Chat Agent API Documentation

This document explains how to interact with the Lyzr Chatbot API using Python scripts. The examples use the requests library to perform HTTP requests to the API endpoints.

Prerequisites

Ensure that the Docker container is running.
Install the requests library if you haven’t already:
pip install requests

Base URL

Update the BASE_URL variable to match the server’s actual address if necessary.

BASE_URL = "<http://localhost:8000>"

Creating a Chatbot Configuration

Sample Configuration

{
    "exclude_hidden": true,
    "filename_as_id": true,
    "recursive": true,
    "required_exts": [
        "string"
    ],
    "system_prompt": "",
    "query_wrapper_prompt": "",
    "embed_model": {
        "embedding_type": "OpenAIEmbedding",
        "model ": "text-embedding-ada-002",
        "api_key": "sk-"
    },
    "llm_params": {
        "model": "gpt-4-1106-preview",
        "api_key": "sk-",
        "stream": true
    },
    "vector_store_params": {
        "vector_store_type": "WeaviateVectorStore",
        "url": "https://<domain>.weaviate.network",
        "api_key": "",
        "index_name": "LyzrCore"
    },
    "service_context_params": {},
    "chat_engine_params": {
        "chat_mode": "context",
        "similarity_top_k": 50,
        "system_prompt": "You are a helpful Assistant"
    },
    "retriever_params": {
        "retriever_type": "Simple"
    }
}

To create a new chatbot configuration, send a POST request to the /create/config endpoint.

import requests
import json

# Define the configuration
config = {
    "exclude_hidden": True,
    "filename_as_id": True,
    "recursive": True,
    "required_exts": ["string"],
    "system_prompt": "",
    "query_wrapper_prompt": "",
    "embed_model": {
        "embedding_type": "OpenAIEmbedding",
        "model": "text-embedding-ada-002",
        "api_key": "sk-"
    },
    "llm_params": {
        "model": "gpt-4-1106-preview",
        "api_key": "sk-",
        "stream": True
    },
    "vector_store_params": {
        "vector_store_type": "WeaviateVectorStore",
        "url": "https://<domain>.weaviate.network",
        "api_key": "",
        "index_name": "LyzrCore"
    },
    "service_context_params": {},
    "chat_engine_params": {
        "chat_mode": "context",
        "similarity_top_k": 50,
        "system_prompt": "You are a helpful Assistant"
    },
    "retriever_params": {
        "retriever_type": "Simple"
    }
}

# Send the request
response = requests.post(f"{BASE_URL}/create/config", json=config)
response.raise_for_status()
config_id = response.json()
print(f"Config ID: {config_id}")

Creating a Chatbot from PDF Files

To create a new chatbot instance from multiple PDF files, send a POST request to the /create/from-pdf/{config_id} endpoint.

import requests

# Define the PDF files to upload
files = [
    ('files', ('test1.pdf', open('path/to/test1.pdf', 'rb'), 'application/pdf')),
    ('files', ('test2.pdf', open('path/to/test2.pdf', 'rb'), 'application/pdf'))
]

# Send the request
response = requests.post(f"{BASE_URL}/create/from-pdf/{config_id}", files=files)
response.raise_for_status()
chatbot_info = response.json()
print(f"Chatbot Info: {chatbot_info}")

Checking Chatbot Status

To check if a chatbot instance exists, send a GET request to the /check/{chatbot_id} endpoint.

import requests

# Send the request
response = requests.get(f"{BASE_URL}/check/{chatbot_info['chatbot_id']}")
response.raise_for_status()
status = response.json()
print(f"Chatbot Status: {status}")

Chatting with the Chatbot

To interact with a specific chatbot instance, send a POST request to the /chat/{chatbot_id} endpoint with a message and chat history.

import requests

# Define the chat history
chat_history = [
    {"role": "user", "content": "Hello, how are you?"}
]

# Define the data to send
data = {
    "message": "Tell me about the PDFs",
    "chat_history": chat_history
}

# Send the request
response = requests.post(f"{BASE_URL}/chat/{chatbot_info['chatbot_id']}", json=data)
response.raise_for_status()
chat_response = response.json()
print(f"Chat Response: {chat_response}")

Health Check

To perform a health check on the API, send a GET request to the /health endpoint.

import requests

# Send the request
response = requests.get(f"{BASE_URL}/health")
response.raise_for_status()
health_status = response.json()
print(f"Health Status: {health_status}")

Recreating a Chatbot with New Configuration

To recreate a chatbot instance with a new configuration, send a POST request to the /recreate/{chatbot_id}/{config_id} endpoint with the new configuration.

import requests
import json

# Define the new configuration
new_config = {
    "name": "UpdatedChatBot",
    "description": "This is an updated test chatbot",
    "version": "1.1"
}

# Send the request
response = requests.post(f"{BASE_URL}/recreate/{chatbot_info['chatbot_id']}/{config_id}", json=new_config)
response.raise_for_status()
recreate_status = response.json()
print(f"Recreate Status: {recreate_status}")

Creating a Chatbot from DOCX Files

To create a new chatbot instance from multiple DOCX files, send a POST request to the /create/from-docx/{config_id} endpoint.

import requests

# Define the DOCX files to upload
files = [
    ('files', ('test1.docx', open('path/to/test1.docx', 'rb'), 'application/vnd.openxmlformats-officedocument.wordprocessingml.document')),
    ('files', ('test2.docx', open('path/to/test2.docx', 'rb'), 'application/vnd.openxmlformats-officedocument.wordprocessingml.document'))
]

# Send the request
response = requests.post(f"{BASE_URL}/create/from-docx/{config_id}", files=files)
response.raise_for_status()
chatbot_info = response.json()
print(f"Chatbot Info: {chatbot_info}")

Creating a Chatbot from Text Files

To create a new chatbot instance from multiple text files, send a POST request to the /create/from-text/{config_id} endpoint.

import requests

# Define the text files to upload
files = [
    ('files', ('test1.txt', open('path/to/test1.txt', 'rb'), 'text/plain')),
    ('files', ('test2.txt', open('path/to/test2.txt', 'rb'), 'text/plain'))
]

# Send the request
response = requests.post(f"{BASE_URL}/create/from-text/{config_id}", files=files)
response.raise_for_status()
chatbot_info = response.json()
print(f"Chatbot Info: {chatbot_info}")

This documentation provides step-by-step instructions on how to interact with the Chatbot API using Python scripts. Adjust the BASE_URL as necessary to match the server’s actual address.

Introduction

Key Concepts

Chat Agent API Documentation

Prerequisites

Base URL

Creating a Chatbot Configuration

Sample Configuration

Creating a Chatbot from PDF Files

Checking Chatbot Status

Chatting with the Chatbot

Health Check

Recreating a Chatbot with New Configuration

Creating a Chatbot from DOCX Files

Creating a Chatbot from Text Files

​Prerequisites

​Base URL

​Creating a Chatbot Configuration

​Sample Configuration

​Creating a Chatbot from PDF Files

​Checking Chatbot Status

​Chatting with the Chatbot

​Health Check

​Recreating a Chatbot with New Configuration

​Creating a Chatbot from DOCX Files

​Creating a Chatbot from Text Files

Prerequisites

Base URL

Creating a Chatbot Configuration

Sample Configuration

Creating a Chatbot from PDF Files

Checking Chatbot Status

Chatting with the Chatbot

Health Check

Recreating a Chatbot with New Configuration

Creating a Chatbot from DOCX Files

Creating a Chatbot from Text Files