Introduction
Last updated
Last updated
A knowledge base is a database of information that an agent can search to improve its responses. This information is stored in a vector database and provides agents with business context, helping them respond in a context-aware manner. The general syntax is:
Vector Databases
While any type of storage can act as a knowledge base, vector databases offer the best solution for retrieving relevant results from dense information quickly. Here’s how vector databases are used with Agents:
Chunk the information
Break down the knowledge into smaller chunks to ensure our search query returns only relevant results.
Load the knowledge base
Convert the chunks into embedding vectors and store them in a vector database.
Search the knowledge base
When the user sends a message, we convert the input message into an embedding and “search” for nearest neighbors in the vector database.
Before you can use a knowledge base, it needs to be loaded with embeddings that will be used for retrieval. Use one of the following knowledge bases to simplify the chunking, loading, searching and optimization process:
: Load ArXiv papers to a knowledge base
: Combine multiple knowledge bases into 1
: Load CSV files to a knowledge base
: Load local docx files to a knowledge base
: Load JSON files to a knowledge base
: Use a Langchain retriever as a knowledge base
: Load local PDF files to a knowledge base
: Load PDF files from a URL to a knowledge base
: Load PDF files from S3 to a knowledge base
: Load text files from S3 to a knowledge base
: Load text/docx files to a knowledge base
: Load website data to a knowledge base
: Load wikipedia articles to a knowledge base