langchain

Commit Graph

Author	SHA1	Message	Date
axiangcoding	63601551b1	fix(llms): improve the ernie chat model (#9289 ) - Description: improve the ernie chat model. - fix missing kwargs to payload - new test cases - add some debug level log - improve description - Issue: None - Dependencies: None - Tag maintainer: @baskaryan	1 year ago
Daniel Chalef	1d55141c50	zep/new ZepVectorStore (#9159 ) - new ZepVectorStore class - ZepVectorStore unit tests - ZepVectorStore demo notebook - update zep-python to ~1.0.2 --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Bagatur	9abf60acb6	Bagatur/vectara regression (#9276 ) Co-authored-by: Ofer Mendelevitch <ofer@vectara.com> Co-authored-by: Ofer Mendelevitch <ofermend@gmail.com>	1 year ago
Xiaoyu Xee	b30f449dae	Add dashvector vectorstore (#9163 ) ## Description Add `Dashvector` vectorstore for langchain - [dashvector quick start](https://help.aliyun.com/document_detail/2510223.html) - [dashvector package description](https://pypi.org/project/dashvector/) ## How to use ```python from langchain.vectorstores.dashvector import DashVector dashvector = DashVector.from_documents(docs, embeddings) ``` --------- Co-authored-by: smallrain.xuxy <smallrain.xuxy@alibaba-inc.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Bagatur	bfbb97b74c	Bagatur/deeplake docs fixes (#9275 ) Co-authored-by: adilkhan <adilkhan.sarsen@nu.edu.kz>	1 year ago
Kunj-2206	1b3942ba74	Added BittensorLLM (#9250 ) Description: Adding NIBittensorLLM via Validator Endpoint to langchain llms Tag maintainer: @Kunj-2206 Maintainer responsibilities: Models / Prompts: @hwchase17, @baskaryan --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Toshish Jawale	852722ea45	Improvements in Nebula LLM (#9226 ) - Description: Added improvements in Nebula LLM to perform auto-retry; more generation parameters supported. Conversation is no longer required to be passed in the LLM object. Examples are updated. - Issue: N/A - Dependencies: N/A - Tag maintainer: @baskaryan - Twitter handle: symbldotai --------- Co-authored-by: toshishjawale <toshish@symbl.ai>	1 year ago
Bagatur	1aae77f26f	fix context nb (#9267 )	1 year ago
Alex Gamble	cf17c58b47	Update documentation for the Context integration with new URL and features (#9259 ) Update documentation and URLs for the Langchain Context integration. We've moved from getcontext.ai to context.ai \o/ Thanks in advance for the review!	1 year ago
Anthony Mahanna	0a04e63811	docs: Update ArangoDB Links (#9251 ) ready for review - mdx link update - colab link update	1 year ago
Hech	4b505060bd	fix: max_marginal_relevance_search and docs in Dingo (#9244 )	1 year ago
axiangcoding	664ff28cba	feat(llms): support ernie chat (#9114 ) Description: support ernie (文心一言) chat model Related issue: #7990 Dependencies: None Tag maintainer: @baskaryan	1 year ago
fanyou-wbd	5e43768f61	docs: update LlamaCpp max_tokens args (#9238 ) This PR updates documentations only, `max_length` should be `max_tokens` according to latest LlamaCpp API doc: https://api.python.langchain.com/en/latest/llms/langchain.llms.llamacpp.LlamaCpp.html	1 year ago
Joshua Sundance Bailey	ef0664728e	ArcGISLoader update (#9240 ) Small bug fixes and added metadata based on user feedback. This PR is from the author of https://github.com/langchain-ai/langchain/pull/8873 .	1 year ago
Joseph McElroy	eac4ddb4bb	Elasticsearch Store Improvements (#8636 ) Todo: - [x] Connection options (cloud, localhost url, es_connection) support - [x] Logging support - [x] Customisable field support - [x] Distance Similarity support - [x] Metadata support - [x] Metadata Filter support - [x] Retrieval Strategies - [x] Approx - [x] Approx with Hybrid - [x] Exact - [x] Custom - [x] ELSER (excluding hybrid as we are working on RRF support) - [x] integration tests - [x] Documentation 👋 this is a contribution to improve Elasticsearch integration with Langchain. Its based loosely on the changes that are in master but with some notable changes: ## Package name & design improvements The import name is now `ElasticsearchStore`, to aid discoverability of the VectorStore. ```py ## Before from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch, ElasticKnnSearch ## Now from langchain.vectorstores.elasticsearch import ElasticsearchStore ``` ## Retrieval Strategy support Before we had a number of classes, depending on the strategy you wanted. `ElasticKnnSearch` for approx, `ElasticVectorSearch` for exact / brute force. With `ElasticsearchStore` we have retrieval strategies: ### Approx Example Default strategy for the vast majority of developers who use Elasticsearch will be inferring the embeddings from outside of Elasticsearch. Uses KNN functionality of _search. ```py texts = ["foo", "bar", "baz"] docsearch = ElasticsearchStore.from_texts( texts, FakeEmbeddings(), es_url="http://localhost:9200", index_name="sample-index" ) output = docsearch.similarity_search("foo", k=1) ``` ### Approx, with hybrid Developers who want to search, using both the embedding and the text bm25 match. Its simple to enable. ```py texts = ["foo", "bar", "baz"] docsearch = ElasticsearchStore.from_texts( texts, FakeEmbeddings(), es_url="http://localhost:9200", index_name="sample-index", strategy=ElasticsearchStore.ApproxRetrievalStrategy(hybrid=True) ) output = docsearch.similarity_search("foo", k=1) ``` ### Approx, with `query_model_id` Developers who want to infer within Elasticsearch, using the model loaded in the ml node. This relies on the developer to setup the pipeline and index if they wish to embed the text in Elasticsearch. Example of this in the test. ```py texts = ["foo", "bar", "baz"] docsearch = ElasticsearchStore.from_texts( texts, FakeEmbeddings(), es_url="http://localhost:9200", index_name="sample-index", strategy=ElasticsearchStore.ApproxRetrievalStrategy( query_model_id="sentence-transformers__all-minilm-l6-v2" ), ) output = docsearch.similarity_search("foo", k=1) ``` ### I want to provide my own custom Elasticsearch Query You might want to have more control over the query, to perform multi-phase retrieval such as LTR, linearly boosting on document parameters like recently updated or geo-distance. You can do this with `custom_query_fn` ```py def my_custom_query(query_body: dict, query: str) -> dict: return {"query": {"match": {"text": {"query": "bar"}}}} texts = ["foo", "bar", "baz"] docsearch = ElasticsearchStore.from_texts( texts, FakeEmbeddings(), **elasticsearch_connection, index_name=index_name ) docsearch.similarity_search("foo", k=1, custom_query=my_custom_query) ``` ### Exact Example Developers who have a small dataset in Elasticsearch, dont want the cost of indexing the dims vs tradeoff on cost at query time. Uses script_score. ```py texts = ["foo", "bar", "baz"] docsearch = ElasticsearchStore.from_texts( texts, FakeEmbeddings(), es_url="http://localhost:9200", index_name="sample-index", strategy=ElasticsearchStore.ExactRetrievalStrategy(), ) output = docsearch.similarity_search("foo", k=1) ``` ### ELSER Example Elastic provides its own sparse vector model called ELSER. With these changes, its really easy to use. The vector store creates a pipeline and index thats setup for ELSER. All the developer needs to do is configure, ingest and query via langchain tooling. ```py texts = ["foo", "bar", "baz"] docsearch = ElasticsearchStore.from_texts( texts, FakeEmbeddings(), es_url="http://localhost:9200", index_name="sample-index", strategy=ElasticsearchStore.SparseVectorStrategy(), ) output = docsearch.similarity_search("foo", k=1) ``` ## Architecture In future, we can introduce new strategies and allow us to not break bwc as we evolve the index / query strategy. ## Credit On release, could you credit @elastic and @phoey1 please? Thank you! --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Lance Martin	17ae2998e7	Update Ollama docs (#9220 ) Based on discussion w/ team.	1 year ago
Krish Dholakia	49f1d8477c	Adding ChatLiteLLM model (#9020 ) Description: Adding a langchain integration for the LiteLLM library Tag maintainer: @hwchase17, @baskaryan Twitter handle: @krrish_dh / @Berri_AI --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Emmanuel Gautier	f11e5442d6	docs: update LlamaCpp input args (#9173 ) This PR only updates the LlamaCpp args documentation. The input arg has been flattened.	1 year ago
Massimiliano Pronesti	d95eeaedbe	feat(llms): support vLLM's OpenAI-compatible server (#9179 ) This PR aims at supporting [vLLM's OpenAI-compatible server feature](https://vllm.readthedocs.io/en/latest/getting_started/quickstart.html#openai-compatible-server), i.e. allowing to call vLLM's LLMs like if they were OpenAI's. I've also udpated the related notebook providing an example usage. At the moment, vLLM only supports the `Completion` API.	1 year ago
Michael Goin	621da3c164	Adds DeepSparse as an LLM (#9184 ) Adds [DeepSparse](https://github.com/neuralmagic/deepsparse) as an LLM backend. DeepSparse supports running various open-source sparsified models hosted on [SparseZoo](https://sparsezoo.neuralmagic.com/) for performance gains on CPUs. Twitter handles: @mgoin_ @neuralmagic --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Bagatur	0fa69d8988	Bagatur/zep python 1.0 (#9186 ) Co-authored-by: Daniel Chalef <131175+danielchalef@users.noreply.github.com>	1 year ago
Bagatur	45741bcc1b	Bagatur/vectara nit (#9140 ) Co-authored-by: Ofer Mendelevitch <ofer@vectara.com>	1 year ago
Dominick DEV	9b64932e55	Add LangChain utility for real-time crypto exchange prices (#4501 ) This commit adds the LangChain utility which allows for the real-time retrieval of cryptocurrency exchange prices. With LangChain, users can easily access up-to-date pricing information by running the command ".run(from_currency, to_currency)". This new feature provides a convenient way to stay informed on the latest exchange rates and make informed decisions when trading crypto. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Joshua Sundance Bailey	eaa505fb09	Create ArcGISLoader & example notebook (#8873 ) - Description: Adds the ArcGISLoader class to `langchain.document_loaders` - Allows users to load data from ArcGIS Online, Portal, and similar - Users can authenticate with `arcgis.gis.GIS` or retrieve public data anonymously - Uses the `arcgis.features.FeatureLayer` class to retrieve the data - Defines the most relevant keywords arguments and accepts `**kwargs` - Dependencies: Using this class requires `arcgis` and, optionally, `bs4.BeautifulSoup`. Tagging maintainers: - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Hai The Dude	e4418d1b7e	Added new use case docs for Web Scraping, Chromium loader, BS4 transformer (#8732 ) - Description: Added a new use case category called "Web Scraping", and a tutorial to scrape websites using OpenAI Functions Extraction chain to the docs. - Tag maintainer:@baskaryan @hwchase17 , - Twitter handle: https://www.linkedin.com/in/haiphunghiem/ (I'm on LinkedIn mostly) --------- Co-authored-by: Lance Martin <lance@langchain.dev>	1 year ago
niklub	16af5f8690	Add LabelStudio integration (#8880 ) This PR introduces [Label Studio](https://labelstud.io/) integration with LangChain via `LabelStudioCallbackHandler`: - sending data to the Label Studio instance - labeling dataset for supervised LLM finetuning - rating model responses - tracking and displaying chat history - support for custom data labeling workflow ### Example ``` chat_llm = ChatOpenAI(callbacks=[LabelStudioCallbackHandler(mode="chat")]) chat_llm([ SystemMessage(content="Always use emojis in your responses."), HumanMessage(content="Hey AI, how's your day going?"), AIMessage(content="🤖 I don't have feelings, but I'm running smoothly! How can I help you today?"), HumanMessage(content="I'm feeling a bit down. Any advice?"), AIMessage(content="🤗 I'm sorry to hear that. Remember, it's okay to seek help or talk to someone if you need to. 💬"), HumanMessage(content="Can you tell me a joke to lighten the mood?"), AIMessage(content="Of course! 🎭 Why did the scarecrow win an award? Because he was outstanding in his field! 🌾"), HumanMessage(content="Haha, that was a good one! Thanks for cheering me up."), AIMessage(content="Always here to help! 😊 If you need anything else, just let me know."), HumanMessage(content="Will do! By the way, can you recommend a good movie?"), ]) ``` <img width="906" alt="image" src="https://github.com/langchain-ai/langchain/assets/6087484/0a1cf559-0bd3-4250-ad96-6e71dbb1d2f3"> ### Dependencies - [label-studio](https://pypi.org/project/label-studio/) - [label-studio-sdk](https://pypi.org/project/label-studio-sdk/) https://twitter.com/labelstudiohq --------- Co-authored-by: nik <nik@heartex.net>	1 year ago
Bagatur	8cb2594562	Bagatur/dingo (#9079 ) Co-authored-by: gary <1625721671@qq.com>	1 year ago
Alvaro Bartolome	f7ae183f40	`ArgillaCallbackHandler` to properly use default values for `api_url` and `api_key` (#9113 ) As of the recent PR at #9043, after some testing we've realised that the default values were not being used for `api_key` and `api_url`. Besides that, the default for `api_key` was set to `argilla.apikey`, but since the default values are intended for people using the Argilla Quickstart (easy to run and setup), the defaults should be instead `owner.apikey` if using Argilla 1.11.0 or higher, or `admin.apikey` if using a lower version of Argilla. Additionally, we've removed the f-string replacements from the docstrings. --------- Co-authored-by: Gabriel Martin <gabriel@argilla.io>	1 year ago
Bagatur	0e5d09d0da	dalle nb fix (#9125 )	1 year ago
Ashutosh Sanzgiri	991b448dfc	minor edits (#9093 ) Description: Minor edit to PR#845 Thanks!	1 year ago
Chenyu Zhao	c0acbdca1b	Update Fireworks model names (#9085 )	1 year ago
Charles Lanahan	a2588d6c57	Update openai embeddings notebook with correct embedding model in section 2 (#5831 ) In second section it looks like a copy/paste from the first section and doesn't include the specific embedding model mentioned in the example so I added it for clarity. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Josh Phillips	5fc07fa524	change id column type to uuid to match function (#7456 ) The table creation process in these examples commands do not match what the recently updated functions in these example commands is looking for. This change updates the type in the table creation command. Issue Number for my report of the doc problem #7446 @rlancemartin and @eyurtsev I believe this is your area Twitter: @j1philli Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Bidhan Roy	02430e25b6	BagelDB (bageldb.ai), VectorStore integration. (#8971 ) - Description: [BagelDB](bageldb.ai) a collaborative vector database. Integrated the bageldb PyPi package with langchain with related tests and code. - Issue: Not applicable. - Dependencies: `betabageldb` PyPi package. - Tag maintainer: @rlancemartin, @eyurtsev, @baskaryan - Twitter handle: bageldb_ai (https://twitter.com/BagelDB_ai) We ran `make format`, `make lint` and `make test` locally. Followed the contribution guideline thoroughly https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --------- Co-authored-by: Towhid1 <nurulaktertowhid@gmail.com>	1 year ago
Piyush Jain	8eea46ed0e	Bedrock embeddings async methods (#9024 ) ## Description This PR adds the `aembed_query` and `aembed_documents` async methods for improving the embeddings generation for large documents. The implementation uses asyncio tasks and gather to achieve concurrency as there is no bedrock async API in boto3. ### Maintainers @agola11 @aarora79 ### Open questions To avoid throttling from the Bedrock API, should there be an option to limit the concurrency of the calls?	1 year ago
Blake (Yung Cher Ho)	8d351bfc20	Takeoff integration (#9045 ) ## Description: This PR adds the Titan Takeoff Server to the available LLMs in LangChain. Titan Takeoff is an inference server created by [TitanML](https://www.titanml.co/) that allows you to deploy large language models locally on your hardware in a single command. Most generative model architectures are included, such as Falcon, Llama 2, GPT2, T5 and many more. Read more about Titan Takeoff here: - [Blog](https://medium.com/@TitanML/introducing-titan-takeoff-6c30e55a8e1e) - [Docs](https://docs.titanml.co/docs/titan-takeoff/getting-started) #### Testing As Titan Takeoff runs locally on port 8000 by default, no network access is needed. Responses are mocked for testing. - [x] Make Lint - [x] Make Format - [x] Make Test #### Dependencies No new dependencies are introduced. However, users will need to install the titan-iris package in their local environment and start the Titan Takeoff inferencing server in order to use the Titan Takeoff integration. Thanks for your help and please let me know if you have any questions. cc: @hwchase17 @baskaryan	1 year ago
Luca Foppiano	dfb93dd2b5	Improved grobid documentation (#9025 ) - Description: Improvement in the Grobid loader documentation, typos and suggesting to use the docker image instead of installing Grobid in local (the documentation was also limited to Mac, while docker allow running in any platform) - Tag maintainer: @rlancemartin, @eyurtsev - Twitter handle: @whitenoise	1 year ago
Jerzy Czopek	539672a7fd	Feature/fix azureopenai model mappings (#8621 ) This pull request aims to ensure that the `OpenAICallbackHandler` can properly calculate the total cost for Azure OpenAI chat models. The following changes have resolved this issue: - The `model_name` has been added to the ChatResult llm_output. Without this, the default values of `gpt-35-turbo` were applied. This was causing the total cost for Azure OpenAI's GPT-4 to be significantly inaccurate. - A new parameter `model_version` has been added to `AzureChatOpenAI`. Azure does not include the model version in the response. With the addition of `model_name`, this is not a significant issue for GPT-4 models, but it's an issue for GPT-3.5-Turbo. Version 0301 (default) of GPT-3.5-Turbo on Azure has a flat rate of 0.002 per 1k tokens for both prompt and completion. However, version 0613 introduced a split in pricing for prompt and completion tokens. - The `OpenAICallbackHandler` implementation has been updated with the proper model names, versions, and cost per 1k tokens. Unit tests have been added to ensure the functionality works as expected; the Azure ChatOpenAI notebook has been updated with examples. Maintainers: @hwchase17, @baskaryan Twitter handle: @jjczopek --------- Co-authored-by: Jerzy Czopek <jerzy.czopek@avanade.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
arjunbansal	a2681f950d	add instructions on integrating Log10 (#8938 ) - Description: Instruction for integration with Log10: an [open source](https://github.com/log10-io/log10) proxiless LLM data management and application development platform that lets you log, debug and tag your Langchain calls - Tag maintainer: @baskaryan - Twitter handle: @log10io @coffeephoenix Several examples showing the integration included [here](https://github.com/log10-io/log10/tree/main/examples/logging) and in the PR	1 year ago
Aarav Borthakur	3f64b8a761	Integrate Rockset as a chat history store (#8940 ) Description: Adds Rockset as a chat history store Dependencies: no changes Tag maintainer: @hwchase17 This PR passes linting and testing. I added a test for the integration and an example notebook showing its use.	1 year ago
Molly Cantillon	99b5a7226c	Weaviate: adding auth example + fixing spelling in ReadME (#8939 ) Added basic auth example to Weaviate notebook @baskaryan	1 year ago
Joe Reuter	8f0cd91d57	Airbyte based loaders (#8586 ) This PR adds 8 new loaders: * `AirbyteCDKLoader` This reader can wrap and run all python-based Airbyte source connectors. * Separate loaders for the most commonly used APIs: * `AirbyteGongLoader` * `AirbyteHubspotLoader` * `AirbyteSalesforceLoader` * `AirbyteShopifyLoader` * `AirbyteStripeLoader` * `AirbyteTypeformLoader` * `AirbyteZendeskSupportLoader` ## Documentation and getting started I added the basic shape of the config to the notebooks. This increases the maintenance effort a bit, but I think it's worth it to make sure people can get started quickly with these important connectors. This is also why I linked the spec and the documentation page in the readme as these two contain all the information to configure a source correctly (e.g. it won't suggest using oauth if that's avoidable even if the connector supports it). ## Document generation The "documents" produced by these loaders won't have a text part (instead, all the record fields are put into the metadata). If a text is required by the use case, the caller needs to do custom transformation suitable for their use case. ## Incremental sync All loaders support incremental syncs if the underlying streams support it. By storing the `last_state` from the reader instance away and passing it in when loading, it will only load updated records. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Harrison Chase	7543a3d70e	Harrison/image (#845 ) Co-authored-by: Ashutosh Sanzgiri <sanzgiri@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Leonid Ganeline	33a2f58fbf	`tensoflow_datasets` document loader (#8721 ) This PR adds `tensoflow_datasets` document loader	1 year ago
Leonid Ganeline	2d078c7767	`PubMed` document loader (#8893 ) - added `PubMed Document Loader` artifacts; ut-s; examples - fixed `PubMed utility`; ut-s @hwchase17	1 year ago
Seif	6327eecdaf	Fix typo in Vectara docs (#8925 ) Fixed a typo in the Vectara docs description.	1 year ago
David vonThenen	bf4a112aa6	Fixes to the Nebula LLM Integration (#8918 ) This addresses some issues with introducing the Nebula LLM to LangChain in this PR: https://github.com/langchain-ai/langchain/pull/8876 This fixes the following: - Removes `SYMBLAI` from variable names - Fixes bug with `Bearer` for the API KEY Thanks again in advance for your help! cc: @hwchase17, @baskaryan --------- Co-authored-by: dvonthenen <david.vonthenen@gmail.com>	1 year ago
Josh Hart	6116cbf0de	Fix imports in awslambda docs (#8916 ) Minor doc fix to awslambda tool notebook. Add missing import for initialize_agent to awslambda agent example Co-authored-by: Josh Hart <josharj@amazon.com>	1 year ago
Maurits de Groot	61c2d918c6	Fixed inaccurate import in integrations:providers:bedrock documentation (#8915 ) Description: Fixed inaccurate import in integrations:providers:bedrock documentation In the current version of the bedrock documentation, page https://python.langchain.com/docs/integrations/providers/bedrock it states that the import is from langchain import Bedrock This has been changed to from langchain.llms.bedrock import Bedrock as stated in https://python.langchain.com/docs/integrations/llms/bedrock Issue: Not applicable Dependencies No dependencies required Tag maintainer @baskaryan Twitter handle: Not applicable	1 year ago
Manuel Soria	e74a605379	SQL use case docs (#8513 )	1 year ago

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137 Commits (3e6cea46e25e9d13b3da14cd90e21b99be2e7ba0)