gcli2api / src /config.py
bibibi12345's picture
added thinking support. added nothinking and maxthinking mode
0185608
raw
history blame
10.3 kB
"""
Configuration constants for the Geminicli2api proxy server.
Centralizes all configuration to avoid duplication across modules.
"""
import os
# API Endpoints
CODE_ASSIST_ENDPOINT = "https://cloudcode-pa.googleapis.com"
# Client Configuration
CLI_VERSION = "0.1.5" # Match current gemini-cli version
# OAuth Configuration
CLIENT_ID = "681255809395-oo8ft2oprdrnp9e3aqf6av3hmdib135j.apps.googleusercontent.com"
CLIENT_SECRET = "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"
SCOPES = [
"https://www.googleapis.com/auth/cloud-platform",
"https://www.googleapis.com/auth/userinfo.email",
"https://www.googleapis.com/auth/userinfo.profile",
]
# File Paths
SCRIPT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
CREDENTIAL_FILE = os.path.join(SCRIPT_DIR, os.getenv("GOOGLE_APPLICATION_CREDENTIALS", "oauth_creds.json"))
# Authentication
GEMINI_AUTH_PASSWORD = os.getenv("GEMINI_AUTH_PASSWORD", "123456")
# Default Safety Settings for Google API
DEFAULT_SAFETY_SETTINGS = [
{"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"},
{"category": "HARM_CATEGORY_CIVIC_INTEGRITY", "threshold": "BLOCK_NONE"}
]
# Base Models (without search variants)
BASE_MODELS = [
{
"name": "models/gemini-2.5-pro-preview-05-06",
"version": "001",
"displayName": "Gemini 2.5 Pro Preview 05-06",
"description": "Preview version of Gemini 2.5 Pro from May 6th",
"inputTokenLimit": 1048576,
"outputTokenLimit": 65535,
"supportedGenerationMethods": ["generateContent", "streamGenerateContent"],
"temperature": 1.0,
"maxTemperature": 2.0,
"topP": 0.95,
"topK": 64
},
{
"name": "models/gemini-2.5-pro-preview-06-05",
"version": "001",
"displayName": "Gemini 2.5 Pro Preview 06-05",
"description": "Preview version of Gemini 2.5 Pro from June 5th",
"inputTokenLimit": 1048576,
"outputTokenLimit": 65535,
"supportedGenerationMethods": ["generateContent", "streamGenerateContent"],
"temperature": 1.0,
"maxTemperature": 2.0,
"topP": 0.95,
"topK": 64
},
{
"name": "models/gemini-2.5-pro",
"version": "001",
"displayName": "Gemini 2.5 Pro",
"description": "Advanced multimodal model with enhanced capabilities",
"inputTokenLimit": 1048576,
"outputTokenLimit": 65535,
"supportedGenerationMethods": ["generateContent", "streamGenerateContent"],
"temperature": 1.0,
"maxTemperature": 2.0,
"topP": 0.95,
"topK": 64
},
{
"name": "models/gemini-2.5-flash-preview-05-20",
"version": "001",
"displayName": "Gemini 2.5 Flash Preview 05-20",
"description": "Preview version of Gemini 2.5 Flash from May 20th",
"inputTokenLimit": 1048576,
"outputTokenLimit": 65535,
"supportedGenerationMethods": ["generateContent", "streamGenerateContent"],
"temperature": 1.0,
"maxTemperature": 2.0,
"topP": 0.95,
"topK": 64
},
{
"name": "models/gemini-2.5-flash-preview-04-17",
"version": "001",
"displayName": "Gemini 2.5 Flash Preview 04-17",
"description": "Preview version of Gemini 2.5 Flash from April 17th",
"inputTokenLimit": 1048576,
"outputTokenLimit": 65535,
"supportedGenerationMethods": ["generateContent", "streamGenerateContent"],
"temperature": 1.0,
"maxTemperature": 2.0,
"topP": 0.95,
"topK": 64
},
{
"name": "models/gemini-2.5-flash",
"version": "001",
"displayName": "Gemini 2.5 Flash",
"description": "Fast and efficient multimodal model with latest improvements",
"inputTokenLimit": 1048576,
"outputTokenLimit": 65535,
"supportedGenerationMethods": ["generateContent", "streamGenerateContent"],
"temperature": 1.0,
"maxTemperature": 2.0,
"topP": 0.95,
"topK": 64
}
]
# Generate search variants for applicable models
def _generate_search_variants():
"""Generate search variants for models that support content generation."""
search_models = []
for model in BASE_MODELS:
# Only add search variants for models that support content generation
if "generateContent" in model["supportedGenerationMethods"]:
search_variant = model.copy()
search_variant["name"] = model["name"] + "-search"
search_variant["displayName"] = model["displayName"] + " with Google Search"
search_variant["description"] = model["description"] + " (includes Google Search grounding)"
search_models.append(search_variant)
return search_models
# Generate thinking variants for applicable models
def _generate_thinking_variants():
"""Generate nothinking and maxthinking variants for models that support thinking."""
thinking_models = []
for model in BASE_MODELS:
# Only add thinking variants for models that support content generation
# and contain "gemini-2.5-flash" or "gemini-2.5-pro" in their name
if ("generateContent" in model["supportedGenerationMethods"] and
("gemini-2.5-flash" in model["name"] or "gemini-2.5-pro" in model["name"])):
# Add -nothinking variant
nothinking_variant = model.copy()
nothinking_variant["name"] = model["name"] + "-nothinking"
nothinking_variant["displayName"] = model["displayName"] + " (No Thinking)"
nothinking_variant["description"] = model["description"] + " (thinking disabled)"
thinking_models.append(nothinking_variant)
# Add -maxthinking variant
maxthinking_variant = model.copy()
maxthinking_variant["name"] = model["name"] + "-maxthinking"
maxthinking_variant["displayName"] = model["displayName"] + " (Max Thinking)"
maxthinking_variant["description"] = model["description"] + " (maximum thinking budget)"
thinking_models.append(maxthinking_variant)
return thinking_models
# Generate combined variants (search + thinking combinations)
def _generate_combined_variants():
"""Generate combined search and thinking variants."""
combined_models = []
for model in BASE_MODELS:
# Only add combined variants for models that support content generation
# and contain "gemini-2.5-flash" or "gemini-2.5-pro" in their name
if ("generateContent" in model["supportedGenerationMethods"] and
("gemini-2.5-flash" in model["name"] or "gemini-2.5-pro" in model["name"])):
# search + nothinking
search_nothinking = model.copy()
search_nothinking["name"] = model["name"] + "-search-nothinking"
search_nothinking["displayName"] = model["displayName"] + " with Google Search (No Thinking)"
search_nothinking["description"] = model["description"] + " (includes Google Search grounding, thinking disabled)"
combined_models.append(search_nothinking)
# search + maxthinking
search_maxthinking = model.copy()
search_maxthinking["name"] = model["name"] + "-search-maxthinking"
search_maxthinking["displayName"] = model["displayName"] + " with Google Search (Max Thinking)"
search_maxthinking["description"] = model["description"] + " (includes Google Search grounding, maximum thinking budget)"
combined_models.append(search_maxthinking)
return combined_models
# Supported Models (includes base models, search variants, and thinking variants)
SUPPORTED_MODELS = BASE_MODELS + _generate_search_variants() + _generate_thinking_variants()
# Helper function to get base model name from any variant
def get_base_model_name(model_name):
"""Convert variant model name to base model name."""
# Remove all possible suffixes in order
suffixes = ["-maxthinking", "-nothinking", "-search"]
for suffix in suffixes:
if model_name.endswith(suffix):
return model_name[:-len(suffix)]
return model_name
# Helper function to check if model uses search grounding
def is_search_model(model_name):
"""Check if model name indicates search grounding should be enabled."""
return "-search" in model_name
# Helper function to check if model uses no thinking
def is_nothinking_model(model_name):
"""Check if model name indicates thinking should be disabled."""
return "-nothinking" in model_name
# Helper function to check if model uses max thinking
def is_maxthinking_model(model_name):
"""Check if model name indicates maximum thinking budget should be used."""
return "-maxthinking" in model_name
# Helper function to get thinking budget for a model
def get_thinking_budget(model_name):
"""Get the appropriate thinking budget for a model based on its name and variant."""
base_model = get_base_model_name(model_name)
if is_nothinking_model(model_name):
if "gemini-2.5-flash" in base_model:
return 0 # No thinking for flash
elif "gemini-2.5-pro" in base_model:
return 128 # Limited thinking for pro
elif is_maxthinking_model(model_name):
if "gemini-2.5-flash" in base_model:
return 24576
elif "gemini-2.5-pro" in base_model:
return 32768
else:
# Default thinking budget for regular models
return -1 # Default for all models
# Helper function to check if thinking should be included in output
def should_include_thoughts(model_name):
"""Check if thoughts should be included in the response."""
if is_nothinking_model(model_name):
# For nothinking mode, still include thoughts if it's a pro model
base_model = get_base_model_name(model_name)
return "gemini-2.5-pro" in base_model
else:
# For all other modes, include thoughts
return True