Build Intelligent Conversational Applications in Minutes with JamAI Base Chat Tables
1. Introduction
This guide will help you quickly get started with JamAI Base chat tables using the Python SDK. You'll learn how to create a simple chat application similar to the example provided.
2. Prerequisites
Python 3.10 or above
JamAI Base account
Personal Access Token (PAT)
Project ID
3. Installation
pip install jamaibase
4. Basic Setup
2. Basic Setup
Get your Personal Access Token (PAT) here:
Get your Project ID here:
from jamaibase import JamAI, protocol as p
import os
# Initialize JamAI client
jamai = JamAI(
project_id="your_project_id", # Replace with your project ID
token="your_pat_token" # Replace with your PAT
)
5. Creating a Chat Agent (UI First)
Go to JamAI Base web interface chat table tab
Navigate to "Agents" section, click + to create new agent.
Name the new agent "example_agent"and select LLM model.
You can further configure your agent in UI but it will not be covered in this tutorial.
Example of some configuration you can do:
Set system prompt
Set multiple parallel agent
Configure RAG settings
Set up knowledge base
6. Creating Chat Sessions
Each chat session is created by duplicating your base agent table. Here's how to do it:
def create_new_chat():
timestamp = int(time.time())
new_table_id = f"Chat_{timestamp}"
try:
jamai.table.duplicate_table(
table_type=p.TableType.chat,
table_id_src="example_agent", # Your base agent ID
table_id_dst=new_table_id,
include_data=True,
create_as_child=True
)
return new_table_id
except Exception as e:
print(f"Error creating new chat: {str(e)}")
return None
7. Basic Chat Interaction
# Send a message and get response
def chat_interaction(table_id, user_message):
response = jamai.table.add_table_rows(
table_type=p.TableType.chat,
request=p.RowAddRequest(
table_id=table_id,
data=[{"User": user_message}],
stream=True # Set to False for non-streaming response
)
)
# For streaming response
full_response = ""
for chunk in response:
if isinstance(chunk, p.GenTableStreamChatCompletionChunk):
if chunk.output_column_name == 'AI':
full_response += chunk.choices[0].message.content
return full_response
8. Simple Implementation Example
# Create a new chat session
chat_id = create_new_chat()
if chat_id:
# Send a message
response = chat_interaction(chat_id, "Hello, how are you?")
print("AI:", response)
9. Key Features
Streaming Responses: Use stream=True for real-time responses
Session Management: Each chat creates a new table instance
Inheritance: New chats inherit settings from the base agent
History Preservation: Chat history is maintained in the table
10. Best Practices
Create a new chat session for each conversation
Handle exceptions during table creation
Store chat IDs for session management
Clean up unused chat tables periodically
11. Complete Runnable Example
Here's a complete, standalone example that you can copy, paste, and run:
import streamlit as st
from jamaibase import JamAI, protocol as p
import time
import os
from dotenv import load_dotenv
# Load environment variables (optional)
load_dotenv()
# Constants
PROJECT_ID = "your_project_id" # Replace with your project ID
PAT = "your_pat_token" # Replace with your PAT
TABLE_TYPE = p.TableType.chat
OPENER = "Hello! How can I help you today?"
# Initialize JamAI
jamai = JamAI(project_id=PROJECT_ID, token=PAT)
def create_new_chat():
timestamp = int(time.time())
new_table_id = f"Chat_{timestamp}"
try:
jamai.table.duplicate_table(
table_type=TABLE_TYPE,
table_id_src="example_agent", # Replace with your agent ID
table_id_dst=new_table_id,
include_data=True,
create_as_child=True
)
return new_table_id
except Exception as e:
st.error(f"Error creating new chat: {str(e)}")
return None
def main():
st.title("Simple Chat Demo")
# Initialize session state
if "table_id" not in st.session_state:
new_table_id = create_new_chat()
st.session_state.table_id = new_table_id
st.session_state.messages = [{"role": "assistant", "content": OPENER}]
# Create New Chat button
if st.button("New Chat"):
new_table_id = create_new_chat()
if new_table_id:
st.session_state.table_id = new_table_id
st.session_state.messages = [{"role": "assistant", "content": OPENER}]
st.rerun()
# Display chat messages
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.write(message["content"])
# Chat input
if prompt := st.chat_input("Type your message here"):
# Display user message
with st.chat_message("user"):
st.write(prompt)
st.session_state.messages.append({"role": "user", "content": prompt})
# Get AI response
with st.chat_message("assistant"):
message_placeholder = st.empty()
full_response = ""
# Stream the response
for chunk in jamai.table.add_table_rows(
table_type=TABLE_TYPE,
request=p.RowAddRequest(
table_id=st.session_state.table_id,
data=[{"User": prompt}],
stream=True
)
):
if isinstance(chunk, p.GenTableStreamChatCompletionChunk):
if chunk.output_column_name == 'AI':
full_response += chunk.choices[0].message.content
message_placeholder.write(full_response + "▌")
message_placeholder.write(full_response)
st.session_state.messages.append({"role": "assistant", "content": full_response})
if __name__ == "__main__":
main()
How to Run:
Save the code in a file (e.g., chat_demo.py)
Install required packages:
pip install streamlit jamaibase python-dotenv
Replace the following values:
your_project_id with your actual Project ID
your_pat_token with your Personal Access Token
example_agent with your agent ID created in the UI
Run the application:
streamlit run chat_demo.py
This example provides:
A simple chat interface
Streaming responses
New chat session creation
Basic error handling
Note: Make sure you have created your agent in the JamAI Base UI before running this code, as it relies on duplicating an existing agent table.
This example serves as a great starting point for building more complex chat applications with JamAI Base.
