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perf: Optimize performance by changing cyclic data retrieval to batch retrieval #6278

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59 changes: 41 additions & 18 deletions apps/application/flow/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -248,33 +248,56 @@ def is_valid_start_node(self):
def is_valid_model_params(self):
node_list = [node for node in self.nodes if (
node.type == 'ai-chat-node' or node.type == 'question-node' or node.type == 'parameter-extraction-node')]

model_ids = []
nodes_requiring_validation = []
for node in node_list:
if (node.properties.get('node_data', {}).get('model_id_type') or 'custom') == 'reference':
continue
model = QuerySet(Model).filter(id=node.properties.get('node_data', {}).get('model_id')).first()
if model is None:
raise ValidationError(ErrorDetail(
_('The node {node} model does not exist').format(node=node.properties.get("stepName"))))
credential = get_model_credential(model.provider, model.model_type, model.model_name)
model_params_setting = node.properties.get('node_data', {}).get('model_params_setting')
model_params_setting_form = credential.get_model_params_setting_form(
model.model_name)
if model_params_setting is None:
model_params_setting = model_params_setting_form.get_default_form_data()
node.properties.get('node_data', {})['model_params_setting'] = model_params_setting
if node.properties.get('status', 200) != 200:
raise ValidationError(
ErrorDetail(_("Node {node} is unavailable").format(node=node.properties.get("stepName"))))
model_id = node.properties.get('node_data', {}).get('model_id')
if model_id:
model_ids.append(model_id)
nodes_requiring_validation.append((node, model_id))
if model_ids:
# 先批量获取数据
models_map = {str(model.id): model for model in QuerySet(Model).filter(id__in=model_ids)}
# 再循环从map中取数据进行处理
for node, model_id in nodes_requiring_validation:
model = models_map.get(str(model_id))
if model is None:
raise ValidationError(ErrorDetail(
_('The node {node} model does not exist').format(node=node.properties.get("stepName"))))
credential = get_model_credential(model.provider, model.model_type, model.model_name)
model_params_setting = node.properties.get('node_data', {}).get('model_params_setting')
model_params_setting_form = credential.get_model_params_setting_form(
model.model_name)
if model_params_setting is None:
model_params_setting = model_params_setting_form.get_default_form_data()
node.properties.get('node_data', {})['model_params_setting'] = model_params_setting
if node.properties.get('status', 200) != 200:
raise ValidationError(
ErrorDetail(_("Node {node} is unavailable").format(node=node.properties.get("stepName"))))

node_list = [node for node in self.nodes if (node.type == 'function-lib-node')]

function_lib_ids = []
nodes_requiring_tool_validation = []
for node in node_list:
function_lib_id = node.properties.get('node_data', {}).get('function_lib_id')
if function_lib_id is None:
raise ValidationError(ErrorDetail(
_('The library ID of node {node} cannot be empty').format(node=node.properties.get("stepName"))))
f_lib = QuerySet(Tool).filter(id=function_lib_id).first()
if f_lib is None:
raise ValidationError(ErrorDetail(_("The function library for node {node} is not available").format(
node=node.properties.get("stepName"))))
function_lib_ids.append(function_lib_id)
nodes_requiring_tool_validation.append((node, function_lib_id))
if function_lib_ids:
# 先批量获取数据
tools_map = {str(tool.id): tool for tool in QuerySet(Tool).filter(id__in=function_lib_ids)}
# 再循环从map中取数据进行处理
for node, function_lib_id in nodes_requiring_tool_validation:
f_lib = tools_map.get(str(function_lib_id))
if f_lib is None:
raise ValidationError(ErrorDetail(_("The function library for node {node} is not available").format(
node=node.properties.get("stepName"))))

def is_valid_base_node(self):
base_node_list = [node for node in self.nodes if node.id == 'base-node']
Expand Down
48 changes: 33 additions & 15 deletions apps/application/flow/step_node/ai_chat_step_node/impl/base_chat_node.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,8 +60,8 @@ def write_context_stream(node_variable: Dict, workflow_variable: Dict, node: INo
@param workflow: 工作流管理器
"""
response = node_variable.get("result")
answer = ""
reasoning_content = ""
answer_chunks = []
reasoning_content_chunks = []
model_setting = node.context.get(
"model_setting",
{"reasoning_content_enable": False, "reasoning_content_end": "</think>", "reasoning_content_start": "<think>"},
Expand All @@ -81,10 +81,10 @@ def write_context_stream(node_variable: Dict, workflow_variable: Dict, node: INo
reasoning_content_chunk = chunk.additional_kwargs.get("reasoning_content", "")
else:
reasoning_content_chunk = reasoning_chunk.get("reasoning_content")
answer += content_chunk
answer_chunks.append(content_chunk)
if reasoning_content_chunk is None:
reasoning_content_chunk = ""
reasoning_content += reasoning_content_chunk
reasoning_content_chunks.append(reasoning_content_chunk)
yield {
"content": content_chunk,
"reasoning_content": reasoning_content_chunk
Expand All @@ -93,14 +93,16 @@ def write_context_stream(node_variable: Dict, workflow_variable: Dict, node: INo
}

reasoning_chunk = reasoning.get_end_reasoning_content()
answer += reasoning_chunk.get("content")
answer_chunks.append(reasoning_chunk.get("content"))
reasoning_content_chunk = ""
if not response_reasoning_content:
reasoning_content_chunk = reasoning_chunk.get("reasoning_content")
yield {
"content": reasoning_chunk.get("content"),
"reasoning_content": reasoning_content_chunk if model_setting.get("reasoning_content_enable", False) else "",
}
answer = "".join(answer_chunks)
reasoning_content = "".join(reasoning_content_chunks)
_write_context(node_variable, workflow_variable, node, workflow, answer, reasoning_content)


Expand Down Expand Up @@ -525,12 +527,20 @@ def _process_videos(self, image, video_model):
if isinstance(image, str) and image.startswith("http"):
videos.append({"type": "video_url", "video_url": {"url": image}})
elif image is not None and len(image) > 0:
# 先批量获取数据
file_ids = [img["file_id"] for img in image if "file_id" in img]
if file_ids:
files_map = {str(f.id): f for f in QuerySet(File).filter(id__in=file_ids)}
else:
files_map = {}
# 再循环从map中取数据进行处理
for img in image:
if "file_id" in img:
file_id = img["file_id"]
file = QuerySet(File).filter(id=file_id).first()
url = video_model.upload_file_and_get_url(file.get_bytes(), file.file_name)
videos.append({"type": "video_url", "video_url": {"url": url}})
file = files_map.get(str(file_id))
if file:
url = video_model.upload_file_and_get_url(file.get_bytes(), file.file_name)
videos.append({"type": "video_url", "video_url": {"url": url}})
elif "url" in img and img["url"].startswith("http"):
videos.append({"type": "video_url", "video_url": {"url": img["url"]}})
return videos
Expand All @@ -543,16 +553,24 @@ def _process_images(self, image):
if isinstance(image, str) and image.startswith("http"):
images.append({"type": "image_url", "image_url": {"url": image}})
elif image is not None and len(image) > 0:
# 先批量获取数据
file_ids = [img["file_id"] for img in image if "file_id" in img]
if file_ids:
files_map = {str(f.id): f for f in QuerySet(File).filter(id__in=file_ids)}
else:
files_map = {}
# 再循环从map中取数据进行处理
for img in image:
if "file_id" in img:
file_id = img["file_id"]
file = QuerySet(File).filter(id=file_id).first()
image_bytes = file.get_bytes()
base64_image = base64.b64encode(image_bytes).decode("utf-8")
image_format = what(None, image_bytes)
images.append(
{"type": "image_url", "image_url": {"url": f"data:image/{image_format};base64,{base64_image}"}}
)
file = files_map.get(str(file_id))
if file:
image_bytes = file.get_bytes()
base64_image = base64.b64encode(image_bytes).decode("utf-8")
image_format = what(None, image_bytes)
images.append(
{"type": "image_url", "image_url": {"url": f"data:image/{image_format};base64,{base64_image}"}}
)
elif "url" in img and img["url"].startswith("http"):
images.append({"type": "image_url", "image_url": {"url": img["url"]}})
return images
Expand Down
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