Chat with the Global Knowledge Base

This guide shows how to create a chat session using the shared global knowledge base — no file uploads needed.

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Prerequisites

• Credentials for a hosted SmartChat RAG API with a CHAT_USER role
• A pre-configured SmartChat RAG API server

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1. Authenticate

import requests
import json

base_url = "<BASE_URL>"

payload = json.dumps({"username": "<USERNAME>", "password": "<PASSWORD>"})
headers = {"Content-Type": "application/json"}

response = requests.post(f"{base_url}/api/v1/auth/user", headers=headers, data=payload)
headers["Authorization"] = f"Bearer {response.json()['access_token']}"

2. Get Default Chat Configuration

response = requests.get(f"{base_url}/config-manager/api/v1/user/configs", headers=headers)

configs = response.json()
default_config = [c for c in configs if c["userGroupId"] == "default"][0]

default_local_config_id = default_config["localKbConfigs"][0]["id"]
allowed_llms = default_config["localKbConfigs"][0]["allowed_llms"]

3. Create a Global Chat Session

body = {
    "title": "Testing the SmartChat RAG API",
    "config": {
        "localConfigId": default_local_config_id,
        "globalContext": True,
        "chatModel": allowed_llms[0]["name"],
    },
}
response = requests.post(f"{base_url}/chat-session-manager/api/v1/sessions/", headers=headers, json=body)
session_id = response.json()["sessionId"]

4. Chat

body = {
    "sessionId": session_id,
    "userPrompt": "Can you summarize the context to me?",
}
response = requests.post(f"{base_url}/query-pipelines/api/v1/chat", headers=headers, json=body)
print(response.json())