diff --git a/CHANGELOG.md b/CHANGELOG.md index 09d2f59..7baaf38 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,13 +5,19 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## [Unreleased] + +### Added + +- Added `FastEmbedVectoriser` as a lightweight local embedding backend for HuggingFace-compatible sentence embedding models. +- Added a `fastembed` optional dependency group and documented the lighter install path. + ## [v1.1.1] - 2026-07-08 ### Fixed - Resolved bug in VectorStore.from_filespace when loading in a VectorStore created by classifai v1.0.0. - - v1.1.1 VectorStore.from_filespace now assigns the default batch_size if a pre v1.1.0 VectorStore is loaded. - + - v1.1.1 VectorStore.from_filespace now assigns the default batch_size if a pre v1.1.0 VectorStore is loaded. ## [v1.1.0] - 2026-07-03 @@ -33,7 +39,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Resolved reverse search Error when no matched Documents - ## [v1.0.0] - 2026-03-27 ### Added @@ -41,8 +46,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - AI Agents - Hooks for using genai to perform tasks on VectorStore results. - Hooks Framework - new framework for hooks to support premade and custom hook development. - Server Class Features: - - new methods for instantiating the FastAPI application and/or routing. - - allows middleware to be used, or the routing to be attached to another FastAPI service. + - new methods for instantiating the FastAPI application and/or routing. + - allows middleware to be used, or the routing to be attached to another FastAPI service. - Documentation - new QuartoDocs documenting the ClassifAI package and new demo notebooks. - Partial String matching - reverse search VectorStore method now does optional partial matching. - Vectoriser Class - More options for instantiating HuggingFace models. @@ -53,10 +58,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Documentation - better docstrings and updated demo notebooks. - Dataclasses - updated for more intuitive dataframe column naming. - Server Class Refactor: - - expanded scope of features. - - renamed start_api method to run_server. + - expanded scope of features. + - renamed start_api method to run_server. ### Fixed + - Server hook data - hook metadata now returned in FastAPI responses. - Reverse Search results - fixed issue where max_n_results defaulted to None causing errors. @@ -84,9 +90,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - REST API - FastAPI served with Uvicorn - Documentation and Demo - README and Jupyter Notebook minimal demo with fake dataset. - + [v1.0.0]: https://github.com/datasciencecampus/classifai/compare/v0.2.1...v1.0.0 [v0.2.1]: https://github.com/datasciencecampus/classifai/compare/v0.2.0...v0.2.1 [v0.2.0]: https://github.com/datasciencecampus/classifai/compare/v0.1.0...v0.2.0 -[v0.1.0]: https://github.com/datasciencecampus/classifai/releases/tag/v0.1.0 \ No newline at end of file +[v0.1.0]: https://github.com/datasciencecampus/classifai/releases/tag/v0.1.0 diff --git a/DEMO/README.md b/DEMO/README.md index 5f6474a..a948ffe 100644 --- a/DEMO/README.md +++ b/DEMO/README.md @@ -137,7 +137,8 @@ add as project dependency: `pip install "classifai[]"` -where `` is one or more of `huggingface`,`gcp`,`ollama`, or `all` to install all of them. +where `` is one or more of `huggingface`,`fastembed`,`gcp`,`ollama`, or `all` to install all of them. +Use `fastembed` for the lightest local sentence-embedding install path. ##### Using uv diff --git a/README.md b/README.md index 0c5dc56..d23aadb 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ Use cases: Key Features of the package include: -- Use included vectorisers (including Google Cloud, Huggingface and Ollama embedders) or implement your own +- Use included vectorisers (including FastEmbed, Google Cloud, Huggingface and Ollama embedders) or implement your own - Built in support for custom hook logic - choose from a library of pre-processing and post-processing functions that control the flow of data (spell checking, results deduplication, etc) or write your own hooks - Deploy Easily with FastAPI - Deploy your semantic search classifier with FastAPI capabilities built into the package for easy REST API deployment @@ -72,9 +72,10 @@ The comparison on other aspects, such as per-request speed or hardware requireme ## Installation You can install the package directly from GitHub in your Python environment, using your preferred package manager. -By default, only the minimum dependencies of the base versions will be installed; you must specify +By default, only the minimum dependencies of the base versions will be installed; you must specify `classifai[all]` to install all sets of optional dependencies, or `classifai[huggingface, ...]` to install one or more specific sets of optional dependencies. -The current sets of optional dependencies are `[all, huggingface, ollama, gcp]`. +The current sets of optional dependencies are `[all, huggingface, fastembed, ollama, gcp]`. +Use `classifai[fastembed]` for a lighter local embedding setup that does not require `torch` or `transformers`. ##### Pip ```bash @@ -98,7 +99,7 @@ The size and quality of the knowledgebase dictates the quality of results you ca #### Step 1: Choose a vectoriser -A vectoriser transforms a query text string into an embedding vector. You can choose from embedding models accessed via HuggingFace, Google or Ollama, or build your own vectoriser. +A vectoriser transforms a query text string into an embedding vector. You can choose from embedding models accessed via HuggingFace, a lighter local FastEmbed backend, Google or Ollama, or build your own vectoriser. ```python from classifai.vectorisers import HuggingFaceVectoriser @@ -111,6 +112,8 @@ vector = vectoriser.transform("Example text to vectorize") print(vector.shape) ``` +If you want a lighter local runtime without `torch` or `transformers`, install `classifai[fastembed]` and use `FastEmbedVectoriser` with the same model name. + #### Step 2: Build a vector store You provide a knowledgebase of labelled examples (currently only allows data to be provided as a csv) to build a vector store @@ -141,7 +144,7 @@ print(results) #### Step 4: Deploy as a REST API In addition to using ClassifAI as a local package, you can use it to create / attach to a FastAPI REST API. -You can create a new FastAPI application which you can modify as required, connect it to an existing FastAPI +You can create a new FastAPI application which you can modify as required, connect it to an existing FastAPI application, or deploy it immediately as an REST API service using `uvicorn`. ```python diff --git a/_quarto.yml b/_quarto.yml index c5248e1..210c967 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -51,6 +51,7 @@ website: - docs/vectorisers.base.VectoriserBase.transform.qmd - section: "Specific Vectorisers" contents: + - docs/vectorisers.fastembed.FastEmbedVectoriser.qmd - docs/vectorisers.huggingface.HuggingFaceVectoriser.qmd - docs/vectorisers.ollama.OllamaVectoriser.qmd - docs/vectorisers.gcp.GcpVectoriser.qmd @@ -131,6 +132,7 @@ quartodoc: - vectorisers.base - vectorisers.base.VectoriserBase - vectorisers.base.VectoriserBase.transform + - vectorisers.fastembed.FastEmbedVectoriser - vectorisers.huggingface.HuggingFaceVectoriser - vectorisers.ollama.OllamaVectoriser - vectorisers.gcp.GcpVectoriser @@ -182,4 +184,4 @@ quartodoc: - evaluation.main.Evaluation - evaluation.metrics - evaluation.metrics.Metric - - evaluation.metrics.MetricResult \ No newline at end of file + - evaluation.metrics.MetricResult diff --git a/pyproject.toml b/pyproject.toml index 4cf5ab0..d0448d9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -46,6 +46,9 @@ huggingface = [ "transformers>=4.52.4", "torch>=2.7.1" ] +fastembed = [ + "fastembed>=0.8.0,<1.0.0" +] ollama = [ "ollama>=0.5.1" ] @@ -54,7 +57,7 @@ gcp = [ "gcsfs>=2026.4.0" ] all = [ - "classifai[huggingface,gcp,ollama]" + "classifai[huggingface,fastembed,gcp,ollama]" ] [tool.ruff] diff --git a/src/classifai/vectorisers/__init__.py b/src/classifai/vectorisers/__init__.py index 8f26a0a..272467f 100644 --- a/src/classifai/vectorisers/__init__.py +++ b/src/classifai/vectorisers/__init__.py @@ -27,6 +27,8 @@ This module contains the following 'ready-made' classes: +- `FastEmbedVectoriser`: A lightweight wrapper class for generating text + embeddings with FastEmbed's ONNX backend. - `GcpVectoriser`: A class for embedding text using either Google Cloud Platform's Gemini API or Gemini Enterprise Agent Platform (formerly VertexAI) API. - `HuggingFaceVectoriser`: A general wrapper class for Huggingface Transformers @@ -37,6 +39,9 @@ Each class is designed to interface with a specific service that provides embedding model functionality. +The `FastEmbedVectoriser` class utilizes FastEmbed's ONNX backend to load +sentence embedding models without requiring `torch` or `transformers`. + The `GcpVectoriser` class leverages Google's GenAI API, The `HuggingFaceVectoriser` class utilizes models from the Huggingface Transformers library. @@ -56,11 +61,13 @@ """ from .base import VectoriserBase +from .fastembed import FastEmbedVectoriser from .gcp import GcpVectoriser from .huggingface import HuggingFaceVectoriser from .ollama import OllamaVectoriser __all__ = [ + "FastEmbedVectoriser", "GcpVectoriser", "HuggingFaceVectoriser", "OllamaVectoriser", diff --git a/src/classifai/vectorisers/base.py b/src/classifai/vectorisers/base.py index 7b26767..6ddda4d 100644 --- a/src/classifai/vectorisers/base.py +++ b/src/classifai/vectorisers/base.py @@ -27,6 +27,8 @@ This module contains the following 'ready-made' classes: +- `FastEmbedVectoriser`: A lightweight wrapper class for generating text + embeddings with FastEmbed's ONNX backend. - `GcpVectoriser`: A class for embedding text using Google Cloud Platform's GenAI API. - `HuggingFaceVectoriser`: A general wrapper class for Huggingface Transformers models to generate text embeddings. @@ -36,6 +38,9 @@ Each class is designed to interface with a specific service that provides embedding model functionality. +The `FastEmbedVectoriser` class utilizes FastEmbed's ONNX backend to load +sentence embedding models without requiring `torch` or `transformers`. + The `GcpVectoriser` class leverages Google's GenAI API, The `HuggingFaceVectoriser` class utilizes models from the Huggingface Transformers library. diff --git a/src/classifai/vectorisers/fastembed.py b/src/classifai/vectorisers/fastembed.py new file mode 100644 index 0000000..b180fc7 --- /dev/null +++ b/src/classifai/vectorisers/fastembed.py @@ -0,0 +1,149 @@ +"""A module that provides a wrapper for FastEmbed models to generate text embeddings.""" + +import numpy as np + +from classifai._optional import check_deps +from classifai.exceptions import ExternalServiceError, VectorisationError + +from .base import VectoriserBase + + +class FastEmbedVectoriser(VectoriserBase): + """A lightweight wrapper class for generating embeddings with FastEmbed. + + The `FastEmbedVectoriser` uses FastEmbed's ONNX backend to generate + embeddings from FastEmbed-compatible sentence embedding models without + requiring `torch` or `transformers` as runtime dependencies. The + `model_name` must be a name recognised by FastEmbed. To see all + supported models, you can run: + `FastEmbedVectoriser.list_supported_models()` + + To use a pre-downloaded model in an air-gapped environment, provide both + its official FastEmbed `model_name` and its local directory through + `specific_model_path`. FastEmbed uses `model_name` to identify the model + configuration and `specific_model_path` to locate its local ONNX files. + + Attributes: + model_name (str): The official FastEmbed name of the embedding model. + model (fastembed.TextEmbedding): The FastEmbed model instance. + specific_model_path (str | None): The path of the local FastEmbed model. + """ + + def __init__( + self, + model_name: str, + specific_model_path: str | None = None, + model_kwargs: dict | None = None, + ): + """Initialises the FastEmbedVectoriser with the specified model name. + + Args: + model_name (str): The official name of the embedding model for FastEmbed + (e.g., "sentence-transformers/all-MiniLM-L6-v2"). + specific_model_path (str | None): The local directory containing + the pre-downloaded ONNX model. To run offline, provide this + value together with the model's official `model_name`. + Defaults to None. + model_kwargs (dict): [optional] Additional keyword arguments to + pass to the model (e.g., `cache_dir`). Defaults to None. + + Raises: + `ExternalServiceError`: If the FastEmbed model cannot be loaded. + """ + check_deps(["fastembed"], extra="fastembed") + from fastembed import TextEmbedding # type: ignore + + self.model_name = model_name + self.specific_model_path = specific_model_path + model_kwargs = dict(model_kwargs or {}) + + if self.specific_model_path is not None: + model_kwargs["specific_model_path"] = str(self.specific_model_path) + + try: + self.model = TextEmbedding(model_name=self.model_name, **model_kwargs) + except Exception as e: + raise ExternalServiceError( + "Failed to load FastEmbed model.", + context={ + "vectoriser": "fastembed", + "model": self.model_name, + "cause": str(e), + "cause_type": type(e).__name__, + }, + ) from e + + def transform(self, texts: str | list[str]) -> np.ndarray: + """Transforms input text(s) into embeddings using FastEmbed. + + Args: + texts (str | list[str]): The input text(s) to embed. Can be a + single string or a list of strings. + + Returns: + numpy.ndarray: A 2D array of embeddings, where each row + corresponds to an input text. + + Raises: + `VectorisationError`: If FastEmbed fails to generate or parse + embeddings. + """ + # If a single string is passed as arg to texts, convert to list + if isinstance(texts, str): + texts = [texts] + + try: + raw_embeddings = list(self.model.embed(texts)) + except Exception as e: + raise VectorisationError( + "Failed to generate embeddings using FastEmbed.", + context={ + "vectoriser": "fastembed", + "model": self.model_name, + "n_texts": len(texts), + "cause": str(e), + "cause_type": type(e).__name__, + }, + ) from e + + try: + embeddings = np.asarray(raw_embeddings, dtype=np.float32) + except Exception as e: + raise VectorisationError( + "Failed to convert FastEmbed embeddings to a numpy array.", + context={ + "vectoriser": "fastembed", + "model": self.model_name, + "n_texts": len(texts), + "cause": str(e), + "cause_type": type(e).__name__, + }, + ) from e + + if embeddings.ndim == 1: + embeddings = embeddings.reshape(1, -1) + + if embeddings.ndim != 2: # noqa: PLR2004 + raise VectorisationError( + "FastEmbed returned embeddings with an unexpected shape.", + context={ + "vectoriser": "fastembed", + "model": self.model_name, + "n_texts": len(texts), + "shape": list(embeddings.shape), + }, + ) + + return embeddings + + @staticmethod + def list_supported_models() -> list[dict[str, any]]: + """Wrapper to list the supported models. + + Returns: + list[dict[str, Any]]: A list of dictionaries containing the model information. + """ + check_deps(["fastembed"], extra="fastembed") + from fastembed import TextEmbedding # type: ignore + + return TextEmbedding.list_supported_models() diff --git a/uv.lock b/uv.lock index 1f321f4..2ec6960 100644 --- a/uv.lock +++ b/uv.lock @@ -580,12 +580,16 @@ dependencies = [ [package.optional-dependencies] all = [ + { name = "fastembed" }, { name = "gcsfs" }, { name = "google-genai" }, { name = "ollama" }, { name = "torch" }, { name = "transformers" }, ] +fastembed = [ + { name = "fastembed" }, +] gcp = [ { name = "gcsfs" }, { name = "google-genai" }, @@ -622,8 +626,9 @@ test = [ [package.metadata] requires-dist = [ - { name = "classifai", extras = ["huggingface", "gcp", "ollama"], marker = "extra == 'all'" }, + { name = "classifai", extras = ["huggingface", "fastembed", "gcp", "ollama"], marker = "extra == 'all'" }, { name = "fastapi", extras = ["standard"], specifier = ">=0.115.11" }, + { name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.8.0,<1.0.0" }, { name = "fsspec", specifier = ">=2026.3.0" }, { name = "gcsfs", marker = "extra == 'gcp'", specifier = ">=2026.4.0" }, { name = "google-genai", marker = "extra == 'gcp'", specifier = ">=1.68.0" }, @@ -638,7 +643,7 @@ requires-dist = [ { name = "transformers", marker = "extra == 'huggingface'", specifier = ">=4.52.4" }, { name = "uvicorn", specifier = ">0.34.0" }, ] -provides-extras = 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