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See tn003_learning_eval.md for more description of how we are doing eval. This is a first pass at evaluation. * Implement a distance metric based on edit distance * Implement the infrastructure to compute it. Add an apply command and use it to run different experiments * Requires starting to move some of the Agent config into the API package because we want to reuse it in the experiment type. Start an initial evaluation dataset. ### API Updates - Added `EvalResult` structure to represent the evaluation outcome, including the actual commands generated, the expected commands, and the evaluation distance. - Introduced `EvalResultStatus` to indicate the status of an evaluation, such as `DONE` or `ERROR`. ### Agent Updates - Updated the `Agent` service to support evaluation mode, allowing it to operate without impacting the learning process. ### Executor Updates - Enhanced the `Executor` service to handle execution in evaluation mode, ensuring that execution traces are marked accordingly. ### Evaluator Implementation - Implemented the `Evaluator` component responsible for orchestrating the evaluation process, loading evaluation examples, generating predictions with the Agent, calculating distances, and updating results. ### Google Sheets Integration - Added functionality to export evaluation results to Google Sheets, enabling easy review and analysis of Foyle's performance. ### CLI Tool Enhancements - Extended the CLI tool with commands for running evaluations ### Miscellaneous - Added necessary protobuf definitions for new data structures related to evaluations. - Updated server setup to handle evaluation logic and integrate with the learning mechanism. - Provided sample evaluation datasets for initial testing and validation of the evaluation process.
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| package api | ||
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| type AgentConfig struct { | ||
| // Model is the name of the model to use to generate completions | ||
| Model string `json:"model" yaml:"model"` | ||
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| // RAG is the configuration for the RAG model | ||
| RAG *RAGConfig `json:"rag,omitempty" yaml:"rag,omitempty"` | ||
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| // EvalMode is whether to run in evaluation mode or not. | ||
| // In EvalMode logs are specially marked so requests won't be used for training. | ||
| EvalMode bool `json:"evalMode" yaml:"evalMode"` | ||
| } | ||
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| // RAGConfig configures the RAG model | ||
| type RAGConfig struct { | ||
| // Enabled is whether to enable the RAG model or not | ||
| Enabled bool `json:"enabled" yaml:"enabled"` | ||
| // MaxResults is the maximum number of results to return | ||
| MaxResults int `json:"maxResults" yaml:"maxResults"` | ||
| } |
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| package api | ||
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| import "k8s.io/apimachinery/pkg/runtime/schema" | ||
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| var ( | ||
| ExperimentGVK = schema.FromAPIVersionAndKind(Group+"/"+Version, "Experiment") | ||
| ) | ||
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| // Experiment is a struct that represents an experiment | ||
| type Experiment struct { | ||
| Metadata Metadata `json:"metadata" yaml:"metadata"` | ||
| Spec ExperimentSpec `json:"spec" yaml:"spec"` | ||
| } | ||
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| type ExperimentSpec struct { | ||
| // EvalDir is the directory containing the evaluation the evaluation input | ||
| EvalDir string `json:"evalDir" yaml:"evalDir"` | ||
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| // DBDir is the directory for the pebble database that will store the results | ||
| DBDir string `json:"dbDir" yaml:"dbDir"` | ||
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| // SheetID is the ID of the Google Sheet to update with the results. | ||
| SheetID string `json:"sheetID" yaml:"sheetID"` | ||
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| // SheetName is the name of the sheet to update. | ||
| SheetName string `json:"sheetName" yaml:"sheetName"` | ||
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| // Agent is the configuration for the agent | ||
| Agent *AgentConfig `json:"agent,omitempty" yaml:"agent,omitempty"` | ||
| } |
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| package api | ||
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| const ( | ||
| Group = "foyle.io" | ||
| Version = "v1alpha1" | ||
| ) | ||
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| // N.B. We need to redefine Metadata and not reuse the version in the K8s libraries | ||
| // because we want it to have yaml tags so we can serialize with the YAML library. | ||
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| // Metadata holds an optional name of the project. | ||
| type Metadata struct { | ||
| Name string `yaml:"name,omitempty"` | ||
| Namespace string `yaml:"namespace,omitempty"` | ||
| Labels map[string]string `yaml:"labels"` | ||
| Annotations map[string]string `yaml:"annotations,omitempty"` | ||
| // ResourceVersion is used for optimistic concurrency. | ||
| // Ref: https://github.com/kubernetes/community/blob/master/contributors/devel/sig-architecture/api-conventions.md#metadata | ||
| // This should be treated as an opaque value by clients. | ||
| ResourceVersion string `yaml:"resourceVersion,omitempty"` | ||
| } |
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| package cmd | ||
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| import ( | ||
| "context" | ||
| "fmt" | ||
| "os" | ||
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| "github.com/go-logr/zapr" | ||
| "github.com/jlewi/foyle/app/pkg/application" | ||
| "github.com/pkg/errors" | ||
| "github.com/spf13/cobra" | ||
| "go.uber.org/zap" | ||
| ) | ||
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| // NewApplyCmd create an apply command | ||
| func NewApplyCmd() *cobra.Command { | ||
| // TODO(jeremy): We should update apply to support the image resource. | ||
| applyCmd := &cobra.Command{ | ||
| Use: "apply <resource.yaml> <resourceDir> <resource.yaml> ...", | ||
| Short: "Apply the specified resource.", | ||
| Run: func(cmd *cobra.Command, args []string) { | ||
| err := func() error { | ||
| log := zapr.NewLogger(zap.L()) | ||
| if len(args) == 0 { | ||
| log.Info("apply takes at least one argument which should be the file or directory YAML to apply.") | ||
| return errors.New("apply takes at least one argument which should be the file or directory YAML to apply.") | ||
| } | ||
| logVersion() | ||
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| app := application.NewApp() | ||
| if err := app.LoadConfig(cmd); err != nil { | ||
| return err | ||
| } | ||
| if err := app.SetupLogging(false); err != nil { | ||
| return err | ||
| } | ||
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| if err := app.SetupRegistry(); err != nil { | ||
| return err | ||
| } | ||
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| return app.ApplyPaths(context.Background(), args) | ||
| }() | ||
| if err != nil { | ||
| fmt.Printf("Error running apply;\n %+v\n", err) | ||
| os.Exit(1) | ||
| } | ||
| }, | ||
| } | ||
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| return applyCmd | ||
| } |
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