9 Commits (893f3014afa9e6584b7bc95c64b1cb077423fc27)

Author	SHA1	Message	Date
William FH	b7c0eb9ecb	Wfh/ref links (#8454)	1 year ago
HeTaoPKU	d5884017a9	Add Minimax llm model to langchain (#7645) ... - Description: Minimax is a great AI startup from China, recently they released their latest model and chat API, and the API is widely-spread in China. As a result, I'd like to add the Minimax llm model to Langchain. - Tag maintainer: @hwchase17, @baskaryan --------- Co-authored-by: the <tao.he@hulu.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Jiayi Ni	1efb9bae5f	FEAT: Integrate Xinference LLMs and Embeddings (#8171) ... - [Xorbits Inference(Xinference)](https://github.com/xorbitsai/inference) is a powerful and versatile library designed to serve language, speech recognition, and multimodal models. Xinference supports a variety of GGML-compatible models including chatglm, whisper, and vicuna, and utilizes heterogeneous hardware and a distributed architecture for seamless cross-device and cross-server model deployment. - This PR integrates Xinference models and Xinference embeddings into LangChain. - Dependencies: To install the depenedencies for this integration, run `pip install "xinference[all]"` - Example Usage: To start a local instance of Xinference, run `xinference`. To deploy Xinference in a distributed cluster, first start an Xinference supervisor using `xinference-supervisor`: `xinference-supervisor -H "${supervisor_host}"` Then, start the Xinference workers using `xinference-worker` on each server you want to run them on. `xinference-worker -e "http://${supervisor_host}:9997"` To use Xinference with LangChain, you also need to launch a model. You can use command line interface (CLI) to do so. Fo example: `xinference launch -n vicuna-v1.3 -f ggmlv3 -q q4_0`. This launches a model named vicuna-v1.3 with `model_format="ggmlv3"` and `quantization="q4_0"`. A model UID is returned for you to use. Now you can use Xinference with LangChain: ```python from langchain.llms import Xinference llm = Xinference( server_url="http://0.0.0.0:9997", # suppose the supervisor_host is "0.0.0.0" model_uid = {model_uid} # model UID returned from launching a model ) llm( prompt="Q: where can we visit in the capital of France? A:", generate_config={"max_tokens": 1024}, ) ``` You can also use RESTful client to launch a model: ```python from xinference.client import RESTfulClient client = RESTfulClient("http://0.0.0.0:9997") model_uid = client.launch_model(model_name="vicuna-v1.3", model_size_in_billions=7, quantization="q4_0") ``` The following code block demonstrates how to use Xinference embeddings with LangChain: ```python from langchain.embeddings import XinferenceEmbeddings xinference = XinferenceEmbeddings( server_url="http://0.0.0.0:9997", model_uid = model_uid ) ``` ```python query_result = xinference.embed_query("This is a test query") ``` ```python doc_result = xinference.embed_documents(["text A", "text B"]) ``` Xinference is still under rapid development. Feel free to [join our Slack community](https://xorbitsio.slack.com/join/shared_invite/zt-1z3zsm9ep-87yI9YZ_B79HLB2ccTq4WA) to get the latest updates! - Request for review: @hwchase17, @baskaryan - Twitter handle: https://twitter.com/Xorbitsio --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Karan V	a003a0baf6	fix(petals) allows to run models that aren't Bloom (Support for LLama and newer models) (#8356) ... In this PR: - Removed restricted model loading logic for Petals-Bloom - Removed petals imports (DistributedBloomForCausalLM, BloomTokenizerFast) - Instead imported more generalized versions of loader (AutoDistributedModelForCausalLM, AutoTokenizer) - Updated the Petals example notebook to allow for a successful installation of Petals in Apple Silicon Macs - Tag maintainer: @hwchase17, @baskaryan --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
William FH	01a9b06400	Add api cross ref linking (#8275) ... Example of how it would show up in our python docs:  Examples added to the reference docs: https://api.python.langchain.com/en/wfh-api_crosslink/vectorstores/langchain.vectorstores.chroma.Chroma.html#langchain.vectorstores.chroma.Chroma 	1 year ago
William FH	0a16b3d84b	Update Integrations links (#8206)	1 year ago
Taqi Jaffri	8f158b72fc	Added stop sequence support to replicate (#8107) ... Stop sequences are useful if you are doing long-running completions and need to early-out rather than running for the full max_length... not only does this save inference cost on Replicate, it is also much faster if you are going to truncate the output later anyway. Other LLMs support stop sequences natively (e.g. OpenAI) but I didn't see this for Replicate so adding this via their prediction cancel method. Housekeeping: I ran `make format` and `make lint`, no issues reported in the files I touched. I did update the replicate integration test and ran `poetry run pytest tests/integration_tests/llms/test_replicate.py` successfully. Finally, I am @tjaffri https://twitter.com/tjaffri for feature announcement tweets... or if you could please tag @docugami https://twitter.com/docugami we would really appreciate that :-) Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>	1 year ago
Liu Ming	24f889f2bc	Change with_history option to False for ChatGLM by default (#8076) ... ChatGLM LLM integration will by default accumulate conversation history(with_history=True) to ChatGLM backend api, which is not expected in most cases. This PR set with_history=False by default, user should explicitly set llm.with_history=True to turn this feature on. Related PR: #8048 #7774 --------- Co-authored-by: mlot <limpo2000@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Bagatur	c8c8635dc9	mv module integrations docs (#8101)	1 year ago