💎 [Feature] Enable gpt-3.5 in chat_api

networks/openai_streamer.py

@@ -0,0 +1,219 @@
+import copy
+import json
+import re
+import tiktoken
+import uuid
+
+from curl_cffi import requests
+from tclogger import logger
+
+from constants.envs import PROXIES
+from constants.headers import OPENAI_GET_HEADERS, OPENAI_POST_DATA
+from constants.models import TOKEN_LIMIT_MAP, TOKEN_RESERVED
+
+from messagers.message_outputer import OpenaiStreamOutputer
+
+
+class OpenaiRequester:
+    def __init__(self):
+        self.init_requests_params()
+
+    def init_requests_params(self):
+        self.api_base = "https://chat.openai.com/backend-anon"
+        self.api_me = f"{self.api_base}/me"
+        self.api_models = f"{self.api_base}/models"
+        self.api_chat_requirements = f"{self.api_base}/sentinel/chat-requirements"
+        self.api_conversation = f"{self.api_base}/conversation"
+        self.uuid = str(uuid.uuid4())
+        self.requests_headers = copy.deepcopy(OPENAI_GET_HEADERS)
+        extra_headers = {
+            "Oai-Device-Id": self.uuid,
+        }
+        self.requests_headers.update(extra_headers)
+
+    def log_request(self, url, method="GET"):
+        logger.note(f"> {method}:", end=" ")
+        logger.mesg(f"{url}", end=" ")
+
+    def log_response(self, res: requests.Response, stream=False, verbose=False):
+        status_code = res.status_code
+        status_code_str = f"[{status_code}]"
+
+        if status_code == 200:
+            logger_func = logger.success
+        else:
+            logger_func = logger.warn
+
+        logger_func(status_code_str)
+
+        if verbose:
+            if stream:
+                if not hasattr(self, "content_offset"):
+                    self.content_offset = 0
+
+                for line in res.iter_lines():
+                    line = line.decode("utf-8")
+                    line = re.sub(r"^data:\s*", "", line)
+                    if re.match(r"^\[DONE\]", line):
+                        logger.success("\n[Finished]")
+                        break
+                    line = line.strip()
+                    if line:
+                        try:
+                            data = json.loads(line, strict=False)
+                            message_role = data["message"]["author"]["role"]
+                            message_status = data["message"]["status"]
+                            if (
+                                message_role == "assistant"
+                                and message_status == "in_progress"
+                            ):
+                                content = data["message"]["content"]["parts"][0]
+                                delta_content = content[self.content_offset :]
+                                self.content_offset = len(content)
+                                logger_func(delta_content, end="")
+                        except Exception as e:
+                            logger.warn(e)
+            else:
+                logger_func(res.json())
+
+    def get_models(self):
+        self.log_request(self.api_models)
+        res = requests.get(
+            self.api_models,
+            headers=self.requests_headers,
+            proxies=PROXIES,
+            timeout=10,
+            impersonate="chrome120",
+        )
+        self.log_response(res)
+
+    def auth(self):
+        self.log_request(self.api_chat_requirements, method="POST")
+        res = requests.post(
+            self.api_chat_requirements,
+            headers=self.requests_headers,
+            proxies=PROXIES,
+            timeout=10,
+            impersonate="chrome120",
+        )
+        self.chat_requirements_token = res.json()["token"]
+        self.log_response(res)
+
+    def transform_messages(self, messages: list[dict]):
+        def get_role(role):
+            if role in ["system", "user", "assistant"]:
+                return role
+            else:
+                return "system"
+
+        new_messages = [
+            {
+                "author": {"role": get_role(message["role"])},
+                "content": {"content_type": "text", "parts": [message["content"]]},
+                "metadata": {},
+            }
+            for message in messages
+        ]
+        return new_messages
+
+    def chat_completions(self, messages: list[dict], verbose=False):
+        extra_headers = {
+            "Accept": "text/event-stream",
+            "Openai-Sentinel-Chat-Requirements-Token": self.chat_requirements_token,
+        }
+        requests_headers = copy.deepcopy(self.requests_headers)
+        requests_headers.update(extra_headers)
+
+        post_data = copy.deepcopy(OPENAI_POST_DATA)
+        extra_data = {
+            "messages": self.transform_messages(messages),
+            "websocket_request_id": str(uuid.uuid4()),
+        }
+        post_data.update(extra_data)
+
+        self.log_request(self.api_conversation, method="POST")
+        s = requests.Session()
+        res = s.post(
+            self.api_conversation,
+            headers=requests_headers,
+            json=post_data,
+            proxies=PROXIES,
+            timeout=10,
+            impersonate="chrome120",
+            stream=True,
+        )
+        if verbose:
+            self.log_response(res, stream=True, verbose=True)
+        return res
+
+
+class OpenaiStreamer:
+    def __init__(self):
+        self.model = "gpt-3.5"
+        self.message_outputer = OpenaiStreamOutputer(owned_by="openai", model="gpt-3.5")
+        self.tokenizer = tiktoken.get_encoding("cl100k_base")
+
+    def count_tokens(self, messages: list[dict]):
+        token_count = sum(
+            len(self.tokenizer.encode(message["content"])) for message in messages
+        )
+        logger.note(f"Prompt Token Count: {token_count}")
+        return token_count
+
+    def check_token_limit(self, messages: list[dict]):
+        token_limit = TOKEN_LIMIT_MAP[self.model]
+        token_redundancy = int(
+            token_limit - TOKEN_RESERVED - self.count_tokens(messages)
+        )
+        if token_redundancy <= 0:
+            raise ValueError(f"Prompt exceeded token limit: {token_limit}")
+        return True
+
+    def chat_response(self, messages: list[dict]):
+        self.check_token_limit(messages)
+        requester = OpenaiRequester()
+        requester.auth()
+        return requester.chat_completions(messages, verbose=False)
+
+    def chat_return_generator(self, stream_response: requests.Response):
+        content_offset = 0
+        is_finished = False
+
+        for line in stream_response.iter_lines():
+            line = line.decode("utf-8")
+            line = re.sub(r"^data:\s*", "", line)
+            line = line.strip()
+
+            if not line:
+                continue
+
+            if re.match(r"^\[DONE\]", line):
+                content_type = "Finished"
+                delta_content = ""
+                logger.success("\n[Finished]")
+                is_finished = True
+            else:
+                content_type = "Completions"
+                try:
+                    data = json.loads(line, strict=False)
+                    message_role = data["message"]["author"]["role"]
+                    message_status = data["message"]["status"]
+                    if message_role == "assistant" and message_status == "in_progress":
+                        content = data["message"]["content"]["parts"][0]
+                        if not len(content):
+                            continue
+                        delta_content = content[content_offset:]
+                        content_offset = len(content)
+                        logger.success(delta_content, end="")
+                    else:
+                        continue
+                except Exception as e:
+                    logger.warn(e)
+
+            output = self.message_outputer.output(
+                content=delta_content, content_type=content_type
+            )
+            yield output
+
+        if not is_finished:
+            yield self.message_outputer.output(content="", content_type="Finished")