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https://github.com/memohai/Memoh.git
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BBQ
83b6ee608c
- Add SetupBotContainer to ContainerLifecycle interface so containers are automatically created when a bot is created, matching the existing cleanup-on-delete behavior. - Refactor schedule tools to use bot-scoped API paths and pass identity context for proper authorization. - Introduce dedicated trigger-schedule endpoint in chat resolver with explicit schedule payload instead of reusing the generic chat path. - Generate short-lived JWT tokens for schedule trigger callbacks with resolved bot owner identity. - Validate required parameters in NewLLMClient and NewOpenAIEmbedder constructors, returning errors instead of falling back to defaults. - Add unit tests for schedule token generation and chat resolver.
109 lines
2.5 KiB
Go
109 lines
2.5 KiB
Go
package embeddings
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import (
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"bytes"
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"context"
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"encoding/json"
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"fmt"
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"io"
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"log/slog"
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"net/http"
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"strings"
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"time"
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)
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type Embedder interface {
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Embed(ctx context.Context, input string) ([]float32, error)
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Dimensions() int
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}
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type OpenAIEmbedder struct {
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apiKey string
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baseURL string
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model string
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dims int
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logger *slog.Logger
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http *http.Client
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}
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type openAIEmbeddingRequest struct {
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Input string `json:"input"`
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Model string `json:"model"`
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}
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type openAIEmbeddingResponse struct {
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Data []struct {
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Embedding []float32 `json:"embedding"`
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} `json:"data"`
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}
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func NewOpenAIEmbedder(log *slog.Logger, apiKey, baseURL, model string, dims int, timeout time.Duration) (*OpenAIEmbedder, error) {
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if strings.TrimSpace(baseURL) == "" {
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return nil, fmt.Errorf("openai embedder: base url is required")
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}
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if strings.TrimSpace(apiKey) == "" {
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return nil, fmt.Errorf("openai embedder: api key is required")
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}
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if strings.TrimSpace(model) == "" {
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return nil, fmt.Errorf("openai embedder: model is required")
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}
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if dims <= 0 {
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return nil, fmt.Errorf("openai embedder: dimensions must be positive")
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}
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if timeout <= 0 {
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timeout = 10 * time.Second
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}
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return &OpenAIEmbedder{
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apiKey: apiKey,
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baseURL: strings.TrimRight(baseURL, "/"),
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model: model,
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dims: dims,
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logger: log.With(slog.String("embedder", "openai")),
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http: &http.Client{
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Timeout: timeout,
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},
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}, nil
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}
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func (e *OpenAIEmbedder) Dimensions() int {
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return e.dims
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}
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func (e *OpenAIEmbedder) Embed(ctx context.Context, input string) ([]float32, error) {
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payload, err := json.Marshal(openAIEmbeddingRequest{
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Input: input,
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Model: e.model,
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})
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if err != nil {
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return nil, err
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}
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req, err := http.NewRequestWithContext(ctx, http.MethodPost, e.baseURL+"/v1/embeddings", bytes.NewReader(payload))
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if err != nil {
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return nil, err
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}
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req.Header.Set("Content-Type", "application/json")
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if e.apiKey != "" {
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req.Header.Set("Authorization", "Bearer "+e.apiKey)
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}
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resp, err := e.http.Do(req)
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if err != nil {
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return nil, err
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}
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defer resp.Body.Close()
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if resp.StatusCode < 200 || resp.StatusCode >= 300 {
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body, _ := io.ReadAll(resp.Body)
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return nil, fmt.Errorf("openai embeddings error: %s", strings.TrimSpace(string(body)))
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}
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var parsed openAIEmbeddingResponse
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if err := json.NewDecoder(resp.Body).Decode(&parsed); err != nil {
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return nil, err
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}
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if len(parsed.Data) == 0 {
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return nil, fmt.Errorf("openai embeddings empty response")
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}
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return parsed.Data[0].Embedding, nil
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}
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