Steve Dudenhoeffer
693ac4e6a7
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.
35 lines
919 B
Go
35 lines
919 B
Go
package tools
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import (
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"github.com/Edw590/go-wolfram"
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gollm "gitea.stevedudenhoeffer.com/steve/go-llm"
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)
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type WolframFunctions struct {
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Imperial *gollm.Function
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Metric *gollm.Function
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}
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func CreateWolframFunctions(appId string) WolframFunctions {
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client := &wolfram.Client{AppID: appId}
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return WolframFunctions{
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Imperial: gollm.NewFunction(
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"wolfram",
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"Query the Wolfram Alpha API",
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func(ctx *gollm.Context, args struct {
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Question string `description:"The question to ask Wolfram|Alpha"`
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}) (string, error) {
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return client.GetShortAnswerQuery(args.Question, wolfram.Imperial, 10)
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}),
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Metric: gollm.NewFunction(
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"wolfram",
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"Query the Wolfram Alpha API",
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func(ctx *gollm.Context, args struct {
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Question string `description:"The question to ask Wolfram|Alpha"`
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}) (string, error) {
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return client.GetShortAnswerQuery(args.Question, wolfram.Metric, 10)
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}),
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}
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}
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