langchain

Commit Graph

Author	SHA1	Message	Date
Harrison Chase	9921f8cc3a	Harrison/update azure nb (#5665 ) Co-authored-by: NEWTON MALLICK <38786893+N-E-W-T-O-N@users.noreply.github.com>	1 year ago
Piyush Jain	562fdfc8f9	Bedrock llm and embeddings (#5464 ) # Bedrock LLM and Embeddings This PR adds a new LLM and an Embeddings class for the [Bedrock](https://aws.amazon.com/bedrock) service. The PR also includes example notebooks for using the LLM class in a conversation chain and embeddings usage in creating an embedding for a query and document. Note: AWS is doing a private release of the Bedrock service on 05/31/2023; users need to request access and added to an allowlist in order to start using the Bedrock models and embeddings. Please use the [Bedrock Home Page](https://aws.amazon.com/bedrock) to request access and to learn more about the models available in Bedrock. <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->	1 year ago
Jeff Vestal	46e181aa8b	Allow ElasticsearchEmbeddings to create a connection with ES Client object (#5321 ) This PR adds a new method `from_es_connection` to the `ElasticsearchEmbeddings` class allowing users to use Elasticsearch clusters outside of Elastic Cloud. Users can create an Elasticsearch Client object and pass that to the new function. The returned object is identical to the one returned by calling `from_credentials` ``` # Create Elasticsearch connection es_connection = Elasticsearch( hosts=['https://es_cluster_url:port'], basic_auth=('user', 'password') ) # Instantiate ElasticsearchEmbeddings using es_connection embeddings = ElasticsearchEmbeddings.from_es_connection( model_id, es_connection, ) ``` I also added examples to the elasticsearch jupyter notebook Fixes # https://github.com/hwchase17/langchain/issues/5239 --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Harrison Chase	d6fb25c439	Harrison/prediction guard update (#5404 ) Co-authored-by: Daniel Whitenack <whitenack.daniel@gmail.com>	1 year ago
Harrison Chase	416c8b1da3	Harrison/deep infra (#5403 ) Co-authored-by: Yessen Kanapin <yessenzhar@gmail.com> Co-authored-by: Yessen Kanapin <yessen@deepinfra.com>	1 year ago
Timothy Ji	100d6655df	Reformat openai proxy setting as code (#5330 ) # Reformat the openai proxy setting as code Only affect the doc for openai Model - @hwchase17 - @agola11	1 year ago
Oleh Kuznetsov	f6615cac41	Update llamacpp demonstration notebook (#5344 ) # Update llamacpp demonstration notebook Add instructions to install with BLAS backend, and update the example of model usage. Fixes #5071. However, it is more like a prevention of similar issues in the future, not a fix, since there was no problem in the framework functionality ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: - @hwchase17 - @agola11	1 year ago
Michael Landis	f75f0dbad6	docs: improve flow of llm caching notebook (#5309 ) # docs: improve flow of llm caching notebook The notebook `llm_caching` demos various caching providers. In the previous version, there was setup common to all examples but under the `In Memory Caching` heading. If a user comes and only wants to try a particular example, they will run the common setup, then the cells for the specific provider they are interested in. Then they will get import and variable reference errors. This commit moves the common setup to the top to avoid this. ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: @dev2049	1 year ago
Xiangrui Meng	aec642febb	LLM wrapper for Databricks (#5142 ) This PR adds LLM wrapper for Databricks. It supports two endpoint types: * serving endpoint * cluster driver proxy app An integration notebook is included to show how it works. Co-authored-by: Davis Chase <130488702+dev2049@users.noreply.github.com> Co-authored-by: Gengliang Wang <gengliang@apache.org> Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Michael Landis	7047a2c1af	feat: add Momento as a standard cache and chat message history provider (#5221 ) # Add Momento as a standard cache and chat message history provider This PR adds Momento as a standard caching provider. Implements the interface, adds integration tests, and documentation. We also add Momento as a chat history message provider along with integration tests, and documentation. [Momento](https://www.gomomento.com/) is a fully serverless cache. Similar to S3 or DynamoDB, it requires zero configuration, infrastructure management, and is instantly available. Users sign up for free and get 50GB of data in/out for free every month. ## Before submitting ✅ We have added documentation, notebooks, and integration tests demonstrating usage. Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Ravindra Marella	b3988621c5	Add C Transformers for GGML Models (#5218 ) # Add C Transformers for GGML Models I created Python bindings for the GGML models: https://github.com/marella/ctransformers Currently it supports GPT-2, GPT-J, GPT-NeoX, LLaMA, MPT, etc. See [Supported Models](https://github.com/marella/ctransformers#supported-models). It provides a unified interface for all models: ```python from langchain.llms import CTransformers llm = CTransformers(model='/path/to/ggml-gpt-2.bin', model_type='gpt2') print(llm('AI is going to')) ``` It can be used with models hosted on the Hugging Face Hub: ```py llm = CTransformers(model='marella/gpt-2-ggml') ``` It supports streaming: ```py from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler llm = CTransformers(model='marella/gpt-2-ggml', callbacks=[StreamingStdOutCallbackHandler()]) ``` Please see [README](https://github.com/marella/ctransformers#readme) for more details. --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Yves Maurer	88ed8e1cd6	Added the option of specifying a proxy for the OpenAI API (#5246 ) # Added the option of specifying a proxy for the OpenAI API Fixes #5243 Co-authored-by: Yves Maurer <>	1 year ago
Archon	5cdd9ab7e1	Add MiniMax embeddings (#5174 ) - Add support for MiniMax embeddings Doc: [MiniMax embeddings](https://api.minimax.chat/document/guides/embeddings?id=6464722084cdc277dfaa966a) --------- Co-authored-by: Archon <archongum@outlook.com> Co-authored-by: Dev 2049 <dev.dev2049@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
Jeff Vestal	cf19a2a59f	example usage (#5182 ) Adding example usage for elasticsearch knn embeddings [per](https://github.com/hwchase17/langchain/pull/3401#issuecomment-1548518389) https://github.com/hwchase17/langchain/blob/master/langchain/embeddings/elasticsearch.py	1 year ago
Ikko Eltociear Ashimine	fff21a0b35	Update rellm_experimental.ipynb (#5189 ) # Your PR Title (What it does) HuggingFace -> Hugging Face	1 year ago
Nolan Tremelling	faa26650c9	Beam (#4996 ) # Beam Calls the Beam API wrapper to deploy and make subsequent calls to an instance of the gpt2 LLM in a cloud deployment. Requires installation of the Beam library and registration of Beam Client ID and Client Secret. Additional calls can then be made through the instance of the large language model in your code or by calling the Beam API. --------- 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
Myeongseop Kim	7a75bb2121	docs: fix minor typo + add wikipedia package installation part in human_input_llm.ipynb (#5118 ) # Fix typo + add wikipedia package installation part in human_input_llm.ipynb This PR 1. Fixes typo ("the the human input LLM"), 2. Addes wikipedia package installation part (in accordance with `WikipediaQueryRun` [documentation](https://python.langchain.com/en/latest/modules/agents/tools/examples/wikipedia.html)) in `human_input_llm.ipynb` (`docs/modules/models/llms/examples/human_input_llm.ipynb`)	1 year ago
Matt Rickard	de6a401a22	Add OpenLM LLM multi-provider (#4993 ) OpenLM is a zero-dependency OpenAI-compatible LLM provider that can call different inference endpoints directly via HTTP. It implements the OpenAI Completion class so that it can be used as a drop-in replacement for the OpenAI API. This changeset utilizes BaseOpenAI for minimal added code. --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
SimFG	f07b9fde74	Update the GPTCache example (#4985 ) # Update the GPTCache example Fixes #4757	1 year ago
so2liu	3002c1d508	fix: error in gptcache example nb (#4930 )	1 year ago
Alexey Nominas	c9e2a01875	Update GPT4ALL integration (#4567 ) # Update GPT4ALL integration GPT4ALL have completely changed their bindings. They use a bit odd implementation that doesn't fit well into base.py and it will probably be changed again, so it's a temporary solution. Fixes #3839, #4628	1 year ago
Ismael G Serrano	41e2394c9c	Fix AzureOpenAI embeddings documentation example. model -> deployment (#4389 ) # Documentation for Azure OpenAI embeddings model - OPENAI_API_VERSION environment variable is needed for the endpoint - The constructor does not work with model, it works with deployment. I fixed it in the notebook. (This is my first contribution) ## Who can review? @hwchase17 @agola Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	1 year ago
Leonid Ganeline	b96ab4b763	docs `retriever` improvements (#4430 ) # Docs: improvements in the `retrievers/examples/` notebooks Its primary purpose is to make the Jupyter notebook examples consistent and more suitable for first-time viewers. - add links to the integration source (if applicable) with a short description of this source; - removed `_retriever` suffix from the file names (where it existed) for consistency; - removed ` retriever` from the notebook title (where it existed) for consistency; - added code to install necessary Python package(s); - added code to set up the necessary API Key. - very small fixes in notebooks from other folders (for consistency): - docs/modules/indexes/vectorstores/examples/elasticsearch.ipynb - docs/modules/indexes/vectorstores/examples/pinecone.ipynb - docs/modules/models/llms/integrations/cohere.ipynb - fixed misspelling in langchain/retrievers/time_weighted_retriever.py comment (sorry, about this change in a .py file ) ## Who can review @dev2049	1 year ago
Sean Morgan	5372a06a8c	DOC: Fix SageMaker example (#4598 ) # Fix SageMaker example typing Since https://github.com/hwchase17/langchain/pull/3249 a new type `LLMContentHandler` is enforced for SageMaker Endpoints Fixes #4168	1 year ago
Harrison Chase	a48810fb21	dont have openai_api_version by default (#4687 ) an alternative to https://github.com/hwchase17/langchain/pull/4234/files	1 year ago
Zander Chase	d85b04be7f	Add RELLM and JSONFormer experimental LLM decoding (#4185 ) [RELLM](https://github.com/r2d4/rellm) is a library that wraps local HuggingFace pipeline models for structured decoding. RELLM works by generating tokens one at a time. At each step, it masks tokens that don't conform to the provided partial regular expression. [JSONFormer](https://github.com/1rgs/jsonformer) is a bit different, where it sequentially adds the keys then decodes each value directly	1 year ago
Harrison Chase	1e322ffc1c	change heading	1 year ago
Harrison Chase	6265cbfb11	Harrison/standard llm interface (#4615 )	1 year ago
Sai Vinay G	cf4c1394a2	feat: Added class to support huggingface text generation inference server (#4447 ) [Text Generation Inference](https://github.com/huggingface/text-generation-inference) is a Rust, Python and gRPC server for generating text using LLMs. This pull request add support for self hosted Text Generation Inference servers. feature: #4280 --------- Co-authored-by: Your Name <you@example.com> Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
SimFG	7bcf238a1a	Optimize the initialization method of GPTCache (#4522 ) Optimize the initialization method of GPTCache, so that users can use GPTCache more quickly.	1 year ago
kYLe	446b60d803	Fix a typo in langchain/docs/modules/models/llms/integrations/anyscale.ipynb (#4526 )	1 year ago
kYLe	0d51a1f12b	Add LLMs support for Anyscale Service (#4350 ) Add Anyscale service integration under LLM Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Ankush Gola	b3ecce0545	fix json saving, update docs to reference anthropic chat model (#4364 ) Fixes # (issue) https://github.com/hwchase17/langchain/issues/4085	1 year ago
PawelFaron	04b74d0446	Adjusted GPT4All llm to streaming API and added support for GPT4All_J (#4131 ) Fix for these issues: https://github.com/hwchase17/langchain/issues/4126 https://github.com/hwchase17/langchain/issues/3839#issuecomment-1534258559 --------- Co-authored-by: Pawel Faron <ext-pawel.faron@vaisala.com>	1 year ago
Harrison Chase	64940e9d0f	docs for azure (#4238 )	1 year ago
Myeongseop Kim	747b5f87c2	Add HumanInputLLM (#4160 ) Related: #4028, I opened a new PR because (1) I was unable to unstage mistakenly committed files (I'm not familiar with git enough to resolve this issue), (2) I felt closing the original PR and opening a new PR would be more appropriate if I changed the class name. This PR creates HumanInputLLM(HumanLLM in #4028), a simple LLM wrapper class that returns user input as the response. I also added a simple Jupyter notebook regarding how and why to use this LLM wrapper. In the notebook, I went over how to use this LLM wrapper and showed example of testing `WikipediaQueryRun` using HumanInputLLM. I believe this LLM wrapper will be useful especially for debugging, educational or testing purpose.	1 year ago
Gengliang Wang	8af25867cb	Simplify HumanMessages in the quick start guide (#4026 ) In the section `Get Message Completions from a Chat Model` of the quick start guide, the HumanMessage doesn't need to include `Translate this sentence from English to French.` when there is a system message. Simplify HumanMessages in these examples can further demonstrate the power of LLM.	1 year ago
Harrison Chase	5f30cc8713	Harrison/knn retriever (#4083 ) Co-authored-by: Yuichi Tateno (secon) <hotchpotch@users.noreply.github.com>	1 year ago
Davis Chase	df3bc707fc	Dev2049/callback example fix (#4010 ) Closes #3997 --------- Co-authored-by: Akshaj Jain <akshaj.jain@gmail.com>	1 year ago
MichaelMDowling	36ee60c96c	Update \docs\modules\models\text_embedding\examples\openai.ipynb (#3976 ) Single edit to: models/text_embedding/examples/openai.ipynb - Line 88: changed from: "embeddings = OpenAIEmbeddings(model_name=\"ada\")" to "embeddings = OpenAIEmbeddings()" as model_name is no longer part of the OpenAIEmbeddings class.	1 year ago
liviuasnash1	6396a4ad8d	Fix documentation typos (#3870 ) Co-authored-by: Liviu Asnash <liviua@maximallearning.com>	1 year ago
Samuel Dion-Girardeau	c5c33786a7	Fix bad spellings for 'convenience' (#3936 ) Found in the docs for chat prompt templates: https://python.langchain.com/en/latest/getting_started/getting_started.html#chat-prompt-templates and fixed similar issues in neighboring notebooks.	1 year ago
Zander Chase	c4cb55a0c5	[Breaking] Migrate GPT4All to use PyGPT4All (#3934 ) Seems the pyllamacpp package is no longer the supported bindings from gpt4all. Tested that this works locally. Given that the older models weren't very performant, I think it's better to migrate now without trying to include a lot of try / except blocks --------- Co-authored-by: Nissan Pow <npow@users.noreply.github.com> Co-authored-by: Nissan Pow <pownissa@amazon.com>	1 year ago
Ankush Gola	d3ec00b566	Callbacks Refactor [base] (#3256 ) Co-authored-by: Nuno Campos <nuno@boringbits.io> Co-authored-by: Davis Chase <130488702+dev2049@users.noreply.github.com> Co-authored-by: Zander Chase <130414180+vowelparrot@users.noreply.github.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	1 year ago
Harrison Chase	be7a8e0824	Harrison/redis cache (#3766 ) Co-authored-by: Tyler Hutcherson <tyler.hutcherson@redis.com>	1 year ago
plutopulp	6d6fd1b9e1	Add PipelineAI LLM integration (#3644 ) Add PipelineAI LLM integration	1 year ago

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