knowledge chunks are returned. For your standard AI Agents, as determined by the rerankerthese matching knowledge chunks are then fed into the conversation history for the agent as a tool-result, which the agent can then use to formulate and write its response

Loading In Knowledge

There are many different ways to load in knowledge to your agent, which will depend a lot on the different use-cases for your agent.

...