Commit Graph

3 Commits

Author SHA1 Message Date
Lorenze Jay
6f2e39c0dd feat: enhance knowledge and guardrail event handling in Agent class (#3672)
Some checks failed
CodeQL Advanced / Analyze (actions) (push) Has been cancelled
CodeQL Advanced / Analyze (python) (push) Has been cancelled
Notify Downstream / notify-downstream (push) Has been cancelled
Update Test Durations / update-durations (3.10) (push) Has been cancelled
Update Test Durations / update-durations (3.11) (push) Has been cancelled
Update Test Durations / update-durations (3.12) (push) Has been cancelled
Update Test Durations / update-durations (3.13) (push) Has been cancelled
* feat: enhance knowledge event handling in Agent class

- Updated the Agent class to include task context in knowledge retrieval events.
- Emitted new events for knowledge retrieval and query processes, capturing task and agent details.
- Refactored knowledge event classes to inherit from a base class for better structure and maintainability.
- Added tracing for knowledge events in the TraceCollectionListener to improve observability.

This change improves the tracking and management of knowledge queries and retrievals, facilitating better debugging and performance monitoring.

* refactor: remove task_id from knowledge event emissions in Agent class

- Removed the task_id parameter from various knowledge event emissions in the Agent class to streamline event handling.
- This change simplifies the event structure and focuses on the essential context of knowledge retrieval and query processes.

This refactor enhances the clarity of knowledge events and aligns with the recent improvements in event handling.

* surface association for guardrail events

* fix: improve LLM selection logic in converter

- Updated the logic for selecting the LLM in the convert_with_instructions function to handle cases where the agent may not have a function_calling_llm attribute.
- This change ensures that the converter can still function correctly by falling back to the standard LLM if necessary, enhancing robustness and preventing potential errors.

This fix improves the reliability of the conversion process when working with different agent configurations.

* fix test

* fix: enforce valid LLM instance requirement in converter

- Updated the convert_with_instructions function to ensure that a valid LLM instance is provided by the agent.
- If neither function_calling_llm nor the standard llm is available, a ValueError is raised, enhancing error handling and robustness.
- Improved error messaging for conversion failures to provide clearer feedback on issues encountered during the conversion process.

This change strengthens the reliability of the conversion process by ensuring that agents are properly configured with a valid LLM.
2025-10-08 11:53:13 -07:00
Lorenze Jay
75b916c85a Lorenze/fix tool call twice (#3495)
* test: add test to ensure tool is called only once during crew execution

- Introduced a new test case to validate that the counting_tool is executed exactly once during crew execution.
- Created a CountingTool class to track execution counts and log call history.
- Enhanced the test suite with a YAML cassette for consistent tool behavior verification.

* ensure tool function called only once

* refactor: simplify error handling in CrewStructuredTool

- Removed unnecessary try-except block around the tool function call to streamline execution flow.
- Ensured that the tool function is called directly, improving readability and maintainability.

* linted

* need to ignore for now as we cant infer the complex generic type within pydantic create_model_func

* fix tests
2025-09-10 15:20:21 -07:00
Lorenze Jay
7ad51d9d05 feat: implement knowledge retrieval events in Agent (#2727)
Some checks failed
Notify Downstream / notify-downstream (push) Has been cancelled
* feat: implement knowledge retrieval events in Agent

This commit introduces a series of knowledge retrieval events in the Agent class, enhancing its ability to handle knowledge queries. New events include KnowledgeRetrievalStartedEvent, KnowledgeRetrievalCompletedEvent, KnowledgeQueryGeneratedEvent, KnowledgeQueryFailedEvent, and KnowledgeSearchQueryCompletedEvent. The Agent now emits these events during knowledge retrieval processes, allowing for better tracking and handling of knowledge queries. Additionally, the console formatter has been updated to handle these new events, providing visual feedback during knowledge retrieval operations.

* refactor: update knowledge query handling in Agent

This commit refines the knowledge query processing in the Agent class by renaming variables for clarity and optimizing the query rewriting logic. The system prompt has been updated in the translation file to enhance clarity and context for the query rewriting process. These changes aim to improve the overall readability and maintainability of the code.

* fix: add missing newline at end of en.json file

* fix broken tests

* refactor: rename knowledge query events and enhance retrieval handling

This commit renames the KnowledgeQueryGeneratedEvent to KnowledgeQueryStartedEvent to better reflect its purpose. It also updates the event handling in the EventListener and ConsoleFormatter classes to accommodate the new event structure. Additionally, the retrieval knowledge is now included in the KnowledgeRetrievalCompletedEvent, improving the overall knowledge retrieval process.

* docs for transparancy

* refactor: improve error handling in knowledge query processing

This commit refactors the knowledge query handling in the Agent class by changing the order of checks for LLM compatibility. It now logs a warning and emits a failure event if the LLM is not an instance of BaseLLM before attempting to call the LLM. Additionally, the task_prompt attribute has been removed from the KnowledgeQueryFailedEvent, simplifying the event structure.

* test: add unit test for knowledge search query and VCR cassette

This commit introduces a new test, `test_get_knowledge_search_query`, to verify that the `_get_knowledge_search_query` method in the Agent class correctly interacts with the LLM using the appropriate prompts. Additionally, a VCR cassette is added to record the interactions with the OpenAI API for this test, ensuring consistent and reliable test results.
2025-05-07 11:55:42 -07:00