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β€ŽREADME.mdβ€Ž

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@@ -36,22 +36,22 @@ This library aims to assist in the development of those types of applications. C
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**❓ Question Answering over specific documents**
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- [Documentation](https://langchain.readthedocs.io/en/latest/use_cases/question_answering.html)
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- [Documentation](https://python.langchain.com/docs/use_cases/question_answering/)
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- End-to-end Example: [Question Answering over Notion Database](https://github.com/hwchase17/notion-qa)
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**πŸ’¬ Chatbots**
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- [Documentation](https://langchain.readthedocs.io/en/latest/use_cases/chatbots.html)
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- [Documentation](https://python.langchain.com/docs/use_cases/chatbots/)
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- End-to-end Example: [Chat-LangChain](https://github.com/hwchase17/chat-langchain)
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**πŸ€– Agents**
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- [Documentation](https://langchain.readthedocs.io/en/latest/modules/agents.html)
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- [Documentation](https://python.langchain.com/docs/modules/agents/)
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- End-to-end Example: [GPT+WolframAlpha](https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain)
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## πŸ“– Documentation
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Please see [here](https://langchain.readthedocs.io/en/latest/?) for full documentation on:
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Please see [here](https://python.langchain.com) for full documentation on:
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- Getting started (installation, setting up the environment, simple examples)
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- How-To examples (demos, integrations, helper functions)
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[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
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For more information on these concepts, please see our [full documentation](https://langchain.readthedocs.io/en/latest/).
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For more information on these concepts, please see our [full documentation](https://python.langchain.com).
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## πŸ’ Contributing
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