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Classification Tutorial: Replaced .dict() with .model_dump() method (#29701)
The .dict() method is deprecated inf Pydantic V2.0 and use `model_dump` method instead. Thank you for contributing to LangChain! - [ ] **PR title**: "package: description" - Where "package" is whichever of langchain, community, core, etc. is being modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI changes. - Example: "community: add foobar LLM" - [ ] **PR message**: ***Delete this entire checklist*** and replace with - **Description:** a description of the change - **Issue:** the issue # it fixes, if applicable - **Dependencies:** any dependencies required for this change - **Twitter handle:** if your PR gets announced, and you'd like a mention, we'll gladly shout you out! - [ ] **Add tests and docs**: 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/docs/integrations` directory. - [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
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@ -154,7 +154,7 @@
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"id": "ff3cf30d",
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"metadata": {},
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"source": [
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"If we want dictionary output, we can just call `.dict()`"
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"If we want dictionary output, we can just call `.model_dump()`"
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]
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},
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{
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@ -179,7 +179,7 @@
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"prompt = tagging_prompt.invoke({\"input\": inp})\n",
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"response = llm.invoke(prompt)\n",
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"\n",
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"response.dict()"
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"response.model_dump()"
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]
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},
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{
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