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fix merge bug.

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This commit is contained in:
Liu Zhicong
2023-09-28 14:03:52 -07:00
parent b2f9ddce5b
commit fc0c91d942
10 changed files with 88 additions and 91 deletions
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src/aios_kernel/agent.py
+6 -2
View File
@@ -340,7 +340,9 @@ class AIAgent:
org_msg.inner_call_chain.append(ineternal_call_record)
if stack_limit > 0:
inner_func_call_node = task_result.result_message.get("function_call")
result_message = task_result.result.get("message")
if result_message:
inner_func_call_node = result_message.get("function_call")
if inner_func_call_node:
return await self._execute_func(inner_func_call_node,prompt,org_msg,stack_limit-1)
@@ -395,7 +397,9 @@ class AIAgent:
final_result = task_result.result_str
inner_func_call_node = task_result.result_message.get("function_call")
result_message = task_result.result.get("message")
if result_message:
inner_func_call_node = result_message.get("function_call")
if inner_func_call_node:
#TODO to save more token ,can i use msg_prompt?
final_result,error_code = await self._execute_func(inner_func_call_node,prompt,msg)
src/aios_kernel/compute_kernel.py
+12 -11
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@@ -107,7 +107,7 @@ class ComputeKernel:
self.run(task_req)
return task_req
async def _send_task(self,task_req:ComputeTask)->ComputeTaskResult:
async def _wait_task(self,task_req:ComputeTask)->ComputeTaskResult:
async def check_timer():
check_times = 0
while True:
@@ -135,7 +135,7 @@ class ComputeKernel:
async def do_llm_completion(self, prompt: AgentPrompt, mode_name: Optional[str] = None, max_token: int = 0, inner_functions = None) -> str:
task_req = self.llm_completion(prompt, mode_name, max_token,inner_functions)
return await self._send_task(task_req)
return await self._wait_task(task_req)
def text_embedding(self,input:str,model_name:Optional[str] = None):
@@ -146,12 +146,13 @@ class ComputeKernel:
async def do_text_embedding(self,input:str,model_name:Optional[str] = None) -> [float]:
task_req = self.text_embedding(input,model_name)
task_result = await self._send_task(task_req)
task_result = await self._wait_task(task_req)
if task_req.state == ComputeTaskState.DONE:
return task_result.result_str
return "error!"
return task_result.result.get("content")
else:
logging.warning(f"do_text_embedding error: {task_req.error_str},input: {input}")
return None
def image_embedding(self,input:ObjectID,model_name:Optional[str] = None):
task_req = ComputeTask()
@@ -161,12 +162,12 @@ class ComputeKernel:
async def do_image_embedding(self,input:ObjectID,model_name:Optional[str] = None) -> [float]:
task_req = self.image_embedding(input,model_name)
task_result = await self._send_task(task_req)
task_result = await self._wait_task(task_req)
if task_req.state == ComputeTaskState.DONE:
return task_result.result_str
return task_result.result.get("content")
return "error!"
return None
async def do_text_to_speech(self,
input:str,
@@ -185,7 +186,7 @@ class ComputeKernel:
task_req.task_type = ComputeTaskType.TEXT_2_VOICE
self.run(task_req)
task_result = await self._send_task(task_req)
task_result = await self._wait_task(task_req)
if task_req.state == ComputeTaskState.DONE:
return task_result.result
@@ -199,7 +200,7 @@ class ComputeKernel:
async def do_text_2_image(self, prompt:str, model_name:Optional[str] = None, negative_prompt = None) -> ComputeTaskResult:
task = self.text_2_image(prompt,model_name, negative_prompt)
task = await self._send_task(task)
task = await self._wait_task(task)
return task.result
# if task_req.state == ComputeTaskState.DONE:
src/aios_kernel/compute_task.py
+3 -1
View File
@@ -110,7 +110,9 @@ class ComputeTaskResult:
self.error_str : str = None
self.result_code: int = 0
self.result_str: str = None # easy to use,can read from result
self.result_message: dict = {}
self.result : dict = {}
self.result_refers: dict = {}
self.pading_data: bytearray = None
src/aios_kernel/knowledge_base.py
+4 -2
View File
@@ -33,7 +33,8 @@ class KnowledgeBase:
text = chunk.read().decode("utf-8")
vector = await self.compute_kernel.do_text_embedding(text, self._default_text_model)
await self.store.get_vector_store(self._default_text_model).insert(vector, chunk_id)
if vector:
await self.store.get_vector_store(self._default_text_model).insert(vector, chunk_id)
async def __embedding_image(self, image: ImageObject):
# desc = {}
@@ -45,7 +46,8 @@ class KnowledgeBase:
# desc["tags"] = image.get_tags()
# vector = await self.compute_kernel.do_text_embedding(json.dumps(desc), self._default_text_model)
vector = await self.compute_kernel.do_image_embedding(image.calculate_id(), self._default_image_model)
await self.store.get_vector_store(self._default_image_model).insert(vector, image.calculate_id())
if vector:
await self.store.get_vector_store(self._default_image_model).insert(vector, image.calculate_id())
async def __embedding_video(self, vedio: VideoObject):
desc = {}
src/aios_kernel/local_llama_compute_node.py
+10 -3
View File
@@ -21,7 +21,11 @@ class LocalLlama_ComputeNode(Queue_ComputeNode):
self.url = url
self.model_name = model_name
async def execute_task(self, task: ComputeTask, result: ComputeTaskResult):
async def execute_task(self, task: ComputeTask)->ComputeTaskResult:
result = ComputeTaskResult()
result.result_code = ComputeTaskResultCode.ERROR
result.set_from_task(task)
result.worker_id = self.node_id
match task.task_type:
case ComputeTaskType.TEXT_EMBEDDING:
model_name = task.params["model_name"]
@@ -56,7 +60,9 @@ class LocalLlama_ComputeNode(Queue_ComputeNode):
result.result_code = ComputeTaskResultCode.ERROR
task.error_str = f"ComputeTask's TaskType : {task.task_type} not support!"
result.error_str = f"ComputeTask's TaskType : {task.task_type} not support!"
return None
return result
return result
async def initial(self) -> bool:
return True
@@ -145,7 +151,8 @@ class LocalLlama_ComputeNode(Queue_ComputeNode):
result.result_code = ComputeTaskResultCode.OK
result.result_str = resp["choices"][0]["message"]["content"]
result.result_message = resp["choices"][0]["message"]
result.result["message"] = resp["choices"][0]["message"]
if token_usage:
result.result_refers["token_usage"] = token_usage
src/aios_kernel/local_st_compute_node.py
+29 -62
View File
@@ -5,14 +5,12 @@ from pydantic import BaseModel
from typing import Union
from PIL import Image
import io
from .compute_task import ComputeTask, ComputeTaskState, ComputeTaskType
from .compute_task import ComputeTask, ComputeTaskState, ComputeTaskType,ComputeTaskResult,ComputeTaskResultCode
from .queue_compute_node import Queue_ComputeNode
from knowledge import ObjectID
logger = logging.getLogger(__name__)
class LocalSentenceTransformer_Text_ComputeNode(Queue_ComputeNode):
# For valid pretrained models, see https://www.sbert.net/docs/pretrained_models.html
def __init__(self, model_name: str = "all-MiniLM-L6-v2"):
@@ -39,18 +37,11 @@ class LocalSentenceTransformer_Text_ComputeNode(Queue_ComputeNode):
self.start()
return True
async def execute_task(
self, task: ComputeTask
) -> {
"task_type": str,
"content": str,
"message": str,
"state": ComputeTaskState,
"error": {
"code": int,
"message": str,
},
}:
async def execute_task(self, task: ComputeTask) :
result = ComputeTaskResult()
result.result_code = ComputeTaskResultCode.ERROR
result.set_from_task(task)
result.worker_id = self.node_id
try:
# logger.debug(f"LocalSentenceTransformer_Text_ComputeNode task: {task}")
if task.task_type == ComputeTaskType.TEXT_EMBEDDING:
@@ -60,25 +51,19 @@ class LocalSentenceTransformer_Text_ComputeNode(Queue_ComputeNode):
)
sentence_embeddings = self.model.encode(input, show_progress_bar=False).tolist()
# logger.debug(f"LocalSentenceTransformer_Text_ComputeNode task sentence_embeddings: {sentence_embeddings}")
return {
"state": ComputeTaskState.DONE,
"content": sentence_embeddings,
"message": None,
}
result.result_code = ComputeTaskResultCode.OK
result.result["content"] = sentence_embeddings
else:
return {
"state": ComputeTaskState.ERROR,
"error": {"code": -1, "message": "unsupport embedding task type"},
}
result.error_str = f"unsupport embedding task type: {task.task_type}"
except Exception as err:
import traceback
logger.error(f"{traceback.format_exc()}, error: {err}")
result.error_str = f"{traceback.format_exc()}, error: {err}"
return result
return {
"state": ComputeTaskState.ERROR,
"error": {"code": -1, "message": "unknown exception: " + str(err)},
}
def display(self) -> str:
return f"LocalSentenceTransformer_Text_ComputeNode: {self.node_id}, {self.model_name}"
@@ -170,16 +155,11 @@ class LocalSentenceTransformer_Image_ComputeNode(Queue_ComputeNode):
async def execute_task(
self, task: ComputeTask
) -> {
"task_type": str,
"content": str,
"message": str,
"state": ComputeTaskState,
"error": {
"code": int,
"message": str,
},
}:
) -> ComputeTaskResult:
result = ComputeTaskResult()
result.result_code = ComputeTaskResultCode.ERROR
result.set_from_task(task)
result.worker_id = self.node_id
try:
# logger.debug(f"LocalSentenceTransformer_Text_ComputeNode task: {task}")
if task.task_type == ComputeTaskType.TEXT_EMBEDDING:
@@ -189,11 +169,9 @@ class LocalSentenceTransformer_Image_ComputeNode(Queue_ComputeNode):
)
sentence_embeddings = self.multi_model.encode(input, show_progress_bar=False).tolist()
# logger.debug(f"LocalSentenceTransformer_Text_ComputeNode task sentence_embeddings: {sentence_embeddings}")
return {
"state": ComputeTaskState.DONE,
"content": sentence_embeddings,
"message": None,
}
result.result_code = ComputeTaskResultCode.OK
result.result["content"] = sentence_embeddings
elif task.task_type == ComputeTaskType.IMAGE_EMBEDDING:
input = task.params["input"]
logger.debug(
@@ -202,33 +180,22 @@ class LocalSentenceTransformer_Image_ComputeNode(Queue_ComputeNode):
img = self._load_image(input)
if img is None:
return {
"state": ComputeTaskState.ERROR,
"error": {"code": -1, "message": "load image failed"},
}
result.error_str = f"load image failed: {input}"
return result
sentence_embeddings = self.model.encode(img, show_progress_bar=False).tolist()
# logger.debug(f"LocalSentenceTransformer_Text_ComputeNode task sentence_embeddings: {sentence_embeddings}")
return {
"state": ComputeTaskState.DONE,
"content": sentence_embeddings,
"message": None,
}
result.result_code = ComputeTaskResultCode.OK
result.result["content"] = sentence_embeddings
else:
return {
"state": ComputeTaskState.ERROR,
"error": {"code": -1, "message": "unsupport embedding task type"},
}
result.error_str = f"unsupport embedding task type: {task.task_type}"
except Exception as err:
import traceback
logger.error(f"{traceback.format_exc()}, error: {err}")
result.error_str = f"{traceback.format_exc()}, error: {err}"
return {
"state": ComputeTaskState.ERROR,
"error": {"code": -1, "message": "unknown exception: " + str(err)},
}
return result
def display(self) -> str:
return f"LocalSentenceTransformer_Image_ComputeNode: {self.node_id}, {self.model_name}"
src/aios_kernel/open_ai_node.py
+2 -1
View File
@@ -150,7 +150,8 @@ class OpenAI_ComputeNode(ComputeNode):
result.result_code = ComputeTaskResultCode.OK
result.worker_id = self.node_id
result.result_str = resp["choices"][0]["message"]["content"]
result.result_message = resp["choices"][0]["message"]
result.result["message"] = resp["choices"][0]["message"]
if token_usage:
result.result_refers["token_usage"] = token_usage
logger.info(f"openai success response: {result.result_str}")
src/aios_kernel/queue_compute_node.py
+11 -4
View File
@@ -16,7 +16,7 @@ class Queue_ComputeNode(ComputeNode):
self.is_start = False
@abstractmethod
async def execute_task(self, task: ComputeTask, result: ComputeTaskResult):
async def execute_task(self, task: ComputeTask)->ComputeTaskResult:
pass
async def push_task(self, task: ComputeTask, proiority: int = 0):
@@ -29,15 +29,22 @@ class Queue_ComputeNode(ComputeNode):
async def _run_task(self, task: ComputeTask):
task.state = ComputeTaskState.RUNNING
result = ComputeTaskResult()
result.result_code = ComputeTaskResultCode.ERROR
result.set_from_task(task)
result.worker_id = self.node_id
await self.execute_task(task, result)
real_result = await self.execute_task(task)
return result
if real_result:
if real_result.result_code == ComputeTaskResultCode.OK:
task.state = ComputeTaskState.DONE
else:
task.state = ComputeTaskState.ERROR
return real_result
else:
task.state = ComputeTaskState.ERROR
return result
def start(self):
if self.is_start is True:
src/aios_kernel/workflow.py
+7 -2
View File
@@ -425,7 +425,10 @@ class Workflow:
ineternal_call_record.done_time = time.time()
org_msg.inner_call_chain.append(ineternal_call_record)
if stack_limit > 0:
inner_func_call_node = task_result.result_message.get("function_call")
result_message = task_result.result.get("message")
if result_message:
inner_func_call_node = result_message.get("function_call")
if inner_func_call_node:
return await self._role_execute_func(the_role,inner_func_call_node,prompt,org_msg,stack_limit-1)
else:
@@ -466,7 +469,9 @@ class Workflow:
result_str = task_result.result_str
logger.info(f"{the_role.role_id} process {msg.sender}:{msg.body},llm str is :{result_str}")
inner_func_call_node = task_result.result_message.get("function_call")
result_message = task_result.result.get("message")
if result_message:
inner_func_call_node = result_message.get("function_call")
if inner_func_call_node:
#TODO to save more token ,can i use msg_prompt?
src/knowledge/vector/chroma_store.py
+2 -1
View File
@@ -28,7 +28,8 @@ class ChromaVectorStore(VectorBase):
self.collection = collection
async def insert(self, vector: [float], id: ObjectID):
logging.info(f"will insert vector: {vector} id: {str(id)}")
logging.info(f"will insert vector: {len(vector)} id: {str(id)}")
logging.debug(f"vector is {vector}")
self.collection.add(
embeddings=vector,
ids=str(id),