13 Commits (c1807d84086c92d1aea2eb7be181204e72ae10d0)

Author	SHA1	Message	Date
Leonid Ganeline	1837caa70d	docs: `ecosystem/integrations` update 1 (#5219) ... # docs: ecosystem/integrations update It is the first in a series of `ecosystem/integrations` updates. The ecosystem/integrations list is missing many integrations. I'm adding the missing integrations in a consistent format: 1. description of the integrated system 2. `Installation and Setup` section with 'pip install ...`, Key setup, and other necessary settings 3. Sections like `LLM`, `Text Embedding Models`, `Chat Models`... with links to correspondent examples and imports of the used classes. This PR keeps new docs, that are presented in the `docs/modules/models/text_embedding/examples` but missed in the `ecosystem/integrations`. The next PRs will cover the next example sections. Also updated `integrations.rst`: added the `Dependencies` section with a link to the packages used in LangChain. ## Who can review? @hwchase17 @eyurtsev @dev2049	1 year ago
Leonid Ganeline	a3598193a0	docs: `ecosystem/integrations` update 2 (#5282) ... # docs: ecosystem/integrations update 2 #5219 - part 1 The second part of this update (parts are independent of each other! no overlap): - added diffbot.md - updated confluence.ipynb; added confluence.md - updated college_confidential.md - updated openai.md - added blackboard.md - added bilibili.md - added azure_blob_storage.md - added azlyrics.md - added aws_s3.md ## Who can review? @hwchase17@agola11 @agola11 @vowelparrot @dev2049	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
Janos Tolgyesi	5f4552391f	Add SKLearnVectorStore (#5305) ... # Add SKLearnVectorStore This PR adds SKLearnVectorStore, a simply vector store based on NearestNeighbors implementations in the scikit-learn package. This provides a simple drop-in vector store implementation with minimal dependencies (scikit-learn is typically installed in a data scientist / ml engineer environment). The vector store can be persisted and loaded from json, bson and parquet format. SKLearnVectorStore has soft (dynamic) dependency on the scikit-learn, numpy and pandas packages. Persisting to bson requires the bson package, persisting to parquet requires the pyarrow package. ## Before submitting Integration tests are provided under `tests/integration_tests/vectorstores/test_sklearn.py` Sample usage notebook is provided under `docs/modules/indexes/vectorstores/examples/sklear.ipynb` 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
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
Ofer Mendelevitch	c81fb88035	Vectara (#5069) ... # Vectara Integration This PR provides integration with Vectara. Implemented here are: * langchain/vectorstore/vectara.py * tests/integration_tests/vectorstores/test_vectara.py * langchain/retrievers/vectara_retriever.py And two IPYNB notebooks to do more testing: * docs/modules/chains/index_examples/vectara_text_generation.ipynb * docs/modules/indexes/vectorstores/examples/vectara.ipynb --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Jamie Broomall	d4fd589638	WhyLabs callback (#4906) ... # Add a WhyLabs callback handler * Adds a simple WhyLabsCallbackHandler * Add required dependencies as optional * protect against missing modules with imports * Add docs/ecosystem basic example based on initial prototype from @andrewelizondo > this integration gathers privacy preserving telemetry on text with whylogs and sends stastical profiles to WhyLabs platform to monitoring these metrics over time. For more information on what WhyLabs is see: https://whylabs.ai After you run the notebook (if you have env variables set for the API Keys, org_id and dataset_id) you get something like this in WhyLabs:  Co-authored-by: Andre Elizondo <andre@whylabs.ai> Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Harrison Chase	224f73e978	move docs	1 year ago
Gengliang Wang	f9f08c4b69	Add documentation for Databricks integration (#5013) ... # Add documentation for Databricks integration This is a follow-up of https://github.com/hwchase17/langchain/pull/4702 It documents the details of how to integrate Databricks using langchain. It also provides examples in a notebook. ## Who can review? @dev2049 @hwchase17 since you are aware of the context. We will promote the integration after this doc is ready. Thanks in advance!	1 year ago
Leonid Ganeline	e2d7677526	docs: compound ecosystem and integrations (#4870) ... # Docs: compound ecosystem and integrations **Problem statement:** We have a big overlap between the References/Integrations and Ecosystem/LongChain Ecosystem pages. It confuses users. It creates a situation when new integration is added only on one of these pages, which creates even more confusion. - removed References/Integrations page (but move all its information into the individual integration pages - in the next PR). - renamed Ecosystem/LongChain Ecosystem into Integrations/Integrations. I like the Ecosystem term. It is more generic and semantically richer than the Integration term. But it mentally overloads users. The `integration` term is more concrete. UPDATE: after discussion, the Ecosystem is the term. Ecosystem/Integrations is the page (in place of Ecosystem/LongChain Ecosystem). As a result, a user gets a single place to start with the individual integration.	1 year ago