langchain

Commit Graph

Author	SHA1	Message	Date
Yoann Poupart	c1807d8408	`encoding_kwargs` for InstructEmbeddings (#5450 ) # What does this PR do? Bring support of `encode_kwargs` for ` HuggingFaceInstructEmbeddings`, change the docstring example and add a test to illustrate with `normalize_embeddings`. Fixes #3605 (Similar to #3914) Use case: ```python from langchain.embeddings import HuggingFaceInstructEmbeddings model_name = "hkunlp/instructor-large" model_kwargs = {'device': 'cpu'} encode_kwargs = {'normalize_embeddings': True} hf = HuggingFaceInstructEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs ) ```	1 year ago
Justin Flick	c09f8e4ddc	Add pagination for Vertex AI embeddings (#5325 ) Fixes #5316 --------- Co-authored-by: Justin Flick <jflick@homesite.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	1 year ago
Harrison Chase	a775aa6389	Harrison/vertex (#5049 ) Co-authored-by: Leonid Kuligin <kuligin@google.com> Co-authored-by: Leonid Kuligin <lkuligin@yandex.ru> Co-authored-by: sasha-gitg <44654632+sasha-gitg@users.noreply.github.com> Co-authored-by: Justin Flick <Justinjayflick@gmail.com> Co-authored-by: Justin Flick <jflick@homesite.com>	1 year ago
Harrison Chase	11c26ebb55	Harrison/modelscope (#5156 ) Co-authored-by: thomas-yanxin <yx20001210@163.com> Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Daniel King	de6e6c764e	Add MosaicML inference endpoints (#4607 ) # Add MosaicML inference endpoints This PR adds support in langchain for MosaicML inference endpoints. We both serve a select few open source models, and allow customers to deploy their own models using our inference service. Docs are here (https://docs.mosaicml.com/en/latest/inference.html), and sign up form is here (https://forms.mosaicml.com/demo?utm_source=langchain). I'm not intimately familiar with the details of langchain, or the contribution process, so please let me know if there is anything that needs fixing or this is the wrong way to submit a new integration, thanks! I'm also not sure what the procedure is for integration tests. I have tested locally with my api key. ## Who can review? @hwchase17 --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	1 year ago
Jeff Vestal	0b542a9706	Add ElasticsearchEmbeddings class for generating embeddings using Elasticsearch models (#3401 ) This PR introduces a new module, `elasticsearch_embeddings.py`, which provides a wrapper around Elasticsearch embedding models. The new ElasticsearchEmbeddings class allows users to generate embeddings for documents and query texts using a [model deployed in an Elasticsearch cluster](https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-model-ref.html#ml-nlp-model-ref-text-embedding). ### Main features: 1. The ElasticsearchEmbeddings class initializes with an Elasticsearch connection object and a model_id, providing an interface to interact with the Elasticsearch ML client through [infer_trained_model](https://elasticsearch-py.readthedocs.io/en/v8.7.0/api.html?highlight=trained%20model%20infer#elasticsearch.client.MlClient.infer_trained_model) . 2. The `embed_documents()` method generates embeddings for a list of documents, and the `embed_query()` method generates an embedding for a single query text. 3. The class supports custom input text field names in case the deployed model expects a different field name than the default `text_field`. 4. The implementation is compatible with any model deployed in Elasticsearch that generates embeddings as output. ### Benefits: 1. Simplifies the process of generating embeddings using Elasticsearch models. 2. Provides a clean and intuitive interface to interact with the Elasticsearch ML client. 3. Allows users to easily integrate Elasticsearch-generated embeddings. Related issue https://github.com/hwchase17/langchain/issues/3400 --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Davis Chase	5db6b796cf	Dev2049/hf emb encode kwargs (#3925 ) Thanks @amogkam for the addition! Refactored slightly --------- Co-authored-by: Amog Kamsetty <amogkam@users.noreply.github.com>	1 year ago
James Brotchie	921894960b	Add ChatModel, LLM, and Embeddings for Google's PaLM APIs (#3575 ) - Add langchain.llms.GooglePalm for text completion, - Add langchain.chat_models.ChatGooglePalm for chat completion, - Add langchain.embeddings.GooglePalmEmbeddings for sentence embeddings, - Add example field to HumanMessage and AIMessage so that users can feed in examples into the PaLM Chat API, - Add system and unit tests. Note async completion for the Text API is not yet supported and will be included in a future PR. Happy for feedback on any aspect of this PR, especially our choice of adding an example field to Human and AI Message objects to enable passing example messages to the API.	1 year ago
Harrison Chase	eda69b13f3	openai embeddings (#3488 )	1 year ago
Zander Chase	20f530e9c5	Add Sentence Transformers Embeddings (#3409 ) Add embeddings based on the sentence transformers library. Add a notebook and integration tests. Co-authored-by: khimaros <me@khimaros.com>	1 year ago
Harrison Chase	d85f57ef9c	Harrison/llama (#2314 ) Co-authored-by: RJ Adriaansen <adriaansen@eshcc.eur.nl>	1 year ago
Harrison Chase	eff5eed719	Harrison/jina (#2043 ) Co-authored-by: numb3r3 <wangfelix87@gmail.com> Co-authored-by: felix-wang <35718120+numb3r3@users.noreply.github.com>	1 year ago
Harrison Chase	c844d1fd46	Harrison/chunk size (#1549 ) Co-authored-by: Florian Leuerer <31259070+floleuerer@users.noreply.github.com>	2 years ago
Harrison Chase	9d6d8f85da	Harrison/self hosted runhouse (#1154 ) Co-authored-by: Donny Greenberg <dongreenberg2@gmail.com> Co-authored-by: John Dagdelen <jdagdelen@users.noreply.github.com> Co-authored-by: Harrison Chase <harrisonchase@Harrisons-MBP.attlocal.net> Co-authored-by: Andrew White <white.d.andrew@gmail.com> Co-authored-by: Peng Qu <82029664+pengqu123@users.noreply.github.com> Co-authored-by: Matt Robinson <mthw.wm.robinson@gmail.com> Co-authored-by: jeff <tangj1122@gmail.com> Co-authored-by: Harrison Chase <harrisonchase@Harrisons-MacBook-Pro.local> Co-authored-by: zanderchase <zander@unfold.ag> Co-authored-by: Charles Frye <cfrye59@gmail.com> Co-authored-by: zanderchase <zanderchase@gmail.com> Co-authored-by: Shahriar Tajbakhsh <sh.tajbakhsh@gmail.com> Co-authored-by: Stefan Keselj <skeselj@princeton.edu> Co-authored-by: Francisco Ingham <fpingham@gmail.com> Co-authored-by: Dhruv Anand <105786647+dhruv-anand-aintech@users.noreply.github.com> Co-authored-by: cragwolfe <cragcw@gmail.com> Co-authored-by: Anton Troynikov <atroyn@users.noreply.github.com> Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com> Co-authored-by: Oliver Klingefjord <oliver@klingefjord.com> Co-authored-by: blob42 <contact@blob42.xyz> Co-authored-by: blob42 <spike@w530> Co-authored-by: Enrico Shippole <henryshippole@gmail.com> Co-authored-by: Ibis Prevedello <ibiscp@gmail.com> Co-authored-by: jped <jonathanped@gmail.com> Co-authored-by: Justin Torre <justintorre75@gmail.com> Co-authored-by: Ivan Vendrov <ivan@anthropic.com> Co-authored-by: Sasmitha Manathunga <70096033+mmz-001@users.noreply.github.com> Co-authored-by: Ankush Gola <9536492+agola11@users.noreply.github.com> Co-authored-by: Matt Robinson <mrobinson@unstructuredai.io> Co-authored-by: Jeff Huber <jeffchuber@gmail.com> Co-authored-by: Akshay <64036106+akshayvkt@users.noreply.github.com> Co-authored-by: Andrew Huang <jhuang16888@gmail.com> Co-authored-by: rogerserper <124558887+rogerserper@users.noreply.github.com> Co-authored-by: seanaedmiston <seane999@gmail.com> Co-authored-by: Hasegawa Yuya <52068175+Hase-U@users.noreply.github.com> Co-authored-by: Ivan Vendrov <ivendrov@gmail.com> Co-authored-by: Chen Wu (吴尘) <henrychenwu@cmu.edu> Co-authored-by: Dennis Antela Martinez <dennis.antela@gmail.com> Co-authored-by: Maxime Vidal <max.vidal@hotmail.fr> Co-authored-by: Rishabh Raizada <110235735+rishabh-ti@users.noreply.github.com>	2 years ago
Hasegawa Yuya	e08961ab25	Fixed openai embeddings to be safe by batching them based on token size calculation. (#991 ) I modified the logic of the batch calculation for embedding according to this cookbook https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb	2 years ago
Harrison Chase	91c6cea227	Harrison/batch embeds (#972 ) Co-authored-by: John Dagdelen <jdagdelen@users.noreply.github.com> Co-authored-by: Harrison Chase <harrisonchase@Harrisons-MBP.attlocal.net>	2 years ago
Harrison Chase	d564308e0f	rfc: instruct embeddings (#811 ) Co-authored-by: seanaedmiston <seane999@gmail.com>	2 years ago
Harrison Chase	7b4882a2f4	Harrison/tf embeddings (#817 ) Co-authored-by: Ryohei Kuroki <10434946+yakigac@users.noreply.github.com>	2 years ago
Harrison Chase	b94244eb12	nits (#210 ) use json.dump move test to integration tests (since it requires huggingface_hub)	2 years ago
Bagatur	b90e25f786	Add HuggingFace Hub Embeddings (#125 ) Add support for calling HuggingFace embedding models using the HuggingFaceHub Inference API. New class mirrors the existing HuggingFaceHub LLM implementation. Currently only supports 'sentence-transformers' models. Closes #86	2 years ago
issam9	28282ad099	Issam9/cohere embeddings (#105 ) Add support for cohere embeddings	2 years ago
Delip Rao	95dd2f140e	Make Integration Tests "work" again (#106 ) This fixes Issue #104 The tests for HF Embeddings is skipped because of the segfault issue mentioned there. Perhaps, a new issue should be created for that?	2 years ago
issam9	990cd821cc	Issam/hf embeddings (#68 ) Add support of HuggingFace embedding models	2 years ago
Harrison Chase	76aff023d7	FAISS and embedding support (#48 ) also adds embeddings and an in memory docstore	2 years ago

24 Commits (1d861dc37a63a41ae2e0983f2ee418efde968ce3)