xinyin025/LangBot
1
0
Fork 0
mirror of https://github.com/langbot-app/LangBot.git synced 2026-08-14 14:31:00 +00:00
Code Issues Packages Projects Releases Wiki Activity

feat: preliminarily implement pipeline invoking

Browse Source
This commit is contained in:
Junyan Qin
2025-03-29 17:50:45 +08:00
parent d01eadc70f
commit 9f15ab5000
57 changed files with 384 additions and 421 deletions
Download Patch File Download Diff File Expand all files Collapse all files
pkg/provider/modelmgr/requesters/ollamachat.py
+61 -46
View File
@@ -22,35 +22,38 @@ REQUESTER_NAME: str = "ollama-chat"
class OllamaChatCompletions(requester.LLMAPIRequester):
"""Ollama平台 ChatCompletion API请求器"""
client: ollama.AsyncClient
default_config: dict[str, typing.Any] = {
'base-url': 'http://127.0.0.1:11434',
'timeout': 120,
"base_url": "http://127.0.0.1:11434",
"timeout": 120,
}
async def initialize(self):
os.environ['OLLAMA_HOST'] = self.requester_cfg['base-url']
self.client = ollama.AsyncClient(
timeout=self.requester_cfg['timeout']
)
os.environ["OLLAMA_HOST"] = self.requester_cfg["base_url"]
self.client = ollama.AsyncClient(timeout=self.requester_cfg["timeout"])
async def _req(self,
args: dict,
) -> Union[Mapping[str, Any], AsyncIterator[Mapping[str, Any]]]:
return await self.client.chat(
**args
)
async def _req(
self,
args: dict,
) -> Union[Mapping[str, Any], AsyncIterator[Mapping[str, Any]]]:
return await self.client.chat(**args)
async def _closure(self, query: core_entities.Query, req_messages: list[dict], use_model: entities.LLMModelInfo,
user_funcs: list[tools_entities.LLMFunction] = None,
extra_args: dict[str, typing.Any] = {}) -> llm_entities.Message:
args: Any = self.requester_cfg['args'].copy()
args["model"] = use_model.name if use_model.model_name is None else use_model.model_name
async def _closure(
self,
query: core_entities.Query,
req_messages: list[dict],
use_model: requester.RuntimeLLMModel,
user_funcs: list[tools_entities.LLMFunction] = None,
extra_args: dict[str, typing.Any] = {},
) -> llm_entities.Message:
args = extra_args.copy()
args["model"] = use_model.model_entity.name
messages: list[dict] = req_messages.copy()
for msg in messages:
if 'content' in msg and isinstance(msg["content"], list):
if "content" in msg and isinstance(msg["content"], list):
text_content: list = []
image_urls: list = []
for me in msg["content"]:
@@ -58,12 +61,16 @@ class OllamaChatCompletions(requester.LLMAPIRequester):
text_content.append(me["text"])
elif me["type"] == "image_base64":
image_urls.append(me["image_base64"])
msg["content"] = "\n".join(text_content)
msg["images"] = [url.split(',')[1] for url in image_urls]
if 'tool_calls' in msg: # LangBot 内部以 str 存储 tool_calls 的参数，这里需要转换为 dict
for tool_call in msg['tool_calls']:
tool_call['function']['arguments'] = json.loads(tool_call['function']['arguments'])
msg["images"] = [url.split(",")[1] for url in image_urls]
if (
"tool_calls" in msg
): # LangBot 内部以 str 存储 tool_calls 的参数，这里需要转换为 dict
for tool_call in msg["tool_calls"]:
tool_call["function"]["arguments"] = json.loads(
tool_call["function"]["arguments"]
)
args["messages"] = messages
args["tools"] = []
@@ -77,8 +84,8 @@ class OllamaChatCompletions(requester.LLMAPIRequester):
return message
async def _make_msg(
self,
chat_completions: ollama.ChatResponse) -> llm_entities.Message:
self, chat_completions: ollama.ChatResponse
) -> llm_entities.Message:
message: ollama.Message = chat_completions.message
if message is None:
raise ValueError("chat_completions must contain a 'message' field")
@@ -86,43 +93,51 @@ class OllamaChatCompletions(requester.LLMAPIRequester):
ret_msg: llm_entities.Message = None
if message.content is not None:
ret_msg = llm_entities.Message(
role="assistant",
content=message.content
)
ret_msg = llm_entities.Message(role="assistant", content=message.content)
if message.tool_calls is not None and len(message.tool_calls) > 0:
tool_calls: list[llm_entities.ToolCall] = []
for tool_call in message.tool_calls:
tool_calls.append(llm_entities.ToolCall(
id=uuid.uuid4().hex,
type="function",
function=llm_entities.FunctionCall(
name=tool_call.function.name,
arguments=json.dumps(tool_call.function.arguments)
tool_calls.append(
llm_entities.ToolCall(
id=uuid.uuid4().hex,
type="function",
function=llm_entities.FunctionCall(
name=tool_call.function.name,
arguments=json.dumps(tool_call.function.arguments),
),
)
))
)
ret_msg.tool_calls = tool_calls
return ret_msg
async def call(
self,
query: core_entities.Query,
model: entities.LLMModelInfo,
messages: typing.List[llm_entities.Message],
funcs: typing.List[tools_entities.LLMFunction] = None,
extra_args: dict[str, typing.Any] = {},
async def invoke_llm(
self,
query: core_entities.Query,
model: requester.RuntimeLLMModel,
messages: typing.List[llm_entities.Message],
funcs: typing.List[tools_entities.LLMFunction] = None,
extra_args: dict[str, typing.Any] = {},
) -> llm_entities.Message:
req_messages: list = []
for m in messages:
msg_dict: dict = m.dict(exclude_none=True)
content: Any = msg_dict.get("content")
if isinstance(content, list):
if all(isinstance(part, dict) and part.get('type') == 'text' for part in content):
if all(
isinstance(part, dict) and part.get("type") == "text"
for part in content
):
msg_dict["content"] = "\n".join(part["text"] for part in content)
req_messages.append(msg_dict)
try:
return await self._closure(query, req_messages, model, funcs, extra_args)
return await self._closure(
query=query,
req_messages=req_messages,
use_model=model,
use_funcs=funcs,
extra_args=extra_args,
)
except asyncio.TimeoutError:
raise errors.RequesterError('请求超时')
raise errors.RequesterError("请求超时")