From 6df08eec520c8ab19590fcd08652a52b45a9a8b1 Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Sat, 12 Nov 2022 14:26:08 -0500 Subject: [PATCH] Readme: Fix link to embeddings example and use python markup for code examples (#123) * Fix URL to embeddings notebook * Specify python is used for the code block --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 6f3cb9c9..af99dfda 100644 --- a/README.md +++ b/README.md @@ -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). -``` +```python from langchain import Prompt, OpenAI, LLMChain template = """Question: {question} @@ -110,9 +110,9 @@ 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/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