# Added another helpful way for developers who want to set OpenAI API
Key dynamically
Previous methods like exporting environment variables are good for
project-wide settings.
But many use cases need to assign API keys dynamically, recently.
```python
from langchain.llms import OpenAI
llm = OpenAI(openai_api_key="OPENAI_API_KEY")
```
## Before submitting
```bash
export OPENAI_API_KEY="..."
```
Or,
```python
import os
os.environ["OPENAI_API_KEY"] = "..."
```
<hr>
Thank you.
Cheers,
Bongsang
- Added links to the vectorstore providers
- Added installation code (it is not clear that we have to go to the
`LangChan Ecosystem` page to get installation instructions.)
My attempt at improving the `Chain`'s `Getting Started` docs and
`LLMChain` docs. Might need some proof-reading as English is not my
first language.
In LLM examples, I replaced the example use case when a simpler one
(shorter LLM output) to reduce cognitive load.
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
