langchain

Commit Graph

Author	SHA1	Message	Date
Eugene Yurtsev	760c278fe0	ChatPromptTemplate: Expand support for message formats and documentation (#8244 ) * Expands support for a variety of message formats in the `from_messages` classmethod. Ideally, we could deprecate the other on-ramps to reduce the amount of classmethods users need to know about. * Expand documentation with code examples.	1 year ago
Bagatur	61dd92f821	bump 246 (#8410 )	1 year ago
Harrison Chase	394b67ab92	add kwargs to llm runnables (#8388 )	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
James Campbell	0ad2d5f27a	[nit] Add default value for ChatOpenAI client (#7939 ) Micro convenience PR to avoid warning regarding missing `client` parameter. It is always set during initialization. @baskaryan Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Harrison Chase	82df923f37	Merge branch 'master' of github.com:hwchase17/langchain	1 year ago
Harrison Chase	1b0bfa54cf	cr	1 year ago
Jeff Vestal	c7ff5f19a8	ElasticKnnSearch rewrite - bug fix - return Document (#8180 ) Fixes: https://github.com/hwchase17/langchain/issues/7117 https://github.com/hwchase17/langchain/issues/5760 Adding back `create_index` , `add_texts`, `from_texts` to ElasticKnnSearch `from_texts` matches standard `from_texts` methods as quick start up method `knn_search` and `hybrid_result` return a list of [`Document()`, `score`,] # Test `from_texts` for quick start ``` # create new index using from_text from langchain.vectorstores.elastic_vector_search import ElasticKnnSearch from langchain.embeddings import ElasticsearchEmbeddings model_id = "sentence-transformers__all-distilroberta-v1" dims = 768 es_cloud_id = "" es_user = "" es_password = "" test_index = "knn_test_index_305" embeddings = ElasticsearchEmbeddings.from_credentials( model_id, #input_field=input_field, es_cloud_id=es_cloud_id, es_user=es_user, es_password=es_password, ) # add texts and create class instance texts = ["This is a test document", "This is another test document"] knnvectorsearch = ElasticKnnSearch.from_texts( texts=texts, embedding=embeddings, index_name= test_index, vector_query_field='vector', query_field='text', model_id=model_id, dims=dims, es_cloud_id=es_cloud_id, es_user=es_user, es_password=es_password ) # Test `add_texts` method texts2 = ["Hello, world!", "Machine learning is fun.", "I love Python."] knnvectorsearch.add_texts(texts2) query = "Hello" knn_result = knnvectorsearch.knn_search(query = query, model_id= model_id, k=2) hybrid_result = knnvectorsearch.knn_hybrid_search(query = query, model_id= model_id, k=2) ``` The mapping is as follows: ``` { "knn_test_index_012": { "mappings": { "properties": { "text": { "type": "text" }, "vector": { "type": "dense_vector", "dims": 768, "index": true, "similarity": "dot_product" } } } } } ``` # Check response type ``` >>> hybrid_result [(Document(page_content='Hello, world!', metadata={}), 0.94232327), (Document(page_content='I love Python.', metadata={}), 0.5321523)] >>> hybrid_result[0] (Document(page_content='Hello, world!', metadata={}), 0.94232327) >>> hybrid_result[0][0] Document(page_content='Hello, world!', metadata={}) >>> type(hybrid_result[0][0]) <class 'langchain.schema.document.Document'> ``` # Test with existing Index ``` from langchain.vectorstores.elastic_vector_search import ElasticKnnSearch from langchain.embeddings import ElasticsearchEmbeddings ## Initialize ElasticsearchEmbeddings model_id = "sentence-transformers__all-distilroberta-v1" dims = 768 es_cloud_id = es_user = "" es_password = "" test_index = "knn_test_index_012" embeddings = ElasticsearchEmbeddings.from_credentials( model_id, es_cloud_id=es_cloud_id, es_user=es_user, es_password=es_password, ) ## Initialize ElasticKnnSearch knn_search = ElasticKnnSearch( es_cloud_id=es_cloud_id, es_user=es_user, es_password=es_password, index_name= test_index, embedding= embeddings ) ## Test adding vectors ### Test `add_texts` method when index created texts = ["Hello, world!", "Machine learning is fun.", "I love Python."] knn_search.add_texts(texts) ``` --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Harrison Chase	a221a9ced0	Harrison/sql query (#8370 ) Co-authored-by: Nuno Campos <nuno@boringbits.io>	1 year ago
Bagatur	a1a650c743	Bagatur/from texts bug fix (#8394 ) --------- Co-authored-by: Davit Buniatyan <davit@loqsh.com> Co-authored-by: Davit Buniatyan <d@activeloop.ai> Co-authored-by: adilkhan <adilkhan.sarsen@nu.edu.kz> Co-authored-by: Ivo Stranic <istranic@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
Bagatur	877d384bc9	Revert "PromptTemplate update documentation and expand kwargs (#8234 )" (#8395 ) fyi @eyurtsev was failing a unit test	1 year ago
Gordon Clark	e66759cc9d	Github add "Create PR" tool + Docs update (#8235 ) Added a new tool to the Github toolkit called Create Pull Request. Now we can make our own langchain contributor in langchain 😁 In order to have somewhere to pull from, I also added a new env var, "GITHUB_BASE_BRANCH." This will allow the existing env var, "GITHUB_BRANCH," to be a working branch for the bot (so that it doesn't have to always commit on the main/master). For example, if you want the bot to work in a branch called `bot_dev` and your repo base is `main`, you would set up the vars like: ``` GITHUB_BASE_BRANCH = "main" GITHUB_BRANCH = "bot_dev" ``` Maintainer responsibilities: - Agents / Tools / Toolkits: @hinthornw	1 year ago
William FH	ecd4aae818	Few Shot Chat Prompt (#8038 ) Proposal for a few shot chat message example selector --------- Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>	1 year ago
Eugene Yurtsev	6dd18eee26	PromptTemplate update documentation and expand kwargs (#8234 ) # PromptTemplate * Update documentation to highlight the classmethod for instantiating a prompt template. * Expand kwargs in the classmethod to make parameters easier to discover	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
lars.gersmann	e758e9e7f5	fix(openapi): openapi chain will work without/empty description/summa… (#8351 ) Description: This PR will enable the Open API chain to work with valid Open API specifications missing `description` and `summary` properties for path and operation nodes in open api specs. Since both `description` and `summary` property are declared optional we cannot be sure they are defined. This PR resolves this problem by providing an empty (`''`) description as fallback. The previous behavior of the Open API chain was that the underlying LLM (OpenAI) throw ed an exception since `None` is not of type string: ``` openai.error.InvalidRequestError: None is not of type 'string' - 'functions.0.description' ``` Using this PR the Open API chain will succeed also using Open API specs lacking `description` and `summary` properties for path and operation nodes. Thanks for your amazing work ! Tag maintainer: @baskaryan --------- Co-authored-by: Lars Gersmann <lars.gersmann@cm4all.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
ljeagle	caa6caeb8a	Upgrade the AwaDB from v0.3.7 to v0.3.9 and change the default embeddings (#8281 ) 1. Upgrade the AwaDB from v0.3.7 to v0.3.9 2. Change the default embedding to AwaEmbedding --------- Co-authored-by: ljeagle <awadb.vincent@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	1 year ago
Harrison Chase	25b8cc7e3d	Harrison/update memory docs (#8384 ) Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Holt Skinner	d7e6770de8	refactor: Code refactoring & simplification for Google Cloud Enterprise Search retriever (#8369 ) Followup to https://github.com/langchain-ai/langchain/pull/7857 - Changes `_convert_search_response()` to use object attributes instead of converting to dictionary - Simplifies logic for readability	1 year ago
Taozhi Wang	594f195e54	Add embeddings for AwaEmbedding (#8353 ) - Description: Adds AwaEmbeddings class for embeddings, which provides users with a convenient way to do fine-tuning, as well as the potential need for multimodality - Tag maintainer: @baskaryan Create `Awa.ipynb`: an example notebook for AwaEmbeddings class Modify `embeddings/__init__.py`: Import the class Create `embeddings/awa.py`: The embedding class Create `embeddings/test_awa.py`: The test file. --------- Co-authored-by: taozhiwang <taozhiwa@gmail.com>	1 year ago
thehunmonkgroup	ba4e82bb47	fix missing _identifying_params() in _VertexAICommon (#8303 ) Full set of params are missing from Vertex* LLMs when `dict()` method is called. ``` >>> from langchain.chat_models.vertexai import ChatVertexAI >>> from langchain.llms.vertexai import VertexAI >>> chat_llm = ChatVertexAI() l>>> llm = VertexAI() >>> chat_llm.dict() {'_type': 'vertexai'} >>> llm.dict() {'_type': 'vertexai'} ``` This PR just uses the same mechanism used elsewhere to expose the full params. Since `_identifying_params()` is on the `_VertexAICommon` class, it should cover the chat and non-chat cases.	1 year ago
bheroder	dc3ca44e05	Add an example for azure ml managed feature store (#8324 ) We are adding an example of how one can connect to azure ml managed feature store and use such a prompt template in a llm chain. @baskaryan	1 year ago
Caitlin2694	b2e4b9dca4	Fix exception caused by restrictions in OWL (#8341 ) Description: Fix exception caused by restrictions in OWL Issue: #8331 Dependencies: none Maintainer: @baskaryan	1 year ago
Harrison Chase	cddd8ae83d	update release yml (#8364 ) only do the step that tags and adds release notes if its langchain	1 year ago
Nikita Pokidyshev	f499e6ea6a	Add FunctionMessage to _message_from_dict (#8374 ) <!-- Thank you for contributing to LangChain! Replace this 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 you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. 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. Maintainer responsibilities: - General / Misc / if you don't know who to tag: @baskaryan - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev - Models / Prompts: @hwchase17, @baskaryan - Memory: @hwchase17 - Agents / Tools / Toolkits: @hinthornw - Tracing / Callbacks: @agola11 - Async: @agola11 If no one reviews your PR within a few days, feel free to @-mention the same people again. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md -->	1 year ago
evelynmitchell	539574670c	Update tot.ipynb (#8387 ) Spelling error fix <!-- Thank you for contributing to LangChain! Replace this 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 you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. 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. Maintainer responsibilities: - General / Misc / if you don't know who to tag: @baskaryan - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev - Models / Prompts: @hwchase17, @baskaryan - Memory: @hwchase17 - Agents / Tools / Toolkits: @hinthornw - Tracing / Callbacks: @agola11 - Async: @agola11 If no one reviews your PR within a few days, feel free to @-mention the same people again. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md -->	1 year ago
emarco177	2ab13ab743	added unit tests for mrkl output_parser.py (#8321 ) - Description: added unit tests for mrkl output_parser.py, - Tag maintainer: @hinthornw - Twitter handle: EdenEmarco177	1 year ago
Sachin Varghese	01217b2247	Update sql database agent example (#8354 ) This PR fixes a minor documentation issue on the SQL database toolkit example notebook.	1 year ago
Bagatur	55beab326c	cleanup warnings (#8379 )	1 year ago
William FH	41524304bf	Update local script for docs build (#8377 )	1 year ago
Harrison Chase	f5bf893035	rename to str output parser (#8373 )	1 year ago
William FH	0e9e5b5202	Retry events on any run type (#8375 )	1 year ago
Bagatur	68763bd25f	mv popular and additional chains to use cases (#8242 )	1 year ago
William FH	ff98fad2d9	Add Retry Events (#8053 ) ![image](https://github.com/hwchase17/langchain/assets/13333726/59a5c3b4-4367-47e6-9f58-5b6557576a8a) --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
William FH	94a693e2ee	Link to use cases from tutorials (#8371 )	1 year ago
Nuno Campos	0eca3e7d90	Add Runnable.bind method to attach kwargs to a Runnable that will be passed to all invoke/stream/batch calls when it is run (#8368 ) <!-- Thank you for contributing to LangChain! Replace this 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 you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. 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. Maintainer responsibilities: - General / Misc / if you don't know who to tag: @baskaryan - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev - Models / Prompts: @hwchase17, @baskaryan - Memory: @hwchase17 - Agents / Tools / Toolkits: @hinthornw - Tracing / Callbacks: @agola11 - Async: @agola11 If no one reviews your PR within a few days, feel free to @-mention the same people again. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md -->	1 year ago
Harrison Chase	cf608f876b	update link	1 year ago
Nuno Campos	1bbadde77b	Support using RunnableMap directly (#8317 ) <!-- Thank you for contributing to LangChain! Replace this 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 you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. 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. Maintainer responsibilities: - General / Misc / if you don't know who to tag: @baskaryan - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev - Models / Prompts: @hwchase17, @baskaryan - Memory: @hwchase17 - Agents / Tools / Toolkits: @hinthornw - Tracing / Callbacks: @agola11 - Async: @agola11 If no one reviews your PR within a few days, feel free to @-mention the same people again. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md -->	1 year ago
Bagatur	944321c6ab	bump 245 (#8359 )	1 year ago
Rubén Barragán	ef6332ead6	Support loading files from Dropbox (#8271 ) ## Description This commit introduces the `DropboxLoader` class, a new document loader that allows loading files from Dropbox into the application. The loader relies on a Dropbox app, which requires creating an app on Dropbox, obtaining the necessary scope permissions, and generating an access token. Additionally, the dropbox Python package is required. The `DropboxLoader` class is designed to be used as a document loader for processing various file types, including text files, PDFs, and Dropbox Paper files. ## Dependencies `pip install dropbox` and `pip install unstructured` for PDF reading. ## Tag maintainer @rlancemartin, @eyurtsev (from Data Loaders). I'd appreciate some feedback here 🙏 . ## Social Networks https://github.com/rubenbarragan https://www.linkedin.com/in/rgbarragan/ https://twitter.com/RubenBarraganP --------- Co-authored-by: Ruben Barragan <rbarragan@Rubens-MacBook-Air.local>	1 year ago
Pranay Chandekar	41bb3a6f9b	fixed the bug #8343 (#8345 ) - Issue: #8343 Signed-off-by: Pranay Chandekar <pranayc6@gmail.com>	1 year ago
Ikko Eltociear Ashimine	934ea80780	Fix typo in Etherscan.ipynb (#8340 ) specifc -> specific	1 year ago
Martin Krasser	93260a9922	Fix broken `make` targets `format_diff` and `lint_diff` (#8344 ) Since the refactoring into sub-projects `libs/langchain` and `libs/experimental`, the `make` targets `format_diff` and `lint_diff` do not work anymore when running `make` from these subdirectories. Reason is that ``` PYTHON_FILES=$(shell git diff --name-only --diff-filter=d master \| grep -E '\.py$$\|\.ipynb$$') ``` generates paths from the project's root directory instead of the corresponding subdirectories. This PR fixes this by adding a `--relative` command line option. - Tag maintainer: @baskaryan	1 year ago
Harrison Chase	ae78ef7fe6	bump experimental to 005 (#8339 )	1 year ago
Vadim Gubergrits	e7e5cb9d08	Tree of Thought introducing a new ToTChain. (#5167 ) # [WIP] Tree of Thought introducing a new ToTChain. This PR adds a new chain called ToTChain that implements the ["Large Language Model Guided Tree-of-Though"](https://arxiv.org/pdf/2305.08291.pdf) paper. There's a notebook example `docs/modules/chains/examples/tot.ipynb` that shows how to use it. Implements #4975 ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: - @hwchase17 - @vowelparrot --------- Co-authored-by: Vadim Gubergrits <vgubergrits@outbox.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	1 year ago
William FH	412e29d436	Fix notebook that 'cannot convert' via nbdoc_build (#8333 )	1 year ago
William FH	9eb7e6e27f	Delete Old Evals Examples (#8252 ) Still retain: - Comparison Examples - Data + QA walkthrough - QA (but really minimize it)	1 year ago
Saurabh Misra	db9d5b213a	Optimize the cosine_similarity_top_k function performance (#8151 ) Optimizing important numerical code and making it run faster. Performance went up by 1.48x (148%). Runtime went down from 138715us to 56020us Optimization explanation: The `cosine_similarity_top_k` function is where we made the most significant optimizations. Instead of sorting the entire score_array which needs considering all elements, `np.argpartition` is utilized to find the top_k largest scores indices, this operation has a time complexity of O(n), higher performance than sorting. Remember, `np.argpartition` doesn't guarantee the order of the values. So we need to use argsort() to get the indices that would sort our top-k values after partitioning, which is much more efficient because it only sorts the top-K elements, not the entire array. Then to get the row and column indices of sorted top_k scores in the original score array, we use `np.unravel_index`. This operation is more efficient and cleaner than a list comprehension. The code has been tested for correctness by running the following snippet on both the original function and the optimized function and averaged over 5 times. ``` def test_cosine_similarity_top_k_large_matrices(): X = np.random.rand(1000, 1000) Y = np.random.rand(1000, 1000) top_k = 100 score_threshold = 0.5 gc.disable() counter = time.perf_counter_ns() return_value = cosine_similarity_top_k(X, Y, top_k, score_threshold) duration = time.perf_counter_ns() - counter gc.enable() ``` @hwaking @hwchase17 @jerwelborn Unit tests pass, I also generated more regression tests which all passed.	1 year ago
Fabrizio Ruocco	ddc353a768	Azure Cognitive Search: Custom index and scoring profile support (#6843 ) Description: Adding support for custom index and scoring profile support in Azure Cognitive Search @hwchase17 --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago

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