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Add Patronus evaluator docs
This commit is contained in:
DarshanDeshpande
64
docs/tools/patronusevaltool.mdx
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64
docs/tools/patronusevaltool.mdx
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---
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title: Patronus Eval Tool
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description: The `PatronusEvalTool` is designed to evaluate agent inputs, outputs and context with a contextually selected criteria and log results to app.patronus.ai
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icon: shield
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---
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# `PatronusEvalTool`
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## Description
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The `PatronusEvalTool` is designed to evaluate agent inputs, outputs and context with a contextually selected criteria and log results to [app.patronus.ai](https://app.patronus.ai)
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It utilizes the [Patronus AI](https://patronus.ai/) API to
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1. Fetch all available criteria for the specific user associated with the `PATRONUS_API_KEY`
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2. Select the most fitting criteria for the task according to the defined `Task`
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3. Evaluates the inputs/outputs/context according to the selected list of criteria and logs them to [app.patronus.ai](https://app.patronus.ai)
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## Installation
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To incorporate this tool into your project, follow the installation instructions below:
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```shell
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pip install 'crewai[tools]'
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```
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## Steps to Get Started
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Follow these steps correctly to use the PatronusEvalTool :
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1. Confirm that the `crewai[tools]` package is installed in your Python environment.
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2. Acquire a Patronus API key by registering for a free account at [patronus.ai](https://patronus.ai/).
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3. Export your API key using `export PATRONUS_API_KEY=[YOUR_KEY_HERE]`
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## Example
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This example demonstrates the use of the PatronusEvalTool for verifying if the generated content is code or not. Here, the agent selects the `contains-code` predefined-criteria, evaluates the output generated for the instruction and logs the results to [app.patronus.ai](https://app.patronus.ai)
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```python
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from crewai_tools import PatronusEvalTool
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tool = PatronusEvalTool()
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coding_agent = Agent(
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role="Coding Agent",
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goal="Generate high quality code and verify that the output is code by using Patronus AI's evaluation tool.",
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backstory="You are an experienced coder who can generate high quality python code. You can follow complex instructions accurately and effectively.",
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tools=[patronus_eval_tool],
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verbose=True,
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)
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generate_code = Task(
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description="Create a simple program to generate the first N numbers in the Fibonacci sequence. Select the most appropriate evaluator and criteria for evaluating your output.",
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expected_output="Program that generates the first N numbers in the Fibonacci sequence.",
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agent=coding_agent,
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)
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crew = Crew(agents=[coding_agent], tasks=[generate_code])
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crew.kickoff()
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```
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## Conclusion
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With the `PatronusEvalTool`, users can build confidence in their agentic systems and improve reliability of their product.
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Using [patronus.ai](https://patronus.ai), agents can choose from several of the pre-defined or custom defined criteria from the platform and evaluate their outputs, making it easier for the user to debug their agentic pipelines.
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Users can also define their own criteria at [app.patronus.ai](https://app.patronus.ai) or local evaluation function (guide [here](https://docs.patronus.ai/docs/experiment-evaluators)) to help with custom evaluation needs. For using custom-defined criteria and local evaluators it is encouraged to use the `PatronusPredifinedCriteriaEvalTool` and `PatronusLocalEvaluatorTool` respectively.
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75
docs/tools/patronuslocalevaluatortool.mdx
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75
docs/tools/patronuslocalevaluatortool.mdx
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---
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title: Patronus Local Evaluator Tool
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description: The `PatronusLocalEvaluatorTool` is designed to evaluate agent inputs,outputs and context based on a user defined function and log evaluation results to [app.patronus.ai](http://app.patronus.ai)
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icon: shield
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---
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# `PatronusLocalEvaluatorTool`
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## Description
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The `PatronusLocalEvaluatorTool` is designed to evaluate agent inputs, outputs and context based on a user defined local function and log evaluation results to [app.patronus.ai](http://app.patronus.ai)
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It utilizes the [Patronus AI](https://patronus.ai/) API to
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1. Evaluate the inputs/outputs/context according to the user defined metric/evaluation criteria
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2. Log the results to [app.patronus.ai](https://app.patronus.ai) where they can be visualized
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## Installation
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To incorporate this tool into your project, follow the installation instructions below:
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```shell
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pip install 'crewai[tools]'
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```
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## Steps to Get Started
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Follow these steps correctly to use the PatronusLocalEvaluatorTool :
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1. Confirm that the `crewai[tools]` package is installed in your Python environment.
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2. Acquire a Patronus API key by registering for a free account at [patronus.ai](https://patronus.ai/).
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3. Export your API key using `export PATRONUS_API_KEY=[YOUR_KEY_HERE]`
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## Example
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This example demonstrates the use of the PatronusLocalEvaluatorTool.
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```python
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from patronus import Client, EvaluationResult
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from crewai_tools import PatronusLocalEvaluatorTool
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client = Client()
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# Register a local evaluation function. For more details refer to https://docs.patronus.ai/docs/experiment-evaluators
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@client.register_local_evaluator("local_evaluator_name")
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def my_evaluator(**kwargs):
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return EvaluationResult(pass_="PASS", score=0.5, explanation="Explanation test")
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patronus_eval_tool = PatronusLocalEvaluatorTool(
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evaluator="local_evaluator_name", evaluated_model_gold_answer="test"
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)
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coding_agent = Agent(
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role="Coding Agent",
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goal="Generate high quality code and verify that the output is code by using Patronus AI's evaluation tool.",
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backstory="You are an experienced coder who can generate high quality python code. You can follow complex instructions accurately and effectively.",
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tools=[patronus_eval_tool],
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verbose=True,
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)
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generate_code = Task(
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description="Create a simple program to generate the first N numbers in the Fibonacci sequence. Select the most appropriate evaluator and criteria for evaluating your output.",
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expected_output="Program that generates the first N numbers in the Fibonacci sequence.",
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agent=coding_agent,
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)
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crew = Crew(agents=[coding_agent], tasks=[generate_code])
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crew.kickoff()
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```
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## Conclusion
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Using `PatronusLocalEvaluatorTool` users can easily and quickly define custom evaluation functions and help improve customer confidence in their agentic systems.
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Using patronus.ai, users can conveniently log their custom metrics to [app.patronus.ai](https://app.patronus.ai), making it easier for the user to visualize trends and debug their agentic pipelines.
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Users can also define their own criteria at [app.patronus.ai](https://app.patronus.ai) or let the agent choose an existing evaluator on the Patronus platform to help with custom evaluation needs.
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For using custom-defined criteria and local evaluators it is encouraged to use the `PatronusPredifinedCriteriaEvalTool` and `PatronusEvalTool` respectively.
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65
docs/tools/patronuspredefinedcriteriaevaltool.mdx
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65
docs/tools/patronuspredefinedcriteriaevaltool.mdx
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@@ -0,0 +1,65 @@
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---
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title: Patronus Eval Tool
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description: The `PatronusPredefinedCriteriaEvalTool` is designed to evaluate agent outputs for a specific criteria on the Patronus platform. The evaluation results for this are logged to [app.patronus.ai](https://app.patronus.ai)
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icon: shield
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---
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# `PatronusPredefinedCriteriaEvalTool`
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## Description
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The `PatronusPredefinedCriteriaEvalTool` is designed to evaluate agent outputs for a specific criteria on the Patronus platform. The evaluation results for this are logged to [app.patronus.ai](https://app.patronus.ai)
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It utilizes the [Patronus AI](https://patronus.ai/) API to
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1. Evaluate the agent input, output, context and gold answer (if available) according to the criteria
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2. Logs the results to [app.patronus.ai](https://app.patronus.ai)
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## Installation
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To incorporate this tool into your project, follow the installation instructions below:
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```shell
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pip install 'crewai[tools]'
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```
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## Steps to Get Started
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Follow these steps correctly to use the PatronusPredefinedCriteriaEvalTool :
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1. Confirm that the `crewai[tools]` package is installed in your Python environment.
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2. Acquire a Patronus API key by registering for a free account at [patronus.ai](https://patronus.ai/).
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3. Export your API key using `export PATRONUS_API_KEY=[YOUR_KEY_HERE]`
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## Example
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This example demonstrates the use of the PatronusPredefinedCriteriaEvalTool for verifying if the generated content is code or not. Here, the agent selects the `contains-code` predefined-criteria, evaluates the output generated for the instruction and logs the results to [app.patronus.ai](https://app.patronus.ai)
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```python
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from crewai_tools import PatronusPredefinedCriteriaEvalTool
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patronus_eval_tool = PatronusPredifinedCriteriaEvalTool(
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evaluators=[{"evaluator": "judge", "criteria": "contains-code"}] # Selecting the "contains-code" criteria and using the default "judge" from Patronus AI
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)
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coding_agent = Agent(
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role="Coding Agent",
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goal="Generate high quality code and verify that the output is code by using Patronus AI's evaluation tool.",
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backstory="You are an experienced coder who can generate high quality python code. You can follow complex instructions accurately and effectively.",
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tools=[patronus_eval_tool],
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verbose=True,
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)
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generate_code = Task(
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description="Create a simple program to generate the first N numbers in the Fibonacci sequence. Select the most appropriate evaluator and criteria for evaluating your output.",
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expected_output="Program that generates the first N numbers in the Fibonacci sequence.",
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agent=coding_agent,
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)
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crew = Crew(agents=[coding_agent], tasks=[generate_code])
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crew.kickoff()
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```
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## Conclusion
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Using `PatronusPredefinedCriteriaEvalTool`, users can conveniently evaluate the inputs, outputs and context provided to the agent.
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Using patronus.ai, agents can choose from several of the pre-defined or custom defined criteria from the platform and evaluate their outputs, making it easier to debug agentic pipelines.
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In the case where the user wants the agent to contextually select the criteria from the list available at [app.patronus.ai](https://app.patronus.ai) or if a local evaluation function is preferred (guide [here](https://docs.patronus.ai/docs/experiment-evaluators)), it is encouraged to use the `PatronusEvalTool` and `PatronusLocalEvaluatorTool` respectively.
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