Theneo

Theneo Assistant for AI Search is a powerful platform designed to assist developers and users in creating comprehensive API documentation. Leveraging the capabilities of GPT AI search, this service offers an intelligent and efficient solution for analyzing documentation and generating accurate descriptions.

With Theneo Assistant, developers can easily navigate through the API documentation and extract relevant information. By utilizing GPT AI search, it goes beyond simple keyword matching and employs natural language understanding to comprehend the context and intent behind queries.

The platform's advanced search capabilities enable users to quickly find specific API methods, parameters, and examples. Theneo Assistant understands the nuances of technical language and can provide accurate descriptions, syntax, and usage guidelines for each API endpoint.

Theneo Assistant for AI Search also assists in creating comprehensive and user-friendly documentation. It can generate clear and concise descriptions, tutorials, and code samples, helping developers understand the API's functionalities and how to integrate them into their projects effectively.

Furthermore, Theneo Assistant for AI Search continuously learns from user interactions, allowing it to improve its understanding of API documentation over time. This adaptive learning feature enhances the accuracy and relevance of the search results and descriptions provided.

In summary, Theneo Assistant for AI Search is a valuable tool for developers and users seeking to create API documentation. By harnessing the power of GPT AI search, it offers an intelligent and efficient solution for navigating, analyzing, and generating comprehensive descriptions for APIs, simplifying the process of understanding and utilizing these powerful tools.


Theneo Assistant Service API

health

Health check for Theneo Assistant Service service

Health check for service

ingestion

## Ingestion of api documentation.

The ingestion is done asynchronously and the ingestion id is returned to the client to track the ingestion status.

The ingestion will be internally by workers, which will like an ETL process.
* It will transform the documents to a format that is suitable for the internal search,
* and then it will be saved in the internal storage with additional embedding returned from Large language model.


Returns: then ingestion id if the ingestion request was successfully accepted

Ingestion

Get Ingestion Metadata By Project Id

Delete Ingestion

Get Ingestion Metadata By Project Id And Published Version

query

Ask sage for an answer related to api documentation of the project_id that was already ingested and processed.
`question` is a `QuestionRequest` which contains the project id and the question to ask sage.
also, the `question` can contain a model name, if the model name is not provided, then the default LLM model will be used.


Return: the answer to the question using streaming response to avoid waiting for the whole answer to be ready

Sections