langchain

Commit Graph

Author	SHA1	Message	Date
Paul-Emile Brotons	92f218207b	removing client+namespace in favor of collection (#5610 ) removing client+namespace in favor of collection for an easier instantiation and to be similar to the typescript library @dev2049	1 year ago
Harrison Chase	ad09367a92	Harrison/pubmed integration (#5664 ) Co-authored-by: younis basher <71520361+younis-ba@users.noreply.github.com> Co-authored-by: Younis Bashir <younis@omicmd.com>	1 year ago
Matt Robinson	a97e4252e3	feat: add `UnstructuredExcelLoader` for `.xlsx` and `.xls` files (#5617 ) # Unstructured Excel Loader Adds an `UnstructuredExcelLoader` class for `.xlsx` and `.xls` files. Works with `unstructured>=0.6.7`. A plain text representation of the Excel file will be available under the `page_content` attribute in the doc. If you use the loader in `"elements"` mode, an HTML representation of the Excel file will be available under the `text_as_html` metadata key. Each sheet in the Excel document is its own document. ### Testing ```python from langchain.document_loaders import UnstructuredExcelLoader loader = UnstructuredExcelLoader( "example_data/stanley-cups.xlsx", mode="elements" ) docs = loader.load() ``` ## Who can review? @hwchase17 @eyurtsev	1 year ago
Zander Chase	20ec1173f4	Update Tracer Auth / Reduce Num Calls (#5517 ) Update the session creation and calls --------- Co-authored-by: Ankush Gola <ankush.gola@gmail.com>	1 year ago
Caleb Ellington	c5a7a85a4e	fix chroma update_document to embed entire documents, fixes a characer-wise embedding bug (#5584 ) # Chroma update_document full document embeddings bugfix Chroma update_document takes a single document, but treats the page_content sting of that document as a list when getting the new document embedding. This is a two-fold problem, where the resulting embedding for the updated document is incorrect (it's only an embedding of the first character in the new page_content) and it calls the embedding function for every character in the new page_content string, using many tokens in the process. Fixes #5582 Co-authored-by: Caleb Ellington <calebellington@Calebs-MBP.hsd1.ca.comcast.net>	1 year ago
Kacper Łukawski	71a7c16ee0	Fix: Qdrant ids (#5515 ) # Fix Qdrant ids creation There has been a bug in how the ids were created in the Qdrant vector store. They were previously calculated based on the texts. However, there are some scenarios in which two documents may have the same piece of text but different metadata, and that's a valid case. Deduplication should be done outside of insertion. It has been fixed and covered with the integration tests. --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Davis Chase	983a213bdc	add maxcompute (#5533 ) cc @pengwork (fresh branch, no creds)	1 year ago
Bharat Ramanathan	22603d19e0	feat(integrations): Add WandbTracer (#4521 ) # WandbTracer This PR adds the `WandbTracer` and deprecates the existing `WandbCallbackHandler`. Added an example notebook under the docs section alongside the `LangchainTracer` Here's an example [colab](https://colab.research.google.com/drive/1pY13ym8ENEZ8Fh7nA99ILk2GcdUQu0jR?usp=sharing) with the same notebook and the [trace](https://wandb.ai/parambharat/langchain-tracing/runs/8i45cst6) generated from the colab run Co-authored-by: Bharat Ramanathan <ramanathan.parameshwaran@gohuddl.com> Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Sheng Han Lim	3bae595182	Add texts with embeddings to PGVector wrapper (#5500 ) Similar to #1813 for faiss, this PR is to extend functionality to pass text and its vector pair to initialize and add embeddings to the PGVector wrapper. Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: - @dev2049	1 year ago
Zander Chase	ea09c0846f	Add Feedback Methods + Evaluation examples (#5166 ) Add CRUD methods to interact with feedback endpoints + added eval examples to the notebook	1 year ago
Kacper Łukawski	8bcaca435a	Feature: Qdrant filters supports (#5446 ) # Support Qdrant filters Qdrant has an [extensive filtering system](https://qdrant.tech/documentation/concepts/filtering/) with rich type support. This PR makes it possible to use the filters in Langchain by passing an additional param to both the `similarity_search_with_score` and `similarity_search` methods. ## Who can review? @dev2049 @hwchase17 --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Ankush Gola	1671c2afb2	py tracer fixes (#5377 )	1 year ago
Kacper Łukawski	f93d256190	Feat: Add batching to Qdrant (#5443 ) # Add batching to Qdrant Several people requested a batching mechanism while uploading data to Qdrant. It is important, as there are some limits for the maximum size of the request payload, and without batching implemented in Langchain, users need to implement it on their own. This PR exposes a new optional `batch_size` parameter, so all the documents/texts are loaded in batches of the expected size (64, by default). The integration tests of Qdrant are extended to cover two cases: 1. Documents are sent in separate batches. 2. All the documents are sent in a single request.	1 year ago
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
Paul-Emile Brotons	a61b7f7e7c	adding MongoDBAtlasVectorSearch (#5338 ) # Add MongoDBAtlasVectorSearch for the python library Fixes #5337 --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Harrison Chase	760632b292	Harrison/spark reader (#5405 ) Co-authored-by: Rithwik Ediga Lakhamsani <rithwik.ediga@databricks.com> Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
UmerHA	8259f9b7fa	DocumentLoader for GitHub (#5408 ) # Creates GitHubLoader (#5257) GitHubLoader is a DocumentLoader that loads issues and PRs from GitHub. Fixes #5257 --------- 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
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
Martin Holecek	44b48d9518	Fix update_document function, add test and documentation. (#5359 ) # Fix for `update_document` Function in Chroma ## Summary This pull request addresses an issue with the `update_document` function in the Chroma class, as described in [#5031](https://github.com/hwchase17/langchain/issues/5031#issuecomment-1562577947). The issue was identified as an `AttributeError` raised when calling `update_document` due to a missing corresponding method in the `Collection` object. This fix refactors the `update_document` method in `Chroma` to correctly interact with the `Collection` object. ## Changes 1. Fixed the `update_document` method in the `Chroma` class to correctly call methods on the `Collection` object. 2. Added the corresponding test `test_chroma_update_document` in `tests/integration_tests/vectorstores/test_chroma.py` to reflect the updated method call. 3. Added an example and explanation of how to use the `update_document` function in the Jupyter notebook tutorial for Chroma. ## Test Plan All existing tests pass after this change. In addition, the `test_chroma_update_document` test case now correctly checks the functionality of `update_document`, ensuring that the function works as expected and updates the content of documents correctly. ## Reviewers @dev2049 This fix will ensure that users are able to use the `update_document` function as expected, without encountering the previous `AttributeError`. This will enhance the usability and reliability of the Chroma class for all users. Thank you for considering this pull request. I look forward to your feedback and suggestions.	1 year ago
Ted Martinez	1cb6498fdb	Tedma4/twilio tool (#5136 ) # Add twilio sms tool --------- 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
Nicholas Liu	7652d2abb0	Add Multi-CSV/DF support in CSV and DataFrame Toolkits (#5009 ) Add Multi-CSV/DF support in CSV and DataFrame Toolkits * CSV and DataFrame toolkits now accept list of CSVs/DFs * Add default prompts for many dataframes in `pandas_dataframe` toolkit Fixes #1958 Potentially fixes #4423 ## Testing * Add single and multi-dataframe integration tests for `pandas_dataframe` toolkit with permutations of `include_df_in_prompt` * Add single and multi-CSV integration tests for csv toolkit --------- Co-authored-by: Harrison Chase <hw.chase.17@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
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
Zander Chase	e76e68b211	Add Delete Session Method (#5193 )	1 year ago
Alon Diament	44abe925df	Add Joplin document loader (#5153 ) # Add Joplin document loader [Joplin](https://joplinapp.org/) is an open source note-taking app. Joplin has a [REST API](https://joplinapp.org/api/references/rest_api/) for accessing its local database. The proposed `JoplinLoader` uses the API to retrieve all notes in the database and their metadata. Joplin needs to be installed and running locally, and an access token is required. - The PR includes an integration test. - The PR includes an example notebook. --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Davis Chase	2b2176a3c1	tfidf retriever (#5114 ) Co-authored-by: vempaliakhil96 <vempaliakhil96@gmail.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
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
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
Jettro Coenradie	b950022894	Fixes issue #5072 - adds additional support to Weaviate (#5085 ) Implementation is similar to search_distance and where_filter # adds 'additional' support to Weaviate queries Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	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
Gergely Imreh	69de33e024	Add Mastodon toots loader (#5036 ) # Add Mastodon toots loader. Loader works either with public toots, or Mastodon app credentials. Toot text and user info is loaded. I've also added integration test for this new loader as it works with public data, and a notebook with example output run now. --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Donger	039f8f1abb	Add the usage of SSL certificates for Elasticsearch and user password authentication (#5058 ) Enhance the code to support SSL authentication for Elasticsearch when using the VectorStore module, as previous versions did not provide this capability. @dev2049 --------- Co-authored-by: caidong <zhucaidong1992@gmail.com> Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Harrison Chase	10ba201d05	Harrison/neo4j (#5078 ) Co-authored-by: Tomaz Bratanic <bratanic.tomaz@gmail.com> Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Zander Chase	785502edb3	Add 'get_token_ids' method (#4784 ) Let user inspect the token ids in addition to getting th enumber of tokens --------- Co-authored-by: Zach Schillaci <40636930+zachschillaci27@users.noreply.github.com>	1 year ago
Matt Robinson	bf3f554357	feat: batch multiple files in a single Unstructured API request (#4525 ) ### Submit Multiple Files to the Unstructured API Enables batching multiple files into a single Unstructured API requests. Support for requests with multiple files was added to both `UnstructuredAPIFileLoader` and `UnstructuredAPIFileIOLoader`. Note that if you submit multiple files in "single" mode, the result will be concatenated into a single document. We recommend using this feature in "elements" mode. ### Testing The following should load both documents, using two of the example docs from the integration tests folder. ```python from langchain.document_loaders import UnstructuredAPIFileLoader file_paths = ["examples/layout-parser-paper.pdf", "examples/whatsapp_chat.txt"] loader = UnstructuredAPIFileLoader( file_paths=file_paths, api_key="FAKE_API_KEY", strategy="fast", mode="elements", ) docs = loader.load() ```	1 year ago
Davis Chase	080eb1b3fc	Fix graphql tool (#4984 ) Fix construction and add unit test.	1 year ago
Eugene Yurtsev	0ff59569dc	Adds 'IN' metadata filter for pgvector for checking set presence (#4982 ) # Adds "IN" metadata filter for pgvector to all checking for set presence PGVector currently supports metadata filters of the form: ``` {"filter": {"key": "value"}} ``` which will return documents where the "key" metadata field is equal to "value". This PR adds support for metadata filters of the form: ``` {"filter": {"key": { "IN" : ["list", "of", "values"]}}} ``` Other vector stores support this via an "$in" syntax. I chose to use "IN" to match postgres' syntax, though happy to switch. Tested locally with PGVector and ChatVectorDBChain. @dev2049 --------- Co-authored-by: jade@spanninglabs.com <jade@spanninglabs.com>	1 year ago
Eugene Yurtsev	06e524416c	power bi api wrapper integration tests & bug fix (#4983 ) # Powerbi API wrapper bug fix + integration tests - Bug fix by removing `TYPE_CHECKING` in in utilities/powerbi.py - Added integration test for power bi api in utilities/test_powerbi_api.py - Added integration test for power bi agent in agent/test_powerbi_agent.py - Edited .env.examples to help set up power bi related environment variables - Updated demo notebook with working code in docs../examples/powerbi.ipynb - AzureOpenAI -> ChatOpenAI Notes: Chat models (gpt3.5, gpt4) are much more capable than davinci at writing DAX queries, so that is important to getting the agent to work properly. Interestingly, gpt3.5-turbo needed the examples=DEFAULT_FEWSHOT_EXAMPLES to write consistent DAX queries, so gpt4 seems necessary as the smart llm. Fixes #4325 ## Before submitting Azure-core and Azure-identity are necessary dependencies check integration tests with the following: `pytest tests/integration_tests/utilities/test_powerbi_api.py` `pytest tests/integration_tests/agent/test_powerbi_agent.py` You will need a power bi account with a dataset id + table name in order to test. See .env.examples for details. ## Who can review? @hwchase17 @vowelparrot --------- Co-authored-by: aditya-pethe <adityapethe1@gmail.com>	1 year ago
Davis Chase	55baa0d153	Update redis integration tests (#4937 )	1 year ago
Harrison Chase	c9a362e482	add alias for model (#4553 ) Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Harrison Chase	9e2227ba11	Harrison/serper api bug (#4902 ) Co-authored-by: Jerry Luan <xmaswillyou@gmail.com>	1 year ago
Eugene Yurtsev	0dc304ca80	Add html parsers (#4874 ) # Add bs4 html parser * Some minor refactors * Extract the bs4 html parsing code from the bs html loader * Move some tests from integration tests to unit tests	1 year ago
yujiosaka	2f8eb95a91	Remove unnecessary comment (#4845 ) # Remove unnecessary comment Remove unnecessary comment accidentally included in #4800 ## Before submitting - no test - no document ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested:	1 year ago
yujiosaka	6561efebb7	Accept uuids kwargs for weaviate (#4800 ) # Accept uuids kwargs for weaviate Fixes #4791	1 year ago
Magnus Friberg	d126276693	Specify which data to return from chromadb (#4393 ) # Improve the Chroma get() method by adding the optional "include" parameter. The Chroma get() method excludes embeddings by default. You can customize the response by specifying the "include" parameter to selectively retrieve the desired data from the collection. --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago

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276 Commits (92f218207be5bf093093d7d695cfc717135b93ee)