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fix merge bugs.

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This commit is contained in:
Liu Zhicong
2023-09-18 11:41:16 -07:00
parent 5a26b9614c
commit a3025274e3
5 changed files with 84 additions and 89 deletions
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build_all_in_one.sh
-2
View File
@@ -1,5 +1,3 @@
#!/usr/bin/bash #!/usr/bin/bash
pipreqs ./src --force
# Build the docker image # Build the docker image
docker build -t aios . docker build -t aios .
src/aios_kernel/compute_task.py
+8 -7
View File
@@ -12,12 +12,13 @@ class ComputeTaskState(Enum):
PENDING = 4 PENDING = 4
class ComputeTaskType(Enum): class ComputeTaskType(Enum):
NONE = -1 NONE = "None"
LLM_COMPLETION = 0 LLM_COMPLETION = "llm_completion"
TEXT_2_IMAGE = 1 TEXT_2_IMAGE = "text_2_image"
IMAGE_2_IMAGE = 2 IMAGE_2_IMAGE = "image_2_image"
VOICE_2_TEXT = 3 VOICE_2_TEXT = "voice_2_text"
TEXT_2_VOICE = 4 TEXT_2_VOICE = "text_2_voice"
TEXT_EMBEDDING ="text_embedding"
class ComputeTask: class ComputeTask:
@@ -54,7 +55,7 @@ class ComputeTask:
self.params["inner_functions"] = inner_functions self.params["inner_functions"] = inner_functions
def set_text_embedding_params(self, input, model_name=None, callchain_id = None): def set_text_embedding_params(self, input, model_name=None, callchain_id = None):
self.task_type = "text_embedding" self.task_type = ComputeTaskType.TEXT_EMBEDDING
self.create_time = time.time() self.create_time = time.time()
self.task_id = uuid.uuid4().hex self.task_id = uuid.uuid4().hex
self.callchain_id = callchain_id self.callchain_id = callchain_id
src/aios_kernel/open_ai_node.py
+70 -71
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@@ -59,85 +59,84 @@ class OpenAI_ComputeNode(ComputeNode):
def _run_task(self, task: ComputeTask): def _run_task(self, task: ComputeTask):
task.state = ComputeTaskState.RUNNING task.state = ComputeTaskState.RUNNING
if task.task_type == "text_embedding": match task.task_type:
model_name = task.params["model_name"] case ComputeTaskType.TEXT_EMBEDDING:
input = task.params["input"] model_name = task.params["model_name"]
logger.info(f"call openai {model_name} input: {input}") input = task.params["input"]
logger.info(f"call openai {model_name} input: {input}")
resp = openai.Embedding.create(model=model_name, resp = openai.Embedding.create(model=model_name,
input=input) input=input)
# resp = { # resp = {
# "object": "list", # "object": "list",
# "data": [ # "data": [
# { # {
# "object": "embedding", # "object": "embedding",
# "index": 0, # "index": 0,
# "embedding": [ # "embedding": [
# -0.00930514745414257, # -0.00930514745414257,
# 0.00765434792265296, # 0.00765434792265296,
# -0.007167573552578688, # -0.007167573552578688,
# -0.012373941019177437, # -0.012373941019177437,
# -0.04884673282504082 # -0.04884673282504082
# ]}] # ]}]
# } # }
logger.info(f"openai response: {resp}") logger.info(f"openai response: {resp}")
result = ComputeTaskResult() result = ComputeTaskResult()
result.set_from_task(task) result.set_from_task(task)
result.worker_id = self.node_id result.worker_id = self.node_id
result.result = resp["data"][0]["embedding"] result.result = resp["data"][0]["embedding"]
return result return result
case ComputeTaskType.LLM_COMPLETION:
if task.task_type == "llm_completion": mode_name = task.params["model_name"]
mode_name = task.params["model_name"] prompts = task.params["prompts"]
# max_token_size = task.params["max_token_size"] max_token_size = task.params.get("max_token_size")
prompts = task.params["prompts"] llm_inner_functions = task.params["inner_functions"]
if max_token_size is None:
max_token_size = 4000
logger.info(f"call openai {mode_name} prompts: {prompts}")
mode_name = task.params["model_name"] if llm_inner_functions is None:
# max_token_size = task.params["max_token_size"] resp = openai.ChatCompletion.create(model=mode_name,
prompts = task.params["prompts"]
logger.info(f"call openai {mode_name} prompts: {prompts}")
if task.params.get("inner_functions") is None:
resp = openai.ChatCompletion.create(model=mode_name,
messages=prompts,
max_tokens=task.params["max_token_size"],
temperature=0.7)
else:
resp = openai.ChatCompletion.create(model=mode_name,
messages=prompts, messages=prompts,
functions=task.params["inner_functions"], max_tokens=max_token_size,
max_tokens=task.params["max_token_size"], temperature=0.7)
temperature=0.7) # TODO: add temperature to task params? else:
resp = openai.ChatCompletion.create(model=mode_name,
messages=prompts,
functions=llm_inner_functions,
max_tokens=max_token_size,
temperature=0.7) # TODO: add temperature to task params?
logger.info(f"openai response: {resp}") logger.info(f"openai response: {resp}")
result = ComputeTaskResult() result = ComputeTaskResult()
result.set_from_task(task) result.set_from_task(task)
status_code = resp["choices"][0]["finish_reason"] status_code = resp["choices"][0]["finish_reason"]
match status_code: match status_code:
case "function_call": case "function_call":
task.state = ComputeTaskState.DONE task.state = ComputeTaskState.DONE
case "stop": case "stop":
task.state = ComputeTaskState.DONE task.state = ComputeTaskState.DONE
case _: case _:
task.state = ComputeTaskState.ERROR task.state = ComputeTaskState.ERROR
task.error_str = f"The status code was {status_code}." task.error_str = f"The status code was {status_code}."
return None return None
result.worker_id = self.node_id
result.result_str = resp["choices"][0]["message"]["content"]
result.result_message = resp["choices"][0]["message"]
return result
result.worker_id = self.node_id
result.result_str = resp["choices"][0]["message"]["content"]
result.result_message = resp["choices"][0]["message"]
return result
case _:
task.state = ComputeTaskState.ERROR
return None
def start(self): def start(self):
if self.is_start is True: if self.is_start is True:
@@ -167,9 +166,9 @@ class OpenAI_ComputeNode(ComputeNode):
def is_support(self, task: ComputeTask) -> bool: def is_support(self, task: ComputeTask) -> bool:
if task.task_type == ComputeTaskType.LLM_COMPLETION: if task.task_type == ComputeTaskType.LLM_COMPLETION:
if (not task.params["model_name"] or task.params["model_name"] == "gpt-4-0613") if not task.params["model_name"] or task.params["model_name"] == "gpt-4-0613":
return True return True
if task.task_type == "text_embedding": if task.task_type == ComputeTaskType.TEXT_EMBEDDING:
if task.params["model_name"] == "text-embedding-ada-002": if task.params["model_name"] == "text-embedding-ada-002":
return True return True
return False return False
src/requirements.txt
+5 -8
View File
@@ -1,7 +1,4 @@
chromadb==0.4 chromadb==0.4
openai==0.28
toml==0.10
moviepy==1.0 moviepy==1.0
base58==2.1 base58==2.1
base36==0.1 base36==0.1
@@ -11,14 +8,14 @@ aioimaplib==1.0.1
aiosmtplib==2.0.2 aiosmtplib==2.0.2
beautifulsoup4==4.12.2 beautifulsoup4==4.12.2
mail_parser==3.15.0 mail_parser==3.15.0
openai==0.27.10
Pillow
prompt_toolkit==3.0.39 prompt_toolkit==3.0.39
protobuf
pydantic==1.10.11 pydantic==1.10.11
python-telegram-bot==20.5 python-telegram-bot==20.5
Requests==2.31.0 Requests==2.31.0
protobuf
stability_sdk stability_sdk
toml==0.10.2 toml
base58
google-cloud-texttospeech google-cloud-texttospeech
openai
Pillow
src/service/aios_shell/aios_shell.py
+1 -1
View File
@@ -102,7 +102,7 @@ class AIOS_Shell:
EmailTunnel.register_to_loader() EmailTunnel.register_to_loader()
user_data_dir = AIStorage.get_instance().get_myai_dir() user_data_dir = AIStorage.get_instance().get_myai_dir()
tunnels_config_path = os.path.abspath(f"{user_data_dir}/tunnels.cfg.toml") tunnels_config_path = os.path.abspath(f"{user_data_dir}/etc/tunnels.cfg.toml")
tunnel_config = None tunnel_config = None
try: try:
tunnel_config = toml.load(tunnels_config_path) tunnel_config = toml.load(tunnels_config_path)