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.

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Prerequisites

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

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Base URL

Update the BASE_URL variable to match the server’s actual address if necessary. 
BASE_URL = "<http://localhost:8000>"

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Creating a Chatbot Configuration

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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}")

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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}")

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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}")

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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}")

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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}")

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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}")

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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.