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document-vectorizer

Description

Note

More information about the service specification can be found in the Core concepts > Service documentation.

This service uses langchain to vectorize a pdf document into a Facebook AI Similarity Search (FAISS) vectorstore.

FAISS is a library that allows developers to quickly search for embeddings of multimedia documents that are similar to each other

The API documentation is automatically generated by FastAPI using the OpenAPI standard. A user friendly interface provided by Swagger is available under the /docs route, where the endpoints of the service are described.

This simple service only has one route /compute that takes a pdf document as input, and then returns a zip archive containing the vectorized document.

This archive can be used in the chatbot-ollama service. This chatbot enables you to ask questions about the content of the document to a large language model.

Environment variables

Check the Core concepts > Service > Environment variables documentation for more details.

Run the tests with Python

Check the Core concepts > Service > Run the tests with Python documentation for more details.

Start the service locally

Check the Core concepts > Service > Start the service locally documentation for more details.