Files
LocalAI/core/http/endpoints/openai/completion.go
Richard Palethorpe 3fa7b2955c feat(pii): NER tier engine — privacy-filter.cpp backend + NER-centric PII filter (#10360)
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>
2026-06-18 11:45:22 +01:00

295 lines
9.2 KiB
Go

package openai
import (
"encoding/json"
"errors"
"fmt"
"time"
"github.com/labstack/echo/v4"
"github.com/mudler/LocalAI/core/backend"
"github.com/mudler/LocalAI/core/config"
"github.com/mudler/LocalAI/core/http/middleware"
"github.com/google/uuid"
"github.com/mudler/LocalAI/core/schema"
"github.com/mudler/LocalAI/core/templates"
"github.com/mudler/LocalAI/pkg/functions"
"github.com/mudler/LocalAI/pkg/model"
"github.com/mudler/xlog"
)
// CompletionEndpoint is the OpenAI Completion API endpoint https://platform.openai.com/docs/api-reference/completions
// @Summary Generate completions for a given prompt and model.
// @Tags inference
// @Param request body schema.OpenAIRequest true "query params"
// @Success 200 {object} schema.OpenAIResponse "Response"
// @Router /v1/completions [post]
func CompletionEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, evaluator *templates.Evaluator, appConfig *config.ApplicationConfig) echo.HandlerFunc {
process := func(id string, s string, req *schema.OpenAIRequest, config *config.ModelConfig, loader *model.ModelLoader, responses chan schema.OpenAIResponse, extraUsage bool) error {
tokenCallback := func(s string, tokenUsage backend.TokenUsage) bool {
created := int(time.Now().Unix())
usage := schema.OpenAIUsage{
PromptTokens: tokenUsage.Prompt,
CompletionTokens: tokenUsage.Completion,
TotalTokens: tokenUsage.Prompt + tokenUsage.Completion,
}
if extraUsage {
usage.TimingTokenGeneration = tokenUsage.TimingTokenGeneration
usage.TimingPromptProcessing = tokenUsage.TimingPromptProcessing
}
// Usage rides on the struct for the consumer to track the
// running cumulative; the consumer strips it before marshalling
// so intermediate chunks stay OpenAI-spec compliant.
usageForChunk := usage
resp := schema.OpenAIResponse{
ID: id,
Created: created,
Model: req.Model, // we have to return what the user sent here, due to OpenAI spec.
Choices: []schema.Choice{
{
Index: 0,
Text: s,
FinishReason: nil,
},
},
Object: "text_completion",
Usage: &usageForChunk,
}
xlog.Debug("Sending goroutine", "text", s)
responses <- resp
return true
}
_, _, _, err := ComputeChoices(req, s, config, cl, appConfig, loader, func(s string, c *[]schema.Choice) {}, tokenCallback)
close(responses)
return err
}
return func(c echo.Context) error {
created := int(time.Now().Unix())
// Handle Correlation
id := c.Request().Header.Get("X-Correlation-ID")
if id == "" {
id = uuid.New().String()
}
extraUsage := c.Request().Header.Get("Extra-Usage") != ""
input, ok := c.Get(middleware.CONTEXT_LOCALS_KEY_LOCALAI_REQUEST).(*schema.OpenAIRequest)
if !ok || input.Model == "" {
return echo.ErrBadRequest
}
config, ok := c.Get(middleware.CONTEXT_LOCALS_KEY_MODEL_CONFIG).(*config.ModelConfig)
if !ok || config == nil {
return echo.ErrBadRequest
}
if config.ResponseFormatMap != nil {
d := schema.ChatCompletionResponseFormat{}
dat, _ := json.Marshal(config.ResponseFormatMap)
_ = json.Unmarshal(dat, &d)
if d.Type == "json_object" {
input.Grammar = functions.JSONBNF
}
}
config.Grammar = input.Grammar
xlog.Debug("Parameter Config", "config", config)
if input.Stream {
xlog.Debug("Stream request received")
c.Response().Header().Set("Content-Type", "text/event-stream")
c.Response().Header().Set("Cache-Control", "no-cache")
c.Response().Header().Set("Connection", "keep-alive")
if len(config.PromptStrings) > 1 {
return errors.New("cannot handle more than 1 `PromptStrings` when Streaming")
}
// Response/output PII redaction is out of scope for now —
// redaction runs request-side via the NER middleware only.
predInput := config.PromptStrings[0]
templatedInput, err := evaluator.EvaluateTemplateForPrompt(templates.CompletionPromptTemplate, *config, templates.PromptTemplateData{
Input: predInput,
SystemPrompt: config.SystemPrompt,
ReasoningEffort: input.ReasoningEffort,
Metadata: input.Metadata,
})
if err == nil {
predInput = templatedInput
xlog.Debug("Template found, input modified", "input", predInput)
}
responses := make(chan schema.OpenAIResponse)
ended := make(chan error)
go func() {
ended <- process(id, predInput, input, config, ml, responses, extraUsage)
}()
var latestUsage *schema.OpenAIUsage
LOOP:
for {
select {
case ev := <-responses:
if len(ev.Choices) == 0 {
xlog.Debug("No choices in the response, skipping")
continue
}
// Capture running cumulative usage for the optional trailer
// emitted after the final stop chunk when include_usage=true.
// Done before the PII filter so a fully-buffered chunk
// (which we drop from the wire) still contributes to the
// running total.
if ev.Usage != nil {
latestUsage = ev.Usage
}
// OpenAI streaming spec: intermediate chunks must NOT
// carry a `usage` field. Strip the tracking copy now.
ev.Usage = nil
respData, err := json.Marshal(ev)
if err != nil {
xlog.Debug("Failed to marshal response", "error", err)
continue
}
xlog.Debug("Sending chunk", "chunk", string(respData))
_, err = fmt.Fprintf(c.Response().Writer, "data: %s\n\n", string(respData))
if err != nil {
return err
}
c.Response().Flush()
case err := <-ended:
if err == nil {
break LOOP
}
xlog.Error("Stream ended with error", "error", err)
stopReason := FinishReasonStop
errorResp := schema.OpenAIResponse{
ID: id,
Created: created,
Model: input.Model,
Choices: []schema.Choice{
{
Index: 0,
FinishReason: &stopReason,
Text: "Internal error: " + err.Error(),
},
},
Object: "text_completion",
}
errorData, marshalErr := json.Marshal(errorResp)
if marshalErr != nil {
xlog.Error("Failed to marshal error response", "error", marshalErr)
// Send a simple error message as fallback
fmt.Fprintf(c.Response().Writer, "data: {\"error\":\"Internal error\"}\n\n")
} else {
fmt.Fprintf(c.Response().Writer, "data: %s\n\n", string(errorData))
}
c.Response().Flush()
return nil
}
}
stopReason := FinishReasonStop
resp := &schema.OpenAIResponse{
ID: id,
Created: created,
Model: input.Model, // we have to return what the user sent here, due to OpenAI spec.
Choices: []schema.Choice{
{
Index: 0,
FinishReason: &stopReason,
},
},
Object: "text_completion",
}
respData, _ := json.Marshal(resp)
pt, ct := 0, 0
if latestUsage != nil {
pt = latestUsage.PromptTokens
ct = latestUsage.CompletionTokens
}
middleware.StampUsage(c, input.Model, pt, ct)
fmt.Fprintf(c.Response().Writer, "data: %s\n\n", respData)
// Trailing usage chunk per OpenAI spec: emit only when the caller
// opted in via stream_options.include_usage.
if input.StreamOptions != nil && input.StreamOptions.IncludeUsage && latestUsage != nil {
trailer := streamUsageTrailerJSON(id, input.Model, created, *latestUsage)
_, _ = fmt.Fprintf(c.Response().Writer, "data: %s\n\n", trailer)
}
fmt.Fprintf(c.Response().Writer, "data: [DONE]\n\n")
c.Response().Flush()
return nil
}
var result []schema.Choice
totalTokenUsage := backend.TokenUsage{}
for k, i := range config.PromptStrings {
templatedInput, err := evaluator.EvaluateTemplateForPrompt(templates.CompletionPromptTemplate, *config, templates.PromptTemplateData{
SystemPrompt: config.SystemPrompt,
Input: i,
ReasoningEffort: input.ReasoningEffort,
Metadata: input.Metadata,
})
if err == nil {
i = templatedInput
xlog.Debug("Template found, input modified", "input", i)
}
r, tokenUsage, _, err := ComputeChoices(
input, i, config, cl, appConfig, ml, func(s string, c *[]schema.Choice) {
stopReason := FinishReasonStop
*c = append(*c, schema.Choice{Text: s, FinishReason: &stopReason, Index: k})
}, nil)
if err != nil {
return err
}
totalTokenUsage.TimingTokenGeneration += tokenUsage.TimingTokenGeneration
totalTokenUsage.TimingPromptProcessing += tokenUsage.TimingPromptProcessing
result = append(result, r...)
}
usage := schema.OpenAIUsage{
PromptTokens: totalTokenUsage.Prompt,
CompletionTokens: totalTokenUsage.Completion,
TotalTokens: totalTokenUsage.Prompt + totalTokenUsage.Completion,
}
if extraUsage {
usage.TimingTokenGeneration = totalTokenUsage.TimingTokenGeneration
usage.TimingPromptProcessing = totalTokenUsage.TimingPromptProcessing
}
resp := &schema.OpenAIResponse{
ID: id,
Created: created,
Model: input.Model, // we have to return what the user sent here, due to OpenAI spec.
Choices: result,
Object: "text_completion",
Usage: &usage,
}
jsonResult, _ := json.Marshal(resp)
xlog.Debug("Response", "response", string(jsonResult))
middleware.StampUsage(c, input.Model, totalTokenUsage.Prompt, totalTokenUsage.Completion)
// Return the prediction in the response body
return c.JSON(200, resp)
}
}