feat: rag

openai/deepseek.py

@@ -0,0 +1,47 @@
+from openai import OpenAI
+
+# client = OpenAI(
+#     base_url="https://api.deepseek.com/v1",
+#     api_key="sk-01931083835f4a539e368b209559c52c",
+# )
+# response = client.chat.completions.create(
+#     model="deepseek-chat",
+#     messages=[
+#         {"role": "system", "content": "你是谁"},
+#     ],
+#     stream=True,
+# )
+
+# for chunk in response:
+#     if chunk.choices[0].delta.content is not None:
+#         print(chunk.choices[0].delta.content, end="", flush=True)
+
+# 调用自己写的
+from openai_client import OpenAI
+
+client = OpenAI(
+    base_url="https://api.deepseek.com",
+    api_key="sk-cc7b983a00f34cec9a12b19b64060f68",
+)
+response = client.chat.completions.create(
+    model="deepseek-chat",
+    messages=[
+        {"role": "system", "content": "西游记作者是谁"},
+    ],
+    stream=True,
+)
+# print(response.choices[0].message.content)
+for chunk in response:
+    if chunk.choices[0].delta.content is not None:
+        print(chunk.choices[0].delta.content, end="", flush=True)
+
+
+# ChatCompletion(
+# id='f8170d75-875c-4b46-bd3b-82a93d6be4c0',
+# choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='你好！我是DeepSeek，一个由深度求索公司创造的AI助手。😊\n\n我是一个纯文本模型，虽然不支持多模态识别功能，但我有文件上传功能，可以帮你处理图像、txt、pdf、ppt、word、excel等文件，并从中读取文字信息进行分析处理。我完全免费使用，拥有128K的上下文长度，还支持联网搜索功能（需要你在Web/App中手动点开联网搜索按键）。\n\n你可以通过官方应用商店下载我的App来使用我。我很乐意帮助你解答问题、处理文档、进行对话交流等等！\n\n有什么我可以帮助你的吗？无论是学习、工作还是日常问题，我都很愿意为你提供帮助！✨', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None))],
+# created=1765348625,
+# model='deepseek-chat',
+# object='chat.completion',
+# service_tier=None,
+# system_fingerprint='fp_eaab8d114b_prod0820_fp8_kvcache',
+# usage=CompletionUsage(completion_tokens=143, prompt_tokens=4, total_tokens=147, completion_tokens_details=None, prompt_tokens_details=PromptTokensDetails(audio_tokens=None, cached_tokens=0), prompt_cache_hit_tokens=0, prompt_cache_miss_tokens=4))

openai/doubao.py

@@ -0,0 +1,20 @@
+from openai import OpenAI
+from dotenv import load_dotenv 
+
+import os
+load_dotenv()
+DATABASE_URL = os.getenv("DATABASE_URL")
+API_KEY = os.getenv("API_KEY")
+
+
+client = OpenAI(
+    base_url=DATABASE_URL,
+    api_key=API_KEY,
+)
+response = client.chat.completions.create(
+    model="doubao-seed-1-6-lite-251015",
+    messages=[
+        {"role": "system", "content": "你是谁"},
+    ],
+)
+print(response)

openai/openai_client.py

@@ -0,0 +1,175 @@
+# 封装统一调用openai的客户端
+from typing import Optional, Iterator
+import os
+
+import requests
+import json
+
+
+class Message:
+    def __init__(self, data):
+        self.content = data.get("content")
+        self.role = data.get("role")
+
+
+class Choice:
+    def __init__(self, choice):
+        self.index = choice.get("index")
+        self.finish_reason = choice.get("finish_reason")
+        self.message = Message(choice.get("message", {}))
+
+
+class ChatCompletionResponse:
+    def __init__(self, data) -> None:
+        self.id = data.get("id")
+        self.object = data.get("object")
+        self.created = data.get("created")
+        self.model = data.get("model")
+        choices_data = data.get("choices", [])
+        self.choices = [Choice(choice) for choice in choices_data]
+        usage_data = data.get("usage", {})
+        self.usage = {
+            "prompt_tokens": usage_data.get("prompt_tokens"),
+            "completion_tokens": usage_data.get("completion_tokens"),
+            "total_tokens": usage_data.get("total_tokens"),
+        }
+
+
+class DeltaMessage:
+    def __init__(self, data) -> None:
+        self.content = data.get("content")
+        self.role = data.get("role")
+
+
+class DeltaChoice:
+    def __init__(self, data):
+        self.index = data.get("index")
+        self.finish_reason = data.get("finish_reason")
+        self.delta = DeltaMessage(data.get("delta", {}))
+
+
+class StreamChunk:
+    def __init__(self, data):
+        self.id = data.get("id")
+        self.object = data.get("object")
+        self.created = data.get("created")
+        self.model = data.get("model")
+        choices_data = data.get("choices", [])
+        self.choices = [DeltaChoice(choice) for choice in choices_data]
+
+
+class Stream:
+    def __init__(self, response: requests.Response):
+        self.response = response
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.response.close()
+
+    def __iter__(self) -> Iterator[StreamChunk]:
+        # 迭代器方法，逐个返回流式数据块
+        try:
+            # 逐行读取响应的内容（SSE格式）
+            for line in self.response.iter_lines(decode_unicode=True):
+                # print(line)
+                # data: {"id":"3eddf823-6ee6-4b14-a231-b0fd9dbc8087","object":"chat.completion.chunk","created":1765355109,"model":"deepseek-chat","system_fingerprint":"fp_eaab8d114b_prod0820_fp8_kvcache","choices":[{"index":0,"delta":{"content":"观点"},"logprobs":null,"finish_reason":null}]}
+                if not line.strip():
+                    continue
+                if line.startswith("data: "):
+                    json_str = line[6:]
+                    # 如果遇到DONE 结束，说明结束
+                    if json_str.strip() == "[DONE]":
+                        break
+                    try:
+                        data = json.loads(json_str)
+                        yield StreamChunk(data)
+                    except json.JSONDecodeError:
+                        continue
+        finally:
+            self.response.close()
+
+
+class ChatCompletions:
+    def __init__(self, client):
+        self._client = client
+
+    def create(
+        self,
+        model,
+        messages,
+        max_tokens=1024,
+        temperature=0.7,
+        stream: bool = False,
+        **kwargs,
+    ):
+        url = f"{self._client.base_url}/chat/completions"
+        body = {
+            "model": model,
+            "messages": messages,
+        }
+        if max_tokens is not None:
+            body["max_tokens"] = max_tokens
+        if temperature is not None:
+            body["temperature"] = temperature
+        if stream:
+            body["stream"] = True
+
+        # 将其他参数添加到body中
+        body.update(kwargs)
+        headers = {
+            "Content-Type": "application/json",
+            "Accept": "application/json",
+            "Authorization": f"Bearer {self._client.api_key}",
+        }
+        if stream:
+            response = requests.post(
+                url,
+                headers=headers,
+                json=body,
+                timeout=self._client.timeout,
+                stream=True,  # 告诉openai的服务器我要使用流式输出
+            )
+            response.raise_for_status()
+            return Stream(response)
+        else:
+            response = requests.post(
+                url, headers=headers, json=body, timeout=self._client.timeout
+            )
+            # 如果响应状态不是2xx则直接报错
+            # response.raise_for_status()是 requests库中一个非常重要的方法，用于自动检查 HTTP 响应状态码，并在状态码表示错误时抛出异常。
+            # 如果状态码是 2xx（成功）：什么都不做，继续执行
+            # 如果状态码是 4xx 或 5xx（客户端或服务器错误）：抛出异常
+            response.raise_for_status()
+            return ChatCompletionResponse(response.json())
+
+
+class ChatResource:
+    def __init__(self, client):
+        self.client = client
+
+    @property
+    def completions(self):
+        return ChatCompletions(self.client)
+
+
+class OpenAI:
+    def __init__(
+        self,
+        base_url: str = "https://api.deepseek.com/v1",
+        api_key: Optional[str] = None,
+        timeout: float = 60.0,
+    ):
+        self.api_key = api_key or os.getenv("OPENAI_API_KEY")
+        if not self.api_key:
+            raise ValueError(
+                f"API秘钥未设置，请设置api_key参数或设置环境变量OPENAI_API_KEY"
+            )
+        self.base_url = base_url.rstrip("/")
+        self.timeout = timeout
+
+    # 可以使用属性.的方式使用方法
+    @property
+    def chat(self):
+        return ChatResource(self)

openai/openai_client_a.py

@@ -0,0 +1,136 @@
+
+from typing import Optional,Iterator
+import os
+import requests
+import json
+class Message:
+    def __init__(self,data):
+        self.role = data.get('role'),
+        self.content = data.get('content')
+class Choice:
+    def __init__(self,data):
+        self.index = data.get('index')
+        self.message = Message(data.get('message',{}))
+        self.finish_reason = data.get('finish_reason')
+class ChatCompletionResponse:
+    def __init__(self,data):
+        self.id = data.get('id')
+        self.object = data.get('object')
+        self.created = data.get('created')
+        self.model = data.get('model')
+        choices_data = data.get('choices',[])
+        self.choices = [
+            Choice(choice_data) for choice_data in choices_data
+        ]
+        usage_data = data.get('usage',{})
+        self.usage = {
+            "prompt_tokens":usage_data.get("prompt_tokens"),
+            "completion_tokens":usage_data.get("completion_tokens"),
+            "total_tokens":usage_data.get("total_tokens"),
+        }
+class DeltaMessage:
+    def __init__(self,data):
+        self.content = data.get('content')  
+        self.role = data.get('role')        
+class DeltaChoice:
+    def __init__(self,data):
+        self.index = data.get('index')  
+        self.delta = DeltaMessage(data.get('delta',{}) ) 
+        self.finish_reason = data.get('finish_reason')       
+#流式响应数据块，表示流式响应中的一个数据块        
+class StreamChunk:
+    def __init__(self,data):
+        self.id = data.get('id')
+        self.object = data.get('object')
+        self.created = data.get('created')
+        self.model = data.get('model')
+        choices_data = data.get('choices',[])
+        self.choices = [DeltaChoice(choice_data) for choice_data in choices_data]
+
+class Stream:
+    def __init__(self,response:requests.Response):
+        self.response=response
+    def __enter__(self):
+        return self
+    def __exit__(self,exc_type,exc_val,exc_tb):
+        self.response.close()
+    def __iter__(self)->Iterator[StreamChunk]:
+        #迭代器方法，逐个返回流式数据块
+        try:
+            # 逐行读取响应的内容(SSE格式)
+            for line in self.response.iter_lines(decode_unicode=True):
+                #print(line)
+                if not line.strip():
+                    continue
+                if line.startswith('data: '):
+                    json_str = line[6:]
+                    # 如果遇到[DONE]说明流式输出结束
+                    if json_str.strip()=="[DONE]":
+                        break
+                    try:
+                        data = json.loads(json_str)
+                        yield StreamChunk(data)
+                    except json.JSONDecodeError:
+                        continue    
+
+        finally:
+            self.response.close()    
+
+class ChatCompletions:
+    def __init__(self,client):
+        self._client = client
+    def create(self,model,messages,max_tokens=1024,temperature=0.7,stream:bool=False,**kwargs):
+        url = f"{self._client.base_url}/chat/completions"
+        body = {
+            "model":model,
+            "messages":messages
+        }
+        if max_tokens is not None:
+            body["max_tokens"]=max_tokens
+        if temperature is not None:
+            body["temperature"]=temperature  
+        if stream:
+            body["stream"]=True    
+        #添加额外的参数到请求体中
+        body.update(kwargs)   
+        headers = {
+            "Authorization":f"Bearer {self._client.api_key}",
+            "Content-Type":"application/json"
+        }   
+        if stream:
+            response = requests.post(
+                url,
+                headers=headers,
+                json=body,
+                timeout=self._client.timeout,
+                stream=True#告诉openai的服务器我要使用流式输出
+            )
+            response.raise_for_status()
+            return Stream(response)
+        else:
+            response = requests.post(
+                url,
+                headers=headers,
+                json=body,
+                timeout=self._client.timeout
+            )
+            # 如果响应的状态不是2XX的话，主抛异常
+            response.raise_for_status()
+            return ChatCompletionResponse(response.json())
+
+class ChatResource:
+    def __init__(self,client):
+        self._client = client
+    @property
+    def completions(self)->ChatCompletions:
+        return ChatCompletions(self._client)
+class OpenAI:
+    def __init__(self,api_key:Optional[str]=None,base_url:str="https://api.openai.com/v1",timeout:float=60.0):
+        self.api_key=api_key or os.getenv("OPENAI_API_KEY")
+        if not self.api_key:
+            raise ValueError(f"API密钥未设置，请设置api_key参数或者环境变量OPENAI_API_KEY")
+        self.base_url = base_url.rstrip('/')
+        self.timeout = timeout
+    @property    
+    def chat(self)->ChatResource:
+        return ChatResource(self)