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Introduction

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:

from bitca.agent import Agent, AgentKnowledge

# Create a knowledge base for the Agent
knowledge_base = AgentKnowledge(vector_db=...)

# Add information to the knowledge base
knowledge_base.load_text("The sky is blue")

# Add the knowledge base to the Agent and
# give it a tool to search the knowledge base as needed
agent = Agent(knowledge=knowledge_base, search_knowledge=True)

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:

1

Chunk the information

Break down the knowledge into smaller chunks to ensure our search query returns only relevant results.

2

Load the knowledge base

Convert the chunks into embedding vectors and store them in a vector database.

3

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.

​Loading the Knowledge Base

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:

PreviousKnowledgesNextArXiv Knowledge Base

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