Function reference

Vectorisers

Utilities to project text into numerical representation in a semantic vector space

vectorisers	This module provides classes for creating and utilizing embedding models from different services.
vectorisers.base	This module provides classes for creating and utilizing embedding models from different services.
vectorisers.base.VectoriserBase	Abstract base class for all vectorisers.
vectorisers.base.VectoriserBase.transform	Transforms input text(s) into embeddings.
vectorisers.huggingface.HuggingFaceVectoriser	A general wrapper class for Huggingface Transformers models to generate text embeddings.
vectorisers.ollama.OllamaVectoriser	A wrapper class allowing a locally-running ollama server to generate text embeddings.
vectorisers.gcp.GcpVectoriser	A class for embedding text using Google Cloud Platform’s GenAI API.

Indexers

Creation of Vector Stores for efficient similarity search and retrieval

indexers	This module provides functionality for creating a vector index from a CSV (text)
indexers.main	This module provides functionality for creating a vector index from a text file.
indexers.VectorStore	A class to model and create ‘VectorStore’ objects for building and searching vector databases from CSV text files.
indexers.dataclasses
indexers.dataclasses.VectorStoreSearchInput	DataFrame-like object for forming and validating search query input data.
indexers.dataclasses.VectorStoreSearchOutput	DataFrame-like object for storing and validating search results with rankings
indexers.dataclasses.VectorStoreReverseSearchInput	DataFrame-like object for forming and validating reverse search query
indexers.dataclasses.VectorStoreReverseSearchOutput	DataFrame-like object for storing reverse search results.
indexers.dataclasses.VectorStoreEmbedInput	DataFrame-like object for forming and validating text data to be embedded.
indexers.dataclasses.VectorStoreEmbedOutput	DataFrame-like object for storing and validating embedded vectors and associated
indexers.VectorStore.embed	Converts text into vector embeddings using the vectoriser and returns a VectorStoreEmbedOutput dataframe with columns ‘id’, ‘text’, and ‘embedding’.
indexers.VectorStore.search	Searches the vector store using queries from a VectorStoreSearchInput object and returns
indexers.VectorStore.reverse_search	Reverse searches the vector store using a VectorStoreReverseSearchInput object
indexers.VectorStore.from_filespace	Creates a VectorStore instance from stored metadata and Parquet files.

Servers

Expose ClassifAI functionality via Fast-API endpoints

servers	This module provides functionality for creating or extending a REST-API service
servers.main	This module provides functionality for creating a start a restAPI service which
servers.get_router	Create and return a FastAPI router with search endpoints.
servers.get_server	Create and return a FastAPI server with search endpoints.
servers.run_server	Create and run a FastAPI server with search endpoints.
servers.make_endpoints	Create and register the different endpoints to your app.