Steve Dudenhoeffer
090b28d956
Refactored LLM handling to use updated langchaingo models and tools, replacing gollm dependencies. Introduced agent-related utilities, tools, and counters for better modular functionality. Added a parser for LLM model configuration and revamped the answering mechanism with enhanced support for tool-based interaction.
47 lines
1.0 KiB
Go
47 lines
1.0 KiB
Go
package agent
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import (
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"encoding/json"
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"fmt"
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)
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var AskTool = FromFunction(
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func(ctx *Context, args struct {
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Question string `description:"the question to answer"`
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}) (FuncResponse, error) {
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var q Question
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q.Question = args.Question
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ctx = ctx.WithQuestion(q)
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answers, err := ask(ctx, q)
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if err != nil {
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return FuncResponse{}, err
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}
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tb := ToolBox{}
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tb.Register(SummarizeAnswers)
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b, err := json.Marshal(answers.Answers)
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if err != nil {
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return FuncResponse{}, fmt.Errorf("failed to marshal answers: %w", err)
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}
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q = Question{Question: string(b)}
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ctx = ctx.WithQuestion(q)
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answers, err = tb.Run(ctx, q)
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if err != nil {
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return FuncResponse{}, fmt.Errorf("failed to summarize answers: %w", err)
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}
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if len(answers.Answers) == 0 {
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return FuncResponse{}, fmt.Errorf("no response from model")
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}
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return FuncResponse{Result: answers.Answers[0].Answer}, nil
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}).
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WithName("ask").
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WithDescription("Ask the agent a question, this is useful for splitting a question into multiple parts")
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