langchain

Commit Graph

Author	SHA1	Message	Date
Liang Zhang	7306600e2f	community[patch]: Support SerDe transform functions in Databricks LLM (#16752 ) Description: Databricks LLM does not support SerDe the transform_input_fn and transform_output_fn. After saving and loading, the LLM will be broken. This PR serialize these functions into a hex string using pickle, and saving the hex string in the yaml file. Using pickle to serialize a function can be flaky, but this is a simple workaround that unblocks many use cases. If more sophisticated SerDe is needed, we can improve it later. Test: Added a simple unit test. I did manual test on Databricks and it works well. The saved yaml looks like: ``` llm: _type: databricks cluster_driver_port: null cluster_id: null databricks_uri: databricks endpoint_name: databricks-mixtral-8x7b-instruct extra_params: {} host: e2-dogfood.staging.cloud.databricks.com max_tokens: null model_kwargs: null n: 1 stop: null task: null temperature: 0.0 transform_input_fn: 80049520000000000000008c085f5f6d61696e5f5f948c0f7472616e73666f726d5f696e7075749493942e transform_output_fn: null ``` @baskaryan ```python from langchain_community.embeddings import DatabricksEmbeddings from langchain_community.llms import Databricks from langchain.chains import RetrievalQA from langchain.document_loaders import TextLoader from langchain.text_splitter import CharacterTextSplitter from langchain.vectorstores import FAISS import mlflow embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en") def transform_input(**request): request["messages"] = [ { "role": "user", "content": request["prompt"] } ] del request["prompt"] return request llm = Databricks(endpoint_name="databricks-mixtral-8x7b-instruct", transform_input_fn=transform_input) persist_dir = "faiss_databricks_embedding" # Create the vector db, persist the db to a local fs folder loader = TextLoader("state_of_the_union.txt") documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) docs = text_splitter.split_documents(documents) db = FAISS.from_documents(docs, embeddings) db.save_local(persist_dir) def load_retriever(persist_directory): embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en") vectorstore = FAISS.load_local(persist_directory, embeddings) return vectorstore.as_retriever() retriever = load_retriever(persist_dir) retrievalQA = RetrievalQA.from_llm(llm=llm, retriever=retriever) with mlflow.start_run() as run: logged_model = mlflow.langchain.log_model( retrievalQA, artifact_path="retrieval_qa", loader_fn=load_retriever, persist_dir=persist_dir, ) # Load the retrievalQA chain loaded_model = mlflow.pyfunc.load_model(logged_model.model_uri) print(loaded_model.predict([{"query": "What did the president say about Ketanji Brown Jackson"}])) ```	5 months ago
François Paupier	929f071513	community[patch]: Fix error in `LlamaCpp` community LLM with Configurable Fields, 'grammar' custom type not available (#16995 ) - Description: Ensure the `LlamaGrammar` custom type is always available when instantiating a `LlamaCpp` LLM - Issue: #16994 - Dependencies: None - Twitter handle: @fpaupier --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	5 months ago
Mohammad Mohtashim	3c4b24b69a	community[patch]: Fix the _call of HuggingFaceHub (#16891 ) Fixed the following identified issue: #16849 @baskaryan	5 months ago
Supreet Takkar	ae33979813	community[patch]: Allow adding ARNs as model_id to support Amazon Bedrock custom models (#16800 ) - Description: Adds an additional class variable to `BedrockBase` called `provider` that allows sending a model provider such as amazon, cohere, ai21, etc. Up until now, the model provider is extracted from the `model_id` using the first part before the `.`, such as `amazon` for `amazon.titan-text-express-v1` (see [supported list of Bedrock model IDs here](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids-arns.html)). But for custom Bedrock models where the ARN of the provisioned throughput must be supplied, the `model_id` is like `arn:aws:bedrock:...` so the `model_id` cannot be extracted from this. A model `provider` is required by the LangChain Bedrock class to perform model-based processing. To allow the same processing to be performed for custom-models of a specific base model type, passing this `provider` argument can help solve the issues. The alternative considered here was the use of `provider.arn:aws:bedrock:...` which then requires ARN to be extracted and passed separately when invoking the model. The proposed solution here is simpler and also does not cause issues for current models already using the Bedrock class. - Issue: N/A - Dependencies: N/A --------- Co-authored-by: Piyush Jain <piyushjain@duck.com>	5 months ago
Bagatur	66e45e8ab7	community[patch]: chat model mypy fixes (#17061 ) Related to #17048	5 months ago
Harrison Chase	4eda647fdd	infra: add -p to mkdir in lint steps (#17013 ) Previously, if this did not find a mypy cache then it wouldnt run this makes it always run adding mypy ignore comments with existing uncaught issues to unblock other prs --------- Co-authored-by: Erick Friis <erick@langchain.dev> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>	5 months ago
Bob Lin	546b757303	community: Add ChatGLM3 (#15265 ) Add [ChatGLM3](https://github.com/THUDM/ChatGLM3) and updated [chatglm.ipynb](https://python.langchain.com/docs/integrations/llms/chatglm) --------- Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	5 months ago
baichuan-assistant	f8f2649f12	community: Add Baichuan LLM to community (#16724 ) Replace this entire comment with: - Description: Add Baichuan LLM to integration/llm, also updated related docs. Co-authored-by: BaiChuanHelper <wintergyc@WinterGYCs-MacBook-Pro.local>	5 months ago
hulitaitai	32cad38ec6	<langchain_community\llms\chatglm.py>: <Correcting "history"> (#16729 ) Use the real "history" provided by the original program instead of putting "None" in the history. - Description: I change one line in the code to make it return the "history" of the chat model. - Issue: At the moment it returns only the answers of the chat model. However the chat model himself provides a history more complet with the questions of the user. - Dependencies: no dependencies required for this change,	5 months ago
Bassem Yacoube	85e93e05ed	community[minor]: Update OctoAI LLM, Embedding and documentation (#16710 ) This PR includes updates for OctoAI integrations: - The LLM class was updated to fix a bug that occurs with multiple sequential calls - The Embedding class was updated to support the new GTE-Large endpoint released on OctoAI lately - The documentation jupyter notebook was updated to reflect using the new LLM sdk Thank you!	5 months ago
Zhuoyun(John) Xu	508bde7f40	community[patch]: Ollama - Pass headers to post request in async method (#16660 ) # Description A previous PR (https://github.com/langchain-ai/langchain/pull/15881) added option to pass headers to ollama endpoint, but headers are not pass to the async method.	5 months ago
Micah Parker	6543e585a5	community[patch]: Added support for Ollama's num_predict option in ChatOllama (#16633 ) Just a simple default addition to the options payload for a ollama generate call to support a max_new_tokens parameter. Should fix issue: https://github.com/langchain-ai/langchain/issues/14715	5 months ago
Bagatur	61e876aad8	openai[patch]: Explicitly support embedding dimensions (#16596 )	5 months ago
Dmitry Tyumentsev	e86e66bad7	community[patch]: YandexGPT models - add sleep_interval (#16566 ) Added sleep between requests to prevent errors associated with simultaneous requests.	5 months ago
Rave Harpaz	c4e9c9ca29	community[minor]: Add OCI Generative AI integration (#16548 ) <!-- Thank you for contributing to LangChain! Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified. Replace this entire comment with: - Description: Adding Oracle Cloud Infrastructure Generative AI integration. Oracle Cloud Infrastructure (OCI) Generative AI is a fully managed service that provides a set of state-of-the-art, customizable large language models (LLMs) that cover a wide range of use cases, and which is available through a single API. Using the OCI Generative AI service you can access ready-to-use pretrained models, or create and host your own fine-tuned custom models based on your own data on dedicated AI clusters. https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm - Issue: None, - Dependencies: OCI Python SDK, - Twitter handle: we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally. Passed See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/ If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. we provide unit tests. However, we cannot provide integration tests due to Oracle policies that prohibit public sharing of api keys. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> --------- Co-authored-by: Arthur Cheng <arthur.cheng@oracle.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	5 months ago
Harel Gal	a91181fe6d	community[minor]: add support for Guardrails for Amazon Bedrock (#15099 ) Added support for optionally supplying 'Guardrails for Amazon Bedrock' on both types of model invocations (batch/regular and streaming) and for all models supported by the Amazon Bedrock service. @baskaryan @hwchase17 ```python llm = Bedrock(model_id="<model_id>", client=bedrock, model_kwargs={}, guardrails={"id": " <guardrail_id>", "version": "<guardrail_version>", "trace": True}, callbacks=[BedrockAsyncCallbackHandler()]) class BedrockAsyncCallbackHandler(AsyncCallbackHandler): """Async callback handler that can be used to handle callbacks from langchain.""" async def on_llm_error( self, error: BaseException, **kwargs: Any, ) -> Any: reason = kwargs.get("reason") if reason == "GUARDRAIL_INTERVENED": # kwargs contains additional trace information sent by 'Guardrails for Bedrock' service. print(f"""Guardrails: {kwargs}""") # streaming llm = Bedrock(model_id="<model_id>", client=bedrock, model_kwargs={}, streaming=True, guardrails={"id": "<guardrail_id>", "version": "<guardrail_version>"}) ``` --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	5 months ago
chyroc	61da2ff24c	community[patch]: use SecretStr for yandex model secrets (#15463 )	5 months ago
Alessio Serra	d628a80a5d	community[patch]: added 'conversational' as a valid task for hugginface endopoint models (#15761 ) - Description: added the conversational task to hugginFace endpoint in order to use models designed for chatbot programming. - Dependencies: None --------- Co-authored-by: Alessio Serra (ext.) <alessio.serra@partner.bmw.de> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	5 months ago
Shivani Modi	4e160540ff	community[minor]: Adding Konko Completion endpoint (#15570 ) This PR introduces update to Konko Integration with LangChain. 1. New Endpoint Addition: Integration of a new endpoint to utilize completion models hosted on Konko. 2. Chat Model Updates for Backward Compatibility: We have updated the chat models to ensure backward compatibility with previous OpenAI versions. 4. Updated Documentation: Comprehensive documentation has been updated to reflect these new changes, providing clear guidance on utilizing the new features and ensuring seamless integration. Thank you to the LangChain team for their exceptional work and for considering this PR. Please let me know if any additional information is needed. --------- Co-authored-by: Shivani Modi <shivanimodi@Shivanis-MacBook-Pro.local> Co-authored-by: Shivani Modi <shivanimodi@Shivanis-MBP.lan>	5 months ago
Facundo Santiago	92e6a641fd	feat: adding paygo api support for Azure ML / Azure AI Studio (#14560 ) - Description: Introducing support for LLMs and Chat models running in Azure AI studio and Azure ML using the new deployment mode pay-as-you-go (model as a service). - Issue: NA - Dependencies: None. - Tag maintainer: @prakharg-msft @gdyre - Twitter handle: @santiagofacundo Examples added: * [docs/docs/integrations/llms/azure_ml.ipynb](https://github.com/santiagxf/langchain/blob/santiagxf/azureml-endpoints-paygo-community/docs/docs/integrations/chat/azureml_endpoint.ipynb) * [docs/docs/integrations/chat/azureml_chat_endpoint.ipynb](https://github.com/santiagxf/langchain/blob/santiagxf/azureml-endpoints-paygo-community/docs/docs/integrations/chat/azureml_chat_endpoint.ipynb) --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	5 months ago
Massimiliano Pronesti	e529939c54	feat(llms): support more tasks in HuggingFaceHub LLM and remove deprecated dep (#14406 ) - Description: this PR upgrades the `HuggingFaceHub` LLM: * support more tasks (`translation` and `conversational`) * replaced the deprecated `InferenceApi` with `InferenceClient` * adjusted the overall logic to use the "recommended" model for each task when no model is provided, and vice-versa. - Tag mainter(s): @baskaryan @hwchase17	5 months ago
DL	b9e7f6f38a	community[minor]: Bedrock async methods (#12477 ) Description: Added support for asynchronous streaming in the Bedrock class and corresponding tests. Primarily: async def aprepare_output_stream async def _aprepare_input_and_invoke_stream async def _astream async def _acall I've ensured that the code adheres to the project's linting and formatting standards by running make format, make lint, and make test. Issue: #12054, #11589 Dependencies: None Tag maintainer: @baskaryan Twitter handle: @dominic_lovric --------- Co-authored-by: Piyush Jain <piyushjain@duck.com>	5 months ago
Guillem Orellana Trullols	aad2aa7188	community[patch]: BedrockChat -> Support Titan express as chat model (#15408 ) Titan Express model was not supported as a chat model because LangChain messages were not "translated" to a text prompt. Co-authored-by: Guillem Orellana Trullols <guillem.orellana_trullols@siemens.com>	5 months ago
Tom Jorquera	1445ac95e8	community[patch]: Enable streaming for GPT4all (#16392 ) `streaming` param was never passed to model	5 months ago
mikeFore4	9d32af72ce	community[patch]: huggingface hub character removal bug fix (#16233 ) - Description: Some text-generation models on huggingface repeat the prompt in their generated response, but not all do! The tests use "gpt2" which DOES repeat the prompt and as such, the HuggingFaceHub class is hardcoded to remove the first few characters of the response (to match the len(prompt)). However, if you are using a model (such as the very popular "meta-llama/Llama-2-7b-chat-hf") that DOES NOT repeat the prompt in it's generated text, then the beginning of the generated text will be cut off. This code change fixes that bug by first checking whether the prompt is repeated in the generated response and removing it conditionally. - Issue: #16232 - Dependencies: N/A - Twitter handle: N/A	6 months ago
Fei Wang	d0e101e4e0	community[patch]: fix ollama astream (#16070 ) Update ollama.py	6 months ago
chyroc	d334efc848	community[patch]: fix top_p type hint (#15452 ) fix: https://github.com/langchain-ai/langchain/issues/15341 @efriis	6 months ago
Mateusz Szewczyk	251afda549	community[patch]: fix stop (stop_sequences) param on WatsonxLLM (#15541 ) - Description: Fix to IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) LLM provider (stop (`stop_sequences`) param on watsonxLLM) - Dependencies: [ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/),	6 months ago
shahrin014	86321a949f	community: Ollama - Parameter structure to follow official documentation (#16035 ) ## Feature - Follow parameter structure as per official documentation - top level parameters (e.g. model, system, template) will be passed as top level parameters - other parameters will be sent in options unless options is provided ![image](https://github.com/langchain-ai/langchain/assets/17451563/d14715d9-9701-4ee3-b44b-89fffea62389) ## Tests - Test if top level parameters handled properly - Test if parameters that are not top level parameters are handled as options - Test if options is provided, it will be passed as is	6 months ago
shahrin014	bdd90ae2ee	community: Ollama - Pass headers to post request (#15881 ) ## Feature - Set additional headers in constructor - Headers will be sent in post request This feature is useful if deploying Ollama on a cloud service such as hugging face, which requires authentication tokens to be passed in the request header. ## Tests - Test if header is passed - Test if header is not passed	6 months ago
Erick Friis	38523d7c57	together[minor]: add llm (#15853 )	6 months ago
Erick Friis	85a4594ed7	community[patch]: more deprecations (#15782 )	6 months ago
Bagatur	ee5bd986de	community[patch]: update oai deprecation message (#15681 ) addresses #15674	6 months ago
Nuno Campos	7ce4cd0709	Do not issue beta or deprecation warnings on internal calls (#15641 )	6 months ago
Erick Friis	d136925c49	community[patch]: fix deprecation warnings on openai subclasses (#15621 )	6 months ago
Erick Friis	ebc75c5ca7	openai[minor]: implement langchain-openai package (#15503 ) Todo - [x] copy over integration tests - [x] update docs with new instructions in #15513 - [x] add linear ticket to bump core -> community, community->langchain, and core->openai deps - [ ] (optional): add `pip install langchain-openai` command to each notebook using it - [x] Update docstrings to not need `openai` install - [x] Add serialization - [x] deprecate old models Contributor steps: - [x] Add secret names to manual integrations workflow in .github/workflows/_integration_test.yml - [x] Add secrets to release workflow (for pre-release testing) in .github/workflows/_release.yml Maintainer steps (Contributors should not do these): - [x] set up pypi and test pypi projects - [x] add credential secrets to Github Actions - [ ] add package to conda-forge Functional changes to existing classes: - now relies on openai client v1 (1.6.1) via concrete dep in langchain-openai package Codebase organization - some function calling stuff moved to `langchain_core.utils.function_calling` in order to be used in both community and langchain-openai	6 months ago
Harutaka Kawamura	73da8f863c	Remove unused `Params` (#14385 ) <!-- Thank you for contributing to LangChain! Replace this entire comment with: - Description: a description of the change, - Issue: the issue # it fixes (if applicable), - Dependencies: any dependencies required for this change, - Tag maintainer: for a quicker response, tag the relevant maintainer (see below), - Twitter handle: we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> Removes unused `Params` in `libs/langchain/langchain/llms/mlflow.py`. Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	6 months ago
Harutaka Kawamura	8ebf55ebbf	Fix `llms.Mlflow` example (#14386 ) <!-- Thank you for contributing to LangChain! Replace this entire comment with: - Description: a description of the change, - Issue: the issue # it fixes (if applicable), - Dependencies: any dependencies required for this change, - Tag maintainer: for a quicker response, tag the relevant maintainer (see below), - Twitter handle: we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> The example code for `llms.Mlflow` is outdated. Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	6 months ago
JuR-0	4dab37741a	Fix Bedrock broad error catching (#14398 ) Fixes #14347 <!-- Thank you for contributing to LangChain! Replace this entire comment with: - Description: Added the traceback of the previous error to keep the initial error type, - Issue: #14347 , - Dependencies: None, - Tag maintainer: Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> --------- Co-authored-by: Julien Raffy <julien.raffy@emeria.eu> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	6 months ago
Bagatur	480626dc99	docs, community[patch], experimental[patch], langchain[patch], cli[pa… (#15412 ) …tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' \| xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' \| xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' \| xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ```	6 months ago
Mateusz Szewczyk	cbfaccc424	WatsonxLLM updates/enhancements (#14598 ) - Description: updates/enhancements to IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) LLM provider (prompt tuned models and prompt templates deployments support) - Dependencies: [ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/), - Tag maintainer: : @hwchase17 , @eyurtsev , @baskaryan - Twitter handle: details in comment below. Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. ✅ --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	6 months ago
David Křístek	a010f29013	fix: call correct stream method in ollama (#15104 ) Co-authored-by: David Kristek <david@David--MacBook-Pro.local>	6 months ago
NuODaniel	7773943a51	community:qianfan endpoint support init params & remove useless params definietion (#15381 ) - Description: - support custom kwargs in object initialization. For instantance, QPS differs from multiple object(chat/completion/embedding with diverse models), for which global env is not a good choice for configuration. - Issue: no - Dependencies: no - Twitter handle: no @baskaryan PTAL	6 months ago
Shuai Liu	4b53440e70	Upgrades the Tongyi LLM and ChatTongyi Model (#14793 ) - Description: fixes and upgrades for the Tongyi LLM and ChatTongyi Model - Fixed typos; it should be `Tongyi`, not `OpenAI`. - Fixed a bug in `stream_generate_with_retry`; it's a real stream generator now. - Fixed a bug in `validate_environment`; the `dashscope_api_key` should be properly handled when set by environment variables or initialization parameters. - Changed the `dashscope` response to incremental output by setting the parameter `incremental_output`, which eliminates the need for the prefix-removal trick. - Removed some unused parameters, like `n`, `prefix_messages`. - Added `_stream` method. - Added async methods support, such as `_astream`, `_agenerate`, `_abatch`. - Dependencies: No new dependencies. - Tag maintainer: @hwchase17 > PS: Some may be confused about the terms `dashscope`, `tongyi`, and `Qwen`: > - `dashscope`: A platform to deploy LLMs and provide APIs to invoke the LLM. > - `tongyi`: A brand name or overall term about Alibaba Cloud's LLM/AI. > - `Qwen`: An LLM that is open-sourced and deployed in `dashscope`. > > We use the `dashscope` SDK to interact with the `tongyi`-`Qwen` LLM. --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	6 months ago
chyroc	1abcf441ae	Refactor: use SecretStr for Predibase llms (#15119 )	6 months ago
chyroc	0a9a73a9c9	Refactor: use SecretStr for PipelineAI llms (#15120 )	6 months ago
chyroc	d63ceb65b3	Refactor: use SecretStr for StochasticAI llms (#15118 )	6 months ago
chyroc	674fde87d2	Refactor: use SecretStr for VolcEngineMaas llms (#15117 )	6 months ago
chyroc	3cc1da2b38	Refactor: use SecretStr for Petals llms (#15121 )	6 months ago
shroominic	e6f0cee896	community: Async Ollama + ChatOllama (#15169 ) Description: Adding async methods to booth OllamaLLM and ChatOllama to enable async streaming and async .on_llm_new_token callbacks. Issue: ChatOllama is not working in combination with an AsyncCallbackManager because the .on_llm_new_token method is not awaited.	6 months ago
chyroc	3a3f880e5a	Patch: improve ollama 404 api error message, fix #15147 (#15156 ) Make this issue more clearly exposed to developers	6 months ago
Philip Kiely - Baseten	6342da333a	community: refactor Baseten integration with new API endpoints & docs (#15017 ) - Description: In response to user feedback, this PR refactors the Baseten integration with updated model endpoints, as well as updates relevant documentation. This PR has been tested by end users in production and works as expected. - Issue: N/A - Dependencies: This PR actually removes the dependency on the `baseten` package! - Twitter handle: https://twitter.com/basetenco	7 months ago
Blane Honeycutt	3fc1b3553b	Community: Adds ability to pass a Config to the boto3 client used by Bedrock (#15029 ) # Description This PR adds the ability to pass a `botocore.config.Config` instance to the boto3 client instantiated by the Bedrock LLM. Currently, the Bedrock LLM doesn't support a way to pass a Config, which means that some settings (e.g., timeouts and retry configuration) require instantiating a new boto3 client with a Config and then replacing the LLM's client: ```python llm = Bedrock( region_name='us-west-2', model_id="anthropic.claude-v2", model_kwargs={'max_tokens_to_sample': 4096, 'temperature': 0}, ) llm.client = boto_client('bedrock-runtime', region_name='us-west-2', config=Config({'read_timeout': 300})) ``` # Issue N/A # Dependencies N/A	7 months ago
Michael Goin	501cc8311d	community[patch]: Fix generation_config not setting properly for DeepSparse (#15036 ) - Description: Tiny but important bugfix to use a more stable interface for specifying generation_config parameters for DeepSparse LLM	7 months ago
MING KANG	ed5e0cfe57	community: add OCI Endpoint (#14250 ) - Description: - [OCI Data Science](https://docs.oracle.com/en-us/iaas/data-science/using/home.htm) is a fully managed and serverless platform for data science teams to build, train, and manage machine learning models in the Oracle Cloud Infrastructure. This PR add integration for using LangChain with an LLM hosted on a [OCI Data Science Model Deployment](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-about.htm). To authenticate, [oracle-ads](https://accelerated-data-science.readthedocs.io/en/latest/user_guide/cli/authentication.html) has been used to automatically load credentials for invoking endpoint. - Issue: None - Dependencies: `oracle-ads` - Tag maintainer: @baskaryan - Twitter handle: None --------- Co-authored-by: Erick Friis <erick@langchain.dev>	7 months ago
Liang Zhang	6479aab74f	community[patch]: Add param "task" to Databricks LLM to work around serialization of transform_output_fn (#14933 ) What is the reproduce code? ```python from langchain.chains import LLMChain, load_chain from langchain.llms import Databricks from langchain.prompts import PromptTemplate def transform_output(response): # Extract the answer from the responses. return str(response["candidates"][0]["text"]) def transform_input(request): full_prompt = f"""{request["prompt"]} Be Concise. """ request["prompt"] = full_prompt return request chat_model = Databricks( endpoint_name="llama2-13B-chat-Brambles", transform_input_fn=transform_input, transform_output_fn=transform_output, verbose=True, ) print(f"Test chat model: {chat_model('What is Apache Spark')}") # This works llm_chain = LLMChain(llm=chat_model, prompt=PromptTemplate.from_template("{chat_input}")) llm_chain("colorful socks") # this works llm_chain.save("databricks_llm_chain.yaml") # transform_input_fn and transform_output_fn are not serialized into the model yaml file loaded_chain = load_chain("databricks_llm_chain.yaml") # The Databricks LLM is recreated with transform_input_fn=None, transform_output_fn=None. loaded_chain("colorful socks") # Thus this errors. The transform_output_fn is needed to produce the correct output ``` Error: ``` File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-6c34afab-3473-421d-877f-1ef18930ef4d/lib/python3.10/site-packages/pydantic/v1/main.py", line 341, in __init__ raise validation_error pydantic.v1.error_wrappers.ValidationError: 1 validation error for Generation text str type expected (type=type_error.str) request payload: {'query': 'What is a databricks notebook?'}'} ``` What does the error mean? When the LLM generates an answer, represented by a Generation data object. The Generation data object takes a str field called text, e.g. Generation(text=”blah”). However, the Databricks LLM tried to put a non-str to text, e.g. Generation(text={“candidates”:[{“text”: “blah”}]}) Thus, pydantic errors. Why the output format becomes incorrect after saving and loading the Databricks LLM? Databrick LLM does not support serializing transform_input_fn and transform_output_fn, so they are not serialized into the model yaml file. When the Databricks LLM is loaded, it is recreated with transform_input_fn=None, transform_output_fn=None. Without transform_output_fn, the output text is not unwrapped, thus errors. Missing transform_output_fn causes this error. Missing transform_input_fn causes the additional prompt “Be Concise.” to be lost after saving and loading. <!-- Thank you for contributing to LangChain! Replace this entire comment with: - Description: a description of the change, - Issue: the issue # it fixes (if applicable), - Dependencies: any dependencies required for this change, - Tag maintainer: for a quicker response, tag the relevant maintainer (see below), - Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/ If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	7 months ago
AlpinDale	b0588774f1	community[minor]: Add Aphrodite Engine support (#14759 ) This PR adds support for PygmalionAI's [Aphrodite Engine](https://github.com/PygmalionAI/aphrodite-engine), based on vLLM's attention mechanism. At the moment, this PR does not include support for the API servers, but they will be added in a later PR. The only dependency as of now is `aphrodite-engine==0.4.2`. We pin the version to prevent breakage due to changes in the aphrodite-engine library. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	7 months ago
Liu Jun	b0c48dc983	community[patch]: make ak and sk optional in qianfan endpoint (#14835 ) - Description: The Qianfan SDK offers multiple authentication methods, but in the `QianfanEndpoint` of Langchain, it currently only supports authentication through AK and SK. In order to accommodate users who wish to use alternative authentication methods, this pull request makes AK and SK optional. This change should not impact existing users, while allowing users to configure other authentication methods as per the Qianfan SDK documentation. - Issue: / - Dependencies: No - Tag maintainer: No - Twitter handle:	7 months ago
Dmitry Tyumentsev	50381abc42	community[patch]: Add retry logic to Yandex GPT API Calls (#14907 ) Description: Added logic for re-calling the YandexGPT API in case of an error --------- Co-authored-by: Dmitry Tyumentsev <dmitry.tyumentsev@raftds.com>	7 months ago
Leonid Ganeline	b2fd41331e	docs: docstrings `langchain_community` update (#14889 ) Addded missed docstrings. Fixed inconsistency in docstrings. Note CC @efriis There were PR errors on `langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py` But, I didn't touch this file in this PR! Can it be some cache problems? I fixed this error.	7 months ago
Erick Friis	5f839beab9	community: replace deprecated davinci models (#14860 ) This is technically a breaking change because it'll switch out default models from `text-davinci-003` to `gpt-3.5-turbo-instruct`, but OpenAI is shutting off those endpoints on 1/4 anyways. Feels less disruptive to switch out the default instead.	7 months ago
Bob Lin	5de1dc72b9	community[patch]: Update Tongyi default model_name (#14844 ) <img width="1305" alt="Screenshot 2023-12-18 at 9 54 01 PM" src="https://github.com/langchain-ai/langchain/assets/10000925/c943fd81-cd48-46eb-8dff-4680424d9ba9"> The current model is no longer available.	7 months ago
Dmitry Tyumentsev	78ae276df7	community[patch]: fix agenerate return value (#14815 ) Fixed: - `_agenerate` return value in the YandexGPT Chat Model - duplicate line in the documentation Co-authored-by: Dmitry Tyumentsev <dmitry.tyumentsev@raftds.com>	7 months ago
Dmitry Tyumentsev	dcead816df	community[patch]: Update YandexGPT API (#14773 ) Update LLMand Chat model to use new api version --------- Co-authored-by: Dmitry Tyumentsev <dmitry.tyumentsev@raftds.com>	7 months ago
Lance Martin	42421860bc	Add image support for Ollama (#14713 ) Support [LLaVA](https://ollama.ai/library/llava): * Upgrade Ollama * `ollama pull llava` Ensure compatibility with [image prompt template](https://github.com/langchain-ai/langchain/pull/14263) --------- Co-authored-by: jacoblee93 <jacoblee93@gmail.com>	7 months ago
Leonid Kuligin	7f42811e14	google-genai[patch], community[patch]: Added support for new Google GenerativeAI models (#14530 ) Replace this entire comment with: - Description: added support for new Google GenerativeAI models - Twitter handle: lkuligin --------- Co-authored-by: Erick Friis <erick@langchain.dev>	7 months ago
William FH	75b8891399	Update Vertex AI to include Gemini (#14670 ) h/t to @lkuligin - Description: added new models on VertexAI - Twitter handle: @lkuligin --------- Co-authored-by: Leonid Kuligin <lkuligin@yandex.ru> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	7 months ago
Bagatur	ed58eeb9c5	community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463 ) Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes	7 months ago

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