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This adds `input_schema` and `output_schema` properties to all runnables, which are Pydantic models for the input and output types respectively. These are inferred from the structure of the Runnable as much as possible, the only manual typing needed is - optionally add type hints to lambdas (which get translated to input/output schemas) - optionally add type hint to RunnablePassthrough These schemas can then be used to create JSON Schema descriptions of input and output types, see the tests - [x] Ensure no InputType and OutputType in our classes use abstract base classes (replace with union of subclasses) - [x] Implement in BaseChain and LLMChain - [x] Implement in RunnableBranch - [x] Implement in RunnableBinding, RunnableMap, RunnablePassthrough, RunnableEach, RunnableRouter - [x] Implement in LLM, Prompt, Chat Model, Output Parser, Retriever - [x] Implement in RunnableLambda from function signature - [x] Implement in Tool <!-- 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. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> |
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