Merge pull request #262 from guangzhengli/main

Fix link and zh documents

pages/techniques/rag.en.mdx

@@ -22,4 +22,4 @@ This shows the potential of RAG as a viable option for enhancing outputs of lang
 
 More recently, these retriever-based approaches have become more popular and are combined with popular LLMs like ChatGPT to improve capabilities and factual consistency.
 
-You can find a [simple example of how to use retrievers and LLMs for question answering with sources](https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa_with_sources.html) from the LangChain documentation.
+You can find a [simple example of how to use retrievers and LLMs for question answering with sources](https://python.langchain.com/docs/use_cases/question_answering/how_to/vector_db_qa) from the LangChain documentation.

pages/techniques/rag.it.mdx

@@ -22,4 +22,4 @@ Questo mostra il potenziale di RAG come opzione valida per migliorare gli output
 
 Più recentemente, questi approcci basati su recupero sono diventati più popolari e sono combinati con LLM popolari come ChatGPT per migliorare le capacità e la coerenza fattuale.
 
-Puoi trovare un [esempio semplice di come usare i recuperatori e i LLM per rispondere alle domande con le fonti](https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa_with_sources.html) dalla documentazione di LangChain.
+Puoi trovare un [esempio semplice di come usare i recuperatori e i LLM per rispondere alle domande con le fonti](https://python.langchain.com/docs/use_cases/question_answering/how_to/vector_db_qa) dalla documentazione di LangChain.

pages/techniques/rag.zh.mdx

@@ -22,4 +22,4 @@ RAG 在 [Natural Questions](https://ai.google.com/research/NaturalQuestions)、[
 
 最近，基于检索器的方法越来越流行，经常与 ChatGPT 等流行 LLM 结合使用来提高其能力和事实一致性。
 
-LangChain 文档中可以找到[一个使用检索器和 LLM 回答问题并给出知识来源的简单例子](https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa_with_sources.html)。
+LangChain 文档中可以找到[一个使用检索器和 LLM 回答问题并给出知识来源的简单例子](https://python.langchain.com/docs/use_cases/question_answering/how_to/vector_db_qa)。

pages/techniques/react.ca.mdx

@@ -173,6 +173,6 @@ La sortida que obtenim és la següent:
 "Harry Styles, el nòvio d'Olivia Wilde, té 29 anys i la seva edat elevada a la potència de 0,23 és 2.169459462491557."
 ```
 
-Hem adaptat l'exemple de la [documentació de LangChain](https://python.langchain.com/en/latest/modules/agents/getting_started.html), així que el crèdit és per a ells. Animen a l'aprenent a explorar diferents combinacions d'eines i tasques.
+Hem adaptat l'exemple de la [documentació de LangChain](https://python.langchain.com/docs/modules/agents), així que el crèdit és per a ells. Animen a l'aprenent a explorar diferents combinacions d'eines i tasques.
 
 Podeu trobar el quadern de codi aquí: https://github.com/dair-ai/Prompt-Engineering-Guide/blob/main/notebooks/react.ipynb

pages/techniques/react.fi.mdx

@@ -163,6 +163,6 @@ Suorituksen tuloksena saamme seuraavanlaisen tuloksen:
 "Harry Styles, Olivia Wilde's boyfriend, is 29 years old and his age raised to the 0.23 power is 2.169459462491557."
 ```
 
-Mukautimme esimerkin [LangChain dokumentaatiosta](https://python.langchain.com/en/latest/modules/agents/getting_started.html). Kannustamme lukijaa tutkimaan erilaisia työkalu- ja tehtäväyhdistelmiä. 
+Mukautimme esimerkin [LangChain dokumentaatiosta](https://python.langchain.com/docs/modules/agents). Kannustamme lukijaa tutkimaan erilaisia työkalu- ja tehtäväyhdistelmiä. 
 
 Löydät tämän koodin sisältävän Notebook-ympäristön täältä: https://github.com/dair-ai/Prompt-Engineering-Guide/blob/main/notebooks/react.ipynb

pages/techniques/react.ru.mdx

@@ -173,6 +173,6 @@ Final Answer: Harry Styles, Olivia Wilde's boyfriend, is 29 years old and his ag
 "Harry Styles, Olivia Wilde's boyfriend, is 29 years old and his age raised to the 0.23 power is 2.169459462491557."
 ```
 
-Мы адаптировали пример из [документации LangChain](https://python.langchain.com/en/latest/modules/agents/getting_started.html), поэтому заслуги принадлежат им. Мы рекомендуем исследовать различные комбинации инструментов и задач.
+Мы адаптировали пример из [документации LangChain](https://python.langchain.com/docs/modules/agents), поэтому заслуги принадлежат им. Мы рекомендуем исследовать различные комбинации инструментов и задач.
 
 Вы можете найти ноутбук для этого кода здесь: https://github.com/dair-ai/Prompt-Engineering-Guide/blob/main/notebooks/react.ipynb

pages/techniques/react.zh.mdx

@@ -163,6 +163,6 @@ agent.run("奥利维亚·王尔德的男朋友是谁?他现在的年龄的0.23
 "哈里·斯泰尔斯, 奥利维亚·王尔德的男朋友, 29 岁。他年龄的 0.23 次方是 2.169459462491557。"
 ```
 
-这个例子我们摘自 [长链文档](https://python.langchain.com/en/latest/modules/agents/getting_started.html) 并改编，所以这些都要归功于他们。我们鼓励学习者去探索工具和任务的不同组合。
+这个例子我们摘自 [LangChain 文档](https://python.langchain.com/docs/modules/agents) 并修改，所以这些都要归功于他们。我们鼓励学习者去探索工具和任务的不同组合。
 
-您可以在这里找到这些代码的笔记本: https://github.com/dair-ai/Prompt-Engineering-Guide/blob/main/notebooks/react.ipynb
+您可以在这里找到这些代码: https://github.com/dair-ai/Prompt-Engineering-Guide/blob/main/notebooks/react.ipynb