1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
// src/providers/chatgpt.rs
use super::{ChatStreamChunk, LlmProvider, Message, MessagePart, TokenUsage, ToolCallChunk};
use anyhow::Result;
use async_openai::{
config::OpenAIConfig,
types::{
ChatCompletionRequestAssistantMessageArgs, ChatCompletionRequestMessage,
ChatCompletionRequestMessageContentPart, ChatCompletionRequestMessageContentPartImageArgs,
ChatCompletionRequestMessageContentPartTextArgs, ChatCompletionRequestSystemMessageArgs,
ChatCompletionRequestToolMessageArgs, ChatCompletionRequestUserMessageArgs,
ChatCompletionRequestUserMessageContent, ChatCompletionStreamOptions, ChatCompletionTool,
CreateChatCompletionRequestArgs, ImageUrlArgs,
},
Client,
};
use async_trait::async_trait;
use futures::{Stream, StreamExt};
use std::pin::Pin;
use std::time::Duration;
pub struct ChatGPTProvider {
client: Client<OpenAIConfig>,
}
impl ChatGPTProvider {
pub fn new() -> Self {
let raw_key = std::env::var("OPENAI_API_KEY").unwrap_or_default();
let api_key = raw_key.trim().to_string();
let api_base = std::env::var("OPENAI_API_BASE")
.unwrap_or_else(|_| "https://api.openai.com/v1".to_string());
let config = OpenAIConfig::new()
.with_api_key(api_key)
.with_api_base(api_base);
let http_client = reqwest::Client::builder()
.connect_timeout(Duration::from_secs(10))
.timeout(Duration::from_secs(300))
.build()
.unwrap_or_default();
Self {
client: Client::with_config(config).with_http_client(http_client),
}
}
fn build_user_content(
&self,
parts_json: &serde_json::Value,
) -> ChatCompletionRequestUserMessageContent {
let mut content_parts = Vec::new();
if let Ok(parts) = serde_json::from_value::<Vec<MessagePart>>(parts_json.clone()) {
for part in parts {
match part.part_type.as_str() {
"text" => {
if let Some(text) = part.content {
if !text.is_empty() {
content_parts.push(ChatCompletionRequestMessageContentPart::Text(
ChatCompletionRequestMessageContentPartTextArgs::default()
.text(text)
.build()
.unwrap(),
));
}
}
}
"image" => {
if let Some(url_val) = part.data {
if let Some(url_str) = url_val.as_str() {
content_parts.push(
ChatCompletionRequestMessageContentPart::ImageUrl(
ChatCompletionRequestMessageContentPartImageArgs::default()
.image_url(
ImageUrlArgs::default()
.url(url_str)
.build()
.unwrap(),
)
.build()
.unwrap(),
),
);
}
}
}
"kline" | "position" | "balance" | "news" | "order" | "symbolInfo" => {
if let Some(data) = part.data {
let label = match part.part_type.as_str() {
"kline" => "Market Chart Data",
"position" => "User Positions",
"balance" => "Wallet Balance",
"news" => "News Article",
_ => "Attached Data",
};
let text_block = format!(
"\n--- {} ---\n{}\n--- End {} ---\n",
label,
data.to_string(),
label
);
content_parts.push(ChatCompletionRequestMessageContentPart::Text(
ChatCompletionRequestMessageContentPartTextArgs::default()
.text(text_block)
.build()
.unwrap(),
));
}
}
"tool_result" => {
if let Some(text) = part.content {
content_parts.push(ChatCompletionRequestMessageContentPart::Text(
ChatCompletionRequestMessageContentPartTextArgs::default()
.text(text)
.build()
.unwrap(),
));
}
}
_ => {}
}
}
}
if content_parts.is_empty() {
ChatCompletionRequestUserMessageContent::Text("".to_string())
} else {
ChatCompletionRequestUserMessageContent::Array(content_parts)
}
}
}
#[async_trait]
impl LlmProvider for ChatGPTProvider {
async fn stream_chat(
&self,
model: &str,
// 【修复】添加 system_prompt 参数以匹配 trait
system_prompt: Option<String>,
history: Vec<Message>,
tools: Option<Vec<serde_json::Value>>,
) -> Result<Pin<Box<dyn Stream<Item = Result<ChatStreamChunk>> + Send>>> {
let mut request_messages: Vec<ChatCompletionRequestMessage> = Vec::new();
let history_len = history.len();
// 【修复】注入 System Prompt (如果存在)
if let Some(sp) = system_prompt {
if !sp.is_empty() {
request_messages.push(
ChatCompletionRequestSystemMessageArgs::default()
.content(sp)
.build()?
.into(),
);
}
}
for (i, msg) in history.iter().enumerate() {
match msg.role.as_str() {
// 如果数据库里偶尔还有 system 消息，可以选择保留或忽略，这里保留以兼容旧数据
"system" => {
let text = if let Ok(parts) =
serde_json::from_value::<Vec<MessagePart>>(msg.parts.clone())
{
parts
.first()
.and_then(|p| p.content.clone())
.unwrap_or_default()
} else {
String::new()
};
request_messages.push(
ChatCompletionRequestSystemMessageArgs::default()
.content(text)
.build()?
.into(),
);
}
"user" => {
let content = self.build_user_content(&msg.parts);
request_messages.push(
ChatCompletionRequestUserMessageArgs::default()
.content(content)
.build()?
.into(),
);
}
"assistant" => {
let text = if let Ok(parts) =
serde_json::from_value::<Vec<MessagePart>>(msg.parts.clone())
{
parts
.first()
.and_then(|p| p.content.clone())
.unwrap_or_default()
} else {
String::new()
};
let mut builder = ChatCompletionRequestAssistantMessageArgs::default();
let mut has_content = false;
if !text.is_empty() {
builder.content(text);
has_content = true;
}
// 检测并丢弃无效消息
let mut has_tools = false;
if let Some(tc_json) = &msg.tool_calls {
let is_next_tool = if i + 1 < history_len {
history[i + 1].role == "tool"
} else {
false
};
if is_next_tool {
if let Ok(tc_vec) = serde_json::from_value::<
Vec<async_openai::types::ChatCompletionMessageToolCall>,
>(tc_json.clone())
{
builder.tool_calls(tc_vec);
has_tools = true;
}
}
}
if has_content || has_tools {
request_messages.push(builder.build()?.into());
}
}
"tool" => {
let tool_call_id = msg
.tool_call_id
.clone()
.ok_or_else(|| anyhow::anyhow!("Tool message missing ID"))?;
let text = if let Ok(parts) =
serde_json::from_value::<Vec<MessagePart>>(msg.parts.clone())
{
parts
.first()
.and_then(|p| p.content.clone())
.unwrap_or_default()
} else {
String::new()
};
request_messages.push(
ChatCompletionRequestToolMessageArgs::default()
.content(text)
.tool_call_id(tool_call_id)
.build()?
.into(),
);
}
_ => {}
}
}
let mut request_tools: Option<Vec<ChatCompletionTool>> = None;
if let Some(t) = tools {
let mut converted_tools = Vec::new();
for tool_json in t {
if let Ok(tool) = serde_json::from_value::<ChatCompletionTool>(tool_json) {
converted_tools.push(tool);
}
}
if !converted_tools.is_empty() {
request_tools = Some(converted_tools);
}
}
let mut args = CreateChatCompletionRequestArgs::default();
args.model(model)
.messages(request_messages)
.stream(true)
.stream_options(ChatCompletionStreamOptions {
include_usage: true,
});
if let Some(t) = request_tools {
args.tools(t);
}
let request = args.build()?;
let stream = self.client.chat().create_stream(request).await?;
let mapped_stream = stream.map(|item| {
match item {
Ok(resp) => {
let choice = resp.choices.first();
let content = choice.and_then(|c| c.delta.content.clone());
let finish_reason = choice
.and_then(|c| c.finish_reason.clone())
.map(|r| format!("{:?}", r));
let mut tool_chunks = Vec::new();
if let Some(c) = choice {
if let Some(tool_calls) = &c.delta.tool_calls {
for tc in tool_calls {
tool_chunks.push(ToolCallChunk {
index: tc.index,
id: tc.id.clone(),
name: tc.function.as_ref().and_then(|f| f.name.clone()),
arguments: tc
.function
.as_ref()
.and_then(|f| f.arguments.clone()),
signature: None,
});
}
}
}
// Extract usage from final chunk (when include_usage is enabled)
let usage = resp.usage.map(|u| TokenUsage {
input_tokens: u.prompt_tokens as i64,
output_tokens: u.completion_tokens as i64,
total_tokens: u.total_tokens as i64,
});
Ok(ChatStreamChunk {
content,
tool_calls: tool_chunks,
usage,
finish_reason,
})
}
Err(e) => Err(anyhow::anyhow!("ChatGPT Provider Error: {}", e)),
}
});
Ok(Box::pin(mapped_stream))
}
}