Releases: cdcai/agents
Releases · cdcai/agents
Version 0.3
What's Changed
Bug fixes
- Fixed issue where tool argument to agent was modified by reference, leading to growing lists in batch calls
- Better handling for agent calling a tool that isn't defined at init time
- v0.2 by @beansrowning in #2
- Version 0.3 by @beansrowning in #4
Full Changelog: v0.3-alpha...v0.3
v0.3 Alpha
Changes
Docs
- Added examples
- Updated README
User-facing
- Removed a lot of the OG classes and framework that I started with, as it wasn't really serving its purpose anymore
- Added
StoppingCondition
s as a standard class to trigger when an Agent has finished rather then handle this internally- This is called at the end of every step to determine whether the Agent should terminate or not, and handles answer extraction
- Added Callbacks as a option to handle triggering additional functions at the end of the run with the answer and scratchpad of the calling agent
- A lot of work making abstract classes and getting typing all correct
- Added
response_model_handler
decorator to handle Pydantic BaseModel validation- Either returns validated BaseModel, or string to pass back to Agent indicating error
- New
StructuredOutputAgent
, which is basically just providing whatPredictionAgent
does, but assumes you can construct the response object before runtime.- Might end up getting rid of the prediction bits as a result
BatchProcessor
ChangesBatchProcessor
now handles the batch object as a kwarg to be inserted into fstring rather than passing as first arg to Agent- You should now include
"{batch}"
in BASE_PROMPT attribute where these data should be inserted - Added additional
_batch_format
method, which supplies logic to convert batch into string (useful in the DataFrame case)
Full Changelog: v0.2...v0.3-alpha
Version 0.2
Changes
- Now allows for passing of GPT args at runtime
- Some additional docs
- prompt_agent now handles auth errors through backoff decorator instead of catch block
- New structured prediction agents
PredictionAgent
provides text classification on a dataframe from a list of possible labels- Using pydantic to ensure correct output
Version 0.1
Merge pull request #1 from cdcai/dev v0.1