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Squashed feat/pii-ner-tier-engine rebased onto master (was 45 commits; see backup/pii-ner-tier-engine-prerebase). Net change: - privacy-filter.cpp: standalone GGML engine for the openai-privacy-filter PII/NER token classifier, wired as a LocalAI gRPC backend (CPU/CUDA/Vulkan). TokenClassify moves off the patched llama.cpp path onto this backend. - PII filter reworked to be NER-centric (encoder/NER detection tier scanning whole conversations as one document), with a recreated bounded restricted- regex secret-matching pattern detector tier alongside it (per-model pii_detection.builtins / .patterns + core/services/routing/piipattern). - Detection labelled by source (ner vs pattern); backend trace / confidence / debug observability; analyze/redact exposed as a synchronous API. - Instance-wide default detector policy + per-usecase default-on; request filtering extended to completions, embeddings, edits & Ollama. - React UI: NER-centric PII editor, detector-models table, pattern/builtins editor, middleware default-policy UI. - Gallery: privacy-filter-multilingual token-classify model + NER install filter; token_classify known_usecase; batch sized to context for NER models. privacy-filter backend registered in the backend gallery (cpu/vulkan/cuda-13 meta + image entries with a capabilities map) matching its CI matrix jobs, and an /import-model auto-detect importer (PrivacyFilterImporter, narrow privacy-filter GGUF detection) replacing the prior pref-only registration. Reconciled against master's independent evolution: - Dropped master's PIIPatternOverrides feature (global-pattern runtime overrides + /api/pii/patterns API + runtime_settings.json persistence). The per-model NER + pattern-detector design supersedes it; it was built on the global redactor pattern set this branch replaced. - Reverted the llama.cpp Score carry-patch (0006-server-task-type-score): removed the patch and restored master's grpc-server.cpp Score RPC (direct llama_decode, slot-loop bypass) and LLAMA_VERSION pin, plus master's model_config validation forbidding score + chat/completion/embeddings on llama-cpp. token_classify is unaffected (it runs on the privacy-filter backend, not llama-cpp). Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com>
295 lines
9.2 KiB
Go
295 lines
9.2 KiB
Go
package openai
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import (
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"encoding/json"
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"errors"
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"fmt"
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"time"
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"github.com/labstack/echo/v4"
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"github.com/mudler/LocalAI/core/backend"
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"github.com/mudler/LocalAI/core/config"
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"github.com/mudler/LocalAI/core/http/middleware"
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"github.com/google/uuid"
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"github.com/mudler/LocalAI/core/schema"
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"github.com/mudler/LocalAI/core/templates"
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"github.com/mudler/LocalAI/pkg/functions"
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"github.com/mudler/LocalAI/pkg/model"
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"github.com/mudler/xlog"
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)
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// CompletionEndpoint is the OpenAI Completion API endpoint https://platform.openai.com/docs/api-reference/completions
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// @Summary Generate completions for a given prompt and model.
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// @Tags inference
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// @Param request body schema.OpenAIRequest true "query params"
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// @Success 200 {object} schema.OpenAIResponse "Response"
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// @Router /v1/completions [post]
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func CompletionEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, evaluator *templates.Evaluator, appConfig *config.ApplicationConfig) echo.HandlerFunc {
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process := func(id string, s string, req *schema.OpenAIRequest, config *config.ModelConfig, loader *model.ModelLoader, responses chan schema.OpenAIResponse, extraUsage bool) error {
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tokenCallback := func(s string, tokenUsage backend.TokenUsage) bool {
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created := int(time.Now().Unix())
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usage := schema.OpenAIUsage{
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PromptTokens: tokenUsage.Prompt,
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CompletionTokens: tokenUsage.Completion,
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TotalTokens: tokenUsage.Prompt + tokenUsage.Completion,
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}
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if extraUsage {
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usage.TimingTokenGeneration = tokenUsage.TimingTokenGeneration
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usage.TimingPromptProcessing = tokenUsage.TimingPromptProcessing
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}
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// Usage rides on the struct for the consumer to track the
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// running cumulative; the consumer strips it before marshalling
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// so intermediate chunks stay OpenAI-spec compliant.
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usageForChunk := usage
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resp := schema.OpenAIResponse{
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ID: id,
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Created: created,
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Model: req.Model, // we have to return what the user sent here, due to OpenAI spec.
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Choices: []schema.Choice{
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{
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Index: 0,
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Text: s,
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FinishReason: nil,
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},
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},
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Object: "text_completion",
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Usage: &usageForChunk,
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}
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xlog.Debug("Sending goroutine", "text", s)
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responses <- resp
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return true
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}
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_, _, _, err := ComputeChoices(req, s, config, cl, appConfig, loader, func(s string, c *[]schema.Choice) {}, tokenCallback)
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close(responses)
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return err
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}
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return func(c echo.Context) error {
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created := int(time.Now().Unix())
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// Handle Correlation
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id := c.Request().Header.Get("X-Correlation-ID")
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if id == "" {
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id = uuid.New().String()
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}
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extraUsage := c.Request().Header.Get("Extra-Usage") != ""
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input, ok := c.Get(middleware.CONTEXT_LOCALS_KEY_LOCALAI_REQUEST).(*schema.OpenAIRequest)
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if !ok || input.Model == "" {
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return echo.ErrBadRequest
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}
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config, ok := c.Get(middleware.CONTEXT_LOCALS_KEY_MODEL_CONFIG).(*config.ModelConfig)
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if !ok || config == nil {
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return echo.ErrBadRequest
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}
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if config.ResponseFormatMap != nil {
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d := schema.ChatCompletionResponseFormat{}
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dat, _ := json.Marshal(config.ResponseFormatMap)
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_ = json.Unmarshal(dat, &d)
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if d.Type == "json_object" {
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input.Grammar = functions.JSONBNF
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}
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}
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config.Grammar = input.Grammar
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xlog.Debug("Parameter Config", "config", config)
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if input.Stream {
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xlog.Debug("Stream request received")
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c.Response().Header().Set("Content-Type", "text/event-stream")
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c.Response().Header().Set("Cache-Control", "no-cache")
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c.Response().Header().Set("Connection", "keep-alive")
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if len(config.PromptStrings) > 1 {
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return errors.New("cannot handle more than 1 `PromptStrings` when Streaming")
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}
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// Response/output PII redaction is out of scope for now —
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// redaction runs request-side via the NER middleware only.
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predInput := config.PromptStrings[0]
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templatedInput, err := evaluator.EvaluateTemplateForPrompt(templates.CompletionPromptTemplate, *config, templates.PromptTemplateData{
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Input: predInput,
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SystemPrompt: config.SystemPrompt,
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ReasoningEffort: input.ReasoningEffort,
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Metadata: input.Metadata,
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})
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if err == nil {
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predInput = templatedInput
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xlog.Debug("Template found, input modified", "input", predInput)
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}
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responses := make(chan schema.OpenAIResponse)
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ended := make(chan error)
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go func() {
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ended <- process(id, predInput, input, config, ml, responses, extraUsage)
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}()
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var latestUsage *schema.OpenAIUsage
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LOOP:
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for {
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select {
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case ev := <-responses:
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if len(ev.Choices) == 0 {
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xlog.Debug("No choices in the response, skipping")
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continue
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}
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// Capture running cumulative usage for the optional trailer
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// emitted after the final stop chunk when include_usage=true.
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// Done before the PII filter so a fully-buffered chunk
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// (which we drop from the wire) still contributes to the
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// running total.
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if ev.Usage != nil {
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latestUsage = ev.Usage
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}
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// OpenAI streaming spec: intermediate chunks must NOT
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// carry a `usage` field. Strip the tracking copy now.
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ev.Usage = nil
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respData, err := json.Marshal(ev)
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if err != nil {
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xlog.Debug("Failed to marshal response", "error", err)
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continue
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}
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xlog.Debug("Sending chunk", "chunk", string(respData))
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_, err = fmt.Fprintf(c.Response().Writer, "data: %s\n\n", string(respData))
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if err != nil {
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return err
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}
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c.Response().Flush()
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case err := <-ended:
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if err == nil {
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break LOOP
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}
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xlog.Error("Stream ended with error", "error", err)
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stopReason := FinishReasonStop
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errorResp := schema.OpenAIResponse{
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ID: id,
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Created: created,
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Model: input.Model,
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Choices: []schema.Choice{
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{
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Index: 0,
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FinishReason: &stopReason,
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Text: "Internal error: " + err.Error(),
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},
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},
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Object: "text_completion",
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}
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errorData, marshalErr := json.Marshal(errorResp)
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if marshalErr != nil {
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xlog.Error("Failed to marshal error response", "error", marshalErr)
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// Send a simple error message as fallback
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fmt.Fprintf(c.Response().Writer, "data: {\"error\":\"Internal error\"}\n\n")
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} else {
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fmt.Fprintf(c.Response().Writer, "data: %s\n\n", string(errorData))
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}
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c.Response().Flush()
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return nil
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}
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}
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stopReason := FinishReasonStop
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resp := &schema.OpenAIResponse{
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ID: id,
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Created: created,
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Model: input.Model, // we have to return what the user sent here, due to OpenAI spec.
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Choices: []schema.Choice{
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{
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Index: 0,
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FinishReason: &stopReason,
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},
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},
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Object: "text_completion",
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}
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respData, _ := json.Marshal(resp)
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pt, ct := 0, 0
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if latestUsage != nil {
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pt = latestUsage.PromptTokens
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ct = latestUsage.CompletionTokens
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}
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middleware.StampUsage(c, input.Model, pt, ct)
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fmt.Fprintf(c.Response().Writer, "data: %s\n\n", respData)
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// Trailing usage chunk per OpenAI spec: emit only when the caller
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// opted in via stream_options.include_usage.
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if input.StreamOptions != nil && input.StreamOptions.IncludeUsage && latestUsage != nil {
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trailer := streamUsageTrailerJSON(id, input.Model, created, *latestUsage)
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_, _ = fmt.Fprintf(c.Response().Writer, "data: %s\n\n", trailer)
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}
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fmt.Fprintf(c.Response().Writer, "data: [DONE]\n\n")
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c.Response().Flush()
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return nil
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}
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var result []schema.Choice
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totalTokenUsage := backend.TokenUsage{}
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for k, i := range config.PromptStrings {
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templatedInput, err := evaluator.EvaluateTemplateForPrompt(templates.CompletionPromptTemplate, *config, templates.PromptTemplateData{
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SystemPrompt: config.SystemPrompt,
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Input: i,
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ReasoningEffort: input.ReasoningEffort,
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Metadata: input.Metadata,
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})
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if err == nil {
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i = templatedInput
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xlog.Debug("Template found, input modified", "input", i)
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}
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r, tokenUsage, _, err := ComputeChoices(
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input, i, config, cl, appConfig, ml, func(s string, c *[]schema.Choice) {
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stopReason := FinishReasonStop
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*c = append(*c, schema.Choice{Text: s, FinishReason: &stopReason, Index: k})
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}, nil)
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if err != nil {
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return err
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}
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totalTokenUsage.TimingTokenGeneration += tokenUsage.TimingTokenGeneration
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totalTokenUsage.TimingPromptProcessing += tokenUsage.TimingPromptProcessing
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result = append(result, r...)
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}
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usage := schema.OpenAIUsage{
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PromptTokens: totalTokenUsage.Prompt,
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CompletionTokens: totalTokenUsage.Completion,
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TotalTokens: totalTokenUsage.Prompt + totalTokenUsage.Completion,
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}
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if extraUsage {
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usage.TimingTokenGeneration = totalTokenUsage.TimingTokenGeneration
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usage.TimingPromptProcessing = totalTokenUsage.TimingPromptProcessing
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}
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resp := &schema.OpenAIResponse{
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ID: id,
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Created: created,
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Model: input.Model, // we have to return what the user sent here, due to OpenAI spec.
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Choices: result,
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Object: "text_completion",
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Usage: &usage,
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}
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jsonResult, _ := json.Marshal(resp)
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xlog.Debug("Response", "response", string(jsonResult))
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middleware.StampUsage(c, input.Model, totalTokenUsage.Prompt, totalTokenUsage.Completion)
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// Return the prediction in the response body
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return c.JSON(200, resp)
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}
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}
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