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TradingAgents
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This commit is contained in:
MarkLo 2025-12-20 07:41:05 +08:00
parent f837d4a2d0
commit c4fc8904eb
11 changed files with 385 additions and 225 deletions
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3
backend/app/api/routes.py
View File

@ -104,6 +104,7 @@ async def run_analysis(
deep_think_api_key=request.deep_think_api_key or "",
embedding_base_url=request.embedding_base_url,
embedding_api_key=request.embedding_api_key or "",
embedding_model=request.embedding_model or "all-MiniLM-L6-v2",
alpha_vantage_api_key=request.alpha_vantage_api_key or "",
finmind_api_key=request.finmind_api_key or "",
))
@ -166,7 +167,7 @@ async def cleanup_task(task_id: str):
after the user has saved the results locally or to cloud storage.
This helps keep Redis storage clean and reduces memory usage.
Note: Tasks are also automatically cleaned up 10 minutes after
Note: Tasks are also automatically cleaned up 1 hour after
completion/failure, so calling this endpoint is optional but recommended.
Args:

8
backend/app/models/schemas.py
View File

@ -46,9 +46,13 @@ class AnalysisRequest(BaseModel):
deep_think_api_key: Optional[str] = Field(None, description="API Key for Deep Thinking Model", min_length=0)
embedding_base_url: Optional[str] = Field(
default="https://api.openai.com/v1",
description="Base URL for Embedding Model"
description="Base URL for Embedding Model (only used for OpenAI embeddings)"
)
embedding_api_key: Optional[str] = Field(None, description="API Key for Embedding Model (only used for OpenAI embeddings)", min_length=0)
embedding_model: Optional[str] = Field(
default="all-MiniLM-L6-v2",
description="Embedding model: 'all-MiniLM-L6-v2' (local, no API key), 'text-embedding-3-small' (OpenAI), etc."
)
embedding_api_key: Optional[str] = Field(None, description="API Key for Embedding Model", min_length=0)
alpha_vantage_api_key: Optional[str] = Field(
None,
description="Alpha Vantage API Key (optional, for US stock fundamental data)",

2
backend/app/services/task_manager.py
View File

@ -40,7 +40,7 @@ class HybridTaskManager:
self._lock = threading.RLock()
self._cleanup_interval = 3600 # 1 hour
self._task_expiry = 86400 # 24 hours for pending/running tasks
self._completed_task_expiry = 600 # 10 minutes for completed/failed tasks (auto cleanup)
self._completed_task_expiry = 3600 # 1 hour for completed/failed tasks (auto cleanup)
# Check Redis availability on startup
if is_redis_available():

20
backend/app/services/trading_service.py
View File

@ -50,6 +50,7 @@ class TradingService:
deep_think_api_key: Optional[str] = None,
embedding_base_url: str = "https://api.openai.com/v1",
embedding_api_key: Optional[str] = None,
embedding_model: str = "all-MiniLM-L6-v2", # Default to local model
alpha_vantage_api_key: Optional[str] = None,
finmind_api_key: Optional[str] = None, # 台灣股市資料 API
market_type: str = "us", # 市場類型：us (美股) 或 tw (台股)
@ -132,8 +133,23 @@ class TradingService:
# Note: For non-OpenAI providers, the user MUST provide the specific key if it differs from the shared one.
config["quick_think_api_key"] = quick_think_api_key if quick_think_api_key else openai_api_key
config["deep_think_api_key"] = deep_think_api_key if deep_think_api_key else openai_api_key
config["embedding_base_url"] = normalize_base_url(embedding_base_url)
config["embedding_api_key"] = embedding_api_key if embedding_api_key else openai_api_key
# Embedding configuration: determine provider based on model name
local_embedding_models = ["all-MiniLM-L6-v2", "all-mpnet-base-v2"]
is_local_embedding = embedding_model in local_embedding_models
if is_local_embedding:
# Local embedding: use sentence-transformers (no API key needed)
config["embedding_provider"] = "local"
config["embedding_model"] = embedding_model
logger.info(f"Using local embedding model: {embedding_model}")
else:
# OpenAI embedding: requires API key
config["embedding_provider"] = "openai"
config["embedding_model"] = embedding_model
config["embedding_base_url"] = normalize_base_url(embedding_base_url)
config["embedding_api_key"] = embedding_api_key if embedding_api_key else openai_api_key
logger.info(f"Using OpenAI embedding model: {embedding_model}")
# 根據 market_type 設定資料供應商
if market_type in ["twse", "tpex"]:

1
backend/requirements.txt
View File

@ -38,6 +38,7 @@ stockstats
eodhd
langgraph
chromadb
sentence-transformers
setuptools
backtrader
akshare

93
frontend/components/analysis/AnalysisForm.tsx
View File

@ -52,6 +52,7 @@ const formSchema = z.object({
research_depth: z.number().int().min(1).max(5),
quick_think_llm: z.string().min(1, "請選擇快速思維模型"),
deep_think_llm: z.string().min(1, "請選擇深層思維模型"),
embedding_model: z.string().min(1, "請選擇嵌入式模型"),
// Market type selection: us=美股, twse=上市, tpex=上櫃/興櫃
market_type: z.enum(["us", "twse", "tpex"]),
@ -71,14 +72,14 @@ const formSchema = z.object({
.url("請輸入有效的 URL")
.optional()
.or(z.literal("")),
quick_think_api_key: z.string().min(1, "請輸入快速思維模型 API Key"),
deep_think_api_key: z.string().min(1, "請輸入深層思維模型 API Key"),
quick_think_api_key: z.string().optional().or(z.literal("")),
deep_think_api_key: z.string().optional().or(z.literal("")),
embedding_base_url: z
.string()
.url("請輸入有效的 URL")
.optional()
.or(z.literal("")),
embedding_api_key: z.string().min(1, "請輸入嵌入模型 API Key"),
embedding_api_key: z.string().optional().or(z.literal("")), // 本地模型不需要 API Key
alpha_vantage_api_key: z.string().optional().or(z.literal("")), // 選填
finmind_api_key: z.string().optional().or(z.literal("")), // 選填
});
@ -106,6 +107,7 @@ export function AnalysisForm({ onSubmit, loading = false }: AnalysisFormProps) {
market_type: "us", // 預設美股
quick_think_llm: "gpt-5-mini",
deep_think_llm: "gpt-5-mini",
embedding_model: "all-MiniLM-L6-v2", // 預設使用本地開源模型
custom_quick_think_model: "",
custom_deep_think_model: "",
quick_think_base_url: "https://api.openai.com/v1",
@ -122,9 +124,11 @@ export function AnalysisForm({ onSubmit, loading = false }: AnalysisFormProps) {
// Load API settings from localStorage and update when models change
const quickThinkLlm = form.watch("quick_think_llm");
const deepThinkLlm = form.watch("deep_think_llm");
const embeddingModel = form.watch("embedding_model");
const marketType = form.watch("market_type");
const isQuickThinkCustom = quickThinkLlm === "custom";
const isDeepThinkCustom = deepThinkLlm === "custom";
const isLocalEmbedding = ["all-MiniLM-L6-v2", "all-mpnet-base-v2"].includes(embeddingModel);
useEffect(() => {
// Use async version to get decrypted API keys
@ -169,14 +173,20 @@ export function AnalysisForm({ onSubmit, loading = false }: AnalysisFormProps) {
);
}
form.setValue(
"embedding_base_url",
savedSettings.custom_base_url || "https://api.openai.com/v1"
);
form.setValue(
"embedding_api_key",
savedSettings.custom_api_key || savedSettings.openai_api_key
);
// 本地模型不需要設定 API Key 和 Base URL
if (!isLocalEmbedding) {
form.setValue(
"embedding_base_url",
savedSettings.custom_base_url || "https://api.openai.com/v1"
);
form.setValue(
"embedding_api_key",
savedSettings.custom_api_key || savedSettings.openai_api_key
);
} else {
form.setValue("embedding_base_url", "");
form.setValue("embedding_api_key", "");
}
form.setValue(
"alpha_vantage_api_key",
savedSettings.alpha_vantage_api_key || ""
@ -186,7 +196,7 @@ export function AnalysisForm({ onSubmit, loading = false }: AnalysisFormProps) {
loadSettings();
// eslint-disable-next-line react-hooks/exhaustive-deps
}, [quickThinkLlm, deepThinkLlm, isQuickThinkCustom, isDeepThinkCustom]);
}, [quickThinkLlm, deepThinkLlm, embeddingModel, isQuickThinkCustom, isDeepThinkCustom, isLocalEmbedding]);
// 當市場類型改變時，更新預設股票代碼和提示
useEffect(() => {
@ -253,6 +263,17 @@ export function AnalysisForm({ onSubmit, loading = false }: AnalysisFormProps) {
return;
}
// Validate API keys are set (they come from localStorage/settings)
if (!values.quick_think_api_key) {
alert("請先在右上角「設定」中設定您的 API Key。\n\n快速思維模型需要對應的 API Key 才能運作。");
return;
}
if (!values.deep_think_api_key) {
alert("請先在右上角「設定」中設定您的 API Key。\n\n深層思維模型需要對應的 API Key 才能運作。");
return;
}
const request: AnalysisRequest = {
...values,
quick_think_llm: finalQuickThinkLlm,
@ -444,8 +465,8 @@ export function AnalysisForm({ onSubmit, loading = false }: AnalysisFormProps) {
/>
</div>
{/* 第二行：研究深度、快速思維模型、深層思維模型（3列） */}
<div className="md:col-span-2 grid grid-cols-1 md:grid-cols-3 gap-6">
{/* 第二行：研究深度、快速思維模型、深層思維模型、嵌入式模型（4列） */}
<div className="md:col-span-2 grid grid-cols-1 md:grid-cols-4 gap-6">
<FormField
control={form.control}
name="research_depth"
@ -764,6 +785,50 @@ export function AnalysisForm({ onSubmit, loading = false }: AnalysisFormProps) {
)}
/>
)}
{/* 嵌入式模型 */}
<FormField
control={form.control}
name="embedding_model"
render={({ field }) => (
<FormItem>
<FormLabel>嵌入式模型</FormLabel>
<Select
onValueChange={field.onChange}
defaultValue={field.value}
>
<FormControl>
<SelectTrigger>
<SelectValue placeholder="選擇嵌入式模型" />
</SelectTrigger>
</FormControl>
<SelectContent>
{/* 本地開源模型 (不需要 API Key) */}
<SelectItem value="all-MiniLM-L6-v2">
🖥️ 本地: all-MiniLM-L6-v2 (推薦)
</SelectItem>
<SelectItem value="all-mpnet-base-v2">
🖥️ 本地: all-mpnet-base-v2
</SelectItem>
{/* OpenAI API 模型 (需要 API Key) */}
<SelectItem value="text-embedding-3-small">
☁️ OpenAI: text-embedding-3-small
</SelectItem>
<SelectItem value="text-embedding-3-large">
☁️ OpenAI: text-embedding-3-large
</SelectItem>
</SelectContent>
</Select>
<FormDescription>
{isLocalEmbedding
? "🆓 本地模型不需 API Key"
: "☁️ 需要 OpenAI API Key"}
</FormDescription>
<FormMessage />
</FormItem>
)}
/>
</div>
</div>

56
frontend/components/settings/ApiSettingsDialog.tsx
View File

@ -40,12 +40,14 @@ import { useAuth } from "@/contexts/auth-context";
import { getCloudSettings, saveCloudSettings, isCloudSyncEnabled } from "@/lib/user-api";
const formSchema = z.object({
// Required
openai_api_key: z.string().min(1, "OpenAI API Key 為必填"),
// All API keys are optional - users only need the ones for their selected models
openai_api_key: z.string().optional().or(z.literal("")),
// Optional
// Stock market data APIs
alpha_vantage_api_key: z.string().optional().or(z.literal("")), // 美股基本面資料
finmind_api_key: z.string().optional().or(z.literal("")), // 台灣股市資料
// LLM Providers
anthropic_api_key: z.string().optional().or(z.literal("")),
google_api_key: z.string().optional().or(z.literal("")),
grok_api_key: z.string().optional().or(z.literal("")),
@ -165,27 +167,11 @@ export function ApiSettingsDialog() {
<Form {...form}>
<form onSubmit={form.handleSubmit(onSubmit)} className="space-y-4">
{/* Required Section */}
<div className="space-y-4">
<h3 className="text-lg font-semibold text-primary">必填項目</h3>
{/* OpenAI API Key */}
<FormField
control={form.control}
name="openai_api_key"
render={({ field }) => (
<FormItem>
<FormLabel>OpenAI API Key *</FormLabel>
<FormControl>
<Input type="password" placeholder="sk-..." {...field} />
</FormControl>
<FormDescription>
用於 OpenAI 模型（GPT-4, GPT-5, o4 等）
</FormDescription>
<FormMessage />
</FormItem>
)}
/>
{/* 注意事項 */}
<div className="space-y-2">
<div className="bg-blue-50 dark:bg-blue-900/20 border border-blue-200 dark:border-blue-800 rounded-lg p-3 text-blue-800 dark:text-blue-300 text-sm">
💡 僅需填寫您選擇的模型供應商的 API。例如，若使用 Claude 模型，只需填寫 Claude API。
</div>
</div>
{/* Stock Market Data APIs Section */}
@ -239,13 +225,29 @@ export function ApiSettingsDialog() {
/>
</div>
{/* Optional LLM Providers Section */}
{/* LLM Providers Section */}
<div className="space-y-4 border-t pt-4">
<h3 className="text-lg font-semibold text-muted-foreground">
選填項目（其他 LLM 供應商）
LLM 模型供應商（依選擇的模型填寫）
</h3>
{/* Anthropic API Key */}
{/* OpenAI API Key */}
<FormField
control={form.control}
name="openai_api_key"
render={({ field }) => (
<FormItem>
<FormLabel>OpenAI API Key</FormLabel>
<FormControl>
<Input type="password" placeholder="sk-..." {...field} />
</FormControl>
<FormDescription>
用於 OpenAI 模型（GPT-4, GPT-5, o4 等）及 OpenAI 嵌入式模型
</FormDescription>
<FormMessage />
</FormItem>
)}
/>
<FormField
control={form.control}
name="anthropic_api_key"

1
frontend/lib/types.ts
View File

@ -20,6 +20,7 @@ export interface AnalysisRequest {
deep_think_api_key?: string;
embedding_base_url?: string;
embedding_api_key?: string;
embedding_model?: string; // Embedding model: 'all-MiniLM-L6-v2' (local), 'text-embedding-3-small' (OpenAI), etc.
alpha_vantage_api_key?: string;
finmind_api_key?: string; // 台灣股市資料 API
}

330
frontend/pnpm-lock.yaml
View File

@ -64,7 +64,7 @@ importers:
version: 19.2.0
react-day-picker:
specifier: ^9.11.3
version: 9.12.0(react@19.2.0)
version: 9.13.0(react@19.2.0)
react-dom:
specifier: 19.2.0
version: 19.2.0(react@19.2.0)
@ -76,7 +76,7 @@ importers:
version: 10.1.0(@types/react@19.2.7)(react@19.2.0)
recharts:
specifier: ^3.4.1
version: 3.5.1(@types/react@19.2.7)(react-dom@19.2.0(react@19.2.0))(react-is@16.13.1)(react@19.2.0)(redux@5.0.1)
version: 3.6.0(@types/react@19.2.7)(react-dom@19.2.0(react@19.2.0))(react-is@16.13.1)(react@19.2.0)(redux@5.0.1)
rehype-raw:
specifier: ^7.0.0
version: 7.0.0
@ -95,7 +95,7 @@ importers:
version: 4.1.18
'@types/node':
specifier: ^20
version: 20.19.26
version: 20.19.27
'@types/react':
specifier: ^19
version: 19.2.7
@ -107,13 +107,13 @@ importers:
version: 1.0.0
baseline-browser-mapping:
specifier: ^2.9.2
version: 2.9.6
version: 2.9.11
eslint:
specifier: ^9
version: 9.39.1(jiti@2.6.1)
version: 9.39.2(jiti@2.6.1)
eslint-config-next:
specifier: 16.0.3
version: 16.0.3(@typescript-eslint/parser@8.49.0(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3))(eslint@9.39.1(jiti@2.6.1))(typescript@5.9.3)
version: 16.0.3(@typescript-eslint/parser@8.50.0(eslint@9.39.2(jiti@2.6.1))(typescript@5.9.3))(eslint@9.39.2(jiti@2.6.1))(typescript@5.9.3)
tailwindcss:
specifier: ^4
version: 4.1.18
@ -235,8 +235,8 @@ packages:
resolution: {integrity: sha512-Kr+LPIUVKz2qkx1HAMH8q1q6azbqBAsXJUxBl/ODDuVPX45Z9DfwB8tPjTi6nNZ8BuM3nbJxC5zCAg5elnBUTQ==}
engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
'@eslint/js@9.39.1':
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engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
'@eslint/object-schema@2.1.7':
@ -900,8 +900,8 @@ packages:
'@radix-ui/rect@1.1.1':
resolution: {integrity: sha512-HPwpGIzkl28mWyZqG52jiqDJ12waP11Pa1lGoiyUkIEuMLBP0oeK/C89esbXrxsky5we7dfd8U58nm0SgAWpVw==}
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peerDependencies:
react: ^16.9.0 || ^17.0.0 || ^18 || ^19
react-redux: ^7.2.1 || ^8.1.3 || ^9.0.0
@ -914,8 +914,8 @@ packages:
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resolution: {integrity: sha512-zt6OdqaDoOnJ1ZYsCYGt9YmWzDXl4vQdKTyJev62gFhRGKdx7mcT54V9KIjg+d2wi9EXsPvAPKe7i7WjfVWB8g==}
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'@standard-schema/spec@1.1.0':
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@ -1065,8 +1065,8 @@ packages:
'@types/ms@2.1.0':
resolution: {integrity: sha512-GsCCIZDE/p3i96vtEqx+7dBUGXrc7zeSK3wwPHIaRThS+9OhWIXRqzs4d6k1SVU8g91DrNRWxWUGhp5KXQb2VA==}
'@types/node@20.19.26':
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'@types/node@20.19.27':
resolution: {integrity: sha512-N2clP5pJhB2YnZJ3PIHFk5RkygRX5WO/5f0WC08tp0wd+sv0rsJk3MqWn3CbNmT2J505a5336jaQj4ph1AdMug==}
'@types/react-dom@19.2.3':
resolution: {integrity: sha512-jp2L/eY6fn+KgVVQAOqYItbF0VY/YApe5Mz2F0aykSO8gx31bYCZyvSeYxCHKvzHG5eZjc+zyaS5BrBWya2+kQ==}
@ -1085,63 +1085,63 @@ packages:
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resolution: {integrity: sha512-zFDAD+tlpf2r4asuHEj0XH6pY6i0g5NeAHPn+15wk3BV6JA69eERFXC1gyGThDkVa1zCyKr5jox1+2LbV/AMLg==}
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engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
peerDependencies:
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'@typescript-eslint/parser': ^8.50.0
eslint: ^8.57.0 || ^9.0.0
typescript: '>=4.8.4 <6.0.0'
'@typescript-eslint/parser@8.49.0':
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engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
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@ -4134,7 +4134,7 @@ snapshots:
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@ -4144,7 +4144,7 @@ snapshots:
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@ -4163,21 +4163,21 @@ snapshots:
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@ -4238,11 +4238,11 @@ snapshots:
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@ -4263,7 +4263,7 @@ snapshots:
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caniuse-lite@1.0.30001760: {}
caniuse-lite@1.0.30001761: {}
ccount@2.0.1: {}
@ -4437,7 +4437,7 @@ snapshots:
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es-abstract@1.24.0:
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@ -4498,12 +4498,12 @@ snapshots:
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@ -4538,7 +4538,7 @@ snapshots:
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@ -4546,18 +4546,18 @@ snapshots:
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@ -4609,9 +4609,9 @@ snapshots:
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@ -4623,13 +4623,13 @@ snapshots:
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@ -4639,7 +4639,7 @@ snapshots:
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@ -4648,26 +4648,26 @@ snapshots:
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@ -4690,15 +4690,15 @@ snapshots:
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@ -5652,7 +5652,7 @@ snapshots:
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react-dom: 19.2.0(react@19.2.0)
@ -5700,14 +5700,14 @@ snapshots:
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@ -5799,7 +5799,7 @@ snapshots:
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@ -5873,12 +5873,12 @@ snapshots:
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@ -5903,7 +5903,7 @@ snapshots:
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@ -6111,14 +6111,14 @@ snapshots:
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@ -6132,7 +6132,7 @@ snapshots:
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@ -6140,7 +6140,7 @@ snapshots:
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@ -6260,13 +6260,13 @@ snapshots:
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@ -6339,7 +6339,7 @@ snapshots:
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escalade: 3.2.0

1
requirements.txt
View File

@ -10,6 +10,7 @@ stockstats
eodhd
langgraph
chromadb
sentence-transformers
setuptools
backtrader
akshare

95
tradingagents/agents/utils/memory.py
View File

@ -1,12 +1,59 @@
import os
import chromadb
from chromadb.config import Settings
from openai import OpenAI
class FinancialSituationMemory:
def __init__(self, name, config):
self.embedding = "text-embedding-3-small"
"""
Initialize the memory with configurable embedding provider.
Config options:
embedding_provider: "local" (default) or "openai"
embedding_model: Model name for the embedding provider
- For local: "all-MiniLM-L6-v2" (default), "all-mpnet-base-v2", etc.
- For OpenAI: "text-embedding-3-small" (default), "text-embedding-3-large", etc.
embedding_base_url: Base URL for OpenAI-compatible API (only used when provider is "openai")
embedding_api_key: API key for OpenAI (only used when provider is "openai")
"""
self.provider = config.get("embedding_provider", "local").lower()
if self.provider == "local":
self._init_local_embedding(config)
elif self.provider == "openai":
self._init_openai_embedding(config)
else:
raise ValueError(f"Unsupported embedding provider: {self.provider}. Use 'local' or 'openai'.")
self.chroma_client = chromadb.Client(Settings(allow_reset=True))
self.situation_collection = self.chroma_client.get_or_create_collection(name=name)
def _init_local_embedding(self, config):
"""Initialize local embedding using sentence-transformers."""
try:
from sentence_transformers import SentenceTransformer
except ImportError:
raise ImportError(
"sentence-transformers is required for local embeddings. "
"Install it with: pip install sentence-transformers"
)
# Default to all-MiniLM-L6-v2 - a lightweight and efficient model
model_name = config.get("embedding_model", "all-MiniLM-L6-v2")
import logging
logging.info(f"Loading local embedding model: {model_name}")
self.model = SentenceTransformer(model_name)
self.embedding_dim = self.model.get_sentence_embedding_dimension()
logging.info(f"Local embedding model loaded. Dimension: {self.embedding_dim}")
def _init_openai_embedding(self, config):
"""Initialize OpenAI embedding client."""
from openai import OpenAI
self.embedding_model = config.get("embedding_model", "text-embedding-3-small")
# Get embedding configuration from config, with fallbacks
embedding_base_url = config.get("embedding_base_url", "https://api.openai.com/v1")
@ -23,23 +70,37 @@ class FinancialSituationMemory:
import logging
logging.warning("Using LLM API key for embeddings. Consider setting embedding_api_key or OPENAI_API_KEY.")
if not embedding_api_key:
raise ValueError(
"OpenAI API key is required for OpenAI embeddings. "
"Set 'embedding_api_key' in config or OPENAI_API_KEY environment variable, "
"or use 'embedding_provider': 'local' for local embeddings without API key."
)
# Use configured endpoint for embeddings
self.client = OpenAI(base_url=embedding_base_url, api_key=embedding_api_key)
self.chroma_client = chromadb.Client(Settings(allow_reset=True))
self.situation_collection = self.chroma_client.get_or_create_collection(name=name)
def get_embedding(self, text):
"""Get OpenAI embedding for a text"""
"""Get embedding for a text using the configured provider."""
# Truncate text to avoid exceeding embedding model's token limit
# text-embedding-3-small has 8192 token limit
# For mixed Chinese/English text, estimate ~1.5-2 tokens per character
# Target: ~4000 characters to stay well under 8192 tokens
max_chars = 4000
if len(text) > max_chars:
text = text[:max_chars]
if self.provider == "local":
return self._get_local_embedding(text)
else:
return self._get_openai_embedding(text)
def _get_local_embedding(self, text):
"""Get embedding using local sentence-transformers model."""
embedding = self.model.encode(text, normalize_embeddings=True)
return embedding.tolist()
def _get_openai_embedding(self, text):
"""Get embedding using OpenAI API."""
response = self.client.embeddings.create(
model=self.embedding, input=text
model=self.embedding_model, input=text
)
return response.data[0].embedding
@ -67,7 +128,7 @@ class FinancialSituationMemory:
)
def get_memories(self, current_situation, n_matches=1):
"""Find matching recommendations using OpenAI embeddings"""
"""Find matching recommendations using embeddings"""
query_embedding = self.get_embedding(current_situation)
results = self.situation_collection.query(
@ -90,8 +151,13 @@ class FinancialSituationMemory:
if __name__ == "__main__":
# Example usage
matcher = FinancialSituationMemory()
# Example usage with local embedding (no API key required!)
config = {
"embedding_provider": "local", # Use local model, no API key needed
"embedding_model": "all-MiniLM-L6-v2", # Lightweight and efficient
}
matcher = FinancialSituationMemory("test_memory", config)
# Example data
example_data = [
@ -114,7 +180,9 @@ if __name__ == "__main__":
]
# Add the example situations and recommendations
print("Adding example situations...")
matcher.add_situations(example_data)
print("Done!")
# Example query
current_situation = """
@ -123,6 +191,7 @@ if __name__ == "__main__":
"""
try:
print("\nSearching for recommendations...")
recommendations = matcher.get_memories(current_situation, n_matches=2)
for i, rec in enumerate(recommendations, 1):
@ -132,4 +201,4 @@ if __name__ == "__main__":
print(f"Recommendation: {rec['recommendation']}")
except Exception as e:
print(f"Error during recommendation: {str(e)}")
print(f"Error during recommendation: {str(e)}")