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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65
pkg/agents/search_terms.go
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65
pkg/agents/search_terms.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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gollm "gitea.stevedudenhoeffer.com/steve/go-llm"
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)
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type SearchTerms struct {
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Model gollm.ChatCompletion
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Context []string
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}
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// SearchTerms will create search terms for the given question.
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// alreadySearched is a list of search terms that have already been used, and should not be used again.
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func (q SearchTerms) SearchTerms(ctx context.Context, question string, alreadySearched []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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"search_terms",
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"search DuckDuckGo with these search terms for the given question",
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func(ctx *gollm.Context, args struct {
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SearchTerms string `description:"The search terms to use for the search"`
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}) (string, error) {
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res = args.SearchTerms
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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: `You are to generate search terms for a question using DuckDuckGo. The question will be provided by the user.`,
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},
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},
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}
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if len(alreadySearched) > 0 {
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req.Messages = append(req.Messages, gollm.Message{
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Role: gollm.RoleSystem,
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Text: fmt.Sprintf("The following search terms have already been used: %v", alreadySearched),
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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: fmt.Sprintf("The question is: %s", 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 "", err
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}
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if len(resp.Choices) == 0 {
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return "", 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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