Function reference

Vectorisers

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

vectorisers	This module provides classes for creating and utilising embedding models from different services.
vectorisers.base	This module provides classes for creating and utilising 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.

Creation of Vector Stores for efficient similarity search and retrieval

indexers	This module provides functionality for creating a `VectorStore` from a CSV (text) file.
indexers.main	Provides functionality for creating a `VectorStore` from a CSV file.
indexers.VectorStore	Models and creates vector databases from CSV text files.
indexers.dataclasses	Dataclasses for structuring and validating VectorStore input and output data.
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.
indexers.dataclasses.VectorStoreReverseSearchInput	DataFrame-like object for forming and validating reverse search query input data.
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.
indexers.VectorStore.embed	Generates vector embeddings from a `VectorStoreEmbedInput` object.
indexers.VectorStore.search	Queries the `vectors` attribute for the most similar documents.
indexers.VectorStore.reverse_search	Looks up documents in `vectors` by label.
indexers.VectorStore.from_filespace	Creates a `VectorStore` instance from a saved filespace folder.

Creation of Pre and post Processing hooks for improved search and RAG

indexers.hooks	This module contains a factory for, and example pre-built hooks. Allowing users to manipulate the content of ClassifAI dataclass objects as they enter or leave the VectorStore.
indexers.hooks.hook_factory
indexers.hooks.default_hooks	Submodule containing the prebuilt hooks for the service.
indexers.hooks.default_hooks.preprocessing
indexers.hooks.default_hooks.postprocessing
indexers.hooks.hook_factory.HookBase	Abstract base class for all hooks requiring customisation.
indexers.hooks.default_hooks.preprocessing.CapitalisationStandardisingHook	A pre-processing hook to handle upper-/lower-/sentence-/title-casing.
indexers.hooks.default_hooks.postprocessing.DeduplicationHook	A post-processing hook to remove duplicate knowledgebase entries.
indexers.hooks.default_hooks.postprocessing.RagHook	A post-processing hook to perform Retrieval Augmented Generation.

Expose ClassifAI functionality via Fast-API endpoints

servers	REST API integration for ClassifAI vector stores.
servers.main	Utilities for building and running the ClassifAI REST API.
servers.get_router	Create and return a FastAPI APIRouter 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.

Utilities for evaluating the performance of VectorStores

evaluation	Evaluation utilities for ClassifAI.
evaluation.main	This module evaluates one or more `classifai.indexers.VectorStore` instances on a ground-truth labelled dataset.
evaluation.main.Evaluation	Evaluation class for assessing the performance of vectorstores against ground truth data.
evaluation.metrics	Metrics.py provides a set of evaluation metrics for multiclass, single-label classification tasks.
evaluation.metrics.Metric	Base class for all classification metrics.
evaluation.metrics.MetricResult	Represents the result of a metric evaluation.