@ -65,7 +65,7 @@ This project was largely inspired by a few projects seen on Twitter for which we
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).
```
```python
from langchain import SelfAskWithSearchChain, OpenAI, SerpAPIChain
llm = OpenAI(temperature=0)
@ -80,7 +80,7 @@ self_ask_with_search.run("What is the hometown of the reigning men's U.S. Open c
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).
```
```python
from langchain import OpenAI, LLMMathChain
llm = OpenAI(temperature=0)
@ -93,7 +93,7 @@ llm_math.run("How many of the integers between 0 and 99 inclusive are divisible
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).
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/notebooks/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/examples/embeddings.ipynb).
```
```python
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.faiss import FAISS
from langchain.text_splitter import CharacterTextSplitter