From 870d5c118d21d5fcb4cc3c96e80458a54155f476 Mon Sep 17 00:00:00 2001
From: Theodore Rolle <trolle@google.com>
Date: Mon, 17 Jul 2023 08:37:51 -0700
Subject: [PATCH] Update README.md

Fixing broken docs links
---
 README.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index fd2de7e20..b5b808c77 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # 🦜️🔗 ChatLangChain
 
-This repo is an implementation of a locally hosted chatbot specifically focused on question answering over the [LangChain documentation](https://langchain.readthedocs.io/en/latest/).
+This repo is an implementation of a locally hosted chatbot specifically focused on question answering over the [LangChain documentation](https://python.langchain.com/docs).
 Built with [LangChain](https://github.com/hwchase17/langchain/) and [FastAPI](https://fastapi.tiangolo.com/).
 
 The app leverages LangChain's streaming support and async API to update the page in real time for multiple users.
@@ -8,9 +8,9 @@ The app leverages LangChain's streaming support and async API to update the page
 ## ✅ Running locally
 1. Install dependencies: `pip install -r requirements.txt`
 1. Run `ingest.sh` to ingest LangChain docs data into the vectorstore (only needs to be done once).
-   1. You can use other [Document Loaders](https://langchain.readthedocs.io/en/latest/modules/document_loaders.html) to load your own data into the vectorstore.
+   1. You can use other [Document Loaders](https://python.langchain.com/docs/modules/data_connection/document_loaders/) to load your own data into the vectorstore.
 1. Run the app: `make start`
-   1. To enable tracing, make sure `langchain-server` is running locally and pass `tracing=True` to `get_chain` in `main.py`. You can find more documentation [here](https://langchain.readthedocs.io/en/latest/tracing.html).
+   1. To enable tracing, make sure `langchain-server` is running locally and pass `tracing=True` to `get_chain` in `main.py`. You can find more documentation [here](https://python.langchain.com/docs/modules/callbacks/how_to/tracing).
 1. Open [localhost:9000](http://localhost:9000) in your browser.
 
 ## 🚀 Important Links
@@ -30,11 +30,11 @@ There are two components: ingestion and question-answering.
 Ingestion has the following steps:
 
 1. Pull html from documentation site
-2. Load html with LangChain's [ReadTheDocs Loader](https://langchain.readthedocs.io/en/latest/modules/document_loaders/examples/readthedocs_documentation.html)
-3. Split documents with LangChain's [TextSplitter](https://langchain.readthedocs.io/en/latest/reference/modules/text_splitter.html)
+2. Load html with LangChain's [ReadTheDocs Loader](https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/readthedocs_documentation)
+3. Split documents with LangChain's [TextSplitter](https://python.langchain.com/docs/modules/data_connection/document_transformers/#text-splitters)
 4. Create a vectorstore of embeddings, using LangChain's [vectorstore wrapper](https://python.langchain.com/en/latest/modules/indexes/vectorstores.html) (with OpenAI's embeddings and FAISS vectorstore).
 
-Question-Answering has the following steps, all handled by [ChatVectorDBChain](https://langchain.readthedocs.io/en/latest/modules/indexes/chain_examples/chat_vector_db.html):
+Question-Answering has the following steps, all handled by [ChatVectorDBChain](https://python.langchain.com/docs/modules/chains/popular/chat_vector_db):
 
 1. Given the chat history and new user input, determine what a standalone question would be (using GPT-3).
 2. Given that standalone question, look up relevant documents from the vectorstore.