Commit Graph

15 Commits

Author SHA1 Message Date
vowelparrot
5ca7ce77cd
Remove pythonrepl from LLM-MathChain (#2943)
Use numexpr evaluate instead of the python REPL to avoid malicious code
injection.

Tested against the (limited) math dataset and got the same score as
before.

For more permissive tools (like the REPL tool itself), other approaches
ought to be provided (some combination of Sanitizer + Restricted python
+ unprivileged-docker + ...), but for a calculator tool, only
mathematical expressions should be permitted.

See https://github.com/hwchase17/langchain/issues/814
2023-04-16 08:50:32 -07:00
Tommertom
0f5d3b3390
Typo docs - Update data_augmented_question_answering.ipynb propriterary-> proprietary (#2626)
Minor typo propritary -> proprietary
2023-04-09 12:24:53 -07:00
Harrison Chase
247a88f2f9
Harrison/move eval (#2533) 2023-04-07 07:53:13 -07:00
SangamSwadiK
8cded3fdad
fix typo (#2532)
1) Any breaking changes  ?
None

2) What does this do ?
Fix typo in QA eval

cc @hwchase17
2023-04-07 07:25:22 -07:00
Vashisht Madhavan
aa439ac2ff
Adding an in-context QA evaluation chain + chain of thought reasoning chain for improved accuracy (#2444)
Right now, eval chains require an answer for every question. It's
cumbersome to collect this ground truth so getting around this issue
with 2 things:

* Adding a context param in `ContextQAEvalChain` and simply evaluating
if the question is answered accurately from context
* Adding chain of though explanation prompting to improve the accuracy
of this w/o GT.

This also gets to feature parity with openai/evals which has the same
contextual eval w/o GT.

TODO in follow-up:
* Better prompt inheritance. No need for seperate prompt for CoT
reasoning. How can we merge them together

---------

Co-authored-by: Vashisht Madhavan <vashishtmadhavan@Vashs-MacBook-Pro.local>
2023-04-06 22:32:41 -07:00
Harrison Chase
c7b083ab56
bump version to 131 (#2391) 2023-04-04 07:21:50 -07:00
Shrined
10dab053b4
Add Enum for agent types (#2321)
This pull request adds an enum class for the various types of agents
used in the project, located in the `agent_types.py` file. Currently,
the project is using hardcoded strings for the initialization of these
agents, which can lead to errors and make the code harder to maintain.
With the introduction of the new enums, the code will be more readable
and less error-prone.

The new enum members include:

- ZERO_SHOT_REACT_DESCRIPTION
- REACT_DOCSTORE
- SELF_ASK_WITH_SEARCH
- CONVERSATIONAL_REACT_DESCRIPTION
- CHAT_ZERO_SHOT_REACT_DESCRIPTION
- CHAT_CONVERSATIONAL_REACT_DESCRIPTION

In this PR, I have also replaced the hardcoded strings with the
appropriate enum members throughout the codebase, ensuring a smooth
transition to the new approach.
2023-04-03 21:56:20 -07:00
Harrison Chase
47d37db2d2
WIP: Harrison/base retriever (#1765) 2023-03-24 07:46:49 -07:00
Harrison Chase
d5b4393bb2
Harrison/llm math (#1808)
Co-authored-by: Vadym Barda <vadim.barda@gmail.com>
2023-03-20 07:53:26 -07:00
Harrison Chase
2d098e8869
Harrison/agent eval (#1620)
Co-authored-by: jerwelborn <jeremy.welborn@gmail.com>
2023-03-14 12:37:48 -07:00
Graham Neubig
31303d0b11
Added other evaluation metrics for data-augmented QA (#1521)
This PR adds additional evaluation metrics for data-augmented QA,
resulting in a report like this at the end of the notebook:

![Screen Shot 2023-03-08 at 8 53 23
AM](https://user-images.githubusercontent.com/398875/223731199-8eb8e77f-5ff3-40a2-a23e-f3bede623344.png)

The score calculation is based on the
[Critique](https://docs.inspiredco.ai/critique/) toolkit, an API-based
toolkit (like OpenAI) that has minimal dependencies, so it should be
easy for people to run if they choose.

The code could further be simplified by actually adding a chain that
calls Critique directly, but that probably should be saved for another
PR if necessary. Any comments or change requests are welcome!
2023-03-08 20:41:03 -08:00
Ikko Eltociear Ashimine
b8a7828d1f
Update huggingface_datasets.ipynb (#1417)
HuggingFace -> Hugging Face
2023-03-04 00:22:31 -08:00
Harrison Chase
7fb33fca47
chroma docs (#1012) 2023-02-12 23:02:01 -08:00
Nicolas
91d7fd20ae
feat: add custom prompt for QAEvalChain chain (#610)
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.
2023-01-14 07:23:48 -08:00
Harrison Chase
985496f4be
Docs refactor (#480)
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>
2023-01-02 08:24:09 -08:00