Creating chat sessions in knowledge models

Below is an example script to chat with the model that creates a new chat session for the specified model and sends messages to it.

Define the constants:

import requests
KM_ID = '{YOUR_KM_ID}' 
API_URL = 'https://training.constructor.app/api/platform-kmapi/v1' 
API_KEY = '{YOUR_API_KEY}'

Define the headers:

# Define the headers 
headers = {
    'X-KM-AccessKey': f'Bearer {API_KEY}' 
}

Select an LLM from the available options to use for generating responses:

print(requests.get(f'{API_URL}/language_models', headers=headers).json())

Example:

{
  "results": [
    {
      "id": "9d38cf09f0ff4fe8ab7b2e714adb55b2",
      "name": "GPT-4o Mini",
      "description": "The most cost-efficient small model",
      "hosted_by": {"name": "OpenAI"},
      "code": "gpt"
    },
    {
      "id": "83db90a3c67648e0ba43b48c2f2c39b6",
      "name": "GPT-4o",
      "description": "Most advanced and efficient model, excelling in non-English languages",
      "hosted_by": {"name": "OpenAI"},
      "code": "gpt"
    },
    {
      "id": "23fb715e7426484ba8d89a2e930d3e9f",
      "name": "GPT-4 Turbo",
      "description": "Offers improved instruction following, JSON mode, reproducible outputs, and parallel function calling, reducing incomplete tasks.",
      "hosted_by": {"name": "OpenAI"},
      "code": "gpt"
    },
    {
      "id": "e4d51d8b3f2a4abca4d5f58b711053a6",
      "name": "GPT-4",
      "description": "Solves complex problems with high accuracy, leveraging broad knowledge and advanced reasoning.",
      "hosted_by": {"name": "OpenAI"},
      "code": "gpt"
    },
    {
      "id": "643282cc66eb4875ab51213438a56a2a",
      "name": "GPT-o1 Preview",
      "description": "Beta version of the o1 model, which performs complex reasoning and think before you answer.",
      "hosted_by": {"name": "OpenAI"},
      "code": "gpt"
    },
    {
      "id": "aaf4df69643e4c728aff9773c7039879",
      "name": "GPT-o1 Mini",
      "description": "Light version of the o1 model with twice the context window size and response speed.",
      "hosted_by": {"name": "OpenAI"},
      "code": "gpt"
    },
    {
      "id": "fc5f80c1b7fb49ffb0b9a059d2cf6522",
      "name": "Claude 3.5 Sonnet",
      "description": "Best combination of performance and speed for efficient, high-throughput tasks.",
      "hosted_by": {"name": "Anthropic"},
      "code": "claude"
    },
    {
      "id": "fa4138f0e3454efda84767c338a06f33",
      "name": "Claude 3 Opus",
      "description": "Highest-performing model, which can handle complex analysis, longer tasks with many steps, and higher-order math and coding tasks.",
      "hosted_by": {"name": "Anthropic"},
      "code": "claude"
    },
    {
      "id": "13c40686125844f590567d0c99d879d7",
      "name": "Gemini 1.5 Pro",
      "description": "Complex reasoning tasks such as code and text generation, text editing, problem solving, data extraction and generation.",
      "hosted_by": {"name": "Google"},
      "code": "gemini"
    },
    {
      "id": "5ae60bf998ee4bc8a1136078dc9ca367",
      "name": "Gemini 1.5 Flash",
      "description": "Fast and versatile performance across a diverse variety of tasks.",
      "hosted_by": {"name": "Google"},
      "code": "gemini"
    },
    {
      "id": "41350d36e6944876bede32d82a9dad01",
      "name": "Gemini 1.0 Pro",
      "description": "Natural language tasks, multi-turn text and code chat, and code generation.",
      "hosted_by": {"name": "Google"},
      "code": "gemini"
    }
  ],
  "total": 11
}

Create a knowledg model if you don’t already have one. See Creating knowledge models.

Create a new chat session using 'gpt-4o' as the chat engine and Knowledge model only for the Chat with setting:

def create_session(km_id):
    data = {
        "llm_id": "9d38cf09f0ff4fe8ab7b2e714adb55b2", # gpt-4o mini, change the id to any other available model if required
        "mode": "model"
    }
    response = requests.post(f'{API_URL}/knowledge-models/{KM_ID}/chat-sessions', headers=headers, json=data)
    return response.json()

Send a message to the chat session:

def send_message(text, session_id, mode="model"):
    headers = {'X-KM-AccessKey': f'Bearer {API_KEY}'}
    data = {"text": text, "mode": mode}
    response = requests.post(f'{API_URL}/knowledge-models/{KM_ID}/chat-sessions/{session_id}/messages', headers=headers, json=data)
    return response.json()

Get responses from the chat session:

 import time
import logging
import requests

# Constants
AI_MESSAGE_TYPE = "ai_message"
PROCESSING_STATUS = "processing"
DONE_STATUS = "done"

# Configure logging
logging.basicConfig(level=logging.INFO)

def ask(text, session_id, mode="model", timeout=60, request_timeout=15, retry_delay=3):
    response = send_message(text, session_id)
    headers = {'X-KM-AccessKey': f'Bearer {API_KEY}'}
    start_time = time.time()
    
    while True:
        response = requests.get(
            f'{API_URL}/knowledge-models/{KM_ID}/chat-sessions/{session_id}/messages',
            headers=headers,
            timeout=request_timeout
        )
        response.raise_for_status()
        message = response.json()["results"][0]
        
        if message["type"] != AI_MESSAGE_TYPE:
            break
        
        status_name = message["status"]["name"]
        
        if status_name == PROCESSING_STATUS:
            logging.info("Waiting for reply...")
            time.sleep(retry_delay)
        elif status_name == DONE_STATUS:
            return message
        
        if time.time() - start_time > timeout:
            logging.warning("Operation timed out.")
            break
    
    return None

Execute the entire flow:

new_model = create_model("ML in Materials Science", "Overview of the current trends in Materials Science", "all") 
KM_ID = new_model["id"] 

session = create_session(KM_ID)      # Create a new session
session_id = session["id"]           # Get session ID from the response
text = "Hello"                       # Define the text you want to send
answer = ask(text, session_id)       # Send message and retrieve the reply
print(answer)                        # Print the reply

{
  "id": "b60f77c5132149fbb859e87f44ef2b0f",
  "type": "ai_message",
  "created_at": "2024-12-18T08:41:18.058713Z",
  "included_documents": [],
  "mode": "model",
  "status": {
    "name": "done",
    "message": "produce_final_answer"
  },
  "like_status": "none",
  "quick_replies": [],
  "content": {
    "type": "text",
    "text": "I don't know.",
    "references": [],
    "documents": [],
    "links": []
  },
  "llm_id": "9d38cf09f0ff4fe8ab7b2e714adb55b2"
}