Introduction

Knowledge Base is a database of information that the Assistant can search to improve its responses. This information is stored in a vector database and provides LLMs with business context, which makes them respond in a context-aware manner. The general syntax is:

from phi.assistant import Assistant, AssistantKnowledge

# Create knowledge base
knowledge_base = AssistantKnowledge(vector_db=...)

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

# Add the knowledge base to the Assistant
assistant = Assistant(knowledge_base=knowledge_base)

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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.

Our goal is to search relevant information from a knowledge base quickly, here’s how vector databases are used with LLMs:

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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:

• PDF knowledge base: Load local PDF files to a knowledge base
• PDF URL knowledge base: Load PDF files from a URL to a knowledge base
• Text knowledge base: Load text/docx files to a knowledge base
• JSON knowledge base: Load JSON files to a knowledge base
• Website knowledge base: Load website data to a knowledge base
• Wikipedia knowledge base: Load wikipedia articles to a knowledge base
• ArXiv knowledge base: Load ArXiv papers to a knowledge base
• Combined knowledge base: Combine multiple knowledge bases into 1
• LangChain knowledge base: Use a Langchain retriever as a knowledge base
• Load knowledge base manually