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request.py
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import os
import re
import json
import httpx
import base64
import urllib.parse
from PIL import Image
import io
from models import RequestModel
from utils import c35s, c3s, c3o, c3h, gemini1, gemini2, BaseAPI, get_model_dict, provider_api_circular_list, safe_get, ThreadSafeCircularList
def get_image_format(file_content):
try:
img = Image.open(io.BytesIO(file_content))
return img.format.lower()
except:
return None
def encode_image(image_path):
with open(image_path, "rb") as image_file:
file_content = image_file.read()
img_format = get_image_format(file_content)
if not img_format:
raise ValueError("无法识别的图片格式")
base64_encoded = base64.b64encode(file_content).decode('utf-8')
if img_format == 'png':
return f"data:image/png;base64,{base64_encoded}"
elif img_format in ['jpg', 'jpeg']:
return f"data:image/jpeg;base64,{base64_encoded}"
else:
raise ValueError(f"不支持的图片格式: {img_format}")
async def get_doc_from_url(url):
filename = urllib.parse.unquote(url.split("/")[-1])
transport = httpx.AsyncHTTPTransport(
http2=True,
verify=False,
retries=1
)
async with httpx.AsyncClient(transport=transport) as client:
try:
response = await client.get(
url,
timeout=30.0
)
with open(filename, 'wb') as f:
f.write(response.content)
except httpx.RequestError as e:
print(f"An error occurred while requesting {e.request.url!r}.")
return filename
async def get_encode_image(image_url):
filename = await get_doc_from_url(image_url)
image_path = os.getcwd() + "/" + filename
base64_image = encode_image(image_path)
os.remove(image_path)
return base64_image
# from PIL import Image
# import io
# def validate_image(image_data, image_type):
# try:
# decoded_image = base64.b64decode(image_data)
# image = Image.open(io.BytesIO(decoded_image))
# # 检查图片格式是否与声明的类型匹配
# # print("image.format", image.format)
# if image_type == "image/png" and image.format != "PNG":
# raise ValueError("Image is not a valid PNG")
# elif image_type == "image/jpeg" and image.format not in ["JPEG", "JPG"]:
# raise ValueError("Image is not a valid JPEG")
# # 如果没有异常,则图片有效
# return True
# except Exception as e:
# print(f"Image validation failed: {str(e)}")
# return False
async def get_image_message(base64_image, engine = None):
if base64_image.startswith("http"):
base64_image = await get_encode_image(base64_image)
colon_index = base64_image.index(":")
semicolon_index = base64_image.index(";")
image_type = base64_image[colon_index + 1:semicolon_index]
if image_type == "image/webp":
# 将webp转换为png
# 解码base64获取图片数据
image_data = base64.b64decode(base64_image.split(",")[1])
# 使用PIL打开webp图片
image = Image.open(io.BytesIO(image_data))
# 转换为PNG格式
png_buffer = io.BytesIO()
image.save(png_buffer, format="PNG")
png_base64 = base64.b64encode(png_buffer.getvalue()).decode('utf-8')
# 返回PNG格式的base64
base64_image = f"data:image/png;base64,{png_base64}"
image_type = "image/png"
if "gpt" == engine or "openrouter" == engine or "azure" == engine:
return {
"type": "image_url",
"image_url": {
"url": base64_image,
}
}
if "claude" == engine or "vertex-claude" == engine:
# if not validate_image(base64_image.split(",")[1], image_type):
# raise ValueError(f"Invalid image format. Expected {image_type}")
return {
"type": "image",
"source": {
"type": "base64",
"media_type": image_type,
"data": base64_image.split(",")[1],
}
}
if "gemini" == engine or "vertex-gemini" == engine:
return {
"inlineData": {
"mimeType": image_type,
"data": base64_image.split(",")[1],
}
}
raise ValueError("Unknown engine")
async def get_text_message(role, message, engine = None):
if "gpt" == engine or "claude" == engine or "openrouter" == engine or "vertex-claude" == engine or "o1" == engine or "azure" == engine:
return {"type": "text", "text": message}
if "gemini" == engine or "vertex-gemini" == engine:
return {"text": message}
if engine == "cloudflare":
return message
if engine == "cohere":
return message
raise ValueError("Unknown engine")
async def get_gemini_payload(request, engine, provider):
headers = {
'Content-Type': 'application/json'
}
model_dict = get_model_dict(provider)
model = model_dict[request.model]
gemini_stream = "streamGenerateContent"
url = provider['base_url']
parsed_url = urllib.parse.urlparse(url)
# print("parsed_url", parsed_url)
if parsed_url.path.endswith("/v1beta") or parsed_url.path.endswith("/v1"):
api_version = parsed_url.path.split('/')[-1] # 获取 v1 或 v1beta
else:
api_version = "v1beta"
# https://generativelanguage.googleapis.com/v1beta/models/
url = f"{parsed_url.scheme}://{parsed_url.netloc}{parsed_url.path}/models/{model}:{gemini_stream}?key={await provider_api_circular_list[provider['provider']].next(model)}"
messages = []
systemInstruction = None
function_arguments = None
for msg in request.messages:
if msg.role == "assistant":
msg.role = "model"
tool_calls = None
if isinstance(msg.content, list):
content = []
for item in msg.content:
if item.type == "text":
text_message = await get_text_message(msg.role, item.text, engine)
content.append(text_message)
elif item.type == "image_url" and provider.get("image", True):
image_message = await get_image_message(item.image_url.url, engine)
content.append(image_message)
else:
content = [{"text": msg.content}]
tool_calls = msg.tool_calls
if tool_calls:
tool_call = tool_calls[0]
function_arguments = {
"functionCall": {
"name": tool_call.function.name,
"args": json.loads(tool_call.function.arguments)
}
}
messages.append(
{
"role": "model",
"parts": [function_arguments]
}
)
elif msg.role == "tool":
function_call_name = function_arguments["functionCall"]["name"]
messages.append(
{
"role": "function",
"parts": [{
"functionResponse": {
"name": function_call_name,
"response": {
"name": function_call_name,
"content": {
"result": msg.content,
}
}
}
}]
}
)
elif msg.role != "system":
messages.append({"role": msg.role, "parts": content})
elif msg.role == "system":
content[0]["text"] = re.sub(r"_+", "_", content[0]["text"])
systemInstruction = {"parts": content}
if "gemini-2.0-flash-exp" in model or "gemini-1.5" in model:
safety_settings = "OFF"
else:
safety_settings = "BLOCK_NONE"
payload = {
"contents": messages or [{"role": "user", "parts": [{"text": "No messages"}]}],
"safetySettings": [
{
"category": "HARM_CATEGORY_HARASSMENT",
"threshold": safety_settings
},
{
"category": "HARM_CATEGORY_HATE_SPEECH",
"threshold": safety_settings
},
{
"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
"threshold": safety_settings
},
{
"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
"threshold": safety_settings
}
]
}
if systemInstruction:
if api_version == "v1beta":
payload["systemInstruction"] = systemInstruction
if api_version == "v1":
first_message = safe_get(payload, "contents", 0, "parts", 0, "text", default=None)
system_instruction = safe_get(systemInstruction, "parts", 0, "text", default=None)
if first_message and system_instruction:
payload["contents"][0]["parts"][0]["text"] = system_instruction + "\n" + first_message
miss_fields = [
'model',
'messages',
'stream',
'tool_choice',
'presence_penalty',
'frequency_penalty',
'n',
'user',
'include_usage',
'logprobs',
'top_logprobs',
'response_format'
]
generation_config = {}
for field, value in request.model_dump(exclude_unset=True).items():
if field not in miss_fields and value is not None:
if field == "tools":
# 处理每个工具的 function 定义
processed_tools = []
for tool in value:
function_def = tool["function"]
# 处理 parameters.properties 中的 default 字段
if safe_get(function_def, "parameters", "properties", default=None):
for prop_value in function_def["parameters"]["properties"].values():
if "default" in prop_value:
# 将 default 值添加到 description 中
default_value = prop_value["default"]
description = prop_value.get("description", "")
prop_value["description"] = f"{description}\nDefault: {default_value}"
# 删除 default 字段
del prop_value["default"]
if function_def["name"] != "googleSearch" and function_def["name"] != "googleSearch":
processed_tools.append({"function": function_def})
if processed_tools:
payload.update({
"tools": [{
"function_declarations": [tool["function"] for tool in processed_tools]
}],
"tool_config": {
"function_calling_config": {
"mode": "AUTO"
}
}
})
elif field == "temperature":
generation_config["temperature"] = value
elif field == "max_tokens":
generation_config["maxOutputTokens"] = value
elif field == "top_p":
generation_config["topP"] = value
else:
payload[field] = value
if generation_config:
payload["generationConfig"] = generation_config
if "maxOutputTokens" not in generation_config:
payload["generationConfig"]["maxOutputTokens"] = 8192
if request.model.endswith("-search"):
if "tools" not in payload:
payload["tools"] = [{
"googleSearch": {}
}]
else:
payload["tools"].append({
"googleSearch": {}
})
return url, headers, payload
import time
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.asymmetric import padding
from cryptography.hazmat.primitives.serialization import load_pem_private_key
def create_jwt(client_email, private_key):
# JWT Header
header = json.dumps({
"alg": "RS256",
"typ": "JWT"
}).encode()
# JWT Payload
now = int(time.time())
payload = json.dumps({
"iss": client_email,
"scope": "https://www.googleapis.com/auth/cloud-platform",
"aud": "https://oauth2.googleapis.com/token",
"exp": now + 3600,
"iat": now
}).encode()
# Encode header and payload
segments = [
base64.urlsafe_b64encode(header).rstrip(b'='),
base64.urlsafe_b64encode(payload).rstrip(b'=')
]
# Create signature
signing_input = b'.'.join(segments)
private_key = load_pem_private_key(private_key.encode(), password=None)
signature = private_key.sign(
signing_input,
padding.PKCS1v15(),
hashes.SHA256()
)
segments.append(base64.urlsafe_b64encode(signature).rstrip(b'='))
return b'.'.join(segments).decode()
def get_access_token(client_email, private_key):
jwt = create_jwt(client_email, private_key)
with httpx.Client() as client:
response = client.post(
"https://oauth2.googleapis.com/token",
data={
"grant_type": "urn:ietf:params:oauth:grant-type:jwt-bearer",
"assertion": jwt
},
headers={'Content-Type': "application/x-www-form-urlencoded"}
)
response.raise_for_status()
return response.json()["access_token"]
async def get_vertex_gemini_payload(request, engine, provider):
headers = {
'Content-Type': 'application/json'
}
if provider.get("client_email") and provider.get("private_key"):
access_token = get_access_token(provider['client_email'], provider['private_key'])
headers['Authorization'] = f"Bearer {access_token}"
if provider.get("project_id"):
project_id = provider.get("project_id")
gemini_stream = "streamGenerateContent"
model_dict = get_model_dict(provider)
model = model_dict[request.model]
search_tool = None
if "gemini-2.0" in model or "gemini-exp" in model:
location = gemini2
search_tool = {"googleSearch": {}}
else:
location = gemini1
search_tool = {"googleSearchRetrieval": {}}
url = "https://{LOCATION}-aiplatform.googleapis.com/v1/projects/{PROJECT_ID}/locations/{LOCATION}/publishers/google/models/{MODEL_ID}:{stream}".format(LOCATION=await location.next(), PROJECT_ID=project_id, MODEL_ID=model, stream=gemini_stream)
messages = []
systemInstruction = None
function_arguments = None
for msg in request.messages:
if msg.role == "assistant":
msg.role = "model"
tool_calls = None
if isinstance(msg.content, list):
content = []
for item in msg.content:
if item.type == "text":
text_message = await get_text_message(msg.role, item.text, engine)
content.append(text_message)
elif item.type == "image_url" and provider.get("image", True):
image_message = await get_image_message(item.image_url.url, engine)
content.append(image_message)
else:
content = [{"text": msg.content}]
tool_calls = msg.tool_calls
if tool_calls:
tool_call = tool_calls[0]
function_arguments = {
"functionCall": {
"name": tool_call.function.name,
"args": json.loads(tool_call.function.arguments)
}
}
messages.append(
{
"role": "model",
"parts": [function_arguments]
}
)
elif msg.role == "tool":
function_call_name = function_arguments["functionCall"]["name"]
messages.append(
{
"role": "function",
"parts": [{
"functionResponse": {
"name": function_call_name,
"response": {
"name": function_call_name,
"content": {
"result": msg.content,
}
}
}
}]
}
)
elif msg.role != "system":
messages.append({"role": msg.role, "parts": content})
elif msg.role == "system":
systemInstruction = {"parts": content}
payload = {
"contents": messages,
# "safetySettings": [
# {
# "category": "HARM_CATEGORY_HARASSMENT",
# "threshold": "BLOCK_NONE"
# },
# {
# "category": "HARM_CATEGORY_HATE_SPEECH",
# "threshold": "BLOCK_NONE"
# },
# {
# "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
# "threshold": "BLOCK_NONE"
# },
# {
# "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
# "threshold": "BLOCK_NONE"
# }
# ]
}
if systemInstruction:
payload["system_instruction"] = systemInstruction
miss_fields = [
'model',
'messages',
'stream',
'tool_choice',
'presence_penalty',
'frequency_penalty',
'n',
'user',
'include_usage',
'logprobs',
'top_logprobs'
]
generation_config = {}
for field, value in request.model_dump(exclude_unset=True).items():
if field not in miss_fields and value is not None:
if field == "tools":
payload.update({
"tools": [{
"function_declarations": [tool["function"] for tool in value]
}],
"tool_config": {
"function_calling_config": {
"mode": "AUTO"
}
}
})
elif field == "temperature":
generation_config["temperature"] = value
elif field == "max_tokens":
generation_config["max_output_tokens"] = value
elif field == "top_p":
generation_config["top_p"] = value
else:
payload[field] = value
if generation_config:
payload["generationConfig"] = generation_config
if "max_output_tokens" not in generation_config:
payload["generationConfig"]["max_output_tokens"] = 8192
if request.model.endswith("-search"):
if "tools" not in payload:
payload["tools"] = [search_tool]
else:
payload["tools"].append(search_tool)
return url, headers, payload
async def get_vertex_claude_payload(request, engine, provider):
headers = {
'Content-Type': 'application/json',
}
if provider.get("client_email") and provider.get("private_key"):
access_token = get_access_token(provider['client_email'], provider['private_key'])
headers['Authorization'] = f"Bearer {access_token}"
if provider.get("project_id"):
project_id = provider.get("project_id")
model_dict = get_model_dict(provider)
model = model_dict[request.model]
if "claude-3-5-sonnet" in model:
location = c35s
elif "claude-3-opus" in model:
location = c3o
elif "claude-3-sonnet" in model:
location = c3s
elif "claude-3-haiku" in model:
location = c3h
claude_stream = "streamRawPredict"
url = "https://{LOCATION}-aiplatform.googleapis.com/v1/projects/{PROJECT_ID}/locations/{LOCATION}/publishers/anthropic/models/{MODEL}:{stream}".format(LOCATION=await location.next(), PROJECT_ID=project_id, MODEL=model, stream=claude_stream)
messages = []
system_prompt = None
tool_id = None
for msg in request.messages:
tool_call_id = None
tool_calls = None
if isinstance(msg.content, list):
content = []
for item in msg.content:
if item.type == "text":
text_message = await get_text_message(msg.role, item.text, engine)
content.append(text_message)
elif item.type == "image_url" and provider.get("image", True):
image_message = await get_image_message(item.image_url.url, engine)
content.append(image_message)
else:
content = msg.content
tool_calls = msg.tool_calls
tool_id = tool_calls[0].id if tool_calls else None or tool_id
tool_call_id = msg.tool_call_id
if tool_calls:
tool_calls_list = []
tool_call = tool_calls[0]
tool_calls_list.append({
"type": "tool_use",
"id": tool_call.id,
"name": tool_call.function.name,
"input": json.loads(tool_call.function.arguments),
})
messages.append({"role": msg.role, "content": tool_calls_list})
elif tool_call_id:
messages.append({"role": "user", "content": [{
"type": "tool_result",
"tool_use_id": tool_id,
"content": content
}]})
elif msg.role == "function":
messages.append({"role": "assistant", "content": [{
"type": "tool_use",
"id": "toolu_017r5miPMV6PGSNKmhvHPic4",
"name": msg.name,
"input": {"prompt": "..."}
}]})
messages.append({"role": "user", "content": [{
"type": "tool_result",
"tool_use_id": "toolu_017r5miPMV6PGSNKmhvHPic4",
"content": msg.content
}]})
elif msg.role != "system":
messages.append({"role": msg.role, "content": content})
elif msg.role == "system":
system_prompt = content
conversation_len = len(messages) - 1
message_index = 0
while message_index < conversation_len:
if messages[message_index]["role"] == messages[message_index + 1]["role"]:
if messages[message_index].get("content"):
if isinstance(messages[message_index]["content"], list):
messages[message_index]["content"].extend(messages[message_index + 1]["content"])
elif isinstance(messages[message_index]["content"], str) and isinstance(messages[message_index + 1]["content"], list):
content_list = [{"type": "text", "text": messages[message_index]["content"]}]
content_list.extend(messages[message_index + 1]["content"])
messages[message_index]["content"] = content_list
else:
messages[message_index]["content"] += messages[message_index + 1]["content"]
messages.pop(message_index + 1)
conversation_len = conversation_len - 1
else:
message_index = message_index + 1
model_dict = get_model_dict(provider)
model = model_dict[request.model]
payload = {
"anthropic_version": "vertex-2023-10-16",
"messages": messages,
"system": system_prompt or "You are Claude, a large language model trained by Anthropic.",
"max_tokens": 8192 if "claude-3-5-sonnet" in model else 4096,
}
if request.max_tokens:
payload["max_tokens"] = int(request.max_tokens)
miss_fields = [
'model',
'messages',
'presence_penalty',
'frequency_penalty',
'n',
'user',
'include_usage',
]
for field, value in request.model_dump(exclude_unset=True).items():
if field not in miss_fields and value is not None:
payload[field] = value
if request.tools and provider.get("tools"):
tools = []
for tool in request.tools:
json_tool = await gpt2claude_tools_json(tool.dict()["function"])
tools.append(json_tool)
payload["tools"] = tools
if "tool_choice" in payload:
if isinstance(payload["tool_choice"], dict):
if payload["tool_choice"]["type"] == "function":
payload["tool_choice"] = {
"type": "tool",
"name": payload["tool_choice"]["function"]["name"]
}
if isinstance(payload["tool_choice"], str):
if payload["tool_choice"] == "auto":
payload["tool_choice"] = {
"type": "auto"
}
if payload["tool_choice"] == "none":
payload["tool_choice"] = {
"type": "any"
}
if provider.get("tools") == False:
payload.pop("tools", None)
payload.pop("tool_choice", None)
return url, headers, payload
async def get_gpt_payload(request, engine, provider):
headers = {
'Content-Type': 'application/json',
}
model_dict = get_model_dict(provider)
model = model_dict[request.model]
if provider.get("api"):
headers['Authorization'] = f"Bearer {await provider_api_circular_list[provider['provider']].next(model)}"
elif provider['provider'].startswith("sk-"):
headers['Authorization'] = f"Bearer {provider['provider']}"
url = provider['base_url']
messages = []
for msg in request.messages:
tool_calls = None
tool_call_id = None
if isinstance(msg.content, list):
content = []
for item in msg.content:
if item.type == "text":
text_message = await get_text_message(msg.role, item.text, engine)
content.append(text_message)
elif item.type == "image_url" and provider.get("image", True) and "o1-mini" not in model:
image_message = await get_image_message(item.image_url.url, engine)
content.append(image_message)
else:
content = msg.content
if msg.role == "system" and "o3-mini" in model and not content.startswith("Formatting re-enabled"):
content = "Formatting re-enabled. " + content
tool_calls = msg.tool_calls
tool_call_id = msg.tool_call_id
if tool_calls:
tool_calls_list = []
for tool_call in tool_calls:
tool_calls_list.append({
"id": tool_call.id,
"type": tool_call.type,
"function": {
"name": tool_call.function.name,
"arguments": tool_call.function.arguments
}
})
if provider.get("tools"):
messages.append({"role": msg.role, "tool_calls": tool_calls_list})
elif tool_call_id:
if provider.get("tools"):
messages.append({"role": msg.role, "tool_call_id": tool_call_id, "content": content})
else:
messages.append({"role": msg.role, "content": content})
if ("o1-mini" in model or "o1-preview" in model) and len(messages) > 1 and messages[0]["role"] == "system":
system_msg = messages.pop(0)
messages[0]["content"] = system_msg["content"] + messages[0]["content"]
payload = {
"model": model,
"messages": messages,
}
miss_fields = [
'model',
'messages',
]
for field, value in request.model_dump(exclude_unset=True).items():
if field not in miss_fields and value is not None:
if field == "max_tokens" and ("o1" in model or "o3" in model):
payload["max_completion_tokens"] = value
else:
payload[field] = value
if provider.get("tools") == False or "o1" in model or "chatgpt-4o-latest" in model or "grok" in model:
payload.pop("tools", None)
payload.pop("tool_choice", None)
if "models.inference.ai.azure.com" in url:
payload["stream"] = False
# request.stream = False
payload.pop("stream_options", None)
if "o3-mini" in model:
if request.model.endswith("high"):
payload["reasoning_effort"] = "high"
elif request.model.endswith("low"):
payload["reasoning_effort"] = "low"
else:
payload["reasoning_effort"] = "medium"
if "o3-mini" in model or "o1" in model:
if "temperature" in payload:
payload.pop("temperature")
if "deepseek-r" in model.lower():
if "temperature" not in payload:
payload["temperature"] = 0.6
if request.model.endswith("-search") and "gemini" in request.model:
if "tools" not in payload:
payload["tools"] = [{
"type": "function",
"function": {
"name": "googleSearch",
"description": "googleSearch"
}
}]
else:
if not any(tool["function"]["name"] == "googleSearch" for tool in payload["tools"]):
payload["tools"].append({
"type": "function",
"function": {
"name": "googleSearch",
"description": "googleSearch"
}
})
return url, headers, payload
def build_azure_endpoint(base_url, deployment_id, api_version="2024-10-21"):
# 移除base_url末尾的斜杠(如果有)
base_url = base_url.rstrip('/')
final_url = base_url
if "models/chat/completions" not in final_url:
# 构建路径
path = f"/openai/deployments/{deployment_id}/chat/completions"
# 使用urljoin拼接base_url和path
final_url = urllib.parse.urljoin(base_url, path)
if "?api-version=" not in final_url:
# 添加api-version查询参数
final_url = f"{final_url}?api-version={api_version}"
return final_url
async def get_azure_payload(request, engine, provider):
headers = {
'Content-Type': 'application/json',
}
model_dict = get_model_dict(provider)
model = model_dict[request.model]
headers['api-key'] = f"{await provider_api_circular_list[provider['provider']].next(model)}"
url = build_azure_endpoint(
base_url=provider['base_url'],
deployment_id=model,
)
messages = []
for msg in request.messages:
tool_calls = None
tool_call_id = None
if isinstance(msg.content, list):
content = []
for item in msg.content:
if item.type == "text":
text_message = await get_text_message(msg.role, item.text, engine)
content.append(text_message)
elif item.type == "image_url" and provider.get("image", True) and "o1-mini" not in model:
image_message = await get_image_message(item.image_url.url, engine)
content.append(image_message)
else:
content = msg.content
tool_calls = msg.tool_calls
tool_call_id = msg.tool_call_id
if tool_calls:
tool_calls_list = []
for tool_call in tool_calls:
tool_calls_list.append({
"id": tool_call.id,
"type": tool_call.type,
"function": {
"name": tool_call.function.name,
"arguments": tool_call.function.arguments
}
})
if provider.get("tools"):
messages.append({"role": msg.role, "tool_calls": tool_calls_list})
elif tool_call_id:
if provider.get("tools"):
messages.append({"role": msg.role, "tool_call_id": tool_call_id, "content": content})
else:
messages.append({"role": msg.role, "content": content})
payload = {
"model": model,
"messages": messages,
}
miss_fields = [
'model',
'messages',
]
for field, value in request.model_dump(exclude_unset=True).items():
if field not in miss_fields and value is not None:
if field == "max_tokens" and "o1" in model:
payload["max_completion_tokens"] = value
else:
payload[field] = value
if provider.get("tools") == False or "o1" in model or "chatgpt-4o-latest" in model or "grok" in model:
payload.pop("tools", None)
payload.pop("tool_choice", None)
return url, headers, payload
async def get_openrouter_payload(request, engine, provider):
headers = {
'Content-Type': 'application/json'
}
model_dict = get_model_dict(provider)
model = model_dict[request.model]
if provider.get("api"):
headers['Authorization'] = f"Bearer {await provider_api_circular_list[provider['provider']].next(model)}"
elif provider['provider'].startswith("sk-"):
headers['Authorization'] = f"Bearer {provider['provider']}"
url = provider['base_url']
messages = []
for msg in request.messages:
name = None
if isinstance(msg.content, list):
content = []
for item in msg.content:
if item.type == "text":
text_message = await get_text_message(msg.role, item.text, engine)
content.append(text_message)
elif item.type == "image_url" and provider.get("image", True):
image_message = await get_image_message(item.image_url.url, engine)
content.append(image_message)
else:
content = msg.content
name = msg.name
if name:
messages.append({"role": msg.role, "name": name, "content": content})
else:
# print("content", content)
if isinstance(content, list):
for item in content:
if item["type"] == "text":
messages.append({"role": msg.role, "content": item["text"]})
elif item["type"] == "image_url":
messages.append({"role": msg.role, "content": [await get_image_message(item["image_url"]["url"], engine)]})
else:
messages.append({"role": msg.role, "content": content})
payload = {
"model": model,
"messages": messages,
}
miss_fields = [
'model',
'messages',
'tools',
'tool_choice',
'temperature',
'top_p',
'max_tokens',
'presence_penalty',
'frequency_penalty',
'n',
'user',
'include_usage',
'logprobs',
'top_logprobs'
]
for field, value in request.model_dump(exclude_unset=True).items():
if field not in miss_fields and value is not None:
payload[field] = value
return url, headers, payload
async def get_cohere_payload(request, engine, provider):
headers = {
'Content-Type': 'application/json'
}
model_dict = get_model_dict(provider)
model = model_dict[request.model]
if provider.get("api"):
headers['Authorization'] = f"Bearer {await provider_api_circular_list[provider['provider']].next(model)}"
url = provider['base_url']
role_map = {
"user": "USER",
"assistant" : "CHATBOT",
"system": "SYSTEM"
}
messages = []
for msg in request.messages:
if isinstance(msg.content, list):
content = []
for item in msg.content:
if item.type == "text":
text_message = await get_text_message(msg.role, item.text, engine)
content.append(text_message)
else:
content = msg.content
if isinstance(content, list):
for item in content: