125 lines
3.7 KiB
Go
125 lines
3.7 KiB
Go
package main
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import (
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"bytes"
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"context"
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"ctxGPT/database"
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"fmt"
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"os"
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"strings"
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"text/template"
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"time"
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"github.com/pkoukk/tiktoken-go"
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"github.com/sashabaranov/go-openai"
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)
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const encodingName = "gpt-4"
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const model = openai.GPT4TurboPreview
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func main() {
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db, err := database.NewDB()
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if err != nil {
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panic(err)
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}
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defer db.Close()
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value, err := db.Get(context.Background(), "context1")
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if err != nil {
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panic(err)
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}
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fmt.Println(value)
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err = db.Save(context.Background(), "context2", "value2")
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if err != nil {
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panic(err)
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}
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// to get text out of PDF, DOC, DOCX, XML, HTML, RTF, ODT pages documents and images to plain text
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// use https://github.com/sajari/docconv
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summarizeConvoPrompt, err := BuildPrompt("summarize.tmpl", struct{ WordLimit int }{WordLimit: 100})
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if err != nil {
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panic(err)
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}
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fmt.Println(summarizeConvoPrompt)
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tokenCount, err := GetTokenCount(summarizeConvoPrompt)
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if err != nil {
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panic(err)
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}
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fmt.Println(tokenCount)
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}
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func BuildPrompt(name string, in interface{}) (string, error) {
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fileLocation := "./prompts/" + name
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tmpl, err := template.New(name).ParseFiles(fileLocation)
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if err != nil {
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return "", fmt.Errorf("error parsing template: %w", err)
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}
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b := bytes.Buffer{}
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err = tmpl.Execute(&b, in)
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if err != nil {
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return "", fmt.Errorf("error executing template: %w", err)
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}
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return b.String(), nil
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}
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func GetTokenCount(input string) (int, error) {
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tke, err := tiktoken.EncodingForModel(encodingName) // cached in "TIKTOKEN_CACHE_DIR"
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if err != nil {
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return 0, fmt.Errorf("error getting encoding: %w", err)
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}
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token := tke.Encode(input, nil, nil)
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return len(token), nil
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}
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// SinglePromptInteraction calls openai chat endpoint with just a system prompt and a user prompt and returns the response
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func SinglePromptInteraction(systemPrompt, prompt string) (openai.ChatCompletionResponse, error) {
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return singlePromptInteraction(systemPrompt, prompt, 5)
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}
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// singlePromptInteraction calls openai chat endpoint with just a system prompt and a user prompt and returns the response
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// it also attempts 5 retries spaced 5 seconds apart in the case of rate limiting errors
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func singlePromptInteraction(systemPrompt, prompt string, retries int) (openai.ChatCompletionResponse, error) {
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client := openai.NewClient(os.Getenv("OPENAI_API_KEY"))
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messages := []openai.ChatCompletionMessage{
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{
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Role: openai.ChatMessageRoleSystem,
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Content: systemPrompt,
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},
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{
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Role: openai.ChatMessageRoleUser,
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Content: prompt,
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},
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}
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resp, err := client.CreateChatCompletion(
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context.Background(),
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openai.ChatCompletionRequest{
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Model: model, // switch to the configured Model
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Messages: messages,
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MaxTokens: 256,
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Temperature: 0,
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},
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)
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if err != nil {
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// if 429, wait and try again
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if strings.Contains(err.Error(), "429") && retries > 0 {
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fmt.Println("429 error, waiting 5 seconds...")
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time.Sleep(5 * time.Second)
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return singlePromptInteraction(systemPrompt, prompt, retries-1) // TODO: establish base case to prevent forever retrying
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}
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return openai.ChatCompletionResponse{}, fmt.Errorf("ChatCompletion request error: %w", err)
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}
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return resp, nil
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}
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// TODO: anything to be stored in the database should be chunked to sizes between 512 and 1024 tokens
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// it should also overlap with the previous chunk by 100-200 tokens
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// When the LLM asks for more context, it should be able to use the database to find the most relevant chunks here is how:
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// We will get the embeddings for each prompt and use those embeddings to search for the closest 6 chunks
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// we will use a separate LLM prompt to make an attempt to select and sort the chunks based on the user's input
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// then we will add the best matched chunks to the main prompt as further context for the given prompt
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