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update readme (#6304)

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@ -36,22 +36,22 @@ This library aims to assist in the development of those types of applications. C
**❓ Question Answering over specific documents** **❓ Question Answering over specific documents**
- [Documentation](https://langchain.readthedocs.io/en/latest/use_cases/question_answering.html) - [Documentation](https://python.langchain.com/docs/use_cases/question_answering/)
- End-to-end Example: [Question Answering over Notion Database](https://github.com/hwchase17/notion-qa) - End-to-end Example: [Question Answering over Notion Database](https://github.com/hwchase17/notion-qa)
**💬 Chatbots** **💬 Chatbots**
- [Documentation](https://langchain.readthedocs.io/en/latest/use_cases/chatbots.html) - [Documentation](https://python.langchain.com/docs/use_cases/chatbots/)
- End-to-end Example: [Chat-LangChain](https://github.com/hwchase17/chat-langchain) - End-to-end Example: [Chat-LangChain](https://github.com/hwchase17/chat-langchain)
**🤖 Agents** **🤖 Agents**
- [Documentation](https://langchain.readthedocs.io/en/latest/modules/agents.html) - [Documentation](https://python.langchain.com/docs/modules/agents/)
- End-to-end Example: [GPT+WolframAlpha](https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain) - End-to-end Example: [GPT+WolframAlpha](https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain)
## 📖 Documentation ## 📖 Documentation
Please see [here](https://langchain.readthedocs.io/en/latest/?) for full documentation on: Please see [here](https://python.langchain.com) for full documentation on:
- Getting started (installation, setting up the environment, simple examples) - Getting started (installation, setting up the environment, simple examples)
- How-To examples (demos, integrations, helper functions) - How-To examples (demos, integrations, helper functions)
@ -87,7 +87,7 @@ Memory refers to persisting state between calls of a chain/agent. LangChain prov
[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. [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.
For more information on these concepts, please see our [full documentation](https://langchain.readthedocs.io/en/latest/). For more information on these concepts, please see our [full documentation](https://python.langchain.com).
## 💁 Contributing ## 💁 Contributing