From 44decb6d1b907f8a2d26e6b9af6a1d98334eec2a Mon Sep 17 00:00:00 2001 From: fzowl Date: Mon, 13 May 2024 22:50:48 +0200 Subject: [PATCH 01/13] VoyageAI vectorizer and reranker --- .github/workflows/run_tests.yml | 2 + README.md | 1 + docs/api/reranker.rst | 12 + docs/api/vectorizer.rst | 11 + docs/user_guide/getting_started_01.ipynb | 2 +- docs/user_guide/rerankers_06.ipynb | 48 +- docs/user_guide/vectorizers_04.ipynb | 71 ++ poetry.lock | 1321 +++++++++++++++------- pyproject.toml | 2 + redisvl/utils/rerank/__init__.py | 2 + redisvl/utils/rerank/voyageai.py | 198 ++++ redisvl/utils/vectorize/__init__.py | 2 + redisvl/utils/vectorize/text/voyageai.py | 341 ++++++ tests/integration/test_rerankers.py | 25 +- tests/integration/test_vectorizers.py | 22 +- 15 files changed, 1659 insertions(+), 401 deletions(-) create mode 100644 redisvl/utils/rerank/voyageai.py create mode 100644 redisvl/utils/vectorize/text/voyageai.py diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml index c5c389a9..b4b63623 100644 --- a/.github/workflows/run_tests.yml +++ b/.github/workflows/run_tests.yml @@ -61,6 +61,7 @@ jobs: GCP_LOCATION: ${{ secrets.GCP_LOCATION }} GCP_PROJECT_ID: ${{ secrets.GCP_PROJECT_ID }} COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }} + VOYAGE_API_KEY: ${{ secrets.VOYAGE_API_KEY }} AZURE_OPENAI_API_KEY: ${{secrets.AZURE_OPENAI_API_KEY}} AZURE_OPENAI_ENDPOINT: ${{secrets.AZURE_OPENAI_ENDPOINT}} AZURE_OPENAI_DEPLOYMENT_NAME: ${{secrets.AZURE_OPENAI_DEPLOYMENT_NAME}} @@ -80,6 +81,7 @@ jobs: GCP_LOCATION: ${{ secrets.GCP_LOCATION }} GCP_PROJECT_ID: ${{ secrets.GCP_PROJECT_ID }} COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }} + VOYAGE_API_KEY: ${{ secrets.VOYAGE_API_KEY }} AZURE_OPENAI_API_KEY: ${{secrets.AZURE_OPENAI_API_KEY}} AZURE_OPENAI_ENDPOINT: ${{secrets.AZURE_OPENAI_ENDPOINT}} AZURE_OPENAI_DEPLOYMENT_NAME: ${{secrets.AZURE_OPENAI_DEPLOYMENT_NAME}} diff --git a/README.md b/README.md index bc8b8029..9d16ae8d 100644 --- a/README.md +++ b/README.md @@ -216,6 +216,7 @@ Integrate with popular embedding models and providers to greatly simplify the pr - [OpenAI](https://www.redisvl.com/api/vectorizer.html#openaitextvectorizer) - [HuggingFace](https://www.redisvl.com/api/vectorizer.html#hftextvectorizer) - [GCP VertexAI](https://www.redisvl.com/api/vectorizer.html#vertexaitextvectorizer) +- [VoyageAI](https://www.redisvl.com/api/vectorizer/html#voyageaitextvectorizer) ```python from redisvl.utils.vectorize import CohereTextVectorizer diff --git a/docs/api/reranker.rst b/docs/api/reranker.rst index dd9fd425..dc4a13d7 100644 --- a/docs/api/reranker.rst +++ b/docs/api/reranker.rst @@ -12,3 +12,15 @@ CohereReranker .. autoclass:: CohereReranker :show-inheritance: :members: + + +VoyageAIReranker +================ + +.. _voyageaireranker_api: + +.. currentmodule:: redisvl.utils.rerank.voyageai + +.. autoclass:: VoyageAIReranker + :show-inheritance: + :members: diff --git a/docs/api/vectorizer.rst b/docs/api/vectorizer.rst index 8f2d8a97..f76d4b0a 100644 --- a/docs/api/vectorizer.rst +++ b/docs/api/vectorizer.rst @@ -49,3 +49,14 @@ CohereTextVectorizer :show-inheritance: :members: + +VoyageAITextVectorizer +==================== + +.. _voyageaitextvectorizer_api: + +.. currentmodule:: redisvl.utils.vectorize.text.voyageai + +.. autoclass:: VoyageAITextVectorizer + :show-inheritance: + :members: diff --git a/docs/user_guide/getting_started_01.ipynb b/docs/user_guide/getting_started_01.ipynb index 8fc40a0e..d4eee5ab 100644 --- a/docs/user_guide/getting_started_01.ipynb +++ b/docs/user_guide/getting_started_01.ipynb @@ -413,7 +413,7 @@ "source": [ "## Creating `VectorQuery` Objects\n", "\n", - "Next we will create a vector query object for our newly populated index. This example will use a simple vector to demonstrate how vector similarity works. Vectors in production will likely be much larger than 3 floats and often require Machine Learning models (i.e. Huggingface sentence transformers) or an embeddings API (Cohere, OpenAI). `redisvl` provides a set of [Vectorizers](https://www.redisvl.com/user_guide/vectorizers_04.html#openai) to assist in vector creation." + "Next we will create a vector query object for our newly populated index. This example will use a simple vector to demonstrate how vector similarity works. Vectors in production will likely be much larger than 3 floats and often require Machine Learning models (i.e. Huggingface sentence transformers) or an embeddings API (Cohere, OpenAI, VoyageAI). `redisvl` provides a set of [Vectorizers](https://www.redisvl.com/user_guide/vectorizers_04.html#openai) to assist in vector creation." ] }, { diff --git a/docs/user_guide/rerankers_06.ipynb b/docs/user_guide/rerankers_06.ipynb index d3ef7a66..72c54e0f 100644 --- a/docs/user_guide/rerankers_06.ipynb +++ b/docs/user_guide/rerankers_06.ipynb @@ -9,7 +9,7 @@ "\n", "In this notebook, we will show how to use RedisVL to rerank search results\n", "(documents or chunks or records) based on the input query. Today RedisVL\n", - "supports reranking through the [Cohere /rerank API](https://docs.cohere.com/docs/rerank-2).\n", + "supports reranking through the [Cohere /rerank API](https://docs.cohere.com/docs/rerank-2) or [VoyageAI /rerank API](https://docs.voyageai.com/docs/reranker).\n", "\n", "Before running this notebook, be sure to:\n", "1. Have installed ``redisvl`` and have that environment active for this notebook.\n", @@ -75,7 +75,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Init the Reranker\n", + "### Init the Cohere Reranker\n", "\n", "Initialize the reranker. Install the cohere library and provide the right Cohere API Key." ] @@ -113,12 +113,54 @@ ] }, { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Init the VoyageAI Reranker\n", + "\n", + "Initialize the reranker. Install the voyageai library and provide the right VoyageAI API Key." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install voyageai" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "\n", + "# setup the API Key\n", + "api_key = os.environ.get(\"VOYAGE_API_KEY\") or getpass.getpass(\"Enter your VoyageAI API key: \")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from redisvl.utils.rerank import VoyageAIReranker\n", + "\n", + "reranker = VoyageAIReranker(model=\"rerank-lite-1\", limit=3, api_config={\"api_key\": api_key})", + "# Please check the available models at https://docs.voyageai.com/docs/reranker" + ] + }, + { "cell_type": "markdown", "metadata": {}, "source": [ "### Rerank documents\n", "\n", - "Below we will use the `CohereReranker` to rerank and also truncate the list of\n", + "Below we will use the `CohereReranker` or the `VoyageAIReranker` to rerank and also truncate the list of\n", "documents above based on relevance to the initial query." ] }, diff --git a/docs/user_guide/vectorizers_04.ipynb b/docs/user_guide/vectorizers_04.ipynb index d102b71e..a6308b28 100644 --- a/docs/user_guide/vectorizers_04.ipynb +++ b/docs/user_guide/vectorizers_04.ipynb @@ -12,6 +12,7 @@ "2. HuggingFace\n", "3. Vertex AI\n", "4. Cohere\n", + "5. VoyageAI\n", "\n", "Before running this notebook, be sure to\n", "1. Have installed ``redisvl`` and have that environment active for this notebook.\n", @@ -501,6 +502,76 @@ ] }, { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### VoyageAI\n", + "\n", + "[VoyageAI](https://dash.voyageai.com/) allows you to implement language AI into your product. The `VoyageAITextVectorizer` makes it simple to use RedisVL with the embeddings models at VoyageAI. For this you will need to install `voyageai`.\n", + "\n", + "```bash\n", + "pip install voyageai\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "# setup the API Key\n", + "api_key = os.environ.get(\"VOYAGE_API_KEY\") or getpass.getpass(\"Enter your VoyageAI API key: \")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Special attention needs to be paid to the `input_type` parameter for each `embed` call. For example, for embedding \n", + "queries, you should set `input_type='query'`; for embedding documents, set `input_type='document'`. See\n", + "more information [here](https://docs.voyageai.com/docs/embeddings)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vector dimensions: 1024\n", + "[0.015814896672964096, 0.046988241374492645, -0.00518248463049531, -0.05383478105068207, -0.015586535446345806, -0.0837097093462944, 0.03744547441601753, -0.007797810714691877, 0.00717928446829319, 0.06857716292142868]\n", + "Vector dimensions: 1024\n", + "[0.006725038401782513, 0.01441393606364727, -0.030212024226784706, -0.06782275438308716, -0.021446991711854935, -0.07667966187000275, 0.01804908737540245, -0.015767497941851616, -0.02152789570391178, 0.049741245806217194]\n" + ] + } + ], + "source": [ + "from redisvl.utils.vectorize import VoyageAITextVectorizer\n", + "\n", + "# create a vectorizer\n", + "vo = VoyageAITextVectorizer(\n", + " model=\"voyage-law-2\", # Please check the available models at https://docs.voyageai.com/docs/embeddings\n", + " api_config={\"api_key\": api_key},\n", + ")\n", + "\n", + "# embed a search query\n", + "test = vo.embed(\"This is a test sentence.\", input_type='query')\n", + "print(\"Vector dimensions: \", len(test))\n", + "print(test[:10])\n", + "\n", + "# embed a document\n", + "test = vo.embed(\"This is a test sentence.\", input_type='document')\n", + "print(\"Vector dimensions: \", len(test))\n", + "print(test[:10])" + ] + }, + { "cell_type": "markdown", "metadata": {}, "source": [ diff --git a/poetry.lock b/poetry.lock index afee9c3d..99aadeeb 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,19 +1,144 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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google-cloud-aiplatform = { version = ">=1.26", optional = true } cohere = { version = ">=4.44", optional = true } +voyageai = { version = ">=0.2.2", optional = true } [tool.poetry.extras] openai = ["openai"] sentence-transformers = ["sentence-transformers"] google_cloud_aiplatform = ["google_cloud_aiplatform"] cohere = ["cohere"] +voyageai = ["voyageai"] [tool.poetry.group.dev.dependencies] black = ">=20.8b1" diff --git a/redisvl/utils/rerank/__init__.py b/redisvl/utils/rerank/__init__.py index ef7fa9e4..b2f2030f 100644 --- a/redisvl/utils/rerank/__init__.py +++ b/redisvl/utils/rerank/__init__.py @@ -1,7 +1,9 @@ from redisvl.utils.rerank.base import BaseReranker from redisvl.utils.rerank.cohere import CohereReranker +from redisvl.utils.rerank.voyageai import VoyageAIReranker __all__ = [ "BaseReranker", "CohereReranker", + "VoyageAIReranker", ] diff --git a/redisvl/utils/rerank/voyageai.py b/redisvl/utils/rerank/voyageai.py new file mode 100644 index 00000000..bcca4982 --- /dev/null +++ b/redisvl/utils/rerank/voyageai.py @@ -0,0 +1,198 @@ +import os +from typing import Any, Dict, List, Optional, Tuple, Union + +from pydantic.v1 import PrivateAttr + +from redisvl.utils.rerank.base import BaseReranker + + +class VoyageAIReranker(BaseReranker): + """ + The VoyageAIReranker class uses VoyageAI's API to rerank documents based on an + input query. + + This reranker is designed to interact with VoyageAI's /rerank API, + requiring an API key for authentication. The key can be provided + directly in the `api_config` dictionary or through the `VOYAGE_API_KEY` + environment variable. User must obtain an API key from VoyageAI's website + (https://dash.voyageai.com/). Additionally, the `voyageai` python + client must be installed with `pip install voyageai`. + + .. code-block:: python + + from redisvl.utils.rerank import VoyageAIReranker + + # set up the VoyageAI reranker with some configuration + reranker = VoyageAIReranker(rank_by=["content"], limit=2) + # rerank raw search results based on user input/query + results = reranker.rank( + query="your input query text here", + docs=[ + {"content": "document 1"}, + {"content": "document 2"}, + {"content": "document 3"} + ] + ) + """ + + _client: Any = PrivateAttr() + _aclient: Any = PrivateAttr() + + def __init__( + self, + model: str, + rank_by: Optional[List[str]] = None, + limit: int = 5, + return_score: bool = True, + api_config: Optional[Dict] = None, + ) -> None: + """ + Initialize the VoyageAIReranker with specified model, ranking criteria, + and API configuration. + + Parameters: + model (str): The identifier for the VoyageAI model used for reranking. + rank_by (Optional[List[str]]): Optional list of keys specifying the + attributes in the documents that should be considered for + ranking. None means ranking will rely on the model's default + behavior. + limit (int): The maximum number of results to return after + reranking. Must be a positive integer. + return_score (bool): Whether to return scores alongside the + reranked results. + api_config (Optional[Dict], optional): Dictionary containing the API key. + Defaults to None. + + Raises: + ImportError: If the voyageai library is not installed. + ValueError: If the API key is not provided. + """ + super().__init__( + model=model, rank_by=rank_by, limit=limit, return_score=return_score + ) + self._initialize_clients(api_config) + + def _initialize_clients(self, api_config: Optional[Dict]): + """ + Setup the VoyageAI clients using the provided API key or an + environment variable. + """ + # Dynamic import of the voyageai module + try: + from voyageai import AsyncClient, Client + except ImportError: + raise ImportError( + "VoyageAI reranker requires the voyageai library. \ + Please install with `pip install voyageai`" + ) + + # Fetch the API key from api_config or environment variable + api_key = ( + api_config.get("api_key") if api_config else os.getenv("VOYAGE_API_KEY") + ) + if not api_key: + raise ValueError( + "VoyageAI API key is required. " + "Provide it in api_config or set the VOYAGE_API_KEY environment variable." + ) + self._client = Client(api_key=api_key) + self._aclient = AsyncClient(api_key=api_key) + + def _preprocess( + self, query: str, docs: Union[List[Dict[str, Any]], List[str]], **kwargs + ): + """ + Prepare and validate reranking config based on provided input and + optional overrides. + """ + limit = kwargs.get("limit", self.limit) + return_score = kwargs.get("return_score", self.return_score) + truncation = kwargs.get("truncation") + rank_by = kwargs.get("rank_by", self.rank_by) or [] + rank_by = [rank_by] if isinstance(rank_by, str) else rank_by + + reranker_kwargs = { + "query": query, + "documents": docs, + "model": self.model, + "top_k": limit, + } + if truncation is not None: + reranker_kwargs["truncation"] = truncation + + # if we are working with list of dicts + if all(isinstance(doc, dict) for doc in docs): + if rank_by: + reranker_kwargs["rank_fields"] = rank_by + else: + raise ValueError( + "If reranking dictionary-like docs, " + "you must provide a list of rank_by fields" + ) + + return reranker_kwargs, return_score + + @staticmethod + def _postprocess( + docs: Union[List[Dict[str, Any]], List[str]], + rankings: List[Any], + ) -> Tuple[List[Any], List[float]]: + """ + Post-process the initial list of documents to include ranking scores, + if specified. + """ + reranked_docs, scores = [], [] + for item in rankings.results: # type: ignore + scores.append(item.relevance_score) + reranked_docs.append(docs[item.index]) + return reranked_docs, scores + + def rank( + self, query: str, docs: Union[List[Dict[str, Any]], List[str]], **kwargs + ) -> Union[Tuple[List[Dict[str, Any]], List[float]], List[Dict[str, Any]]]: + """ + Rerank documents based on the provided query using the VoyageAI rerank API. + + This method processes the user's query and the provided documents to + rerank them in a manner that is potentially more relevant to the + query's context. + + Parameters: + query (str): The user's search query. + docs (Union[List[Dict[str, Any]], List[str]]): The list of documents + to be ranked, either as dictionaries or strings. + + Returns: + Union[Tuple[Union[List[Dict[str, Any]], List[str]], float], List[Dict[str, Any]]]: The reranked list of documents and optionally associated scores. + """ + reranker_kwargs, return_score = self._preprocess(query, docs, **kwargs) + rankings = self._client.rerank(**reranker_kwargs) + reranked_docs, scores = self._postprocess(docs, rankings) + if return_score: + return reranked_docs, scores + return reranked_docs + + async def arank( + self, query: str, docs: Union[List[Dict[str, Any]], List[str]], **kwargs + ) -> Union[Tuple[List[Dict[str, Any]], List[float]], List[Dict[str, Any]]]: + """ + Rerank documents based on the provided query using the VoyageAI rerank API. + + This method processes the user's query and the provided documents to + rerank them in a manner that is potentially more relevant to the + query's context. + + Parameters: + query (str): The user's search query. + docs (Union[List[Dict[str, Any]], List[str]]): The list of documents + to be ranked, either as dictionaries or strings. + + Returns: + Union[Tuple[Union[List[Dict[str, Any]], List[str]], float], List[Dict[str, Any]]]: The reranked list of documents and optionally associated scores. + """ + reranker_kwargs, return_score = self._preprocess(query, docs, **kwargs) + rankings = await self._aclient.rerank(**reranker_kwargs) + reranked_docs, scores = self._postprocess(docs, rankings) + if return_score: + return reranked_docs, scores + return reranked_docs diff --git a/redisvl/utils/vectorize/__init__.py b/redisvl/utils/vectorize/__init__.py index ea9d7bee..5d0b5fdb 100644 --- a/redisvl/utils/vectorize/__init__.py +++ b/redisvl/utils/vectorize/__init__.py @@ -4,6 +4,7 @@ from redisvl.utils.vectorize.text.huggingface import HFTextVectorizer from redisvl.utils.vectorize.text.openai import OpenAITextVectorizer from redisvl.utils.vectorize.text.vertexai import VertexAITextVectorizer +from redisvl.utils.vectorize.text.voyageai import VoyageAITextVectorizer __all__ = [ "BaseVectrorizer", @@ -12,4 +13,5 @@ "OpenAITextVectorizer", "VertexAITextVectorizer", "AzureOpenAITextVectorizer", + "VoyageAITextVectorizer", ] diff --git a/redisvl/utils/vectorize/text/voyageai.py b/redisvl/utils/vectorize/text/voyageai.py new file mode 100644 index 00000000..07f65cdc --- /dev/null +++ b/redisvl/utils/vectorize/text/voyageai.py @@ -0,0 +1,341 @@ +import os +from typing import Any, Callable, Dict, List, Optional + +from pydantic.v1 import PrivateAttr +from tenacity import retry, stop_after_attempt, wait_random_exponential +from tenacity.retry import retry_if_not_exception_type + +from redisvl.utils.vectorize.base import BaseVectorizer + +# ignore that voyageai isn't imported +# mypy: disable-error-code="name-defined" + + +class VoyageAITextVectorizer(BaseVectorizer): + """The VoyageAITextVectorizer class utilizes VoyageAI's API to generate + embeddings for text data. + + This vectorizer is designed to interact with VoyageAI's /embed API, + requiring an API key for authentication. The key can be provided + directly in the `api_config` dictionary or through the `VOYAGE_API_KEY` + environment variable. User must obtain an API key from VoyageAI's website + (https://dash.voyageai.com/). Additionally, the `voyageai` python + client must be installed with `pip install voyageai`. + + The vectorizer supports both synchronous and asynchronous operations, allows for batch + processing of texts and flexibility in handling preprocessing tasks. + + .. code-block:: python + + from redisvl.utils.vectorize import VoyageAITextVectorizer + + vectorizer = VoyageAITextVectorizer( + model="voyage-large-2", + api_config={"api_key": "your-voyageai-api-key"} # OR set VOYAGE_API_KEY in your env + ) + query_embedding = vectorizer.embed( + text="your input query text here", + input_type="search_query" + ) + doc_embeddings = vectorizer.embed_many( + texts=["your document text", "more document text"], + input_type="search_document" + ) + + """ + + _client: Any = PrivateAttr() + _aclient: Any = PrivateAttr() + + def __init__( + self, model: str, api_config: Optional[Dict] = None + ): + """Initialize the VoyageAI vectorizer. + + Visit https://docs.voyageai.com/docs/embeddings to learn about embeddings and check the available models. + + Args: + model (str): Model to use for embedding. + api_config (Optional[Dict], optional): Dictionary containing the API key. + Defaults to None. + + Raises: + ImportError: If the voyageai library is not installed. + ValueError: If the API key is not provided. + + """ + self._initialize_client(api_config) + super().__init__(model=model, dims=self._set_model_dims(model)) + + def _initialize_client(self, api_config: Optional[Dict]): + """ + Setup the VoyageAI clients using the provided API key or an + environment variable. + """ + # Dynamic import of the voyageai module + try: + from voyageai import AsyncClient, Client + except ImportError: + raise ImportError( + "VoyageAI vectorizer requires the voyageai library. \ + Please install with `pip install voyageai`" + ) + + # Fetch the API key from api_config or environment variable + api_key = ( + api_config.get("api_key") if api_config else os.getenv("VOYAGE_API_KEY") + ) + if not api_key: + raise ValueError( + "VoyageAI API key is required. " + "Provide it in api_config or set the VOYAGE_API_KEY environment variable." + ) + self._client = Client(api_key=api_key) + self._aclient = AsyncClient(api_key=api_key) + + def _set_model_dims(self, model) -> int: + try: + embedding = self._client.embed( + texts=["dimension test"], + model=model, + input_type="document", + ).embeddings[0] + except (KeyError, IndexError) as ke: + raise ValueError(f"Unexpected response from the VoyageAI API: {str(ke)}") + except Exception as e: # pylint: disable=broad-except + # fall back (TODO get more specific) + raise ValueError(f"Error setting embedding model dimensions: {str(e)}") + return len(embedding) + + def embed( + self, + text: str, + preprocess: Optional[Callable] = None, + as_buffer: bool = False, + **kwargs, + ) -> List[float]: + """Embed a chunk of text using the VoyageAI Embeddings API. + + Can provide the embedding `input_type` as a `kwarg` to this method + that specifies the type of input you're giving to the model. For retrieval/search use cases, + we recommend specifying this argument when encoding queries or documents to enhance retrieval quality. + Embeddings generated with and without the input_type argument are compatible. + + Supported input types are ``document`` and ``query`` + + When hydrating your Redis DB, the documents you want to search over + should be embedded with input_type="document" and when you are + querying the database, you should set the input_type="query". + + Args: + text (str): Chunk of text to embed. + preprocess (Optional[Callable], optional): Optional preprocessing callable to + perform before vectorization. Defaults to None. + as_buffer (bool, optional): Whether to convert the raw embedding + to a byte string. Defaults to False. + input_type (str): Specifies the type of input passed to the model. + truncation (bool): Whether to truncate the input texts to fit within the context length. + Check https://docs.voyageai.com/docs/embeddings + + Returns: + List[float]: Embedding. + + Raises: + TypeError: In an invalid input_type is provided. + """ + return self.embed_many( + texts=[text], + preprocess=preprocess, + as_buffer=as_buffer, + **kwargs + )[0] + + @retry( + wait=wait_random_exponential(min=1, max=60), + stop=stop_after_attempt(6), + retry=retry_if_not_exception_type(TypeError), + ) + def embed_many( + self, + texts: List[str], + preprocess: Optional[Callable] = None, + batch_size: Optional[int] = None, + as_buffer: bool = False, + **kwargs, + ) -> List[List[float]]: + """Embed many chunks of text using the VoyageAI Embeddings API. + + Can provide the embedding `input_type` as a `kwarg` to this method + that specifies the type of input you're giving to the model. For retrieval/search use cases, + we recommend specifying this argument when encoding queries or documents to enhance retrieval quality. + Embeddings generated with and without the input_type argument are compatible. + + Supported input types are ``document`` and ``query`` + + When hydrating your Redis DB, the documents you want to search over + should be embedded with input_type="document" and when you are + querying the database, you should set the input_type="query". + + Args: + texts (List[str]): List of text chunks to embed. + preprocess (Optional[Callable], optional): Optional preprocessing callable to + perform before vectorization. Defaults to None. + batch_size (int, optional): Batch size of texts to use when creating + embeddings. . + as_buffer (bool, optional): Whether to convert the raw embedding + to a byte string. Defaults to False. + input_type (str): Specifies the type of input passed to the model. + truncation (bool): Whether to truncate the input texts to fit within the context length. + Check https://docs.voyageai.com/docs/embeddings + + Returns: + List[List[float]]: List of embeddings. + + Raises: + TypeError: In an invalid input_type is provided. + + """ + input_type = kwargs.get("input_type") + truncation = kwargs.get("truncation") + + if not isinstance(texts, list): + raise TypeError("Must pass in a list of str values to embed.") + if len(texts) > 0 and not isinstance(texts[0], str): + raise TypeError("Must pass in a list of str values to embed.") + if input_type is not None and input_type not in ['document', 'query']: + raise TypeError( + "Must pass in a allowed value for voyageai embedding input_type. \ + See https://docs.voyageai.com/docs/embeddings." + ) + + if truncation is not None and not isinstance(truncation, bool): + raise TypeError("Truncation (optional) parameter is a bool.") + + if batch_size is None: + batch_size = 72 if self.model in ["voyage-2", "voyage-02"] else 7 + + embeddings: List = [] + for batch in self.batchify(texts, batch_size, preprocess): + response = self._client.embed( + texts=batch, model=self.model, input_type=input_type + ) + embeddings += [ + self._process_embedding(embedding, as_buffer) + for embedding in response.embeddings + ] + return embeddings + + async def aembed_many( + self, + texts: List[str], + preprocess: Optional[Callable] = None, + batch_size: int = None, + as_buffer: bool = False, + **kwargs, + ) -> List[List[float]]: + """Embed many chunks of text using the VoyageAI Embeddings API. + + Can provide the embedding `input_type` as a `kwarg` to this method + that specifies the type of input you're giving to the model. For retrieval/search use cases, + we recommend specifying this argument when encoding queries or documents to enhance retrieval quality. + Embeddings generated with and without the input_type argument are compatible. + + Supported input types are ``document`` and ``query`` + + When hydrating your Redis DB, the documents you want to search over + should be embedded with input_type="document" and when you are + querying the database, you should set the input_type="query". + + Args: + texts (List[str]): List of text chunks to embed. + preprocess (Optional[Callable], optional): Optional preprocessing callable to + perform before vectorization. Defaults to None. + batch_size (int, optional): Batch size of texts to use when creating + embeddings. . + as_buffer (bool, optional): Whether to convert the raw embedding + to a byte string. Defaults to False. + input_type (str): Specifies the type of input passed to the model. + truncation (bool): Whether to truncate the input texts to fit within the context length. + Check https://docs.voyageai.com/docs/embeddings + + Returns: + List[List[float]]: List of embeddings. + + Raises: + TypeError: In an invalid input_type is provided. + + """ + input_type = kwargs.get("input_type") + truncation = kwargs.get("truncation") + + if not isinstance(texts, list): + raise TypeError("Must pass in a list of str values to embed.") + if len(texts) > 0 and not isinstance(texts[0], str): + raise TypeError("Must pass in a list of str values to embed.") + if input_type is not None and input_type not in ['document', 'query']: + raise TypeError( + "Must pass in a allowed value for voyageai embedding input_type. \ + See https://docs.voyageai.com/docs/embeddings." + ) + + if truncation is not None and not isinstance(truncation, bool): + raise TypeError("Truncation (optional) parameter is a bool.") + + if batch_size is None: + batch_size = 72 if self.model in ["voyage-2", "voyage-02"] else 7 + + embeddings: List = [] + for batch in self.batchify(texts, batch_size, preprocess): + response = await self._aclient.embed( + texts=batch, model=self.model, input_type=input_type + ) + embeddings += [ + self._process_embedding(embedding, as_buffer) + for embedding in response.embeddings + ] + return embeddings + + async def aembed( + self, + text: str, + preprocess: Optional[Callable] = None, + as_buffer: bool = False, + **kwargs, + ) -> List[float]: + """Embed a chunk of text using the VoyageAI Embeddings API. + + Can provide the embedding `input_type` as a `kwarg` to this method + that specifies the type of input you're giving to the model. For retrieval/search use cases, + we recommend specifying this argument when encoding queries or documents to enhance retrieval quality. + Embeddings generated with and without the input_type argument are compatible. + + Supported input types are ``document`` and ``query`` + + When hydrating your Redis DB, the documents you want to search over + should be embedded with input_type="document" and when you are + querying the database, you should set the input_type="query". + + Args: + text (str): Chunk of text to embed. + preprocess (Optional[Callable], optional): Optional preprocessing callable to + perform before vectorization. Defaults to None. + as_buffer (bool, optional): Whether to convert the raw embedding + to a byte string. Defaults to False. + input_type (str): Specifies the type of input passed to the model. + truncation (bool): Whether to truncate the input texts to fit within the context length. + Check https://docs.voyageai.com/docs/embeddings + + Returns: + List[float]: Embedding. + + Raises: + TypeError: In an invalid input_type is provided. + """ + + result = await self.aembed_many( + texts=[text], + preprocess=preprocess, + as_buffer=as_buffer, + **kwargs + ) + return result[0] diff --git a/tests/integration/test_rerankers.py b/tests/integration/test_rerankers.py index 4866aa58..bd03434e 100644 --- a/tests/integration/test_rerankers.py +++ b/tests/integration/test_rerankers.py @@ -2,16 +2,31 @@ import pytest -from redisvl.utils.rerank import CohereReranker +from redisvl.utils.rerank import CohereReranker, VoyageAIReranker -# Fixture for the reranker instance @pytest.fixture -def reranker(): - skip_reranker = os.getenv("SKIP_RERANKERS", "False").lower() == "true" +def skip_reranker() -> bool: + # os.getenv returns a string + v = os.getenv("SKIP_RERANKERS", "False").lower() == "true" + return v + + +# Fixture for the reranker instance +@pytest.fixture( + params=[ + CohereReranker, + VoyageAIReranker, + ] +) +def reranker(request, skip_reranker): if skip_reranker: pytest.skip("Skipping reranker instantiation...") - return CohereReranker() + + if request.param == CohereReranker: + return request.param() + elif request.param == VoyageAIReranker: + return request.param(model="rerank-lite-1") # Test for basic ranking functionality diff --git a/tests/integration/test_vectorizers.py b/tests/integration/test_vectorizers.py index 23952c65..7ae51ab7 100644 --- a/tests/integration/test_vectorizers.py +++ b/tests/integration/test_vectorizers.py @@ -8,6 +8,7 @@ HFTextVectorizer, OpenAITextVectorizer, VertexAITextVectorizer, + VoyageAITextVectorizer, ) @@ -25,6 +26,7 @@ def skip_vectorizer() -> bool: VertexAITextVectorizer, CohereTextVectorizer, AzureOpenAITextVectorizer, + VoyageAITextVectorizer, ] ) def vectorizer(request, skip_vectorizer): @@ -39,6 +41,8 @@ def vectorizer(request, skip_vectorizer): return request.param() elif request.param == CohereTextVectorizer: return request.param() + elif request.param == VoyageAITextVectorizer: + return request.param(model="voyage-large-2") elif request.param == AzureOpenAITextVectorizer: return request.param( model=os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME", "text-embedding-ada-002") @@ -49,6 +53,8 @@ def test_vectorizer_embed(vectorizer): text = "This is a test sentence." if isinstance(vectorizer, CohereTextVectorizer): embedding = vectorizer.embed(text, input_type="search_document") + elif isinstance(vectorizer, VoyageAITextVectorizer): + embedding = vectorizer.embed(text, input_type="document") else: embedding = vectorizer.embed(text) @@ -60,6 +66,8 @@ def test_vectorizer_embed_many(vectorizer): texts = ["This is the first test sentence.", "This is the second test sentence."] if isinstance(vectorizer, CohereTextVectorizer): embeddings = vectorizer.embed_many(texts, input_type="search_document") + elif isinstance(vectorizer, VoyageAITextVectorizer): + embeddings = vectorizer.embed_many(texts, input_type="document") else: embeddings = vectorizer.embed_many(texts) @@ -81,7 +89,7 @@ def test_vectorizer_bad_input(vectorizer): vectorizer.embed_many(42) -@pytest.fixture(params=[OpenAITextVectorizer]) +@pytest.fixture(params=[OpenAITextVectorizer, VoyageAITextVectorizer]) def avectorizer(request, skip_vectorizer): if skip_vectorizer: pytest.skip("Skipping vectorizer instantiation...") @@ -89,12 +97,17 @@ def avectorizer(request, skip_vectorizer): # Here we use actual models for integration test if request.param == OpenAITextVectorizer: return request.param() + elif request.param == VoyageAITextVectorizer: + return request.param(model="voyage-large-2") @pytest.mark.asyncio async def test_vectorizer_aembed(avectorizer): text = "This is a test sentence." - embedding = await avectorizer.aembed(text) + if isinstance(avectorizer, VoyageAITextVectorizer): + embedding = await avectorizer.aembed(text) + else: + embedding = await avectorizer.aembed(text) assert isinstance(embedding, list) assert len(embedding) == avectorizer.dims @@ -103,7 +116,10 @@ async def test_vectorizer_aembed(avectorizer): @pytest.mark.asyncio async def test_vectorizer_aembed_many(avectorizer): texts = ["This is the first test sentence.", "This is the second test sentence."] - embeddings = await avectorizer.aembed_many(texts) + if isinstance(avectorizer, VoyageAITextVectorizer): + embeddings = await avectorizer.aembed_many(texts) + else: + embeddings = await avectorizer.aembed_many(texts) assert isinstance(embeddings, list) assert len(embeddings) == len(texts) From 21246afd9626a3f18a57e7ab41f1b02ffe514d90 Mon Sep 17 00:00:00 2001 From: fzowl Date: Tue, 14 May 2024 01:38:54 +0200 Subject: [PATCH 02/13] VoyageAI vectorizer and reranker --- redisvl/utils/vectorize/text/voyageai.py | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/redisvl/utils/vectorize/text/voyageai.py b/redisvl/utils/vectorize/text/voyageai.py index 07f65cdc..85602970 100644 --- a/redisvl/utils/vectorize/text/voyageai.py +++ b/redisvl/utils/vectorize/text/voyageai.py @@ -47,9 +47,7 @@ class VoyageAITextVectorizer(BaseVectorizer): _client: Any = PrivateAttr() _aclient: Any = PrivateAttr() - def __init__( - self, model: str, api_config: Optional[Dict] = None - ): + def __init__(self, model: str, api_config: Optional[Dict] = None): """Initialize the VoyageAI vectorizer. Visit https://docs.voyageai.com/docs/embeddings to learn about embeddings and check the available models. @@ -144,10 +142,7 @@ def embed( TypeError: In an invalid input_type is provided. """ return self.embed_many( - texts=[text], - preprocess=preprocess, - as_buffer=as_buffer, - **kwargs + texts=[text], preprocess=preprocess, as_buffer=as_buffer, **kwargs )[0] @retry( @@ -202,7 +197,7 @@ def embed_many( raise TypeError("Must pass in a list of str values to embed.") if len(texts) > 0 and not isinstance(texts[0], str): raise TypeError("Must pass in a list of str values to embed.") - if input_type is not None and input_type not in ['document', 'query']: + if input_type is not None and input_type not in ["document", "query"]: raise TypeError( "Must pass in a allowed value for voyageai embedding input_type. \ See https://docs.voyageai.com/docs/embeddings." @@ -272,7 +267,7 @@ async def aembed_many( raise TypeError("Must pass in a list of str values to embed.") if len(texts) > 0 and not isinstance(texts[0], str): raise TypeError("Must pass in a list of str values to embed.") - if input_type is not None and input_type not in ['document', 'query']: + if input_type is not None and input_type not in ["document", "query"]: raise TypeError( "Must pass in a allowed value for voyageai embedding input_type. \ See https://docs.voyageai.com/docs/embeddings." @@ -333,9 +328,6 @@ async def aembed( """ result = await self.aembed_many( - texts=[text], - preprocess=preprocess, - as_buffer=as_buffer, - **kwargs + texts=[text], preprocess=preprocess, as_buffer=as_buffer, **kwargs ) return result[0] From 09efff51d8f140c845608bcd66e9191c66de45e0 Mon Sep 17 00:00:00 2001 From: fzowl Date: Tue, 14 May 2024 01:43:40 +0200 Subject: [PATCH 03/13] VoyageAI vectorizer and reranker --- redisvl/utils/vectorize/text/voyageai.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/redisvl/utils/vectorize/text/voyageai.py b/redisvl/utils/vectorize/text/voyageai.py index 85602970..7f93fc31 100644 --- a/redisvl/utils/vectorize/text/voyageai.py +++ b/redisvl/utils/vectorize/text/voyageai.py @@ -224,7 +224,7 @@ async def aembed_many( self, texts: List[str], preprocess: Optional[Callable] = None, - batch_size: int = None, + batch_size: Optional[int] = None, as_buffer: bool = False, **kwargs, ) -> List[List[float]]: From ae78dd7ae6acace1cc11fe9838724479058a757f Mon Sep 17 00:00:00 2001 From: fzowl Date: Fri, 7 Jun 2024 16:51:45 +0200 Subject: [PATCH 04/13] VoyageAI vectorizer and reranker --- redisvl/utils/rerank/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/redisvl/utils/rerank/__init__.py b/redisvl/utils/rerank/__init__.py index 6720e393..3a2d28ba 100644 --- a/redisvl/utils/rerank/__init__.py +++ b/redisvl/utils/rerank/__init__.py @@ -1,11 +1,11 @@ from redisvl.utils.rerank.base import BaseReranker from redisvl.utils.rerank.cohere import CohereReranker -from redisvl.utils.rerank.voyageai import VoyageAIReranker from redisvl.utils.rerank.hf_cross_encoder import HFCrossEncoderReranker +from redisvl.utils.rerank.voyageai import VoyageAIReranker __all__ = [ "BaseReranker", "CohereReranker", - "VoyageAIReranker", "HFCrossEncoderReranker", + "VoyageAIReranker", ] From 3e014403e208fcf978ca8a09d2e90e5943bbe858 Mon Sep 17 00:00:00 2001 From: fzowl Date: Fri, 7 Jun 2024 20:36:31 +0200 Subject: [PATCH 05/13] VoyageAI vectorizer and reranker --- docs/user_guide/rerankers_06.ipynb | 145 +++++++++++++++++++---------- redisvl/utils/rerank/voyageai.py | 23 ++--- 2 files changed, 108 insertions(+), 60 deletions(-) diff --git a/docs/user_guide/rerankers_06.ipynb b/docs/user_guide/rerankers_06.ipynb index f357611a..e253b99e 100644 --- a/docs/user_guide/rerankers_06.ipynb +++ b/docs/user_guide/rerankers_06.ipynb @@ -188,58 +188,16 @@ "source": [ "from redisvl.utils.rerank import CohereReranker\n", "\n", - "reranker = CohereReranker(limit=3, api_config={\"api_key\": api_key})" + "cohere_reranker = CohereReranker(limit=3, api_config={\"api_key\": api_key})" ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Init the VoyageAI Reranker\n", - "\n", - "Initialize the reranker. Install the voyageai library and provide the right VoyageAI API Key." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#!pip install voyageai" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import getpass\n", - "\n", - "# setup the API Key\n", - "api_key = os.environ.get(\"VOYAGE_API_KEY\") or getpass.getpass(\"Enter your VoyageAI API key: \")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from redisvl.utils.rerank import VoyageAIReranker\n", - "\n", - "reranker = VoyageAIReranker(model=\"rerank-lite-1\", limit=3, api_config={\"api_key\": api_key})", - "# Please check the available models at https://docs.voyageai.com/docs/reranker" - ] - }, - { "cell_type": "markdown", "metadata": {}, "source": [ "### Rerank documents with CohereReranker\n", "\n", - "Below we will use the `CohereReranker` or the `VoyageAIReranker` to rerank and also truncate the list of\n", + "Below we will use the `CohereReranker` to rerank and also truncate the list of\n", "documents above based on relevance to the initial query." ] }, @@ -251,7 +209,7 @@ }, "outputs": [], "source": [ - "results, scores = reranker.rank(query=query, docs=docs)" + "results, scores = cohere_reranker.rank(query=query, docs=docs)" ] }, { @@ -325,7 +283,7 @@ }, "outputs": [], "source": [ - "results, scores = reranker.rank(query=query, docs=docs, rank_by=[\"passage\", \"source\"])" + "results, scores = cohere_reranker.rank(query=query, docs=docs, rank_by=[\"passage\", \"source\"])" ] }, { @@ -349,7 +307,100 @@ "for result, score in zip(results, scores):\n", " print(score, \" -- \", result)" ] - } + }, + + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using the VoyageAI Reranker\n", + "\n", + "To initialize the VoyageAI reranker you'll need to install the voyaeai library and provide the right VoyageAI API Key." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "metadata": {} + }, + "outputs": [], + "source": [ + "#!pip install voyageai" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "metadata": {} + }, + "outputs": [], + "source": [ + "import getpass\n", + "\n", + "# setup the API Key\n", + "api_key = os.environ.get(\"VOYAGE_API_KEY\") or getpass.getpass(\"Enter your VoyageAI API key: \")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "metadata": {} + }, + "outputs": [], + "source": [ + "from redisvl.utils.rerank import VoyageAIReranker\n", + "\n", + "reranker = VoyageAIReranker(model=\"rerank-lite-1\", limit=3, api_config={\"api_key\": api_key})", + "# Please check the available models at https://docs.voyageai.com/docs/reranker" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rerank documents with VoyageAIReranker\n", + "\n", + "Below we will use the `VoyageAIReranker` to rerank and also truncate the list of\n", + "documents above based on relevance to the initial query." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "metadata": {} + }, + "outputs": [], + "source": [ + "results, scores = reranker.rank(query=query, docs=docs)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "metadata": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.796875 -- Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district. The President of the USA and many major national government offices are in the territory. This makes it the political center of the United States of America.\n", + "0.578125 -- Charlotte Amalie is the capital and largest city of the United States Virgin Islands. It has about 20,000 people. The city is on the island of Saint Thomas.\n", + "0.5625 -- Carson City is the capital city of the American state of Nevada. At the 2010 United States Census, Carson City had a population of 55,274.\n" + ] + } + ], + "source": [ + "for result, score in zip(results, scores):\n", + " print(score, \" -- \", result)" + ] + } + ], "metadata": { "kernelspec": { diff --git a/redisvl/utils/rerank/voyageai.py b/redisvl/utils/rerank/voyageai.py index bcca4982..3c1c345b 100644 --- a/redisvl/utils/rerank/voyageai.py +++ b/redisvl/utils/rerank/voyageai.py @@ -108,28 +108,25 @@ def _preprocess( limit = kwargs.get("limit", self.limit) return_score = kwargs.get("return_score", self.return_score) truncation = kwargs.get("truncation") - rank_by = kwargs.get("rank_by", self.rank_by) or [] - rank_by = [rank_by] if isinstance(rank_by, str) else rank_by + + if all(isinstance(doc, dict) and "content" in doc for doc in docs): + texts = [ + str(doc["content"]) + for doc in docs + if isinstance(doc, dict) and "content" in doc + ] + else: + texts = [str(doc) for doc in docs] reranker_kwargs = { "query": query, - "documents": docs, + "documents": texts, "model": self.model, "top_k": limit, } if truncation is not None: reranker_kwargs["truncation"] = truncation - # if we are working with list of dicts - if all(isinstance(doc, dict) for doc in docs): - if rank_by: - reranker_kwargs["rank_fields"] = rank_by - else: - raise ValueError( - "If reranking dictionary-like docs, " - "you must provide a list of rank_by fields" - ) - return reranker_kwargs, return_score @staticmethod From 55f63808a01d4b61b35a5b897744ac292f68ce68 Mon Sep 17 00:00:00 2001 From: fzowl Date: Mon, 16 Sep 2024 16:15:11 +0200 Subject: [PATCH 06/13] Batching logic Reformatting Adding VoyageAI to enum --- redisvl/utils/vectorize/__init__.py | 2 ++ redisvl/utils/vectorize/base.py | 1 + redisvl/utils/vectorize/text/voyageai.py | 21 +++++++++++++++++++-- 3 files changed, 22 insertions(+), 2 deletions(-) diff --git a/redisvl/utils/vectorize/__init__.py b/redisvl/utils/vectorize/__init__.py index 4b25d114..97e637d1 100644 --- a/redisvl/utils/vectorize/__init__.py +++ b/redisvl/utils/vectorize/__init__.py @@ -36,3 +36,5 @@ def vectorizer_from_dict(vectorizer: dict) -> BaseVectorizer: return MistralAITextVectorizer(model) elif vectorizer_type == Vectorizers.vertexai: return VertexAITextVectorizer(model) + elif vectorizer_type == Vectorizers.voyageai: + return VoyageAITextVectorizer(model) diff --git a/redisvl/utils/vectorize/base.py b/redisvl/utils/vectorize/base.py index 858e94b1..76c60834 100644 --- a/redisvl/utils/vectorize/base.py +++ b/redisvl/utils/vectorize/base.py @@ -14,6 +14,7 @@ class Vectorizers(Enum): mistral = "mistral" vertexai = "vertexai" hf = "hf" + voyageai = "voyageai" class BaseVectorizer(BaseModel, ABC): diff --git a/redisvl/utils/vectorize/text/voyageai.py b/redisvl/utils/vectorize/text/voyageai.py index 7f93fc31..cd1a7c07 100644 --- a/redisvl/utils/vectorize/text/voyageai.py +++ b/redisvl/utils/vectorize/text/voyageai.py @@ -7,6 +7,7 @@ from redisvl.utils.vectorize.base import BaseVectorizer + # ignore that voyageai isn't imported # mypy: disable-error-code="name-defined" @@ -207,7 +208,15 @@ def embed_many( raise TypeError("Truncation (optional) parameter is a bool.") if batch_size is None: - batch_size = 72 if self.model in ["voyage-2", "voyage-02"] else 7 + batch_size = ( + 72 + if self.model in ["voyage-2", "voyage-02"] + else ( + 30 + if self.model == "voyage-3-lite" + else (10 if self.model == "voyage-3" else 7) + ) + ) embeddings: List = [] for batch in self.batchify(texts, batch_size, preprocess): @@ -277,7 +286,15 @@ async def aembed_many( raise TypeError("Truncation (optional) parameter is a bool.") if batch_size is None: - batch_size = 72 if self.model in ["voyage-2", "voyage-02"] else 7 + batch_size = ( + 72 + if self.model in ["voyage-2", "voyage-02"] + else ( + 30 + if self.model == "voyage-3-lite" + else (10 if self.model == "voyage-3" else 7) + ) + ) embeddings: List = [] for batch in self.batchify(texts, batch_size, preprocess): From a5b4218ee8e57b50567f986677f6308bf65da368 Mon Sep 17 00:00:00 2001 From: fzowl Date: Sat, 21 Sep 2024 14:01:02 +0200 Subject: [PATCH 07/13] Corrections --- redisvl/utils/vectorize/text/voyageai.py | 4 ++-- tests/integration/test_rerankers.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/redisvl/utils/vectorize/text/voyageai.py b/redisvl/utils/vectorize/text/voyageai.py index cd1a7c07..37bcbf6a 100644 --- a/redisvl/utils/vectorize/text/voyageai.py +++ b/redisvl/utils/vectorize/text/voyageai.py @@ -36,11 +36,11 @@ class VoyageAITextVectorizer(BaseVectorizer): ) query_embedding = vectorizer.embed( text="your input query text here", - input_type="search_query" + input_type="query" ) doc_embeddings = vectorizer.embed_many( texts=["your document text", "more document text"], - input_type="search_document" + input_type="document" ) """ diff --git a/tests/integration/test_rerankers.py b/tests/integration/test_rerankers.py index 5ea7a652..f7773480 100644 --- a/tests/integration/test_rerankers.py +++ b/tests/integration/test_rerankers.py @@ -40,7 +40,7 @@ def hfCrossEncoderRerankerWithCustomModel(): # Test for basic ranking functionality -def test_rank_documents_cohere(reranker): +def test_rank_documents(reranker): docs = ["document one", "document two", "document three"] query = "search query" From 7e92632eb079264e09ed44d124af37f9b71ab015 Mon Sep 17 00:00:00 2001 From: fzowl Date: Sat, 21 Sep 2024 14:02:21 +0200 Subject: [PATCH 08/13] Corrections --- tests/integration/test_rerankers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/integration/test_rerankers.py b/tests/integration/test_rerankers.py index f7773480..28625e46 100644 --- a/tests/integration/test_rerankers.py +++ b/tests/integration/test_rerankers.py @@ -53,7 +53,7 @@ def test_rank_documents(reranker): # Test for asynchronous ranking functionality @pytest.mark.asyncio -async def test_async_rank_documents_cohere(reranker): +async def test_async_rank_documents(reranker): docs = ["document one", "document two", "document three"] query = "search query" From 5f389f8be4579c1a2d55060f2143b70880aeaa56 Mon Sep 17 00:00:00 2001 From: Tyler Hutcherson Date: Sat, 21 Sep 2024 10:05:34 -0400 Subject: [PATCH 09/13] formatting --- redisvl/utils/vectorize/text/voyageai.py | 1 - tests/integration/test_rerankers.py | 6 +++++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/redisvl/utils/vectorize/text/voyageai.py b/redisvl/utils/vectorize/text/voyageai.py index 37bcbf6a..81778da9 100644 --- a/redisvl/utils/vectorize/text/voyageai.py +++ b/redisvl/utils/vectorize/text/voyageai.py @@ -7,7 +7,6 @@ from redisvl.utils.vectorize.base import BaseVectorizer - # ignore that voyageai isn't imported # mypy: disable-error-code="name-defined" diff --git a/tests/integration/test_rerankers.py b/tests/integration/test_rerankers.py index 28625e46..8a95c634 100644 --- a/tests/integration/test_rerankers.py +++ b/tests/integration/test_rerankers.py @@ -2,7 +2,11 @@ import pytest -from redisvl.utils.rerank import CohereReranker, VoyageAIReranker, HFCrossEncoderReranker +from redisvl.utils.rerank import ( + CohereReranker, + HFCrossEncoderReranker, + VoyageAIReranker, +) @pytest.fixture From 6c581ca203fce15081d8b1f063542a0933371bce Mon Sep 17 00:00:00 2001 From: Tyler Hutcherson Date: Sat, 21 Sep 2024 10:10:17 -0400 Subject: [PATCH 10/13] update lockfile --- poetry.lock | 4269 +++++++++++++++++++++++++++------------------------ 1 file changed, 2224 insertions(+), 2045 deletions(-) diff --git a/poetry.lock b/poetry.lock index a7d6bdc9..641331c0 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"] +cover = ["pytest-cov"] +doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +enabler = ["pytest-enabler (>=2.2)"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +type = ["pytest-mypy"] [extras] cohere = ["cohere"] @@ -6066,4 +6245,4 @@ voyageai = ["voyageai"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<4.0" -content-hash = "be9b5df2ff3600823749e4d0bfffe148c6bb04f88fa287a3dfae712ade9fd06e" +content-hash = "ab52d8827ddf7139fe7a94c50e344ceb5affb31a1d70cf5100b3df45ac019a1e" From f2156bba3782ad5cb56f8b71157d8cbe22924719 Mon Sep 17 00:00:00 2001 From: Tyler Hutcherson Date: Sat, 21 Sep 2024 10:26:44 -0400 Subject: [PATCH 11/13] more linting issues --- redisvl/utils/rerank/hf_cross_encoder.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/redisvl/utils/rerank/hf_cross_encoder.py b/redisvl/utils/rerank/hf_cross_encoder.py index 2fc0f908..d3f282d7 100644 --- a/redisvl/utils/rerank/hf_cross_encoder.py +++ b/redisvl/utils/rerank/hf_cross_encoder.py @@ -98,14 +98,14 @@ def rank( doc_subset = [{"content": doc} for doc in docs] scores = self.model.predict([(query, text) for text in texts]) - scores = [float(score) for score in scores] + scores = [float(score) for score in scores] # type: ignore docs_with_scores = list(zip(doc_subset, scores)) docs_with_scores.sort(key=lambda x: x[1], reverse=True) reranked_docs = [doc for doc, _ in docs_with_scores[:limit]] scores = scores[:limit] if return_score: - return reranked_docs, scores + return reranked_docs, scores # type: ignore return reranked_docs async def arank( From ee68921bda5f5f69bda4e4f8aa856c0588f931be Mon Sep 17 00:00:00 2001 From: Tyler Hutcherson Date: Tue, 1 Oct 2024 10:08:16 -0400 Subject: [PATCH 12/13] check for list of dicts --- redisvl/utils/rerank/voyageai.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/redisvl/utils/rerank/voyageai.py b/redisvl/utils/rerank/voyageai.py index 3c1c345b..2c3741ce 100644 --- a/redisvl/utils/rerank/voyageai.py +++ b/redisvl/utils/rerank/voyageai.py @@ -109,6 +109,9 @@ def _preprocess( return_score = kwargs.get("return_score", self.return_score) truncation = kwargs.get("truncation") + if not isinstance(docs, list): + raise ValueError("Docs to rerank should either be a list of str or dict.") + if all(isinstance(doc, dict) and "content" in doc for doc in docs): texts = [ str(doc["content"]) From 8b1b520c1d563d5e7b428d518b454093bd65e336 Mon Sep 17 00:00:00 2001 From: Tyler Hutcherson Date: Tue, 1 Oct 2024 10:22:51 -0400 Subject: [PATCH 13/13] check reranker type first before test --- tests/integration/test_rerankers.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/tests/integration/test_rerankers.py b/tests/integration/test_rerankers.py index 8a95c634..65aad333 100644 --- a/tests/integration/test_rerankers.py +++ b/tests/integration/test_rerankers.py @@ -79,10 +79,11 @@ def test_bad_input(reranker): with pytest.raises(Exception): reranker.rank("valid query", "not a list") # Invalid type for documents - with pytest.raises(Exception): - reranker.rank( - "valid query", [{"field": "valid document"}], rank_by=["invalid_field"] - ) # Invalid rank_by field + if isinstance(reranker, CohereReranker): + with pytest.raises(Exception): + reranker.rank( + "valid query", [{"field": "valid document"}], rank_by=["invalid_field"] + ) # Invalid rank_by field def test_rank_documents_cross_encoder(hfCrossEncoderReranker):