From abe4fc04fa86e695084993c81cfd4ead56eee035 Mon Sep 17 00:00:00 2001 From: Nicholas Larus-Stone Date: Wed, 9 Nov 2022 13:23:29 -0800 Subject: [PATCH] docs: fix some minor typos in README (#107) Small docs fixes --- README.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index d5b47b9c..b32ac6a8 100644 --- a/README.md +++ b/README.md @@ -96,16 +96,17 @@ template = """Question: {question} Answer: Let's think step by step.""" prompt = Prompt(template=template, input_variables=["question"]) -llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0)) +llm = OpenAI(temperature=0) +llm_chain = LLMChain(prompt=prompt, llm=llm) -question = "What NFL team won the Super Bowl in the year Justin Beiber was born?" +question = "What NFL team won the Super Bowl in the year Justin Bieber was born?" 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/notebooks/examples/embeddings.ipynb). ``` from langchain.embeddings.openai import OpenAIEmbeddings