With Autonomous Assistants, the LLM can run functions to:

  • Search the knowledge base
  • Search the chat history
  • Achieve other tasks

The LLM decides if it needs to search the knowledge base and what search parameters it needs to query the knowledge base.

cookbook/assistants/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)

​
Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.

2

Run PgVector

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
3

Install libraries

pip install -U pgvector pypdf "psycopg[binary]" sqlalchemy openai phidata
4

Run the assistant

​
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