Archives/langchain
2
0
Fork 1
mirror of https://github.com/hwchase17/langchain synced 2024-11-18 09:25:54 +00:00
Code Issues Packages Projects Releases Wiki Activity

Fix notebook links (#149)

Browse Source 
Example notebook links were broken.
This commit is contained in:
thesved 2022-11-17 00:13:12 +01:00 committed by GitHub
parent 4f1bf159f4
commit 47e35d7d0e
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 4 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
README.md
View File

@ -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