| Name | Description |
|--------------------------|---------------------------------------------------------------------------------------|
| Google.CreateSpreadsheet | Create a new spreadsheet with the provided
title and data in its first sheet |
| Google.GetSpreadsheet | Get the user entered and formatted data for
all sheets in the spreadsheet |
| Google.WriteToCell | Write a value to a single cell in a spreadsheet.
|
## Google.CreateSpreadsheet
This tool can create a new spreadsheet with data in its first sheet
This tool takes in the data as a JSON string. Here's an example input:
```
// Good at large payloads, sparse payloads, and contiguous data payloads.
// For example data[1]["D"] represents the value of the cell in the first row in the D column
{
// All data in row 1
1: {
"A": 42,
"B": 2,
"D":"=A1+B1"
},
// All data in row 54
54: {
"A": "my string",
"QQ": "my far away string"
}
}
```
The above data format performed better on evals than the other two that
I tested:
```
// Performed poorly at sparse data and also at larger amounts of data
[
[42, 2, "", "=A1+B1"],
[],
[],
...,
["A": "my string", "", "", ..., "my far away string"]
]
```
```
// Good at small payloads and sparse payloads, but very bad at payloads with contiguous data
{
"A1": 42", "B1": 2, "D1": "=A1+B1", "A54": "my string", "QQ": "my far away string"
}
```
## Google.GetSpreadsheet
Gets the formatted values for all non empty cells in all sheets of the
spreadsheet. The data returned is in a similar format as the
`Google.CreateSpreadsheet` tool's `data` input parameter. The difference
is that `get_spreadsheet` will return the user entered value (=A1+B1)
and also the formatted value (23.4) for each cell.
## Google.WriteToCell
Writes to a single cell. At this point in time we do not support batch
updating a sheet.
when testing earlier, 0.1.0 was used for a failed pypi upload. I'm
assuming this is why `This filename has already been used, use a
different version` is occuring
1. The Arcade Worker doesn't like it when the package name is different
than the directory name. This PR renames the directory from
`arcade_notion` to `arcade_notion_toolkit`
2. `Notion` is not a well-known provider name in `arcade-ai==1.0.5`,
I've updated the dep to represent this.
1. Add the following tools:
* Google Finance
- get_stock_summary
- get_stock_historical_data
* Google Flights
- search_roundtrip_flights
- search_one_way_flights
* Google Hotels
- search_hotels
2. Add some common helper functions for serpAPI tools.
Migrated all interfaces to get and return strings.
Added tests and evals for all functions (except the random generation)
Math functions are now organized into different math categories
---------
Co-authored-by: Nate Barbettini <nate@arcade-ai.com>
This tool will be useful in scenarios akin to RAG, where someone wants
to ask questions or request the production of a summary, for instance,
about a bunch of documents related to a particular topic. Currently, to
fulfill such requests, the LLM needs to first `list_documents`, then
`get_document_by_id` for each document.
We also implement a utility functions to return documents in Markdown
and HTML, since the Drive API JSON is verbose and would waste too many
tokens unnecessarily.
Limitations: the Markdown/HTML utilities do not handle table of contents
(which I think aren't really useful here), headers, footers, or
footnotes.
---
This PR deprecates `list_documents` and implements `search_documents`,
apart from `search_and_retrieve_documents`). This configuration makes it
easier for LLMs to understand when to call each tool.
Both tools had their interfaces refactored to remove Google API-specific
arguments that were confusing LLMs sometimes, such as "corpora" and
"support_all_drives". It now accepts arguments that better relate to
expected user requests.
---------
Co-authored-by: Eric Gustin <eric@arcade.dev>
~~Note: Don't merge until the correct secrets have been added to Arcade
Cloud.~~
Ready to merge, the feature is already on its way to prod.
---------
Co-authored-by: Eric Gustin <eric@arcade.dev>
Break down `search_contacts` into `search_contacts_by_name` and
`search_contacts_by_email`. The search_contacts' `query` argument was
not clear enough for LLMs.
Improved gmail toolkit. Added support for threading in draft replies,
multipart email parsing, and label management. Fixed the DateRange
parameter issue in list_emails_by_headers. Added logging and removed
print statements. Created custom exceptions for each specific google
toolkit.
-----
Summary of changes by @byrro:
- Fixed minor bug related to the `date_range` argument of
`list_emails_by_header`
- A few utility functions (`build_email_message`,
`build_reply_recipients`, `build_reply_body`) to centralize logic and
remove repeated code from email-sending tools
- New `reply_to_email` tool (apart from `write_draft_reply_email`,
implemented by Alex) to keep the toolkit consistent
- Evals and unit tests
- Handling of reply-to (only sender) and reply-to-all recipients
- Removed some unnecessary debug messages, which Alex had added to
replace print statements
- Removed HTML handling implemented by Alex in `write_draft_reply_email`
> I think we should either support HTML across all applicable tools or
not at all; I decided to remove it and leave this feature for a future
PR.
---------
Co-authored-by: Renato Byrro <rmbyrro@gmail.com>
## PR Description
Search ([see API docs
here](https://docs.x.com/x-api/posts/recent-search)) returns a 'data'
field that maps to a list of tweets returned. We've observed that the
'data' field is not present if no tweets match the search. This PR
handles that case safely.
Currently, retrieving DMs with a given username requires several
actions: first get the current user's ID; list all users and find the ID
of the username; then scan all DM conversations and find the one with
the current user's ID and the username's ID, to finally retrieve the
messages using that conversation ID.
This tool abstracts all that in a single call.
PS: we'll implement a similar tool for multi-person DM conversations in
a subsequent PR.
# PR Description
This PR adds ~~four~~ three improvements to evals.
~~## 1. Add parameterized eval cases~~
~~Adds a new method named `add_parameterized_case`. Just like pytest’s
parameterized tests, eval cases can be parameterized with multiple user
messages. Adds a case to the `EvalSuite` for each user message. All
cases have the same expected tool call(s), params, additional_messages.
This reduces duplicate code and makes it easy to observe how a model
performs based on increasingly more difficult prompts.~~
```python
""" NO LONGER IN THIS PR
user_messages = [
"Call the delete tweet by id tool with the tweet ID '148975632'.",
"Delete the tweet with ID '148975632'.",
"I don't want to have this tweet (148975632) on my account anymore.",
"do the opposite of post for https://x.com/x/status/148975632",
]
suite.add_parameterized_case(
name="Delete a tweet by ID",
user_messages=user_messages,
expected_tool_calls=[
ExpectedToolCall(
func=delete_tweet_by_id,
args={"tweet_id": "148975632"},
)
],
critics=[
BinaryCritic(
critic_field="tweet_id",
weight=1.0,
),
],
)
"""
```
~~PASSED Delete a tweet by ID (user_message 1 of 4) -- Score: 100.00%~~
~~PASSED Delete a tweet by ID (user_message 2 of 4) -- Score: 100.00%~~
~~PASSED Delete a tweet by ID (user_message 3 of 4) -- Score: 100.00%~~
~~FAILED Delete a tweet by ID (user_message 4 of 4) -- Score: 0.00%~~
~~Summary -- Total: 4 -- Passed: 3 -- Failed: 1~~
## 2. Parameters that are not explicitly criticized are assigned a
`NoneCritic`.
A NoneCritic has no effect on the evaluation results and does not
actually evaluate. Parameters that have a NoneCritic will be displayed
as ‘un-criticized’ in the evaluation summary (if `-d` flag is used).
## 3. Add a hardcoded `seed` parameter for evals.
The seed parameter aides in receiving (mostly) consistent outputs -
aiding in reproducibility for evaluations.
## 4. Disallow more than one critic for the same field.
Raises a `ValueError` if more than one critic is assigned to a field.
---------
Co-authored-by: Eric Gustin <eric@arcade-ai.com>
# PR Description
* This PR updates code in `examples/` to be compatible with version
1.0.0
* This PR removes the Spotify examples since the Arcade hosted worker
doesn't currently cataloge the Spotify toolkit. We can reintroduce these
examples when it does.
* This PR performs various renames across the codebase for
`arcade-ai.com` --> `arcade.dev` and `Arcade AI` --> `Arcade`
# PR Description
The `github.event.pull_request.author_association` in the "Prevent
Unauthorized Version Updates" workflow was returning inconsistent
results by saying that MEMBERS were CONTRIBUTORS. This PR moves away
from `author_association` in favor of a whitelist text file containing
the GitHub usernames of authorized toolkit release managers.
A toolkit release manager has the following special permissions:
* Can change the version of an existing toolkit
* Can delete an existing toolkit
* Can rename an existing toolkit
The `get_conversation_metadata_by_name` tool retrieves conversation
metadata from another tool, `list_conversations_metadata`, but was
accessing the `next_cursor` using the Slack API response dict structure,
instead of the tool response structure. As a result, in that tool, the
tool would never actually paginate to the second page. This PR fixes it
and also adjust tests to capture the issue appropriately.
`starred` is a required argument of the
`arcade_github.activity.set_starred` tool, but when it is not provided
in the tool call, the engine is somehow passing it with a falsy value,
instead of raising an error. the falsy value makes the tool unstar a
repo by default, which is not the desired behavior.
we're setting the `starred` arg to True in the tool interface to prevent
that.
