Praison AI

Deepseek Streamlit UI

Create interactive chat interfaces with Deepseek models using Streamlit

Diagram

Query

Input

Streamlit UI

Vector DB

Knowledge Agent

Deepseek

Output

Prerequisites

  1. Install Package

    Install required packages:

    bash
    pip install "praisonaiagents[knowledge]" streamlit ollama
  2. Setup Model

    Pull Deepseek model:

    bash
    # Large Language Model
ollama pull deepseek-r1

# Embedding Model
ollama pull nomic-embed-text
  3. Setup Environment

    Configure environment:

    bash
    export OPENAI_MODEL_NAME=ollama/deepseek-r1
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:

    bash
    streamlit run app.py

Code

python
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"],
        memory={"embedder": config["embedder"]},
        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 Deepseek through Ollama.

 Session Management

Maintains chat history in session state.