Refactor: different endpoint types

llm.go

@@ -0,0 +1,116 @@
+package main
+
+import (
+	"bytes"
+	"elefant/models"
+	"encoding/json"
+	"io"
+	"strings"
+)
+
+type ChunkParser interface {
+	ParseChunk([]byte) (string, bool, error)
+	FormMsg(msg, role string) (io.Reader, error)
+}
+
+func initChunkParser() {
+	chunkParser = LlamaCPPeer{}
+	if strings.Contains(cfg.APIURL, "v1") {
+		logger.Info("chosen openai parser")
+		chunkParser = OpenAIer{}
+		return
+	}
+	logger.Info("chosen llamacpp parser")
+}
+
+type LlamaCPPeer struct {
+}
+type OpenAIer struct {
+}
+
+func (lcp LlamaCPPeer) FormMsg(msg, role string) (io.Reader, error) {
+	if msg != "" { // otherwise let the bot continue
+		// if role == cfg.UserRole {
+		// 	msg = msg + cfg.AssistantRole + ":"
+		// }
+		newMsg := models.RoleMsg{Role: role, Content: msg}
+		chatBody.Messages = append(chatBody.Messages, newMsg)
+		// if rag
+		if cfg.RAGEnabled {
+			ragResp, err := chatRagUse(newMsg.Content)
+			if err != nil {
+				logger.Error("failed to form a rag msg", "error", err)
+				return nil, err
+			}
+			ragMsg := models.RoleMsg{Role: cfg.ToolRole, Content: ragResp}
+			chatBody.Messages = append(chatBody.Messages, ragMsg)
+		}
+	}
+	messages := make([]string, len(chatBody.Messages))
+	for i, m := range chatBody.Messages {
+		messages[i] = m.ToPrompt()
+	}
+	prompt := strings.Join(messages, "\n")
+	botMsgStart := "\n" + cfg.AssistantRole + ":\n"
+	payload := models.NewLCPReq(prompt+botMsgStart, role)
+	data, err := json.Marshal(payload)
+	if err != nil {
+		logger.Error("failed to form a msg", "error", err)
+		return nil, err
+	}
+	return bytes.NewReader(data), nil
+}
+
+func (lcp LlamaCPPeer) ParseChunk(data []byte) (string, bool, error) {
+	llmchunk := models.LlamaCPPResp{}
+	if err := json.Unmarshal(data, &llmchunk); err != nil {
+		logger.Error("failed to decode", "error", err, "line", string(data))
+		return "", false, err
+	}
+	if llmchunk.Stop {
+		if llmchunk.Content != "" {
+			logger.Error("text inside of finish llmchunk", "chunk", llmchunk)
+		}
+		return llmchunk.Content, true, nil
+	}
+	return llmchunk.Content, false, nil
+}
+
+func (op OpenAIer) ParseChunk(data []byte) (string, bool, error) {
+	llmchunk := models.LLMRespChunk{}
+	if err := json.Unmarshal(data, &llmchunk); err != nil {
+		logger.Error("failed to decode", "error", err, "line", string(data))
+		return "", false, err
+	}
+	content := llmchunk.Choices[len(llmchunk.Choices)-1].Delta.Content
+	if llmchunk.Choices[len(llmchunk.Choices)-1].FinishReason == "stop" {
+		if content != "" {
+			logger.Error("text inside of finish llmchunk", "chunk", llmchunk)
+		}
+		return content, true, nil
+	}
+	return content, false, nil
+}
+
+func (op OpenAIer) FormMsg(msg, role string) (io.Reader, error) {
+	if msg != "" { // otherwise let the bot continue
+		newMsg := models.RoleMsg{Role: role, Content: msg}
+		chatBody.Messages = append(chatBody.Messages, newMsg)
+		// if rag
+		if cfg.RAGEnabled {
+			ragResp, err := chatRagUse(newMsg.Content)
+			if err != nil {
+				logger.Error("failed to form a rag msg", "error", err)
+				return nil, err
+			}
+			ragMsg := models.RoleMsg{Role: cfg.ToolRole, Content: ragResp}
+			chatBody.Messages = append(chatBody.Messages, ragMsg)
+		}
+	}
+	data, err := json.Marshal(chatBody)
+	if err != nil {
+		logger.Error("failed to form a msg", "error", err)
+		return nil, err
+	}
+	return bytes.NewReader(data), nil
+}