Add core implementation for AI-powered question answering
Introduce multiple agents, tools, and utilities for processing, extracting, and answering user-provided questions using LLMs and external data. Key features include knowledge processing, question splitting, search term generation, and contextual knowledge handling.
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66
pkg/agents/question_splitter.go
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66
pkg/agents/question_splitter.go
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package agents
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import (
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"context"
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"fmt"
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"strings"
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gollm "gitea.stevedudenhoeffer.com/steve/go-llm"
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)
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type QuestionSplitter struct {
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Model gollm.ChatCompletion
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ContextualInfo []string
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}
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func (q QuestionSplitter) SplitQuestion(ctx context.Context, question string) ([]string, error) {
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var res []string
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req := gollm.Request{
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Toolbox: gollm.NewToolBox(
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gollm.NewFunction(
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"questions",
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"split the provided question by the user into sub-questions",
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func(ctx *gollm.Context, args struct {
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Questions []string `description:"The questions to evaluate"`
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}) (string, error) {
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res = args.Questions
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return "", nil
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}),
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),
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Messages: []gollm.Message{
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{
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Role: gollm.RoleSystem,
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Text: `The user is going to ask you a question, if the question would be better answered split into multiple questions, please do so.
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Respond using the "questions" function.
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If the question is fine as is, respond with the original question passed to the "questions" function.`,
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},
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},
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}
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if len(q.ContextualInfo) > 0 {
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req.Messages = append(req.Messages, gollm.Message{
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Role: gollm.RoleSystem,
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Text: "Some contextual information you should be aware of: " + strings.Join(q.ContextualInfo, "\n"),
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})
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}
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req.Messages = append(req.Messages, gollm.Message{
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Role: gollm.RoleUser,
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Text: question,
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})
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resp, err := q.Model.ChatComplete(ctx, req)
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if err != nil {
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return nil, err
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}
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if len(resp.Choices) == 0 {
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return nil, fmt.Errorf("no choices found")
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}
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choice := resp.Choices[0]
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_, _ = req.Toolbox.ExecuteCallbacks(gollm.NewContext(ctx, req, &choice, nil), choice.Calls, nil, nil)
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return res, nil
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}
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