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ai_scheduler
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fix: 1. 调整vllm配置及其相关 2. 意图识别模型切换 ollama -> vllm

This commit is contained in:
fuzhongyun 2026-02-28 14:16:53 +08:00
parent 376c08e836
commit ca671694f9
8 changed files with 237 additions and 24 deletions
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14
config/config.yaml
View File

@ -17,10 +17,16 @@ ollama:
format: "json"
vllm:
base_url: "http://172.17.0.1:8001/v1"
vl_model: "qwen2.5-vl-3b-awq"
timeout: "120s"
level: "info"
vl_model:
base_url: "http://192.168.6.115:8001/v1"
model: "qwen2.5-vl-3b-awq"
timeout: "120s"
level: "info"
text_model:
base_url: "http://192.168.6.115:8002/v1"
model: "qwen3-8b-fp8"
timeout: "120s"
level: "info"
coze:
base_url: "https://api.coze.cn"

14
config/config_env.yaml
View File

@ -14,10 +14,16 @@ ollama:
format: "json"
vllm:
base_url: "http://117.175.169.61:16001/v1"
vl_model: "qwen2.5-vl-3b-awq"
timeout: "120s"
level: "info"
vl_model:
base_url: "http://192.168.6.115:8001/v1"
model: "qwen2.5-vl-3b-awq"
timeout: "120s"
level: "info"
text_model:
base_url: "http://192.168.6.115:8002/v1"
model: "qwen3-8b-fp8"
timeout: "120s"
level: "info"
coze:
base_url: "https://api.coze.cn"

14
config/config_test.yaml
View File

@ -14,10 +14,16 @@ ollama:
format: "json"
vllm:
base_url: "http://host.docker.internal:8001/v1"
vl_model: "qwen2.5-vl-3b-awq"
timeout: "120s"
level: "info"
vl_model:
base_url: "http://192.168.6.115:8001/v1"
model: "qwen2.5-vl-3b-awq"
timeout: "120s"
level: "info"
text_model:
base_url: "http://192.168.6.115:8002/v1"
model: "qwen3-8b-fp8"
timeout: "120s"
level: "info"
coze:
base_url: "https://api.coze.cn"

6
internal/biz/do/handle.go
View File

@ -36,6 +36,7 @@ import (
type Handle struct {
Ollama *llm_service.OllamaService
Vllm *llm_service.VllmService
toolManager *tools.Manager
conf *config.Config
sessionImpl *impl.SessionImpl
@ -47,6 +48,7 @@ type Handle struct {
func NewHandle(
Ollama *llm_service.OllamaService,
Vllm *llm_service.VllmService,
toolManager *tools.Manager,
conf *config.Config,
sessionImpl *impl.SessionImpl,
@ -57,6 +59,7 @@ func NewHandle(
) *Handle {
return &Handle{
Ollama: Ollama,
Vllm: Vllm,
toolManager: toolManager,
conf: conf,
sessionImpl: sessionImpl,
@ -72,7 +75,8 @@ func (r *Handle) Recognize(ctx context.Context, rec *entitys.Recognize, promptPr
prompt, err := promptProcessor.CreatePrompt(ctx, rec)
//意图识别
recognizeMsg, err := r.Ollama.IntentRecognize(ctx, &entitys.ToolSelect{
// recognizeMsg, err := r.Ollama.IntentRecognize(ctx, &entitys.ToolSelect{
recognizeMsg, err := r.Vllm.IntentRecognize(ctx, &entitys.ToolSelect{
Prompt: prompt,
Tools: rec.Tasks,
})

153
internal/biz/llm_service/vllm.go Normal file
View File

@ -0,0 +1,153 @@
package llm_service
import (
"ai_scheduler/internal/config"
"ai_scheduler/internal/entitys"
"ai_scheduler/internal/pkg"
"ai_scheduler/internal/pkg/utils_vllm"
"context"
"encoding/base64"
"errors"
"fmt"
"strings"
"github.com/cloudwego/eino/schema"
"github.com/ollama/ollama/api"
)
type VllmService struct {
client *utils_vllm.Client
config *config.Config
}
func NewVllmService(
client *utils_vllm.Client,
config *config.Config,
) *VllmService {
return &VllmService{
client: client,
config: config,
}
}
func (s *VllmService) IntentRecognize(ctx context.Context, req *entitys.ToolSelect) (msg string, err error) {
msgs := s.convertMessages(req.Prompt)
tools := s.convertTools(req.Tools)
resp, err := s.client.ToolSelect(ctx, msgs, tools)
if err != nil {
return
}
if resp.Content == "" {
if len(resp.ToolCalls) > 0 {
call := resp.ToolCalls[0]
var matchFromTools = &entitys.Match{
Confidence: 1,
Index: call.Function.Name,
Parameters: call.Function.Arguments,
IsMatch: true,
}
msg = pkg.JsonStringIgonErr(matchFromTools)
} else {
err = errors.New("不太明白你想表达的意思呢，可以在仔细描述一下您所需要的内容吗，感谢感谢")
return
}
} else {
msg = resp.Content
}
return
}
func (s *VllmService) convertMessages(prompts []api.Message) []*schema.Message {
msgs := make([]*schema.Message, 0, len(prompts))
for _, p := range prompts {
msg := &schema.Message{
Role: schema.RoleType(p.Role),
Content: p.Content,
}
// 这里实际应该不会走进来
if len(p.Images) > 0 {
parts := []schema.MessageInputPart{
{Type: schema.ChatMessagePartTypeText, Text: p.Content},
}
for _, imgData := range p.Images {
b64 := base64.StdEncoding.EncodeToString(imgData)
mimeType := "image/jpeg"
parts = append(parts, schema.MessageInputPart{
Type: schema.ChatMessagePartTypeImageURL,
Image: &schema.MessageInputImage{
MessagePartCommon: schema.MessagePartCommon{
MIMEType: mimeType,
Base64Data: &b64,
},
},
})
}
msg.UserInputMultiContent = parts
}
msgs = append(msgs, msg)
}
return msgs
}
func (s *VllmService) convertTools(tasks []entitys.RegistrationTask) []*schema.ToolInfo {
tools := make([]*schema.ToolInfo, 0, len(tasks))
for _, task := range tasks {
params := make(map[string]*schema.ParameterInfo)
for k, v := range task.TaskConfigDetail.Param.Properties {
dt := schema.String
// Handle v.Type dynamically to support both string and []string (compiler suggests []string)
// Using fmt.Sprint handles both cases safely without knowing exact type structure
typeStr := fmt.Sprintf("%v", v.Type)
typeStr = strings.Trim(typeStr, "[]") // normalize "[string]" -> "string"
switch typeStr {
case "string":
dt = schema.String
case "integer", "int":
dt = schema.Integer
case "number", "float":
dt = schema.Number
case "boolean", "bool":
dt = schema.Boolean
case "object":
dt = schema.Object
case "array":
dt = schema.Array
}
required := false
for _, r := range task.TaskConfigDetail.Param.Required {
if r == k {
required = true
break
}
}
desc := v.Description
if len(v.Enum) > 0 {
var enumStrs []string
for _, e := range v.Enum {
enumStrs = append(enumStrs, fmt.Sprintf("%v", e))
}
desc += " Enum: " + strings.Join(enumStrs, ", ")
}
params[k] = &schema.ParameterInfo{
Type: dt,
Desc: desc,
Required: required,
}
}
tools = append(tools, &schema.ToolInfo{
Name: task.Name,
Desc: task.Desc,
ParamsOneOf: schema.NewParamsOneOfByParams(params),
})
}
return tools
}

1
internal/biz/provider_set.go
View File

@ -13,6 +13,7 @@ var ProviderSetBiz = wire.NewSet(
NewChatHistoryBiz,
//llm_service.NewLangChainGenerate,
llm_service.NewOllamaGenerate,
llm_service.NewVllmService,
//handle.NewHandle,
do.NewDo,
do.NewHandle,

7
internal/config/config.go
View File

@ -122,8 +122,13 @@ type OllamaConfig struct {
}
type VllmConfig struct {
VLModel VllmModel `mapstructure:"vl_model"`
TextModel VllmModel `mapstructure:"text_model"`
}
type VllmModel struct {
BaseURL string `mapstructure:"base_url"`
VlModel string `mapstructure:"vl_model"`
Model string `mapstructure:"model"`
Timeout time.Duration `mapstructure:"timeout"`
Level string `mapstructure:"level"`
}

52
internal/pkg/utils_vllm/client.go
View File

@ -7,33 +7,63 @@ import (
"encoding/base64"
"github.com/cloudwego/eino-ext/components/model/openai"
"github.com/cloudwego/eino/components/model"
"github.com/cloudwego/eino/schema"
)
type Client struct {
model *openai.ChatModel
config *config.Config
vlModel *openai.ChatModel
generateModel *openai.ChatModel
config *config.Config
}
func NewClient(config *config.Config) (*Client, func(), error) {
m, err := openai.NewChatModel(context.Background(), &openai.ChatModelConfig{
BaseURL: config.Vllm.BaseURL,
Model: config.Vllm.VlModel,
Timeout: config.Vllm.Timeout,
// 初始化视觉模型
vl, err := openai.NewChatModel(context.Background(), &openai.ChatModelConfig{
BaseURL: config.Vllm.VLModel.BaseURL,
Model: config.Vllm.VLModel.Model,
Timeout: config.Vllm.VLModel.Timeout,
})
if err != nil {
return nil, nil, err
}
c := &Client{model: m, config: config}
// 初始化生成模型
gen, err := openai.NewChatModel(context.Background(), &openai.ChatModelConfig{
BaseURL: config.Vllm.TextModel.BaseURL,
Model: config.Vllm.TextModel.Model,
Timeout: config.Vllm.TextModel.Timeout,
ExtraFields: map[string]any{
"chat_template_kwargs": map[string]any{
"enable_thinking": false,
},
},
})
if err != nil {
return nil, nil, err
}
c := &Client{
vlModel: vl,
generateModel: gen,
config: config,
}
cleanup := func() {}
return c, cleanup, nil
}
func (c *Client) Chat(ctx context.Context, msgs []*schema.Message) (*schema.Message, error) {
return c.model.Generate(ctx, msgs)
// 默认聊天使用生成模型
return c.generateModel.Generate(ctx, msgs)
}
func (c *Client) ToolSelect(ctx context.Context, msgs []*schema.Message, tools []*schema.ToolInfo) (*schema.Message, error) {
// 工具选择使用生成模型
return c.generateModel.Generate(ctx, msgs, model.WithTools(tools))
}
func (c *Client) RecognizeWithImg(ctx context.Context, systemPrompt, userPrompt string, imgURLs []string) (*schema.Message, error) {
// 图片识别使用视觉模型
in := []*schema.Message{
{
Role: schema.System,
@ -58,11 +88,12 @@ func (c *Client) RecognizeWithImg(ctx context.Context, systemPrompt, userPrompt
}
in[1].UserInputMultiContent = parts
return c.model.Generate(ctx, in)
return c.vlModel.Generate(ctx, in)
}
// 识别图片by二进制文件
func (c *Client) RecognizeWithImgBytes(ctx context.Context, systemPrompt, userPrompt string, imgBytes []byte, imgType string) (*schema.Message, error) {
// 图片识别使用视觉模型
in := []*schema.Message{
{
Role: schema.System,
@ -82,9 +113,10 @@ func (c *Client) RecognizeWithImgBytes(ctx context.Context, systemPrompt, userPr
MIMEType: imgType,
Base64Data: util.AnyToPoint(base64.StdEncoding.EncodeToString(imgBytes)),
},
Detail: schema.ImageURLDetailHigh,
},
})
in[1].UserInputMultiContent = parts
return c.model.Generate(ctx, in)
return c.vlModel.Generate(ctx, in)
}