> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lyzr.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# 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

1. Ensure that the Docker container is running.
2. Install the `requests` library if you haven't already:

   ```bash theme={null}
   pip install requests

   ```

### Base URL

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

```python theme={null}
BASE_URL = "<http://localhost:8000>"

```

### Creating a Chatbot Configuration

### Sample Configuration

```json theme={null}
{
    "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.

```python theme={null}
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.

```python theme={null}
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.

```python theme={null}
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.

```python theme={null}
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.

```python theme={null}
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.

```python theme={null}
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.

```python theme={null}
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.

```python theme={null}
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.