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:

1

Chunk the information

Break down the knowledge into smaller chunks to ensure our search query matches 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.

​
Example: Assistant with a PDF Knowledge Base

Let’s build an Assistant that answers questions from a PDF.

  1. We’ll load our knowledge base with the PDF of a recipe book.
  2. Our Assistant will respond with recipes from the knowledge base.

​
Step 1: Run PgVector

Let’s use PgVector as our vector db as it can also provide storage for our Assistants.

Install docker desktop and run PgVector on port 5532 using:

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql/data/pgdata \
  -v pgvolume:/var/lib/postgresql/data \
  -p 5532:5432 \
  --name pgvector \
  phidata/pgvector:16

​
Step 2: RAG Assistant

Retrieval Augmented Generation means “stuffing the prompt with relevant information” to improve LLM responses. This is a 2 step process:

  1. Retrieve relevant information from a vector database.
  2. Augment the prompt to provide context to the LLM.

Let’s build a PDF Assistant that helps us with food recipes using RAG.

1

Install libraries

Install the required libraries using pip

2

Create a RAG Assistant

Create a file rag_assistant.py with the following contents

rag_assistant.py
from phi.assistant import Assistant
from phi.knowledge.pdf import PDFUrlKnowledgeBase
from phi.vectordb.pgvector import PgVector2

from resources import vector_db

knowledge_base = PDFUrlKnowledgeBase(
    # Read PDF from this URL
    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    # Store embeddings in the `ai.recipes` table
    vector_db=PgVector2(
        collection="recipes",
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    ),
)
# Load the knowledge base
knowledge_base.load(recreate=False)

assistant = Assistant(
    knowledge_base=knowledge_base,
    # The add_references_to_prompt will update the prompt with references from the knowledge base.
    add_references_to_prompt=True,
)
assistant.print_response("How do I make pad thai?", markdown=True)
3

Run the Assistant

Run the Assistant (it takes a few seconds to load the knowledge base).


The Assistant uses the OpenAI LLM and Embeddings by default.

​
Step 3: Autonomous Assistant

With the RAG assistant above, the add_references_to_prompt=True always adds information from the knowledge base to the prompt, regardless of whether it is relevant to the question.

With Autonomous assistants, we let the LLM decide if it needs to access the knowledge base and what search parameters it needs to query the knowledge base.

Make the Assistant Autonomous by setting the search_knowledge and read_chat_history flags, giving it tools to search the knowledge base and chat history on demand.

1

Create an Autonomous Assistant

Create a file auto_assistant.py with the following contents

auto_assistant.py
from phi.assistant import Assistant
from phi.knowledge.pdf import PDFUrlKnowledgeBase
from phi.vectordb.pgvector import PgVector2

knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=PgVector2(
        collection="recipes",
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    ),
)
# Comment out as the knowledge base is already loaded.
# knowledge_base.load(recreate=False)

assistant = Assistant(
    knowledge_base=knowledge_base,
    # Show tool calls in the response
    show_tool_calls=True,
    # Enable the assistant to search the knowledge base
    search_knowledge=True,
    # Enable the assistant to read the chat history
    read_chat_history=True,
)
assistant.print_response("How do I make pad thai?", markdown=True)
assistant.print_response("What was my last question?", markdown=True)
2

Run the assistant

Run the Assistant

Notice how it searches the knowledge base and chat history when needed

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