204 lines
6.7 KiB
Go
204 lines
6.7 KiB
Go
package oci
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import (
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"context"
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"encoding/json"
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"fmt"
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"net/http"
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"time"
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"github.com/oracle/oci-go-sdk/v65/generativeai"
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"github.com/oracle/oci-go-sdk/v65/generativeaiinference"
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"oci-portal/internal/aiwire"
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)
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// GenAiModel 是区域可用基础模型的摘要(管理面 ListModels)。
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type GenAiModel struct {
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Ocid string `json:"ocid"`
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Name string `json:"name"`
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Vendor string `json:"vendor"`
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Caps []string `json:"capabilities"`
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// Capability 是网关侧归一能力:CHAT / EMBEDDING(兼具时算 CHAT)
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Capability string `json:"capability"`
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// Deprecated 是 OCI 宣布的弃用时间(TimeDeprecated),nil 表示未宣布;
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// 弃用后模型仍可调用,直到 Retired(TimeOnDemandRetired 按需推理退役)。
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Deprecated *time.Time `json:"deprecated"`
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// Retired 是按需推理退役时间;已过表示调用必 404,同步层直接剔除
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Retired *time.Time `json:"retired"`
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}
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func (c *RealClient) genAiClient(cred Credentials, region string) (generativeai.GenerativeAiClient, error) {
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gc, err := generativeai.NewGenerativeAiClientWithConfigurationProvider(provider(cred))
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if err != nil {
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return gc, fmt.Errorf("new generative ai client: %w", err)
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}
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applyProxy(&gc.BaseClient, cred)
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if region != "" {
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gc.SetRegion(normalizeRegion(region))
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}
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return gc, nil
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}
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func (c *RealClient) genAiInferenceClient(cred Credentials, region string) (generativeaiinference.GenerativeAiInferenceClient, error) {
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ic, err := generativeaiinference.NewGenerativeAiInferenceClientWithConfigurationProvider(provider(cred))
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if err != nil {
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return ic, fmt.Errorf("new generative ai inference client: %w", err)
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}
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applyProxy(&ic.BaseClient, cred)
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if region != "" {
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ic.SetRegion(normalizeRegion(region))
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}
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return ic, nil
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}
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// ListGenAiModels 实现 Client:列出区域的 CHAT / EMBEDDING 能力 ACTIVE 基础模型(去重按名称)。
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func (c *RealClient) ListGenAiModels(ctx context.Context, cred Credentials, region string) ([]GenAiModel, error) {
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gc, err := c.genAiClient(cred, region)
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if err != nil {
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return nil, err
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}
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resp, err := gc.ListModels(ctx, generativeai.ListModelsRequest{CompartmentId: &cred.TenancyOCID})
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if err != nil {
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return nil, fmt.Errorf("list genai models: %w", err)
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}
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return dedupGenAiModels(resp.Items, time.Now()), nil
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}
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// dedupGenAiModels 压平并按名称去重;同名多条目时优先保留不含 FINE_TUNE 能力的条目
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// (微调基座条目在部分区域不支持按需调用,缓存其 OCID 会导致调用 400)。
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func dedupGenAiModels(items []generativeai.ModelSummary, now time.Time) []GenAiModel {
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seen := map[string]int{}
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var out []GenAiModel
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for _, m := range items {
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gm, ok := toGenAiModel(m, now)
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if !ok {
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continue
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}
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if i, dup := seen[gm.Name]; dup {
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if hasFineTune(out[i].Caps) && !hasFineTune(gm.Caps) {
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out[i] = gm
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}
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continue
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}
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seen[gm.Name] = len(out)
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out = append(out, gm)
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}
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return out
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}
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// hasFineTune 判断能力列表是否含 FINE_TUNE(微调基座条目)。
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func hasFineTune(caps []string) bool {
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for _, c := range caps {
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if c == string(generativeai.ModelCapabilityFineTune) {
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return true
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}
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}
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return false
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}
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// toGenAiModel 压平模型摘要;无归一能力、无名称或按需推理已退役
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// (调用必 404,ListModels 仍会返回且 state 为 ACTIVE)时返回 false 不入池。
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func toGenAiModel(m generativeai.ModelSummary, now time.Time) (GenAiModel, bool) {
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capability := modelCapability(m)
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name := deref(m.DisplayName)
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if capability == "" || name == "" {
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return GenAiModel{}, false
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}
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if m.TimeOnDemandRetired != nil && m.TimeOnDemandRetired.Time.Before(now) {
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return GenAiModel{}, false
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}
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gm := GenAiModel{Ocid: deref(m.Id), Name: name, Vendor: deref(m.Vendor),
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Caps: capStrings(m.Capabilities), Capability: capability}
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if m.TimeDeprecated != nil {
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gm.Deprecated = &m.TimeDeprecated.Time
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}
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if m.TimeOnDemandRetired != nil {
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gm.Retired = &m.TimeOnDemandRetired.Time
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}
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return gm, true
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}
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// modelCapability 归一模型能力:ACTIVE 且具 CHAT / TEXT_EMBEDDINGS 才纳入(兼具时算 CHAT)。
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func modelCapability(m generativeai.ModelSummary) string {
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if m.LifecycleState != generativeai.ModelLifecycleStateActive {
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return ""
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}
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capability := ""
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for _, cap := range m.Capabilities {
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switch cap {
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case generativeai.ModelCapabilityChat:
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return "CHAT"
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case generativeai.ModelCapabilityTextEmbeddings:
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capability = "EMBEDDING"
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}
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}
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return capability
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}
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func capStrings(caps []generativeai.ModelCapabilityEnum) []string {
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out := make([]string, 0, len(caps))
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for _, c := range caps {
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out = append(out, string(c))
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}
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return out
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}
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// GenAiProbeChat 实现 Client:经 OpenAI 兼容面(直通同链路)发一次极小请求探测渠道;配额探测专用的最小聊天(maxTokens=1),返回 HTTP 状态码;
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// modelName 决定请求格式(cohere.* 走 COHERE)。
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func (c *RealClient) GenAiProbeChat(ctx context.Context, cred Credentials, region, modelOcid, modelName string) (int, error) {
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body, err := json.Marshal(map[string]any{"model": modelName, "input": "hi",
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"max_output_tokens": 1, "store": false})
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if err != nil {
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return 0, err
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}
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if _, err = c.GenAiCompatResponses(ctx, cred, region, body); err == nil {
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return http.StatusOK, nil
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}
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if status, ok := ServiceStatus(err); ok {
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return status, err
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}
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return 0, err
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}
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// sdkUsageToIR 把 SDK 用量转为内部记账结构(缓存命中挂 details,仅命中时出现)。
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func sdkUsageToIR(u *generativeaiinference.Usage) *aiwire.Usage {
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if u == nil {
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return nil
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}
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out := &aiwire.Usage{}
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if u.PromptTokens != nil {
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out.PromptTokens = *u.PromptTokens
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}
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if u.CompletionTokens != nil {
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out.CompletionTokens = *u.CompletionTokens
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}
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if u.TotalTokens != nil {
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out.TotalTokens = *u.TotalTokens
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}
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if u.PromptTokensDetails != nil && u.PromptTokensDetails.CachedTokens != nil && *u.PromptTokensDetails.CachedTokens > 0 {
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out.PromptTokensDetails = &aiwire.PromptTokensDetails{CachedTokens: *u.PromptTokensDetails.CachedTokens}
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}
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return out
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}
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// GenAiEmbed 实现 Client:文本向量化(on-demand serving);dimensions 透传 OutputDimensions。
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func (c *RealClient) GenAiEmbed(ctx context.Context, cred Credentials, region, modelOcid string, inputs []string, dimensions *int) ([][]float32, *aiwire.Usage, error) {
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ic, err := c.genAiInferenceClient(cred, region)
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if err != nil {
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return nil, nil, err
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}
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resp, err := ic.EmbedText(ctx, generativeaiinference.EmbedTextRequest{
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EmbedTextDetails: generativeaiinference.EmbedTextDetails{
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CompartmentId: &cred.TenancyOCID,
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ServingMode: generativeaiinference.OnDemandServingMode{ModelId: &modelOcid},
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Inputs: inputs,
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OutputDimensions: dimensions,
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},
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})
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if err != nil {
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return nil, nil, fmt.Errorf("genai embed: %w", err)
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}
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return resp.Embeddings, sdkUsageToIR(resp.Usage), nil
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}
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