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This commit is contained in:
Joel
2024-11-08 13:55:39 +08:00
parent 6759c6d5e6 033ab5490b
commit a9de7f24a2
175 changed files with 5472 additions and 303 deletions
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2
api/core/workflow/nodes/base/node.py
View File

@ -69,7 +69,7 @@ class BaseNode(Generic[GenericNodeData]):
try:
result = self._run()
except Exception as e:
logger.error(f"Node {self.node_id} failed to run: {e}")
logger.exception(f"Node {self.node_id} failed to run: {e}")
result = NodeRunResult(
status=WorkflowNodeExecutionStatus.FAILED,
error=str(e),

22
api/core/workflow/nodes/http_request/executor.py
View File

@ -97,15 +97,6 @@ class Executor:
headers = self.variable_pool.convert_template(self.node_data.headers).text
self.headers = _plain_text_to_dict(headers)
body = self.node_data.body
if body is None:
return
if "content-type" not in (k.lower() for k in self.headers) and body.type in BODY_TYPE_TO_CONTENT_TYPE:
self.headers["Content-Type"] = BODY_TYPE_TO_CONTENT_TYPE[body.type]
if body.type == "form-data":
self.boundary = f"----WebKitFormBoundary{_generate_random_string(16)}"
self.headers["Content-Type"] = f"multipart/form-data; boundary={self.boundary}"
def _init_body(self):
body = self.node_data.body
if body is not None:
@ -154,9 +145,8 @@ class Executor:
for k, v in files.items()
if v.related_id is not None
}
self.data = form_data
self.files = files
self.files = files or None
def _assembling_headers(self) -> dict[str, Any]:
authorization = deepcopy(self.auth)
@ -217,6 +207,7 @@ class Executor:
"timeout": (self.timeout.connect, self.timeout.read, self.timeout.write),
"follow_redirects": True,
}
# request_args = {k: v for k, v in request_args.items() if v is not None}
response = getattr(ssrf_proxy, self.method)(**request_args)
return response
@ -244,6 +235,13 @@ class Executor:
raw += f"Host: {url_parts.netloc}\r\n"
headers = self._assembling_headers()
body = self.node_data.body
boundary = f"----WebKitFormBoundary{_generate_random_string(16)}"
if body:
if "content-type" not in (k.lower() for k in self.headers) and body.type in BODY_TYPE_TO_CONTENT_TYPE:
headers["Content-Type"] = BODY_TYPE_TO_CONTENT_TYPE[body.type]
if body.type == "form-data":
headers["Content-Type"] = f"multipart/form-data; boundary={boundary}"
for k, v in headers.items():
if self.auth.type == "api-key":
authorization_header = "Authorization"
@ -256,7 +254,6 @@ class Executor:
body = ""
if self.files:
boundary = self.boundary
for k, v in self.files.items():
body += f"--{boundary}\r\n"
body += f'Content-Disposition: form-data; name="{k}"\r\n\r\n'
@ -271,7 +268,6 @@ class Executor:
elif self.data and self.node_data.body.type == "x-www-form-urlencoded":
body = urlencode(self.data)
elif self.data and self.node_data.body.type == "form-data":
boundary = self.boundary
for key, value in self.data.items():
body += f"--{boundary}\r\n"
body += f'Content-Disposition: form-data; name="{key}"\r\n\r\n'

5
api/core/workflow/nodes/llm/node.py
View File

@ -14,6 +14,7 @@ from core.model_runtime.entities import (
PromptMessage,
PromptMessageContentType,
TextPromptMessageContent,
VideoPromptMessageContent,
)
from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
from core.model_runtime.entities.model_entities import ModelType
@ -560,7 +561,9 @@ class LLMNode(BaseNode[LLMNodeData]):
# cuz vision detail is related to the configuration from FileUpload feature.
content_item.detail = vision_detail
prompt_message_content.append(content_item)
elif isinstance(content_item, TextPromptMessageContent | AudioPromptMessageContent):
elif isinstance(
content_item, TextPromptMessageContent | AudioPromptMessageContent | VideoPromptMessageContent
):
prompt_message_content.append(content_item)
if len(prompt_message_content) > 1:

35
api/core/workflow/nodes/question_classifier/question_classifier_node.py
View File

@ -49,11 +49,13 @@ class QuestionClassifierNode(LLMNode):
variable_pool = self.graph_runtime_state.variable_pool
# extract variables
variable = variable_pool.get(node_data.query_variable_selector) if node_data.query_variable_selector else None
variable = variable_pool.get(
node_data.query_variable_selector) if node_data.query_variable_selector else None
query = variable.value if variable else None
variables = {"query": query}
# fetch model config
model_instance, model_config = self._fetch_model_config(node_data.model)
model_instance, model_config = self._fetch_model_config(
node_data.model)
# fetch memory
memory = self._fetch_memory(
node_data_memory=node_data.memory,
@ -61,7 +63,8 @@ class QuestionClassifierNode(LLMNode):
)
# fetch instruction
node_data.instruction = node_data.instruction or ""
node_data.instruction = variable_pool.convert_template(node_data.instruction).text
node_data.instruction = variable_pool.convert_template(
node_data.instruction).text
files: Sequence[File] = (
self._fetch_files(
@ -127,7 +130,7 @@ class QuestionClassifierNode(LLMNode):
category_id = category_id_result
except OutputParserError:
logging.error(f"Failed to parse result text: {result_text}")
logging.exception(f"Failed to parse result text: {result_text}")
try:
process_data = {
"model_mode": model_config.mode,
@ -184,12 +187,15 @@ class QuestionClassifierNode(LLMNode):
variable_mapping = {"query": node_data.query_variable_selector}
variable_selectors = []
if node_data.instruction:
variable_template_parser = VariableTemplateParser(template=node_data.instruction)
variable_selectors.extend(variable_template_parser.extract_variable_selectors())
variable_template_parser = VariableTemplateParser(
template=node_data.instruction)
variable_selectors.extend(
variable_template_parser.extract_variable_selectors())
for variable_selector in variable_selectors:
variable_mapping[variable_selector.variable] = variable_selector.value_selector
variable_mapping = {node_id + "." + key: value for key, value in variable_mapping.items()}
variable_mapping = {node_id + "." + key: value for key,
value in variable_mapping.items()}
return variable_mapping
@ -210,7 +216,8 @@ class QuestionClassifierNode(LLMNode):
context: Optional[str],
) -> int:
prompt_transform = AdvancedPromptTransform(with_variable_tmpl=True)
prompt_template = self._get_prompt_template(node_data, query, None, 2000)
prompt_template = self._get_prompt_template(
node_data, query, None, 2000)
prompt_messages = prompt_transform.get_prompt(
prompt_template=prompt_template,
inputs={},
@ -223,13 +230,15 @@ class QuestionClassifierNode(LLMNode):
)
rest_tokens = 2000
model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
model_context_tokens = model_config.model_schema.model_properties.get(
ModelPropertyKey.CONTEXT_SIZE)
if model_context_tokens:
model_instance = ModelInstance(
provider_model_bundle=model_config.provider_model_bundle, model=model_config.model
)
curr_message_tokens = model_instance.get_llm_num_tokens(prompt_messages)
curr_message_tokens = model_instance.get_llm_num_tokens(
prompt_messages)
max_tokens = 0
for parameter_rule in model_config.model_schema.parameter_rules:
@ -270,7 +279,8 @@ class QuestionClassifierNode(LLMNode):
prompt_messages: list[LLMNodeChatModelMessage] = []
if model_mode == ModelMode.CHAT:
system_prompt_messages = LLMNodeChatModelMessage(
role=PromptMessageRole.SYSTEM, text=QUESTION_CLASSIFIER_SYSTEM_PROMPT.format(histories=memory_str)
role=PromptMessageRole.SYSTEM, text=QUESTION_CLASSIFIER_SYSTEM_PROMPT.format(
histories=memory_str)
)
prompt_messages.append(system_prompt_messages)
user_prompt_message_1 = LLMNodeChatModelMessage(
@ -311,4 +321,5 @@ class QuestionClassifierNode(LLMNode):
)
else:
raise InvalidModelTypeError(f"Model mode {model_mode} not support.")
raise InvalidModelTypeError(
f"Model mode {model_mode} not support.")