9 Commits (9298a0b9412a24c574cbeb87eb44f1bd3b6028fc)

Author	SHA1	Message	Date
Leonid Ganeline	3f6bf852ea	experimental: docstrings update (#18048) ... Added missed docstrings. Formatted docsctrings to the consistent format.	7 months ago
Mattt394	7c6009b76f	experimental[patch]: Fixed typos in SmartLLMChain ideation and critique prompts (#11507) ... Noticed and fixed a few typos in the SmartLLMChain default ideation and critique prompts --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	7 months ago
Anish Nag	6da0cfea0e	experimental[patch]: SmartLLMChain Output Key Customization (#14466) ... **Description** The `SmartLLMChain` was was fixed to output key "resolution". Unfortunately, this prevents the ability to use multiple `SmartLLMChain` in a `SequentialChain` because of colliding output keys. This change simply gives the option the customize the output key to allow for sequential chaining. The default behavior is the same as the current behavior. Now, it's possible to do the following: ``` from langchain.chat_models import ChatOpenAI from langchain.prompts import PromptTemplate from langchain_experimental.smart_llm import SmartLLMChain from langchain.chains import SequentialChain joke_prompt = PromptTemplate( input_variables=["content"], template="Tell me a joke about {content}.", ) review_prompt = PromptTemplate( input_variables=["scale", "joke"], template="Rate the following joke from 1 to {scale}: {joke}" ) llm = ChatOpenAI(temperature=0.9, model_name="gpt-4-32k") joke_chain = SmartLLMChain(llm=llm, prompt=joke_prompt, output_key="joke") review_chain = SmartLLMChain(llm=llm, prompt=review_prompt, output_key="review") chain = SequentialChain( chains=[joke_chain, review_chain], input_variables=["content", "scale"], output_variables=["review"], verbose=True ) response = chain.run({"content": "chickens", "scale": "10"}) print(response) ``` --------- Co-authored-by: Erick Friis <erick@langchain.dev>	10 months ago
kavinraj A S	ab6b41937a	Fixed a typo in smart_llm prompt (#13052) ... <!-- 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. -->	10 months ago
Predrag Gruevski	7c0f1bf23f	Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) ... Part of upgrading our CI to use Poetry 1.6.1.	11 months ago
Aashish Saini	f9f1340208	Fixed some grammatical and spelling errors (#10595) ... Fixed some grammatical and spelling errors	1 year ago
Nuno Campos	c0d67420e5	Use a submodule for pydantic v1 compat (#9371) ... <!-- 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/hwchase17/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. These live is docs/extras directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin. -->	1 year ago
Eugene Yurtsev	2673b3a314	Create pydantic v1 namespace in langchain (#9254) ... Create pydantic v1 namespace in langchain experimental	1 year ago
UmerHA	8aab39e3ce	Added SmartGPT workflow (issue #4463) (#4816) ... # Added SmartGPT workflow by providing SmartLLM wrapper around LLMs Edit: As @hwchase17 suggested, this should be a chain, not an LLM. I have adapted the PR. It is used like this: ``` from langchain.prompts import PromptTemplate from langchain.chains import SmartLLMChain from langchain.chat_models import ChatOpenAI hard_question = "I have a 12 liter jug and a 6 liter jug. I want to measure 6 liters. How do I do it?" hard_question_prompt = PromptTemplate.from_template(hard_question) llm = ChatOpenAI(model_name="gpt-4") prompt = PromptTemplate.from_template(hard_question) chain = SmartLLMChain(llm=llm, prompt=prompt, verbose=True) chain.run({}) ``` Original text: Added SmartLLM wrapper around LLMs to allow for SmartGPT workflow (as in https://youtu.be/wVzuvf9D9BU). SmartLLM can be used wherever LLM can be used. E.g: ``` smart_llm = SmartLLM(llm=OpenAI()) smart_llm("What would be a good company name for a company that makes colorful socks?") ``` or ``` smart_llm = SmartLLM(llm=OpenAI()) prompt = PromptTemplate( input_variables=["product"], template="What is a good name for a company that makes {product}?", ) chain = LLMChain(llm=smart_llm, prompt=prompt) chain.run("colorful socks") ``` SmartGPT consists of 3 steps: 1. Ideate - generate n possible solutions ("ideas") to user prompt 2. Critique - find flaws in every idea & select best one 3. Resolve - improve upon best idea & return it Fixes #4463 ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: - @hwchase17 - @agola11 Twitter: [@UmerHAdil](https://twitter.com/@UmerHAdil) | Discord: RicChilligerDude#7589 --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago