For using Azure OpenAI API, we need to set multiple env vars. But as can
be seen in openai package
[here](48b69293a3/openai/__init__.py (L35)),
the env var for setting base url is named `OPENAI_API_BASE` and not
`OPENAI_API_BASE_URL`. This PR fixes that part in the documentation.
I originally had only modified the `from_llm` to include the prompt but
I realized that if the prompt keys used on the custom prompt didn't
match the default prompt, it wouldn't work because of how `apply` works.
So I made some changes to the evaluate method to check if the prompt is
the default and if not, it will check if the input keys are the same as
the prompt key and update the inputs appropriately.
Let me know if there is a better way to do this.
Also added the custom prompt to the QA eval notebook.
add a chain that applies a prompt to all inputs and then returns not
only an answer but scores it
add examples for question answering and question answering with sources
Small quick fix:
Suggest making the order of the menu the same as it is written on the
page (Getting Started -> Key Concepts). Before the menu order was not
the same as it was on the page. Not sure if this is the only place the
menu is affected.
Mismatch is found here:
https://langchain.readthedocs.io/en/latest/modules/llms.html
- Add support for local build and linkchecking of docs
- Add GitHub Action to automatically check links before prior to
publication
- Minor reformat of Contributing readme
- Fix existing broken links
Co-authored-by: Hunter Gerlach <hunter@huntergerlach.com>
Co-authored-by: Hunter Gerlach <HunterGerlach@users.noreply.github.com>
Co-authored-by: Hunter Gerlach <hunter@huntergerlach.com>
I noticed (after publication) that the getting_started link on the main
page was borked. This should fix it.
Co-authored-by: Hunter Gerlach <hunter@huntergerlach.com>
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>
