langchain

Commit Graph

Author	SHA1	Message	Date
Bagatur	a20e8f8bb0	experimental[patch]: release 0.0.43 (#13570 )	10 months ago
Bagatur	48fbc5513d	infra[patch], langchain[patch]: fix test deps and upper bound langchain dep on core(#13984 )	10 months ago
Nuno Campos	8329f81072	Use pytest asyncio auto mode (#13643 ) <!-- 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. -->	11 months ago
Bagatur	78a1f4b264	bump 338, exp 42 (#13564 )	11 months ago
Martin Krasser	79ed66f870	EXPERIMENTAL Generic LLM wrapper to support chat model interface with configurable chat prompt format (#8295 ) ## Update 2023-09-08 This PR now supports further models in addition to Lllama-2 chat models. See [this comment](#issuecomment-1668988543) for further details. The title of this PR has been updated accordingly. ## Original PR description This PR adds a generic `Llama2Chat` model, a wrapper for LLMs able to serve Llama-2 chat models (like `LlamaCPP`, `HuggingFaceTextGenInference`, ...). It implements `BaseChatModel`, converts a list of chat messages into the [required Llama-2 chat prompt format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2) and forwards the formatted prompt as `str` to the wrapped `LLM`. Usage example: ```python # uses a locally hosted Llama2 chat model llm = HuggingFaceTextGenInference( inference_server_url="http://127.0.0.1:8080/", max_new_tokens=512, top_k=50, temperature=0.1, repetition_penalty=1.03, ) # Wrap llm to support Llama2 chat prompt format. # Resulting model is a chat model model = Llama2Chat(llm=llm) messages = [ SystemMessage(content="You are a helpful assistant."), MessagesPlaceholder(variable_name="chat_history"), HumanMessagePromptTemplate.from_template("{text}"), ] prompt = ChatPromptTemplate.from_messages(messages) memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) chain = LLMChain(llm=model, prompt=prompt, memory=memory) # use chat model in a conversation # ... ``` Also part of this PR are tests and a demo notebook. - Tag maintainer: @hwchase17 - Twitter handle: `@mrt1nz` --------- Co-authored-by: Erick Friis <erick@langchain.dev>	11 months ago
Bagatur	a9b2c943e6	bump 336, exp 44 (#13420 )	11 months ago
Predrag Gruevski	d63d4994c0	Bump all libraries to the latest `ruff` version. (#13350 ) This version of `ruff` is the one we'll be using to lint the docs and cookbooks (#12677), so I'm making it used everywhere else too.	11 months ago
Bagatur	24386e0860	bump 334, exp 40 (#13211 )	11 months ago
Bagatur	1f85ec34d5	bump 331rc3 exp 39 (#13086 )	11 months ago
Bagatur	cf481c9418	bump exp 38 (#13016 )	11 months ago
Bagatur	eee5181b7a	bump 328, exp 37 (#12722 )	11 months ago
Predrag Gruevski	f94e24dfd7	Install and use `ruff format` instead of black for code formatting. (#12585 ) Best to review one commit at a time, since two of the commits are 100% autogenerated changes from running `ruff format`: - Install and use `ruff format` instead of black for code formatting. - Output of `ruff format .` in the `langchain` package. - Use `ruff format` in experimental package. - Format changes in experimental package by `ruff format`. - Manual formatting fixes to make `ruff .` pass.	11 months ago
Harrison Chase	eb903e211c	bump to 36 (#12487 )	11 months ago
Bagatur	c6a733802b	bump 324 and 35 (#12352 )	12 months ago
Bagatur	286a29a49e	bump 322 and 34 (#12228 )	12 months ago
Bagatur	963ff93476	bump 321 (#12161 )	12 months ago
Bagatur	85302a9ec1	Add CI check that integration tests compile (#12090 )	12 months ago
Bagatur	35c7c1f050	bump 317 (#11986 )	12 months ago
Predrag Gruevski	dcd0392423	Upgrade to newer black (23.10) and ruff (first 0.1.x!) versions. (#11944 ) Minor lint dependency version upgrade to pick up latest functionality. Ruff's new v0.1 version comes with lots of nice features, like fix-safety guarantees and a preview mode for not-yet-stable features: https://astral.sh/blog/ruff-v0.1.0	12 months ago
Bagatur	ba0d729961	bump 316 (#11928 )	12 months ago
Bagatur	25b1d65305	bump 315 (#11850 )	12 months ago
Erick Friis	1861cc7100	General anthropic functions, steps towards experimental integration tests (#11727 ) To match change in js here https://github.com/langchain-ai/langchainjs/pull/2892 Some integration tests need a bit more work in experimental: ![Screenshot 2023-10-12 at 12 02 49 PM](https://github.com/langchain-ai/langchain/assets/9557659/262d7d22-c405-40e9-afef-669e8d585307) Pretty sure the sqldatabase ones are an actual regression or change in interface because it's returning a placeholder. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	12 months ago
Bagatur	9c0584be74	bump 313 (#11718 )	12 months ago
Bagatur	7232e082de	bump 312 (#11621 )	1 year ago
Bagatur	53887242a1	bump 310 (#11486 )	1 year ago
Bagatur	8fafa1af91	merge	1 year ago
Bagatur	8b6b8bf68c	bump 309 (#11443 )	1 year ago
Predrag Gruevski	c9986bc3a9	Tweak type hints to match dependency's behavior. (#11355 ) Needs #11353 to merge first, and a new `langchain` to be published with those changes.	1 year ago
Bagatur	16a80779b9	bump 307 (#11380 )	1 year ago
Bagatur	8eec43ed91	bump 306 (#11289 )	1 year ago
Bagatur	77c7c9ab97	bump 305 (#11224 )	1 year ago
Bagatur	12fb393a43	bump 302 (#11070 )	1 year ago
Bagatur	aa6e6db8c7	bump 301 (#11018 )	1 year ago
Bagatur	24cb5cd379	bump 298 (#10892 )	1 year ago
Bagatur	46aa90062b	bump exp 19 (#10851 )	1 year ago
Bagatur	0d1550da91	Bagatur/bump 295 (#10785 )	1 year ago
Bagatur	f7f3c02585	bump 287 (#10498 )	1 year ago
olgavrou	b78d672a43	merge from upstream/master	1 year ago
olgavrou	11f20cded1	move everything into experimental	1 year ago
Bagatur	d2d11ccf63	bump 285 (#10373 )	1 year ago
Bagatur	672907bbbb	bump 284 (#10330 )	1 year ago
Tomaz Bratanic	db73c9d5b5	Diffbot Graph Transformer / Neo4j Graph document ingestion (#9979 ) Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Bagatur	098b4aa465	bump 281 (#10189 )	1 year ago
Bagatur	0e4c5dd176	bump 13 (#10130 )	1 year ago
maks-operlejn-ds	a8f804a618	Add data anonymizer (#9863 ) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. Identification: Identify all data fields that contain personally identifiable information (PII). 2. Replacement: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use Microsoft Presidio together with Faker framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - deanonymization - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - instance anonymization - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Bagatur	d6957921f0	bump 276 (#9931 )	1 year ago
Bagatur	9731ce5a40	bump 273 (#9751 )	1 year ago
Predrag Gruevski	eee0d1d0dd	Update repository links in the package metadata. (#9454 )	1 year ago
Bagatur	a69d1b84f4	bump 267 (#9403 )	1 year ago
Bagatur	5935767056	bump lc 246, lce 9 (#9207 )	1 year ago

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64 Commits (d0b818b634ff72ad6e50205abebeb7e822b21c14)