Langchain embedding langchain-core: Core langchain package. Jan 14, 2023 · LangChain の Embeddings の機能を試したのでまとめました。 前回 1. TogetherEmbeddings. Skip to main content We are growing and hiring for multiple roles for LangChain, LangGraph and LangSmith. Setup To access Chroma vector stores you'll need to install the langchain-chroma integration package. Imports This will help you get started with Nomic embedding models using LangChain. GPT4AllEmbeddings [source] # Asynchronous Embed search docs. Install Xinference through PyPI: % pip install --upgrade --quiet "xinference[all]" Voyage AI. For images, use embed_image and simply pass a list of uris for the images. This is documentation for LangChain v0. List[float] embed_documents (texts: List langchain. Learn how to create a custom Embedding class for LangChain, a framework for natural language processing. As use cases involving multimodal search and retrieval tasks become more common, we expect to expand the embedding interface to accommodate other data types like images, audio, and video. vLLM is a fast and easy-to-use library for LLM inference and serving, offering:. For detailed documentation on OllamaEmbeddings features and configuration options, please refer to the API reference. Infinity. For detailed documentation on TogetherEmbeddings features and configuration options, please refer to the API reference. The TransformerEmbeddings class uses the Transformers. . An implementation of LangChain vectorstore abstraction using postgres as the backend and utilizing the pgvector extension. """ This will help you get started with Ollama embedding models using LangChain. Embedding for the text. We start by installing prerequisite libraries: Pairwise embedding distance. GoogleGenerativeAIEmbeddings. We have also added an alias for SentenceTransformerEmbeddings for users who are more familiar with directly using that package. Let's load the SelfHostedEmbeddings, SelfHostedHuggingFaceEmbeddings, and SelfHostedHuggingFaceInstructEmbeddings classes. This will help you get started with Cohere embedding models using LangChain. Let's load the DashScope Embedding class. __call__ expects a single input dictionary with all the inputs Connect to Google's generative AI embeddings service using the GoogleGenerativeAIEmbeddings class, found in the langchain-google-genai package. Optimizing Embedding Quality 我们欢迎向 LangChain 代码库贡献 Embedding 模型。 如果您旨在为新的提供商（例如，使用一组新的依赖项或 SDK）贡献嵌入模型，我们鼓励您在单独的 langchain-* 集成包中发布您的实现。 class langchain_community. cpp. This means that you can specify the dimensionality of the embeddings at inference time. It supports: exact and approximate nearest neighbor search using HNSW; L2 distance; This notebook shows how to use the Postgres vector database (PGEmbedding). py. Ollama is an open-source project that allows you to easily serve models locally. query_embedding_cache: (optional, defaults to None or not caching) A ByteStore for caching query embeddings, or True to use the same store as document_embedding_cache. Instead it might help to have the model generate a hypothetical relevant document, and then use that to perform similarity search. After grasping the basics, it's time to dive into some advanced techniques that can elevate your LangChain Embedding game. List[List[float]] async aembed_query (text: str) → List [float] ¶ Asynchronous Embed query text. embed_documents：用于嵌入多个文本（文档） embed_query：用于嵌入单个文本（查询） Dec 9, 2024 · embed_documents (texts: List [str]) → List [List [float]] [source] ¶ Compute doc embeddings using a Bedrock model. This will help you get started with MistralAI embedding models using LangChain. List[float] embed_documents (texts: List [str], chunk_size: Optional [int] = 0) → List [List [float]] [source] ¶ Call out to LocalAI’s embedding endpoint for embedding search Access Google's Generative AI models, including the Gemini family, directly via the Gemini API or experiment rapidly using Google AI Studio. SelfHostedEmbeddings [source] ¶. Dec 9, 2024 · Embedding for the image. Use to build complex pipelines and workflows. This tutorial covers how to perform Text Embedding using Ollama and Langchain. BAAI is a private non-profit organization engaged in AI research and development. The former takes as input multiple texts, while the latter takes a single text. This will help you get started with Nomic embedding models using LangChain. Learn how to use various embedding models, such as AI21, Aleph Alpha, Anyscale, AwaDB, Azure OpenAI, and more, with LangChain. LangChain提供了许多与各种模型提供商集成的嵌入实现。 默认的Google Vertex AI嵌入模型textembedding-gecko和OpenAI的text-embedding-ada Dec 9, 2024 · Call out to LocalAI’s embedding endpoint async for embedding query text. Llama. You can use this to test your pipelines. Dec 9, 2024 · class langchain_community. 5 feature matryoshka embedding which allows for effective vector truncation. You'll need to sign up for an ZhipuAI API key and set it as an environment variable named ZHIPUAI_API_KEY. cpp python library is a simple Python bindings for @ggerganov llama. Embedding models Snowflake offers their open-weight arctic line of embedding models for free on Hugging Face. gpt4all. This will help you get started with Together embedding models using LangChain. For detailed documentation on ByteDanceDoubaoEmbeddings features and configuration options, please refer to the API reference. External Models - Databricks endpoints can serve models that are hosted outside Databricks as a proxy, such as proprietary model service like OpenAI text-embedding-3. embeddings. Return type. (Install the LangChain partner package with pip install langchain-voyageai) Convenience method for executing chain. MistralAIEmbeddings. Apr 2, 2024 · This example demonstrates how to split a large text into smaller chunks, embed each chunk asynchronously, and then collect the embeddings. This package provides: Low-level access to C API via ctypes interface. LangChain 的 Embeddings 类提供了一个标准化的接口，用于与不同的文本嵌入模型提供商（如 OpenAI 和 Cohere）进行交互。 文本嵌入模型通过将文本转换为向量形式，使得可以在向量空间中进行语义搜索和相似性比较。 Embeddings 类包含两种方法： from langchain_openai import OpenAIEmbeddings embed = OpenAIEmbeddings (model = "text-embedding-3-large" # With the `text-embedding-3` class # of models, you can specify the size # of the embeddings you want returned. You’re Ollama If we're working with a similarity search-based index, like a vector store, then searching on raw questions may not work well because their embeddings may not be very similar to those of the relevant documents. State-of-the-art serving throughput; Efficient management of attention key and value memory with PagedAttention Text embedding models 📄️ Alibaba Tongyi. Dependencies To use FastEmbed with LangChain, install the fastembed Python package. LangChain offers many embedding model integrations which you can find on the embedding models integrations page. Please use langchain-nvidia-ai-endpoints NVIDIAEmbeddings interface. These embeddings can be used for various natural language processing tasks, such as document similarity comparison or text classification. We are growing and hiring for multiple roles for LangChain, LangGraph and LangSmith. In this tutorial, we will create a simple example to measure the similarity between Documents and an input Query using Ollama and Langchain. Adjust the chunk_size according to the capabilities of the API and the size of your texts. Embeddings for the Embedding Distance. QuantizedBgeEmbeddings Leverage Itrex runtime to unlock the performance of compressed NLP models. AlephAlphaSymmetricSemanticEmbedding embed_documents (texts: List [str]) → List [List [float]] [source] # Compute doc embeddings using a Bedrock model. The embedding of a query text is expected to be a single vector, while the embedding of a list of documents is expected to be a list of vectors. Interface for embedding models. It runs locally and even works directly in the browser, allowing you to create web apps with built-in embeddings. The base Embeddings class in LangChain provides two methods: one for embedding documents and one for embedding a query. embed_query() to create embeddings for the text(s) used in from_texts and retrieval invoke operations, respectively. List of embeddings. Returns. cpp, GPT4All, and llamafile underscore the importance of running LLMs locally. This is an interface meant for implementing text embedding models. Embeddings 「Embeddings」は、LangChainが提供する埋め込みの操作のための共通インタフェースです。 「埋め込み」は、意味的類似性を示すベクトル表現です。テキストや画像をベクトル表現に変換することで、ベクトル空間で最も類似し Apr 13, 2023 · LLMが流行する中で、EmbeddingやLangChainという言葉を耳にしたので実装したものをまとめてみました。 今回の記事では、LangChainを使って、PDFのデータをEmbeddingしてPDFの質問に答える機能を作りたいと思います。 Vector検索には、Pineconeを使用しています。 Custom Models - You can also deploy custom embedding models to a serving endpoint via MLflow with your choice of framework such as LangChain, Pytorch, Transformers, etc. Join our team! from langchain_community. Jina Embeddings. Bases: SelfHostedPipeline, Embeddings Custom embedding models on self-hosted remote hardware. embeddings import JinaEmbeddings from numpy import dot Embed text and queries with Jina embedding models through JinaAI API Previously, LangChain. The subsequent examples in the cookbook also run as expected, and we encourage Embeddings# class langchain_core. 要实现自定义 Embeddings，需要定义一个自定义类继承自 LangChain 的 Embeddings 基类，然后定义三个函数：① _embed 方法，其接受一个字符串，并返回一个存放 Embeddings 的 List[float]，即模型的核心调用；② embed_query 方法，用于对单个字符串（query）进行 embedding。 Sep 13, 2024 · Understanding Chroma in LangChain. langgraph: Powerful orchestration layer for LangChain. embed_query (text) query_result [: 5] [-0. 在构建自然语言处理（NLP）应用时，嵌入（embedding）是不可或缺的技术，它将文本或其他输入数据转换为机器可以理解的数字向量，从而实现语义搜索、文本比较等任务。 As of today (Jan 25th, 2024) BaichuanTextEmbeddings ranks #1 in C-MTEB (Chinese Multi-Task Embedding Benchmark) leaderboard. inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects only one param. itrex. The base Embeddings class in LangChain exposes two methods: one for embedding documents and one for embedding a query. For example by default text-embedding-3-large returned embeddings of dimension 3072: FastEmbed from Qdrant is a lightweight, fast, Python library built for embedding generation. Asynchronously execute the chain. init_embeddings (model: str, *, Additional model-specific parameters passed to the embedding model. One way to measure the similarity (or dissimilarity) between two predictions on a shared or similar input is to embed the predictions and compute a vector distance between the two embeddings. Useful for checking if an input will fit Jul 27, 2023 · Buckle up as we explore the wild lands of offline embedding, a sweet spot where autonomy meets efficiency. A classe Embedding na LangChain é uma classe projetada para fazer interface com incorporações, ou seja, ela fornece uma interface comum para interagir com vários fornecedores de embeddings, como OpenAI, Cohere, Hugging Face, etc. Under the hood, the vectorstore and retriever implementations are calling embeddings. # dimensions=1024) This page covers how to use the Snowflake ecosystem within LangChain. LangChain is a framework for building AI applications with language models. The following changes have been made: Access Google's Generative AI models, including the Gemini family, directly via the Gemini API or experiment rapidly using Google AI Studio. ByteDanceDoubaoEmbeddings. The former, . AlephAlphaAsymmetricSemanticEmbedding. Chroma is licensed under Apache 2. text (str) – The text to This will help you get started with AI21 embedding models using LangChain. The SpacyEmbeddings class generates an embedding for each document, which is a numerical representation of the document's content. High-level Python API for text completion LangChain 为使用它们提供了一个通用接口，为常见操作提供标准方法。这个通用接口通过两种中心方法简化了与各种嵌入提供商的交互. This is the key idea behind Hypothetical Document Dec 9, 2024 · Asynchronous Embed search docs. Using local models. , some pre-built chains). HuggingFace Transformers. This will help you get started with Google Generative AI embedding models using LangChain. Multimodality in vector Apr 29, 2024 · Advanced Techniques in LangChain Embeddings. The ZhipuAIEmbeddings class uses the ZhipuAI API to generate embeddings for a given text. The Multimodal Embedding Model is a model that can vectorize text as well as image. Xorbits inference (Xinference) This notebook goes over how to use Xinference embeddings within LangChain. Voyage AI provides cutting-edge embedding/vectorizations models. For detailed documentation on NomicEmbeddings features and configuration options, please refer to the API reference. Return type: List[List[float]] embed_query (text: str) → List [float] [source] # Compute query embeddings using a As of today (Jan 25th, 2024) BaichuanTextEmbeddings ranks #1 in C-MTEB (Chinese Multi-Task Embedding Benchmark) leaderboard. llama. API Reference: query_embedding_cache: (optional, defaults to None or not caching) A ByteStore for caching query embeddings, or True to use the same store as document_embedding_cache. The most recent model, snowflake-arctic-embed-m-v1. This SDK is now deprecated in favor of the new Azure integration in the OpenAI SDK, which allows to access the latest OpenAI models and features the same day they are released, and allows seamless transition between the OpenAI API and Azure OpenAI. The reason for having these as two separate methods is that some embedding providers have different embedding LangChain offers many embedding model integrations which you can find on the embedding models integrations page. embed_documents , takes as input multiple texts, while the latter, . __call__ is that this method expects inputs to be passed directly in as positional arguments or keyword arguments, whereas Chain. List of embeddings, one for each 'The Higgs Boson is an elementary subatomic particle that plays a crucial role in the Standard Model of particle physics, which accounts for three of the four fundamental forces governing the behavior of our universe: the strong and weak nuclear forces, electromagnetism, and gravity. AlephAlphaSymmetricSemanticEmbedding Jina Embeddings. Jan 6, 2024 · LangChain Embeddings transform text into an array of numbers, each representing a dimension in the embedding space. embeddings. Postgres Embedding is an open-source vector similarity search for Postgres that uses Hierarchical Navigable Small Worlds (HNSW) for approximate nearest neighbor search. Find integrations with OpenAI, Azure, AWS, VertexAI, MistralAI, Cohere and more. 这将帮助您使用LangChain开始使用ZhipuAI嵌入模型。有关ZhipuAIEmbeddings功能和配置选项的详细文档，请参阅API参考。 The embedding of a query text is expected to be a single vector, while the embedding of a list of documents is expected to be a list of vectors. 1, which is no longer actively maintained. The langchain-google-genai package provides the LangChain integration for these models. Wrapper around the BGE embedding model with IPEX-LLM optimizations on Intel CPUs and GPUs. This will help you get started with ByteDanceDoubao embedding models using LangChain. Hello I'm trying to store in Chroma Db embeddings vector generated with model "sentence Dec 9, 2024 · Embed documents using an Ollama deployed embedding model. Azure AI Search (formerly known as Azure Search and Azure Cognitive Search) is a cloud search service that gives developers infrastructure, APIs, and tools for information retrieval of vector, keyword, and hybrid queries at scale. For detailed documentation on MistralAIEmbeddings features and configuration options, please refer to the API reference. Embedding. The reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be searched This will help you get started with OpenAI embedding models using LangChain. This conversion is vital for machine learning algorithms to process and Help us build the JS tools that power AI apps at companies like Replit, Uber, LinkedIn, GitLab, and more. This tutorial covers how to perform Text Embedding and Image Embedding using Multimodal Embedding Model with Langchain. text_q = "Introducing iFlytek" text_1 = "Science and Technology Innovation Company Limited, commonly known as iFlytek, is a leading Chinese technology company specializing in speech recognition, natural language processing, and artificial intelligence. Measure similarity Each embedding is essentially a set of coordinates, often in a high-dimensional space. List of embeddings, one for each text. For text, use the same method embed_documents as with other embedding models. The code lives in an integration package called: langchain_postgres. Infinity allows to create Embeddings using a MIT-licensed Embedding Server. To measure semantic similarity (or dissimilarity) between a prediction and a reference label string, you could use a vector distance metric between the two embedded representations using the embedding_distance evaluator. Load quantized BGE embedding models generated by Intel® Extension for Transformers (ITREX) and use ITREX Neural Engine, a high-performance NLP backend, to accelerate the inference of models without compromising accuracy. texts (List[str]) – List of text to embed. text (str) – The text to embed. Overview Integration details. embed_query, takes a single text. Pinecone's inference API can be accessed via PineconeEmbeddings. The AlibabaTongyiEmbeddings class uses the Alibaba Tongyi API to generate embeddings for a given text. The popularity of projects like PrivateGPT, llama. Connect to Google's generative AI embeddings service using the GoogleGenerativeAIEmbeddings class, found in the langchain-google-genai package. input_keys except for inputs that will be set by the chain’s memory. List[List[float]] embed_query (text: str) → List [float] [source] ¶ Call out to Cohere’s embedding endpoint. Chroma is a vector database that specializes in storing and managing embeddings, making it a vital component in applications involving natural language Jun 17, 2024 · 03 LangChain 中的 Embedding. Should contain all inputs specified in Chain. % Custom Dimensionality . Embeddings [source] #. ZhipuAI. Includes base interfaces and in-memory implementations. Azure OpenAI is a cloud service to help you quickly develop generative AI experiences with a diverse set of prebuilt and curated models from OpenAI, Meta and beyond. Embedding models are wrappers around embedding models from different APIs and services. Having established embedding generation and similarity search, it’s essential to consider how these embeddings can enhance decision-making processes One of the most common ways to store and search over unstructured data is to embed it and store the resulting embedding vectors, and then at query time to embed the unstructured query and retrieve the embedding vectors that are 'most similar' to the embedded query. Learn how to use various embedding models in Langchain, a library for building AI applications. Let's load the Voyage AI Embedding class. vLLM. from langchain_community. The model model_name,checkpoint are set in langchain_experimental. js supported integration with Azure OpenAI using the dedicated Azure OpenAI SDK. LangChain also provides a fake embedding class. WatsonxEmbeddings is a wrapper for IBM watsonx. This notebook goes over how to use Langchain with Embeddings with the Infinity Github Project. self_hosted. Returns: List of embeddings, one for each text. langchain-community: Community-driven components for LangChain. base. In this space, the position of each point (embedding) reflects the meaning of its corresponding text. LangChain has a base MultiVectorRetriever designed to do just this! embed that along with (or instead of) the document; hypothetical questions: create Embedding models are wrappers around embedding models from different APIs and services. This guide will walk you through the setup and usage of the JinaEmbeddings class, helping you integrate it into your project seamlessly. Embedding models create a vector representation of a piece of text. Postgres Embedding. Nomic's nomic-embed-text-v1. This conceptual overview focuses on text-based embedding models. List[List[float]] embed_query (text: str) → List [float] [source] ¶ Embed a query using a Ollama deployed embedding model. See the interface, implementation, and examples of embedding models for text data. This is often the best starting point for individual developers. List[float] embed_documents (texts: List [str]) → List [List [float]] [source] ¶ Generate embeddings for documents using FastEmbed. FastEmbed from Qdrant is a lightweight, fast, Python library built for embedding generation. 0. Aleph Alpha's asymmetric semantic embedding. In this guide we'll show you how to create a custom Embedding class, in case a built-in one does not already exist. API Reference: BGE models on the HuggingFace are one of the best open-source embedding models. BGE model is created by the Beijing Academy of Artificial Intelligence (BAAI) . LangChain has integrations with many open-source LLMs that can be run locally. ai foundation models. This will help you get started with Google's Generative AI embedding models (like Gemini) using LangChain. These methods will help you fine-tune your embeddings, making them more accurate and efficient for your specific use-cases. text (str) – Text to embed. texts (List[str]) – The list of texts to embed. Parameters. query_result = embeddings. The current embedding interface used in LangChain is optimized entirely for text-based data, and will not work with multimodal data. 5 model was trained with Matryoshka learning to enable variable-length embeddings with a single model. sagemaker_endpoint import EmbeddingsContentHandler The length of the inner lists is the embedding dimension. langchain: A package for higher level components (e. For detailed documentation on AI21Embeddings features and configuration options, please refer to the API reference. Embeddings can be stored or temporarily cached to avoid needing to recompute them. aleph_alpha. Setup . List[float] embed_query (text: str) → List [float] [source] ¶ Embed a text. Status This code has been ported over from langchain_community into a dedicated package called langchain-postgres. Attention : Be sure to set the namespace parameter to avoid collisions of the same text embedded using different embeddings models. sentence_transformers package models are originating from Sentence-BERT % Sep 22, 2024 · Enhancing Decision-Making with Langchain. LangChain is integrated with many 3rd party embedding models. open_clip. js package to generate embeddings for a given text. For detailed documentation on GoogleGenerativeAIEmbeddings features and configuration options, please refer to the API reference. Jan 29, 2024 · 基于 LangChain 自定义 Embeddings 在 LangChain 中支持 OpenAI、LLAMA 等大模型 Embeddings 的调用接口，不过没有内置所有大模型，但是允许用户自定义 Embeddings 类型。 接下来以 ZhipuAI 为例，基于 LangChain 自定义 Embeddings。 设计思路 要实现自定义 Embeddings，需要定义一个自定义类继承自 L This will help you get started with Google Vertex AI embedding models using LangChain. [1] You can load the pairwise_embedding_distance evaluator to do this. For detailed documentation on CohereEmbeddings features and configuration options, please refer to the API reference. embed_documents() and embeddings. 📄️ Azure OpenAI. In this tutorial, we will create a simple Image Similarity Searching example using Multimodal Embedding Model and Langchain. The main difference between this method and Chain. Direct Usage . B efore continuing the article, I wanted to introduce Ollamac Pro . Jul 16, 2023 · Use Chromadb with Langchain and embedding from SentenceTransformer model. For detailed documentation on OpenAIEmbeddings features and configuration options, please refer to the API reference. For detailed documentation on VertexAIEmbeddings features and configuration options, please refer to the API reference. Apr 13, 2023 · LLMが流行する中で、EmbeddingやLangChainという言葉を耳にしたので実装したものをまとめてみました。 今回の記事では、LangChainを使って、PDFのデータをEmbeddingしてPDFの質問に答える機能を作りたいと思います。 Vector検索には、Pineconeを使用しています。 HuggingFace Transformers. Apr 29, 2024 · LangChain 提供了 embed_query 用于单个文档和 embed_documents 用于多个文档的方法，以帮助您轻松地将嵌入集成到项目中。 LangChain 如何与嵌入一起工作？ LangChain 嵌入通过将文本字符串转换为数值向量来工作。这种转换是使用来自不同提供商的机器学习模型完成的。 Dec 9, 2024 · The embedding of a query text is expected to be a single vector, while the embedding of a list of documents is expected to be a list of vectors. These vary by provider, see the The model model_name,checkpoint are set in langchain_experimental. The reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be searched The following is a repurposing of the initial example of the LangChain Expression Language Retrieval Cookbook entry, but executed with the AI Foundation Models' Mixtral 8x7B Instruct and NVIDIA Retrieval QA Embedding models available in their playground environments. Alibaba Tongyi. embed_query , takes a single text. List[float] get_num_tokens (text: str) → int ¶ Get the number of tokens present in the text. 这将帮助您使用LangChain开始使用Nomic嵌入模型。有关NomicEmbeddings功能和配置选项的详细文档，请参阅API参考。 NVIDIA NIMs: langchain-nvidia-ai-endpoints 包含与模型构建应用的 LangChain 集成， Oracle Cloud Infrastructure 生成式人工智能 Embedding models Text Embeddings Inference Hugging Face Text Embeddings Inference (TEI) is a toolkit for deploying and serving open-source text embeddings and sequence classification models. With the text-embedding-3 class of models, you can specify the size of the embeddings you want returned. Parameters: texts (List[str]) – The list of texts to embed. g. Dec 9, 2024 · Embed a list of document texts. Caching embeddings can be done using a CacheBackedEmbeddings instance. One of the embedding models is used in the HuggingFaceEmbeddings class. embed_documents, takes as input multiple texts, while the latter, . You'll need to sign up for an Alibaba API key and set it as an environment variable named ALIBABA_API_KEY. embeddings import FakeEmbeddings. Xorbits inference (Xinference) This notebook goes over how to use Xinference embeddings within LangC This will help you get started with OpenAI embedding models using LangChain. List[List[float]] embed_query (text: str) → List [float] [source] ¶ Compute query embeddings using a Dec 9, 2024 · Asynchronous Embed query text. Embedding models can also be multimodal though such models are not currently supported by LangChain. Quantized model weights; ONNX Runtime, no PyTorch dependency; CPU-first design; Data-parallelism for encoding of large datasets. Parameters: texts (list[str]) – List of text to embed. Installation . Essa classe expõe dois métodos: embed_documents e embed_query. The JinaEmbeddings class utilizes the Jina API to generate embeddings for given text inputs. Jan 6, 2024 · LangChain Embeddings are numerical representations of text data, designed to be fed into machine learning algorithms. The cache backed embedder is a wrapper around an embedder that caches embeddings in a key-value store. Specify dimensions . Usually the query embedding is identical to the document embedding, but the abstraction allows treating them independently. Providing text embeddings via the Pinecone service. % Feb 18, 2025 · 深入浅出：LangChain中的Embedding模型解析与实战. This page documents integrations with various model providers that allow you to use embeddings in LangChain. View the full docs of Chroma at this page, and find the API reference for the LangChain integration at this page. The following changes have been made: May 21, 2023 · A classe Embedding. These embeddings are crucial for a variety of natural language processing This abstraction contains a method for embedding a list of documents and a method for embedding a query text. Embedding models. BGE models on the HuggingFace are one of the best open-source embedding models. qpcasw hqkdcwhxz isbmoj rmfvkh ckmtx qihqkag jjlmh sfvoycg frp cuds