kaiyuan/chatgpt-on-wechat
1
0
Fork 0
mirror of https://github.com/zhayujie/chatgpt-on-wechat.git synced 2026-07-17 11:07:11 +08:00
Code Issues Packages Projects Releases Wiki Activity

Merge remote-tracking branch 'upstream/master'

Browse Source 
# Please enter a commit message to explain why this merge is necessary,
# especially if it merges an updated upstream into a topic branch.
#
# Lines starting with '#' will be ignored, and an empty message aborts
# the commit.
This commit is contained in:
yangziyu-hhh
2026-06-23 17:18:24 +08:00
parent f1cdc2d2cc 5db2998e3d
commit 9ea0017778
61 changed files with 3162 additions and 183 deletions
Download Patch File Download Diff File Expand all files Collapse all files

247
channel/web/web_channel.py
View File

@@ -1180,6 +1180,9 @@ class WebChannel(ChatChannel):
'/api/knowledge/action', 'KnowledgeActionHandler',
'/api/knowledge/import', 'KnowledgeImportHandler',
'/api/scheduler', 'SchedulerHandler',
'/api/scheduler/toggle', 'SchedulerToggleHandler',
'/api/scheduler/update', 'SchedulerUpdateHandler',
'/api/scheduler/delete', 'SchedulerDeleteHandler',
'/api/sessions', 'SessionsHandler',
'/api/sessions/(.*)/generate_title', 'SessionTitleHandler',
'/api/sessions/(.*)/clear_context', 'SessionClearContextHandler',
@@ -1500,10 +1503,10 @@ class ConfigHandler:
const.CLAUDE_4_8_OPUS, const.CLAUDE_4_7_OPUS, const.CLAUDE_FABLE_5, const.CLAUDE_4_6_SONNET, const.CLAUDE_4_6_OPUS,
const.GEMINI_35_FLASH, const.GEMINI_31_FLASH_LITE_PRE, const.GEMINI_31_PRO_PRE, const.GEMINI_3_FLASH_PRE,
const.GPT_55, const.GPT_54, const.GPT_54_MINI, const.GPT_54_NANO, const.GPT_5, const.GPT_41, const.GPT_4o,
const.GLM_5_1, const.GLM_5_TURBO, const.GLM_5, const.GLM_4_7,
const.GLM_5_2, const.GLM_5_1, const.GLM_5_TURBO, const.GLM_5, const.GLM_4_7,
const.QWEN37_PLUS, const.QWEN37_MAX, const.QWEN36_PLUS,
const.DOUBAO_SEED_2_PRO, const.DOUBAO_SEED_2_CODE,
const.KIMI_K2_6, const.KIMI_K2_5, const.KIMI_K2,
const.KIMI_K2_7_CODE, const.KIMI_K2_7_CODE_HIGHSPEED, const.KIMI_K2_6, const.KIMI_K2_5, const.KIMI_K2,
const.ERNIE_5_1, const.ERNIE_5, const.ERNIE_X1_1, const.ERNIE_45_TURBO_128K, const.ERNIE_45_TURBO_32K,
const.MIMO_V2_5_PRO, const.MIMO_V2_5,
]
@@ -1566,7 +1569,7 @@ class ConfigHandler:
"api_base_key": "zhipu_ai_api_base",
"api_base_default": "https://open.bigmodel.cn/api/paas/v4",
"api_base_placeholder": _PLACEHOLDER_ZHIPU,
"models": [const.GLM_5_1, const.GLM_5_TURBO, const.GLM_5, const.GLM_4_7],
"models": [const.GLM_5_2, const.GLM_5_1, const.GLM_5_TURBO, const.GLM_5, const.GLM_4_7],
}),
("dashscope", {
"label": {"zh": "通义千问", "en": "Qwen"},
@@ -1590,7 +1593,7 @@ class ConfigHandler:
"api_base_key": "moonshot_base_url",
"api_base_default": "https://api.moonshot.cn/v1",
"api_base_placeholder": _PLACEHOLDER_V1,
"models": [const.KIMI_K2_6, const.KIMI_K2_5, const.KIMI_K2],
"models": [const.KIMI_K2_7_CODE, const.KIMI_K2_7_CODE_HIGHSPEED, const.KIMI_K2_6, const.KIMI_K2_5, const.KIMI_K2],
}),
("qianfan", {
"label": {"zh": "百度千帆", "en": "ERNIE"},
@@ -2083,7 +2086,20 @@ class ModelsHandler:
],
},
}
_EMBEDDING_PROVIDERS = ["openai", "dashscope", "doubao", "zhipu", "linkai"]
_EMBEDDING_PROVIDERS = ["openai", "dashscope", "doubao", "zhipu", "linkai", "custom"]
# Embedding model catalog per provider. Mirrors the default_model in
# agent/memory/embedding/provider.py::EMBEDDING_VENDORS.
# Custom providers have no preset list — model names vary per vendor,
# so the user always types the model id manually.
_EMBEDDING_PROVIDER_MODELS = {
"openai": ["text-embedding-3-small", "text-embedding-3-large"],
"dashscope": ["text-embedding-v4"],
"doubao": ["doubao-embedding-vision-251215"],
"zhipu": ["embedding-3"],
"linkai": ["text-embedding-3-small"],
"custom": [],
}
# Capability-scoped model catalogs. The chat dropdown can reuse the
# provider's generic model list, but vision and image generation are
@@ -2133,6 +2149,9 @@ class ModelsHandler:
const.CLAUDE_4_6_SONNET,
const.GEMINI_31_FLASH_LITE_PRE,
],
# Custom OpenAI-compatible providers have no preset list — model
# names vary per vendor, so the user types the model id manually.
"custom": [],
}
# Image-generation catalog. Source of truth: skills/image-generation/SKILL.md.
@@ -2446,18 +2465,31 @@ class ModelsHandler:
user_specified = (vision_conf.get("model") or "").strip()
explicit_provider = (vision_conf.get("provider") or "").strip()
# Build provider list: built-in providers + expanded custom:<id> entries.
# Same pattern as _embedding_capability — each user-created custom
# provider gets its own dropdown entry showing the user-chosen name.
providers = []
custom_cards = cls._custom_provider_cards(local_config)
for pid in cls._VISION_PROVIDER_MODELS:
if pid == "custom":
if custom_cards:
providers.extend(c["id"] for c in custom_cards)
else:
providers.append(pid)
# Provider resolution priority:
# 1. Explicit `tools.vision.provider` (persisted via UI; supports
# custom model names that prefix-inference can't recognize).
# 2. Scan per-provider model lists by model name.
# Empty provider keeps the dropdown on "auto" when we can't tell.
inferred_provider = ""
if explicit_provider and explicit_provider in cls._VISION_PROVIDER_MODELS:
if explicit_provider and explicit_provider in providers:
inferred_provider = explicit_provider
elif user_specified:
for pid, models in cls._VISION_PROVIDER_MODELS.items():
if user_specified in models:
inferred_provider = pid
# For "custom" key, map to the first custom card
inferred_provider = custom_cards[0]["id"] if pid == "custom" and custom_cards else pid
break
# In auto mode the hint should reflect what vision.py will actually
@@ -2473,7 +2505,7 @@ class ModelsHandler:
"current_model": user_specified,
"fallback_provider": predicted["provider"],
"fallback_model": predicted["model"],
"providers": list(cls._VISION_PROVIDER_MODELS.keys()),
"providers": providers,
"provider_models": cls._VISION_PROVIDER_MODELS,
}
@@ -2546,18 +2578,40 @@ class ModelsHandler:
suggested = ""
if not explicit:
for pid in cls._EMBEDDING_PROVIDERS:
if pid == "custom":
continue
meta = ConfigHandler.PROVIDER_MODELS.get(pid) or {}
key_field = meta.get("api_key_field")
if key_field and cls._is_real_key(local_config.get(key_field, "")):
suggested = pid
break
if not suggested:
custom_cards = cls._custom_provider_cards(local_config)
if custom_cards:
suggested = custom_cards[0]["id"]
# Build provider list: built-in providers + expanded custom:<id> entries
# Same pattern as _chat_capability — each user-created custom provider
# gets its own dropdown entry showing the user-chosen name.
providers = []
custom_cards = cls._custom_provider_cards(local_config)
for pid in cls._EMBEDDING_PROVIDERS:
if pid == "custom":
if custom_cards:
providers.extend(c["id"] for c in custom_cards)
# No custom providers configured — skip the bare "custom" entry
# since the runtime cannot resolve its credentials.
else:
providers.append(pid)
return {
"editable": True,
"current_provider": explicit,
"suggested_provider": suggested,
"current_model": local_config.get("embedding_model", "") or "",
"current_dim": int(local_config.get("embedding_dimensions") or 0) or None,
"providers": cls._EMBEDDING_PROVIDERS,
"providers": providers,
"provider_models": cls._EMBEDDING_PROVIDER_MODELS,
}
# Auto-fallback order for image generation. Mirrors the global priority
@@ -2919,10 +2973,10 @@ class ModelsHandler:
{
"action": "set_custom_provider",
"id": "3f2a9c1b", # required for edit; omit for create
"name": "siliconflow", # required, display label
"name": "my-provider", # required, display label
"api_base": "https://...", # required when creating
"api_key": "sk-...", # optional on edit (keep existing)
"model": "deepseek-ai/...", # optional default model
"model": "model-name", # optional default model
"make_active": true # optional, also activate it
}
"""
@@ -3143,6 +3197,25 @@ class ModelsHandler:
# is persisted so users picking a custom model under a specific vendor
# still get routed there — runtime falls back to model-name prefix
# inference only when provider is empty.
# Validate provider_id — mirrors _set_chat / _set_embedding pattern.
if provider_id.startswith("custom:"):
from models.custom_provider import parse_custom_bot_type
_, custom_id = parse_custom_bot_type(provider_id)
providers = self._normalize_custom_providers(conf().get("custom_providers"))
custom_provider = next((p for p in providers if p.get("id") == custom_id), None)
if custom_provider is None:
return json.dumps({"status": "error", "message": f"unknown custom provider id: {custom_id}"})
if not model:
model = custom_provider.get("model") or ""
elif provider_id and provider_id not in {k for k in ModelsHandler._VISION_PROVIDER_MODELS if k != "custom"}:
return json.dumps({"status": "error", "message": f"unknown provider: {provider_id}"})
if provider_id and not model:
return json.dumps({
"status": "error",
"message": "vision model is required when a provider is selected",
})
local_config = conf()
file_cfg = self._read_file_config()
self._set_nested_namespace_value(file_cfg, "tools", "vision", "model", model)
@@ -3268,7 +3341,20 @@ class ModelsHandler:
logger.warning(f"[ModelsHandler] Bridge voice refresh failed: {e}")
def _set_embedding(self, provider_id: str, model: str) -> str:
# Two valid states: both empty (reset to pick-or-empty) OR both set.
# Validate provider_id — mirrors _set_chat's validation pattern.
if provider_id.startswith("custom:"):
from models.custom_provider import parse_custom_bot_type
_, custom_id = parse_custom_bot_type(provider_id)
providers = self._normalize_custom_providers(conf().get("custom_providers"))
custom_provider = next((p for p in providers if p.get("id") == custom_id), None)
if custom_provider is None:
return json.dumps({"status": "error", "message": f"unknown custom provider id: {custom_id}"})
# Fall back to the custom provider's default model when none is given.
if not model:
model = custom_provider.get("model") or ""
elif provider_id and provider_id not in {p for p in ModelsHandler._EMBEDDING_PROVIDERS if p != "custom"}:
return json.dumps({"status": "error", "message": f"unknown provider: {provider_id}"})
# A provider without a model leaves the runtime in a broken half-state,
# so reject that explicitly instead of silently writing it through.
if provider_id and not model:
@@ -4186,6 +4272,141 @@ class SchedulerHandler:
return json.dumps({"status": "error", "message": str(e)})
class SchedulerToggleHandler:
def POST(self):
_require_auth()
web.header('Content-Type', 'application/json; charset=utf-8')
try:
body = json.loads(web.data())
task_id = body.get("task_id")
enabled = body.get("enabled", True)
if not task_id:
return json.dumps({"status": "error", "message": "task_id required"})
from agent.tools.scheduler.task_store import TaskStore
workspace_root = _get_workspace_root()
store_path = os.path.join(workspace_root, "scheduler", "tasks.json")
store = TaskStore(store_path)
store.enable_task(task_id, enabled)
task = store.get_task(task_id)
return json.dumps({"status": "success", "task": task}, ensure_ascii=False)
except Exception as e:
logger.error(f"[WebChannel] Scheduler toggle error: {e}")
return json.dumps({"status": "error", "message": str(e)})
class SchedulerUpdateHandler:
def POST(self):
_require_auth()
web.header('Content-Type', 'application/json; charset=utf-8')
try:
body = json.loads(web.data())
task_id = body.get("task_id")
if not task_id:
return json.dumps({"status": "error", "message": "task_id required"})
from agent.tools.scheduler.task_store import TaskStore
from agent.tools.scheduler.scheduler_service import SchedulerService
from datetime import datetime
workspace_root = _get_workspace_root()
store_path = os.path.join(workspace_root, "scheduler", "tasks.json")
store = TaskStore(store_path)
# Get original task (single query to avoid repeated I/O)
original_task = store.get_task(task_id)
if not original_task:
return json.dumps({"status": "error", "message": f"Task '{task_id}' not found"})
# Build updates dict
updates = {}
if "name" in body:
updates["name"] = body["name"]
if "enabled" in body:
updates["enabled"] = body["enabled"]
# Update schedule
if "schedule" in body:
updates["schedule"] = body["schedule"]
# If schedule config changed, recalculate next_run_at
# Build merged temp task data for calculation (without modifying the original object)
merged = dict(original_task)
merged.update(updates)
if "action" in body:
merged["action"] = body["action"]
temp_service = SchedulerService(store, lambda t: None)
next_run = temp_service._calculate_next_run(merged, datetime.now())
if next_run:
updates["next_run_at"] = next_run.isoformat()
else:
# Cannot calculate next run time, schedule config may be invalid
return json.dumps({
"status": "error",
"message": "Cannot calculate next run time. Please check the schedule config (e.g., cron expression format, or whether the one-time task time has already passed)."
}, ensure_ascii=False)
# Update action
if "action" in body:
action = body["action"]
channel_type = action.get("channel_type", "web")
# Get the task's original channel_type
old_channel = original_task.get("action", {}).get("channel_type", "web")
# If channel type changed or no receiver, reject the update.
# Note: the web UI disables the channel selector, so this branch
# is only reachable via direct API calls. Changing a task's channel
# after creation is not supported because the receiver identity is
# channel-bound and cannot be trivially re-populated (e.g. weixin
# requires a valid context_token tied to the original user-session).
if old_channel and old_channel != channel_type:
return json.dumps({
"status": "error",
"message": f"Cannot change channel type from '{old_channel}' to '{channel_type}'. Please create a new task on the target channel instead."
}, ensure_ascii=False)
if not action.get("receiver"):
return json.dumps({
"status": "error",
"message": "Receiver is required. Please create a new task through the chat interface."
}, ensure_ascii=False)
updates["action"] = action
# If schedule was not updated but action was, ensure next_run_at exists
if "schedule" not in body and "next_run_at" not in original_task:
merged = dict(original_task)
merged.update(updates)
temp_service = SchedulerService(store, lambda t: None)
next_run = temp_service._calculate_next_run(merged, datetime.now())
if next_run:
updates["next_run_at"] = next_run.isoformat()
store.update_task(task_id, updates)
task = store.get_task(task_id)
return json.dumps({"status": "success", "task": task}, ensure_ascii=False)
except Exception as e:
logger.error(f"[WebChannel] Scheduler update error: {e}")
return json.dumps({"status": "error", "message": str(e)})
class SchedulerDeleteHandler:
def POST(self):
_require_auth()
web.header('Content-Type', 'application/json; charset=utf-8')
try:
body = json.loads(web.data())
task_id = body.get("task_id")
if not task_id:
return json.dumps({"status": "error", "message": "task_id required"})
from agent.tools.scheduler.task_store import TaskStore
workspace_root = _get_workspace_root()
store_path = os.path.join(workspace_root, "scheduler", "tasks.json")
store = TaskStore(store_path)
store.delete_task(task_id)
return json.dumps({"status": "success"}, ensure_ascii=False)
except Exception as e:
logger.error(f"[WebChannel] Scheduler delete error: {e}")
return json.dumps({"status": "error", "message": str(e)})
class SessionsHandler:
def GET(self):
_require_auth()
@@ -4362,7 +4583,7 @@ class MessageDeleteHandler:
# 2. Sync agent's in-memory context so its next turn sees the
# same history as the DB. Handled by the agent_bridge helper.
try:
from bridge import Bridge
from bridge.bridge import Bridge
Bridge().get_agent_bridge().sync_session_messages_from_store(session_id)
except Exception as sync_err:
logger.warning(f"[WebChannel] Failed to sync agent memory: {sync_err}")