langchain

Commit Graph

Author	SHA1	Message	Date
kYLe	17ecf6e119	community[patch]: Remove model limitation on Anyscale LLM (#17662 ) Description: Llama Guard is deprecated from Anyscale public endpoint. Issue: Change the default model. and remove the limitation of only use Llama Guard with Anyscale LLMs Anyscale LLM can also works with all other Chat model hosted on Anyscale. Also added `async_client` for Anyscale LLM	7 months ago
Erick Friis	29e0445490	community[patch]: BaseLLM typing in init (#18029 )	7 months ago
Guangdong Liu	4197efd67a	community: Fix SparkLLM error (#18015 ) Thank you for contributing to LangChain! - [x] PR title: "package: description" - Where "package" is whichever of langchain, community, core, experimental, etc. is being modified. Use "docs: ..." for purely docs changes, "templates: ..." for template changes, "infra: ..." for CI changes. - Example: "community: add foobar LLM" - Description: fix SparkLLM error - Issue: the issue # it fixes, if applicable - Dependencies: any dependencies required for this change - Twitter handle: if your PR gets announced, and you'd like a mention, we'll gladly shout you out!	7 months ago
Brad Erickson	ecd72d26cf	community: Bugfix - correct Ollama API path to avoid HTTP 307 (#17895 ) Sets the correct /api/generate path, without ending /, to reduce HTTP requests. Reference: https://github.com/ollama/ollama/blob/efe040f8/docs/api.md#generate-request-streaming Before: DEBUG: Starting new HTTP connection (1): localhost:11434 DEBUG: http://localhost:11434 "POST /api/generate/ HTTP/1.1" 307 0 DEBUG: http://localhost:11434 "POST /api/generate HTTP/1.1" 200 None After: DEBUG: Starting new HTTP connection (1): localhost:11434 DEBUG: http://localhost:11434 "POST /api/generate HTTP/1.1" 200 None	7 months ago
Guangdong Liu	47b1b7092d	community[minor]: Add SparkLLM to community (#17702 )	7 months ago
Aymeric Roucher	0d294760e7	Community: Fuse HuggingFace Endpoint-related classes into one (#17254 ) ## Description Fuse HuggingFace Endpoint-related classes into one: - [HuggingFaceHub](`5ceaf784f3/libs/community/langchain_community/llms/huggingface_hub.py`) - [HuggingFaceTextGenInference](`5ceaf784f3/libs/community/langchain_community/llms/huggingface_text_gen_inference.py`) - and [HuggingFaceEndpoint](`5ceaf784f3/libs/community/langchain_community/llms/huggingface_endpoint.py`) Are fused into - HuggingFaceEndpoint ## Issue The deduplication of classes was creating a lack of clarity, and additional effort to develop classes leads to issues like [this hack](`5ceaf784f3/libs/community/langchain_community/llms/huggingface_endpoint.py (L159)`). ## Dependancies None, this removes dependancies. ## Twitter handle If you want to post about this: @AymericRoucher --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	7 months ago
Mohammad Mohtashim	43dc5d3416	community[patch]: OpenLLM Client Fixes + Added Timeout Parameter (#17478 ) - OpenLLM was using outdated method to get the final text output from openllm client invocation which was raising the error. Therefore corrected that. - OpenLLM `_identifying_params` was getting the openllm's client configuration using outdated attributes which was raising error. - Updated the docstring for OpenLLM. - Added timeout parameter to be passed to underlying openllm client.	7 months ago
Mateusz Szewczyk	916332ef5b	ibm: added partners package `langchain_ibm`, added llm (#16512 ) - Description: Added `langchain_ibm` as an langchain partners package of IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) LLM provider (`WatsonxLLM`) - Dependencies: [ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/), - Tag maintainer: : --------- Co-authored-by: Erick Friis <erick@langchain.dev>	7 months ago
wulixuan	c776cfc599	community[minor]: integrate with model Yuan2.0 (#15411 ) 1. integrate with [`Yuan2.0`](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/README-EN.md) 2. update `langchain.llms` 3. add a new doc for [Yuan2.0 integration](docs/docs/integrations/llms/yuan2.ipynb) --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	7 months ago
Alex Peplowski	70c296ae96	community[patch]: Expose Anthropic Retry Logic (#17069 ) Description: Expose Anthropic's retry logic, so that `max_retries` can be configured via langchain. Anthropic's retry logic is implemented in their Python SDK here: https://github.com/anthropics/anthropic-sdk-python?tab=readme-ov-file#retries --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	7 months ago
Kate Silverstein	0bc4a9b3fc	community[minor]: Adds Llamafile as an LLM (#17431 ) * Description: Adds a simple LLM implementation for interacting with [llamafile](https://github.com/Mozilla-Ocho/llamafile)-based models. * Dependencies: N/A * Issue: N/A Detail [llamafile](https://github.com/Mozilla-Ocho/llamafile) lets you run LLMs locally from a single file on most computers without installing any dependencies. To use the llamafile LLM implementation, the user needs to: 1. Download a llamafile e.g. https://huggingface.co/jartine/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile?download=true 2. Make the file executable. 3. Run the llamafile in 'server mode'. (All llamafiles come packaged with a lightweight server; by default, the server listens at `http://localhost:8080`.) ```bash wget https://url/of/model.llamafile chmod +x model.llamafile ./model.llamafile --server --nobrowser ``` Now, the user can invoke the LLM via the LangChain client: ```python from langchain_community.llms.llamafile import Llamafile llm = Llamafile() llm.invoke("Tell me a joke.") ```	7 months ago
Nat Noordanus	8a3b74fe1f	community[patch]: Fix pydantic ForwardRef error in BedrockBase (#17416 ) - Description: Fixes a type annotation issue in the definition of BedrockBase. This issue was that the annotation for the `config` attribute includes a ForwardRef to `botocore.client.Config` which is only imported when `TYPE_CHECKING`. This can cause pydantic to raise an error like `pydantic.errors.ConfigError: field "config" not yet prepared so type is still a ForwardRef, ...`. - Issue: N/A - Dependencies: N/A - Twitter handle: `@__nat_n__`	7 months ago
Theo / Taeyoon Kang	1987f905ed	core[patch]: Support .yml extension for YAML (#16783 ) - Description: [AS-IS] When dealing with a yaml file, the extension must be .yaml. [TO-BE] In the absence of extension length constraints in the OS, the extension of the YAML file is yaml, but control over the yml extension must still be made. It's as if it's an error because it's a .jpg extension in jpeg support. - Issue: - - Dependencies: no dependencies required for this change,	7 months ago
Robby	0653aa469a	community[patch]: Invoke callback prior to yielding token (#17346 ) Description: Invoke callback prior to yielding token in stream method for watsonx. Issue: [Callback for on_llm_new_token should be invoked before the token is yielded by the model #16913](https://github.com/langchain-ai/langchain/issues/16913) Co-authored-by: Robby <h0rv@users.noreply.github.com>	7 months ago
Erick Friis	3a2eb6e12b	infra: add print rule to ruff (#16221 ) Added noqa for existing prints. Can slowly remove / will prevent more being intro'd	7 months ago
kYLe	c9999557bf	community[patch]: Modify LLMs/Anyscale work with OpenAI API v1 (#14206 ) <!-- 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. --> - Description: 1. Modify LLMs/Anyscale to work with OAI v1 2. Get rid of openai_ prefixed variables in Chat_model/ChatAnyscale 3. Modify `anyscale_api_base` to `anyscale_base_url` to follow OAI name convention (reverted) --------- Co-authored-by: Erick Friis <erick@langchain.dev>	7 months ago
Leonid Ganeline	932c52c333	community[patch]: docstrings (#16810 ) - added missed docstrings - formated docstrings to the consistent form	7 months ago
Armin Stepanyan	641efcf41c	community: add runtime kwargs to HuggingFacePipeline (#17005 ) This PR enables changing the behaviour of huggingface pipeline between different calls. For example, before this PR there's no way of changing maximum generation length between different invocations of the chain. This is desirable in cases, such as when we want to scale the maximum output size depending on a dynamic prompt size. Usage example: ```python from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_id = "gpt2" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) hf = HuggingFacePipeline(pipeline=pipe) hf("Say foo:", pipeline_kwargs={"max_new_tokens": 42}) ``` --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	7 months ago
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"}])) ```	7 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>	7 months ago
Mohammad Mohtashim	3c4b24b69a	community[patch]: Fix the _call of HuggingFaceHub (#16891 ) Fixed the following identified issue: #16849 @baskaryan	7 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>	7 months ago
Bagatur	66e45e8ab7	community[patch]: chat model mypy fixes (#17061 ) Related to #17048	7 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>	7 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>	8 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>	8 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,	8 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!	8 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.	8 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	8 months ago
Bagatur	61e876aad8	openai[patch]: Explicitly support embedding dimensions (#16596 )	8 months ago
Dmitry Tyumentsev	e86e66bad7	community[patch]: YandexGPT models - add sleep_interval (#16566 ) Added sleep between requests to prevent errors associated with simultaneous requests.	8 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>	8 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>	8 months ago
chyroc	61da2ff24c	community[patch]: use SecretStr for yandex model secrets (#15463 )	8 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>	8 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>	8 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>	8 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	8 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>	8 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>	8 months ago
Tom Jorquera	1445ac95e8	community[patch]: Enable streaming for GPT4all (#16392 ) `streaming` param was never passed to model	8 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	8 months ago
Fei Wang	d0e101e4e0	community[patch]: fix ollama astream (#16070 ) Update ollama.py	8 months ago
chyroc	d334efc848	community[patch]: fix top_p type hint (#15452 ) fix: https://github.com/langchain-ai/langchain/issues/15341 @efriis	8 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/),	8 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	8 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	8 months ago
Erick Friis	38523d7c57	together[minor]: add llm (#15853 )	8 months ago
Erick Friis	85a4594ed7	community[patch]: more deprecations (#15782 )	8 months ago

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