​
Prerequisites

1

Install Package

Install required packages:
pip install "praisonaiagents[knowledge]" streamlit ollama
streamlit for UI ollama for model hosting praisonaiagents[knowledge] for RAG capabilities
2

Setup Model

Pull Ollama models:
# Large Language Model
ollama pull deepseek-r1

# Embedding Model
ollama pull nomic-embed-text
3

Setup Environment

Configure environment:
export OPENAI_BASE_URL=http://localhost:11434/v1
export OPENAI_API_KEY=fake-key
4

Create File

Create a new file called app.py and add the following code:
5

Run Application

Start the Streamlit application:
streamlit run app.py

​
Code

import streamlit as st
from praisonaiagents import Agent

def init_agent():
    config = {
        "vector_store": {
            "provider": "chroma",
            "config": {
                "collection_name": "praison",
                "path": ".praison"
            }
        },
        "llm": {
            "provider": "ollama",
            "config": {
                "model": "deepseek-r1:latest",
                "temperature": 0,
                "max_tokens": 8000,
                "ollama_base_url": "http://localhost:11434",
            },
        },
        "embedder": {
            "provider": "ollama",
            "config": {
                "model": "nomic-embed-text:latest",
                "ollama_base_url": "http://localhost:11434",
                "embedding_dims": 1536
            },
        },
    }
    
    return Agent(
        name="Knowledge Agent",
        instructions="You answer questions based on the provided knowledge.",
        knowledge=["kag-research-paper.pdf"],
        knowledge_config=config,
        user_id="user1",
        llm="deepseek-r1"
    )

st.title("Knowledge Agent Chat")

if "agent" not in st.session_state:
    st.session_state.agent = init_agent()
    st.session_state.messages = []

if "messages" in st.session_state:
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

prompt = st.chat_input("Ask a question...")

if prompt:
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(prompt)

    with st.chat_message("assistant"):
        response = st.session_state.agent.start(prompt)
        st.markdown(response)
        st.session_state.messages.append({"role": "assistant", "content": response})

​
Features

Interactive Chat

Real-time chat interface with message history.

Knowledge Base

RAG capabilities with ChromaDB integration.

Model Integration

Uses Ollama for local model hosting.

Session Management

Maintains chat history in session state.
Make sure your system meets the requirements for running models locally through Ollama.