Data Types
🗺️ Website
Integrating Entire Website Content into Your Chat Agent
The ability to integrate content from an entire website into your chat agent significantly enhances its knowledge base and conversational capabilities. The website_chat
method is designed for this purpose, allowing for the comprehensive inclusion of information from a website, thereby expanding the agent’s ability to provide detailed and accurate responses.
Function Overview
The website_chat
function enables the integration of an entire website’s content into the chat agent, using a series of parameters to fine-tune the content processing and ensure effective integration.
Parameters
- url (
Optional[str]
): The base URL of the website you want to integrate. This should point to the homepage or a major section of the site whose content you wish to make searchable. - system_prompt (
str
): An optional prompt to guide the system’s approach to processing the website’s content. This can be useful for focusing on particular types of information. - query_wrapper_prompt (
str
): An optional prompt that can improve the relevance of user queries by providing a context specific to the website’s content. - embed_model (
Union[str, EmbedType]
): Specifies the embedding model used for extracting and embedding text from the website. Defaults to a model optimized for general web content. - llm_params (
dict
): Configuration parameters for integrating Large Language Models, enhancing the chat agent’s comprehension of the website content. - vector_store_params (
dict
): Configuration for vector storage, outlining how and where the extracted content embeddings are stored. - service_context_params (
dict
): Additional parameters for customizing the service context for the integrated website content. - chat_engine_params (
dict
): Customization parameters for the chat engine, affecting how the agent utilizes the website content in conversations. - retriever_params (
dict
): Configuration for the document retriever component, determining how the website content is indexed and retrieved based on user queries.
Example Usage
Adding an Entire Website
This example adds the content from the entire Example.com
website to the chat agent.