Fix notebook links (#149)

Example notebook links were broken.

README.md

@@ -37,7 +37,7 @@ This project was largely inspired by a few projects seen on Twitter for which we
 
 **[Self-ask-with-search](https://ofir.io/self-ask.pdf)**
 
-To recreate this paper, use the following code snippet or checkout the [example notebook](https://github.com/hwchase17/langchain/blob/master/examples/self_ask_with_search.ipynb).
+To recreate this paper, use the following code snippet or checkout the [example notebook](https://github.com/hwchase17/langchain/blob/master/docs/examples/demos/self_ask_with_search.ipynb).
 
 ```python
 from langchain import SelfAskWithSearchChain, OpenAI, SerpAPIChain
@@ -52,7 +52,7 @@ self_ask_with_search.run("What is the hometown of the reigning men's U.S. Open c
 
 **[LLM Math](https://twitter.com/amasad/status/1568824744367259648?s=20&t=-7wxpXBJinPgDuyHLouP1w)**
 
-To recreate this example, use the following code snippet or check out the [example notebook](https://github.com/hwchase17/langchain/blob/master/examples/llm_math.ipynb).
+To recreate this example, use the following code snippet or check out the [example notebook](https://github.com/hwchase17/langchain/blob/master/docs/examples/demos/llm_math.ipynb).
 
 ```python
 from langchain import OpenAI, LLMMathChain
@@ -65,7 +65,7 @@ llm_math.run("How many of the integers between 0 and 99 inclusive are divisible
 
 **Generic Prompting**
 
-You can also use this for simple prompting pipelines, as in the below example and this [example notebook](https://github.com/hwchase17/langchain/blob/master/examples/simple_prompts.ipynb).
+You can also use this for simple prompting pipelines, as in the below example and this [example notebook](https://github.com/hwchase17/langchain/blob/master/docs/examples/demos/simple_prompts.ipynb).
 
 ```python
 from langchain import Prompt, OpenAI, LLMChain
@@ -84,7 +84,7 @@ llm_chain.predict(question=question)
 
 **Embed & Search Documents**
 
-We support two vector databases to store and search embeddings -- FAISS and Elasticsearch. Here's a code snippet showing how to use FAISS to store embeddings and search for text similar to a query. Both database backends are featured in this [example notebook](https://github.com/hwchase17/langchain/blob/master/examples/embeddings.ipynb).
+We support two vector databases to store and search embeddings -- FAISS and Elasticsearch. Here's a code snippet showing how to use FAISS to store embeddings and search for text similar to a query. Both database backends are featured in this [example notebook](https://github.com/hwchase17/langchain/blob/master/docs/examples/integrations/embeddings.ipynb).
 
 ```python
 from langchain.embeddings.openai import OpenAIEmbeddings