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Enhance Code Consistency Across Repository with .editorconfig (#19023)

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This commit is contained in:
QuantumGhost
2025-04-29 18:04:33 +08:00
committed by GitHub
parent 226afd4550
commit bd1bbfee4b
274 changed files with 1271 additions and 1217 deletions
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2
api/.dockerignore
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@@ -16,4 +16,4 @@ logs
.ruff_cache
# venv
.venv
.venv

8
api/commands.py
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@@ -444,13 +444,13 @@ def convert_to_agent_apps():
WHERE a.mode = 'chat'
AND am.agent_mode is not null
AND (
am.agent_mode like '%"strategy": "function_call"%'
am.agent_mode like '%"strategy": "function_call"%'
OR am.agent_mode like '%"strategy": "react"%'
)
)
AND (
am.agent_mode like '{"enabled": true%'
am.agent_mode like '{"enabled": true%'
OR am.agent_mode like '{"max_iteration": %'
) ORDER BY a.created_at DESC LIMIT 1000
) ORDER BY a.created_at DESC LIMIT 1000
"""
with db.engine.begin() as conn:

4
api/core/agent/prompt/template.py
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@@ -1,4 +1,4 @@
ENGLISH_REACT_COMPLETION_PROMPT_TEMPLATES = """Respond to the human as helpfully and accurately as possible.
ENGLISH_REACT_COMPLETION_PROMPT_TEMPLATES = """Respond to the human as helpfully and accurately as possible.
{{instruction}}
@@ -47,7 +47,7 @@ Thought:""" # noqa: E501
ENGLISH_REACT_COMPLETION_AGENT_SCRATCHPAD_TEMPLATES = """Observation: {{observation}}
Thought:"""
ENGLISH_REACT_CHAT_PROMPT_TEMPLATES = """Respond to the human as helpfully and accurately as possible.
ENGLISH_REACT_CHAT_PROMPT_TEMPLATES = """Respond to the human as helpfully and accurately as possible.
{{instruction}}

2
api/core/external_data_tool/api/__builtin__
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@@ -1 +1 @@
1
1

6
api/core/helper/code_executor/javascript/javascript_transformer.py
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@@ -10,13 +10,13 @@ class NodeJsTemplateTransformer(TemplateTransformer):
f"""
// declare main function
{cls._code_placeholder}
// decode and prepare input object
var inputs_obj = JSON.parse(Buffer.from('{cls._inputs_placeholder}', 'base64').toString('utf-8'))
// execute main function
var output_obj = main(inputs_obj)
// convert output to json and print
var output_json = JSON.stringify(output_obj)
var result = `<<RESULT>>${{output_json}}<<RESULT>>`

16
api/core/helper/code_executor/jinja2/jinja2_transformer.py
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@@ -21,20 +21,20 @@ class Jinja2TemplateTransformer(TemplateTransformer):
import jinja2
template = jinja2.Template('''{cls._code_placeholder}''')
return template.render(**inputs)
import json
from base64 import b64decode
# decode and prepare input dict
inputs_obj = json.loads(b64decode('{cls._inputs_placeholder}').decode('utf-8'))
# execute main function
output = main(**inputs_obj)
# convert output and print
result = f'''<<RESULT>>{{output}}<<RESULT>>'''
print(result)
""")
return runner_script
@@ -43,15 +43,15 @@ class Jinja2TemplateTransformer(TemplateTransformer):
preload_script = dedent("""
import jinja2
from base64 import b64decode
def _jinja2_preload_():
# prepare jinja2 environment, load template and render before to avoid sandbox issue
template = jinja2.Template('{{s}}')
template.render(s='a')
if __name__ == '__main__':
_jinja2_preload_()
""")
return preload_script

8
api/core/helper/code_executor/python3/python3_transformer.py
View File

@@ -9,16 +9,16 @@ class Python3TemplateTransformer(TemplateTransformer):
runner_script = dedent(f"""
# declare main function
{cls._code_placeholder}
import json
from base64 import b64decode
# decode and prepare input dict
inputs_obj = json.loads(b64decode('{cls._inputs_placeholder}').decode('utf-8'))
# execute main function
output_obj = main(**inputs_obj)
# convert output to json and print
output_json = json.dumps(output_obj, indent=4)
result = f'''<<RESULT>>{{output_json}}<<RESULT>>'''

44
api/core/llm_generator/prompts.py
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@@ -1,5 +1,5 @@
# Written by YORKI MINAKO🤡, Edited by Xiaoyi
CONVERSATION_TITLE_PROMPT = """You need to decompose the user's input into "subject" and "intention" in order to accurately figure out what the user's input language actually is.
CONVERSATION_TITLE_PROMPT = """You need to decompose the user's input into "subject" and "intention" in order to accurately figure out what the user's input language actually is.
Notice: the language type user uses could be diverse, which can be English, Chinese, Italian, Español, Arabic, Japanese, French, and etc.
ENSURE your output is in the SAME language as the user's input!
Your output is restricted only to: (Input language) Intention + Subject(short as possible)
@@ -58,7 +58,7 @@ User Input: yo, 你今天咋样？
"Your Output": "查询今日我的状态☺️"
}
User Input:
User Input:
""" # noqa: E501
PYTHON_CODE_GENERATOR_PROMPT_TEMPLATE = (
@@ -163,11 +163,11 @@ Here is a task description for which I would like you to create a high-quality p
{{TASK_DESCRIPTION}}
</task_description>
Based on task description, please create a well-structured prompt template that another AI could use to consistently complete the task. The prompt template should include:
- Do not include <input> or <output> section and variables in the prompt, assume user will add them at their own will.
- Clear instructions for the AI that will be using this prompt, demarcated with <instruction> tags. The instructions should provide step-by-step directions on how to complete the task using the input variables. Also Specifies in the instructions that the output should not contain any xml tag.
- Relevant examples if needed to clarify the task further, demarcated with <example> tags. Do not include variables in the prompt. Give three pairs of input and output examples.
- Do not include <input> or <output> section and variables in the prompt, assume user will add them at their own will.
- Clear instructions for the AI that will be using this prompt, demarcated with <instruction> tags. The instructions should provide step-by-step directions on how to complete the task using the input variables. Also Specifies in the instructions that the output should not contain any xml tag.
- Relevant examples if needed to clarify the task further, demarcated with <example> tags. Do not include variables in the prompt. Give three pairs of input and output examples.
- Include other relevant sections demarcated with appropriate XML tags like <examples>, <instruction>.
- Use the same language as task description.
- Use the same language as task description.
- Output in ``` xml ``` and start with <instruction>
Please generate the full prompt template with at least 300 words and output only the prompt template.
""" # noqa: E501
@@ -178,28 +178,28 @@ Here is a task description for which I would like you to create a high-quality p
{{TASK_DESCRIPTION}}
</task_description>
Based on task description, please create a well-structured prompt template that another AI could use to consistently complete the task. The prompt template should include:
- Descriptive variable names surrounded by {{ }} (two curly brackets) to indicate where the actual values will be substituted in. Choose variable names that clearly indicate the type of value expected. Variable names have to be composed of number, english alphabets and underline and nothing else.
- Clear instructions for the AI that will be using this prompt, demarcated with <instruction> tags. The instructions should provide step-by-step directions on how to complete the task using the input variables. Also Specifies in the instructions that the output should not contain any xml tag.
- Relevant examples if needed to clarify the task further, demarcated with <example> tags. Do not use curly brackets any other than in <instruction> section.
- Descriptive variable names surrounded by {{ }} (two curly brackets) to indicate where the actual values will be substituted in. Choose variable names that clearly indicate the type of value expected. Variable names have to be composed of number, english alphabets and underline and nothing else.
- Clear instructions for the AI that will be using this prompt, demarcated with <instruction> tags. The instructions should provide step-by-step directions on how to complete the task using the input variables. Also Specifies in the instructions that the output should not contain any xml tag.
- Relevant examples if needed to clarify the task further, demarcated with <example> tags. Do not use curly brackets any other than in <instruction> section.
- Any other relevant sections demarcated with appropriate XML tags like <input>, <output>, etc.
- Use the same language as task description.
- Use the same language as task description.
- Output in ``` xml ``` and start with <instruction>
Please generate the full prompt template and output only the prompt template.
""" # noqa: E501
RULE_CONFIG_PARAMETER_GENERATE_TEMPLATE = """
I need to extract the following information from the input text. The <information to be extracted> tag specifies the 'type', 'description' and 'required' of the information to be extracted.
I need to extract the following information from the input text. The <information to be extracted> tag specifies the 'type', 'description' and 'required' of the information to be extracted.
<information to be extracted>
variables name bounded two double curly brackets. Variable name has to be composed of number, english alphabets and underline and nothing else.
variables name bounded two double curly brackets. Variable name has to be composed of number, english alphabets and underline and nothing else.
</information to be extracted>
Step 1: Carefully read the input and understand the structure of the expected output.
Step 2: Extract relevant parameters from the provided text based on the name and description of object.
Step 2: Extract relevant parameters from the provided text based on the name and description of object.
Step 3: Structure the extracted parameters to JSON object as specified in <structure>.
Step 4: Ensure that the list of variable_names is properly formatted and valid. The output should not contain any XML tags. Output an empty list if there is no valid variable name in input text.
Step 4: Ensure that the list of variable_names is properly formatted and valid. The output should not contain any XML tags. Output an empty list if there is no valid variable name in input text.
### Structure
Here is the structure of the expected output, I should always follow the output structure.
Here is the structure of the expected output, I should always follow the output structure.
["variable_name_1", "variable_name_2"]
### Input Text
@@ -214,13 +214,13 @@ I should always output a valid list. Output nothing other than the list of varia
RULE_CONFIG_STATEMENT_GENERATE_TEMPLATE = """
<instruction>
Step 1: Identify the purpose of the chatbot from the variable {{TASK_DESCRIPTION}} and infer chatbot's tone (e.g., friendly, professional, etc.) to add personality traits.
Step 1: Identify the purpose of the chatbot from the variable {{TASK_DESCRIPTION}} and infer chatbot's tone (e.g., friendly, professional, etc.) to add personality traits.
Step 2: Create a coherent and engaging opening statement.
Step 3: Ensure the output is welcoming and clearly explains what the chatbot is designed to do. Do not include any XML tags in the output.
Please use the same language as the user's input language. If user uses chinese then generate opening statement in chinese, if user uses english then generate opening statement in english.
Example Input:
Please use the same language as the user's input language. If user uses chinese then generate opening statement in chinese, if user uses english then generate opening statement in english.
Example Input:
Provide customer support for an e-commerce website
Example Output:
Example Output:
Welcome! I'm here to assist you with any questions or issues you might have with your shopping experience. Whether you're looking for product information, need help with your order, or have any other inquiries, feel free to ask. I'm friendly, helpful, and ready to support you in any way I can.
<Task>
Here is the task description: {{INPUT_TEXT}}
@@ -276,15 +276,15 @@ Your task is to convert simple user descriptions into properly formatted JSON Sc
{
"type": "object",
"properties": {
"email": {
"email": {
"type": "string",
"format": "email"
},
"password": {
"password": {
"type": "string",
"minLength": 8
},
"age": {
"age": {
"type": "integer",
"minimum": 18
}

2
api/core/model_runtime/docs/en_US/customizable_model_scale_out.md
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@@ -307,4 +307,4 @@ Runtime Errors:
"""
```
For interface method details, see: [Interfaces](./interfaces.md). For specific implementations, refer to: [llm.py](https://github.com/langgenius/dify-runtime/blob/main/lib/model_providers/anthropic/llm/llm.py).
For interface method details, see: [Interfaces](./interfaces.md). For specific implementations, refer to: [llm.py](https://github.com/langgenius/dify-runtime/blob/main/lib/model_providers/anthropic/llm/llm.py).

2
api/core/model_runtime/docs/en_US/predefined_model_scale_out.md
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@@ -170,4 +170,4 @@ Runtime Errors:
"""
```
For interface method explanations, see: [Interfaces](./interfaces.md). For detailed implementation, refer to: [llm.py](https://github.com/langgenius/dify-runtime/blob/main/lib/model_providers/anthropic/llm/llm.py).
For interface method explanations, see: [Interfaces](./interfaces.md). For detailed implementation, refer to: [llm.py](https://github.com/langgenius/dify-runtime/blob/main/lib/model_providers/anthropic/llm/llm.py).

2
api/core/model_runtime/docs/zh_Hans/customizable_model_scale_out.md
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@@ -294,4 +294,4 @@ provider_credential_schema:
"""
```
接口方法说明见：[Interfaces](./interfaces.md)，具体实现可参考：[llm.py](https://github.com/langgenius/dify-runtime/blob/main/lib/model_providers/anthropic/llm/llm.py)。
接口方法说明见：[Interfaces](./interfaces.md)，具体实现可参考：[llm.py](https://github.com/langgenius/dify-runtime/blob/main/lib/model_providers/anthropic/llm/llm.py)。

2
api/core/model_runtime/docs/zh_Hans/predefined_model_scale_out.md
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@@ -169,4 +169,4 @@ pricing: # 价格信息
"""
```
接口方法说明见：[Interfaces](./interfaces.md)，具体实现可参考：[llm.py](https://github.com/langgenius/dify-runtime/blob/main/lib/model_providers/anthropic/llm/llm.py)。
接口方法说明见：[Interfaces](./interfaces.md)，具体实现可参考：[llm.py](https://github.com/langgenius/dify-runtime/blob/main/lib/model_providers/anthropic/llm/llm.py)。

2
api/core/moderation/api/__builtin__
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@@ -1 +1 @@
3
3

2
api/core/moderation/keywords/__builtin__
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@@ -1 +1 @@
2
2

2
api/core/moderation/openai_moderation/__builtin__
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@@ -1 +1 @@
1
1

4
api/core/plugin/backwards_invocation/model.py
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@@ -239,8 +239,8 @@ class PluginModelBackwardsInvocation(BaseBackwardsInvocation):
content = payload.text
SUMMARY_PROMPT = """You are a professional language researcher, you are interested in the language
and you can quickly aimed at the main point of an webpage and reproduce it in your own words but
retain the original meaning and keep the key points.
and you can quickly aimed at the main point of an webpage and reproduce it in your own words but
retain the original meaning and keep the key points.
however, the text you got is too long, what you got is possible a part of the text.
Please summarize the text you got.

2
api/core/prompt/prompt_templates/baichuan_chat.json
View File

@@ -10,4 +10,4 @@
],
"query_prompt": "\n\n用户：{{#query#}}",
"stops": ["用户："]
}
}

2
api/core/prompt/prompt_templates/baichuan_completion.json
View File

@@ -6,4 +6,4 @@
],
"query_prompt": "{{#query#}}",
"stops": null
}
}

2
api/core/prompt/prompt_templates/common_completion.json
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@@ -6,4 +6,4 @@
],
"query_prompt": "{{#query#}}",
"stops": null
}
}

6
api/core/rag/datasource/vdb/analyticdb/analyticdb_vector_sql.py
View File

@@ -156,8 +156,8 @@ class AnalyticdbVectorBySql:
values = []
id_prefix = str(uuid.uuid4()) + "_"
sql = f"""
INSERT INTO {self.table_name}
(id, ref_doc_id, vector, page_content, metadata_, to_tsvector)
INSERT INTO {self.table_name}
(id, ref_doc_id, vector, page_content, metadata_, to_tsvector)
VALUES (%s, %s, %s, %s, %s, to_tsvector('zh_cn', %s));
"""
for i, doc in enumerate(documents):
@@ -242,7 +242,7 @@ class AnalyticdbVectorBySql:
where_clause += f"AND metadata_->>'document_id' IN ({document_ids})"
with self._get_cursor() as cur:
cur.execute(
f"""SELECT id, vector, page_content, metadata_,
f"""SELECT id, vector, page_content, metadata_,
ts_rank(to_tsvector, to_tsquery_from_text(%s, 'zh_cn'), 32) AS score
FROM {self.table_name}
WHERE to_tsvector@@to_tsquery_from_text(%s, 'zh_cn') {where_clause}

2
api/core/rag/datasource/vdb/oceanbase/oceanbase_vector.py
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@@ -203,7 +203,7 @@ class OceanBaseVector(BaseVector):
full_sql = f"""SELECT metadata, text, MATCH (text) AGAINST (:query) AS score
FROM {self._collection_name}
WHERE MATCH (text) AGAINST (:query) > 0
WHERE MATCH (text) AGAINST (:query) > 0
{where_clause}
ORDER BY score DESC
LIMIT {top_k}"""

4
api/core/rag/datasource/vdb/opengauss/opengauss.py
View File

@@ -59,12 +59,12 @@ CREATE TABLE IF NOT EXISTS {table_name} (
"""
SQL_CREATE_INDEX_PQ = """
CREATE INDEX IF NOT EXISTS embedding_{table_name}_pq_idx ON {table_name}
CREATE INDEX IF NOT EXISTS embedding_{table_name}_pq_idx ON {table_name}
USING hnsw (embedding vector_cosine_ops) WITH (m = 16, ef_construction = 64, enable_pq=on, pq_m={pq_m});
"""
SQL_CREATE_INDEX = """
CREATE INDEX IF NOT EXISTS embedding_cosine_{table_name}_idx ON {table_name}
CREATE INDEX IF NOT EXISTS embedding_cosine_{table_name}_idx ON {table_name}
USING hnsw (embedding vector_cosine_ops) WITH (m = 16, ef_construction = 64);
"""

12
api/core/rag/datasource/vdb/oracle/oraclevector.py
View File

@@ -59,8 +59,8 @@ CREATE TABLE IF NOT EXISTS {table_name} (
)
"""
SQL_CREATE_INDEX = """
CREATE INDEX IF NOT EXISTS idx_docs_{table_name} ON {table_name}(text)
INDEXTYPE IS CTXSYS.CONTEXT PARAMETERS
CREATE INDEX IF NOT EXISTS idx_docs_{table_name} ON {table_name}(text)
INDEXTYPE IS CTXSYS.CONTEXT PARAMETERS
('FILTER CTXSYS.NULL_FILTER SECTION GROUP CTXSYS.HTML_SECTION_GROUP LEXER world_lexer')
"""
@@ -164,7 +164,7 @@ class OracleVector(BaseVector):
with conn.cursor() as cur:
try:
cur.execute(
f"""INSERT INTO {self.table_name} (id, text, meta, embedding)
f"""INSERT INTO {self.table_name} (id, text, meta, embedding)
VALUES (:1, :2, :3, :4)""",
value,
)
@@ -227,8 +227,8 @@ class OracleVector(BaseVector):
conn.outputtypehandler = self.output_type_handler
with conn.cursor() as cur:
cur.execute(
f"""SELECT meta, text, vector_distance(embedding,(select to_vector(:1) from dual),cosine)
AS distance FROM {self.table_name}
f"""SELECT meta, text, vector_distance(embedding,(select to_vector(:1) from dual),cosine)
AS distance FROM {self.table_name}
{where_clause} ORDER BY distance fetch first {top_k} rows only""",
[numpy.array(query_vector)],
)
@@ -290,7 +290,7 @@ class OracleVector(BaseVector):
document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
where_clause = f" AND metadata->>'document_id' in ({document_ids}) "
cur.execute(
f"""select meta, text, embedding FROM {self.table_name}
f"""select meta, text, embedding FROM {self.table_name}
WHERE CONTAINS(text, :kk, 1) > 0 {where_clause}
order by score(1) desc fetch first {top_k} rows only""",
kk=" ACCUM ".join(entities),

2
api/core/rag/datasource/vdb/pgvector/pgvector.py
View File

@@ -61,7 +61,7 @@ CREATE TABLE IF NOT EXISTS {table_name} (
"""
SQL_CREATE_INDEX = """
CREATE INDEX IF NOT EXISTS embedding_cosine_v1_idx ON {table_name}
CREATE INDEX IF NOT EXISTS embedding_cosine_v1_idx ON {table_name}
USING hnsw (embedding vector_cosine_ops) WITH (m = 16, ef_construction = 64);
"""

2
api/core/rag/datasource/vdb/pyvastbase/vastbase_vector.py
View File

@@ -58,7 +58,7 @@ CREATE TABLE IF NOT EXISTS {table_name} (
"""
SQL_CREATE_INDEX = """
CREATE INDEX IF NOT EXISTS embedding_cosine_v1_idx ON {table_name}
CREATE INDEX IF NOT EXISTS embedding_cosine_v1_idx ON {table_name}
USING hnsw (embedding floatvector_cosine_ops) WITH (m = 16, ef_construction = 64);
"""

4
api/core/rag/datasource/vdb/tidb_vector/tidb_vector.py
View File

@@ -205,9 +205,9 @@ class TiDBVector(BaseVector):
with Session(self._engine) as session:
select_statement = sql_text(f"""
SELECT meta, text, distance
SELECT meta, text, distance
FROM (
SELECT
SELECT
meta,
text,
{tidb_dist_func}(vector, :query_vector_str) AS distance

4
api/core/rag/retrieval/template_prompts.py
View File

@@ -50,7 +50,7 @@ You are a text metadata extract engine that extract text's metadata based on use
# Your task is to ONLY extract the metadatas that exist in the input text from the provided metadata list and Use the following operators ["=", "!=", ">", "<", ">=", "<="] to express logical relationships, then return result in JSON format with the key "metadata_fields" and value "metadata_field_value" and comparison operator "comparison_operator".
### Format
The input text is in the variable input_text. Metadata are specified as a list in the variable metadata_fields.
### Constraint
### Constraint
DO NOT include anything other than the JSON array in your response.
### Example
Here is the chat example between human and assistant, inside <example></example> XML tags.
@@ -59,7 +59,7 @@ User:{{"input_text": ["I want to know which company’s email address test@examp
Assistant:{{"metadata_map": [{{"metadata_field_name": "email", "metadata_field_value": "test@example.com", "comparison_operator": "="}}]}}
User:{{"input_text": "What are the movies with a score of more than 9 in 2024?", "metadata_fields": ["name", "year", "rating", "country"]}}
Assistant:{{"metadata_map": [{{"metadata_field_name": "year", "metadata_field_value": "2024", "comparison_operator": "="}, {{"metadata_field_name": "rating", "metadata_field_value": "9", "comparison_operator": ">"}}]}}
</example>
</example>
### User Input
{{"input_text" : "{input_text}", "metadata_fields" : {metadata_fields}}}
### Assistant Output

4
api/core/tools/builtin_tool/tool.py
View File

@@ -6,8 +6,8 @@ from core.tools.entities.tool_entities import ToolProviderType
from core.tools.utils.model_invocation_utils import ModelInvocationUtils
_SUMMARY_PROMPT = """You are a professional language researcher, you are interested in the language
and you can quickly aimed at the main point of an webpage and reproduce it in your own words but
retain the original meaning and keep the key points.
and you can quickly aimed at the main point of an webpage and reproduce it in your own words but
retain the original meaning and keep the key points.
however, the text you got is too long, what you got is possible a part of the text.
Please summarize the text you got.
"""

4
api/core/workflow/nodes/knowledge_retrieval/template_prompts.py
View File

@@ -50,7 +50,7 @@ You are a text metadata extract engine that extract text's metadata based on use
# Your task is to ONLY extract the metadatas that exist in the input text from the provided metadata list and Use the following operators ["=", "!=", ">", "<", ">=", "<="] to express logical relationships, then return result in JSON format with the key "metadata_fields" and value "metadata_field_value" and comparison operator "comparison_operator".
### Format
The input text is in the variable input_text. Metadata are specified as a list in the variable metadata_fields.
### Constraint
### Constraint
DO NOT include anything other than the JSON array in your response.
### Example
Here is the chat example between human and assistant, inside <example></example> XML tags.
@@ -59,7 +59,7 @@ User:{{"input_text": ["I want to know which company’s email address test@examp
Assistant:{{"metadata_map": [{{"metadata_field_name": "email", "metadata_field_value": "test@example.com", "comparison_operator": "="}}]}}
User:{{"input_text": "What are the movies with a score of more than 9 in 2024?", "metadata_fields": ["name", "year", "rating", "country"]}}
Assistant:{{"metadata_map": [{{"metadata_field_name": "year", "metadata_field_value": "2024", "comparison_operator": "="}, {{"metadata_field_name": "rating", "metadata_field_value": "9", "comparison_operator": ">"}}]}}
</example>
</example>
### User Input
{{"input_text" : "{input_text}", "metadata_fields" : {metadata_fields}}}
### Assistant Output

14
api/core/workflow/nodes/parameter_extractor/prompts.py
View File

@@ -17,7 +17,7 @@ Some additional information is provided below. Always adhere to these instructio
</instruction>
Steps:
1. Review the chat history provided within the <histories> tags.
2. Extract the relevant information based on the criteria given, output multiple values if there is multiple relevant information that match the criteria in the given text.
2. Extract the relevant information based on the criteria given, output multiple values if there is multiple relevant information that match the criteria in the given text.
3. Generate a well-formatted output using the defined functions and arguments.
4. Use the `extract_parameter` function to create structured outputs with appropriate parameters.
5. Do not include any XML tags in your output.
@@ -89,13 +89,13 @@ Some extra information are provided below, I should always follow the instructio
</instructions>
### Extract parameter Workflow
I need to extract the following information from the input text. The <information to be extracted> tag specifies the 'type', 'description' and 'required' of the information to be extracted.
I need to extract the following information from the input text. The <information to be extracted> tag specifies the 'type', 'description' and 'required' of the information to be extracted.
<information to be extracted>
{{ structure }}
</information to be extracted>
Step 1: Carefully read the input and understand the structure of the expected output.
Step 2: Extract relevant parameters from the provided text based on the name and description of object.
Step 2: Extract relevant parameters from the provided text based on the name and description of object.
Step 3: Structure the extracted parameters to JSON object as specified in <structure>.
Step 4: Ensure that the JSON object is properly formatted and valid. The output should not contain any XML tags. Only the JSON object should be outputted.
@@ -106,10 +106,10 @@ Here are the chat histories between human and assistant, inside <histories></his
</histories>
### Structure
Here is the structure of the expected output, I should always follow the output structure.
Here is the structure of the expected output, I should always follow the output structure.
{{γγγ
'properties1': 'relevant text extracted from input',
'properties2': 'relevant text extracted from input',
'properties1': 'relevant text extracted from input',
'properties2': 'relevant text extracted from input',
}}γγγ
### Input Text
@@ -119,7 +119,7 @@ Inside <text></text> XML tags, there is a text that I should extract parameters
</text>
### Answer
I should always output a valid JSON object. Output nothing other than the JSON object.
I should always output a valid JSON object. Output nothing other than the JSON object.
```JSON
""" # noqa: E501

4
api/core/workflow/nodes/question_classifier/template_prompts.py
View File

@@ -55,7 +55,7 @@ You are a text classification engine that analyzes text data and assigns categor
Your task is to assign one categories ONLY to the input text and only one category may be assigned returned in the output. Additionally, you need to extract the key words from the text that are related to the classification.
### Format
The input text is in the variable input_text. Categories are specified as a category list with two filed category_id and category_name in the variable categories. Classification instructions may be included to improve the classification accuracy.
### Constraint
### Constraint
DO NOT include anything other than the JSON array in your response.
### Example
Here is the chat example between human and assistant, inside <example></example> XML tags.
@@ -64,7 +64,7 @@ User:{{"input_text": ["I recently had a great experience with your company. The
Assistant:{{"keywords": ["recently", "great experience", "company", "service", "prompt", "staff", "friendly"],"category_id": "f5660049-284f-41a7-b301-fd24176a711c","category_name": "Customer Service"}}
User:{{"input_text": ["bad service, slow to bring the food"], "categories": [{{"category_id":"80fb86a0-4454-4bf5-924c-f253fdd83c02","category_name":"Food Quality"}},{{"category_id":"f6ff5bc3-aca0-4e4a-8627-e760d0aca78f","category_name":"Experience"}},{{"category_id":"cc771f63-74e7-4c61-882e-3eda9d8ba5d7","category_name":"Price"}}], "classification_instructions": []}}
Assistant:{{"keywords": ["bad service", "slow", "food", "tip", "terrible", "waitresses"],"category_id": "f6ff5bc3-aca0-4e4a-8627-e760d0aca78f","category_name": "Experience"}}
</example>
</example>
### Memory
Here are the chat histories between human and assistant, inside <histories></histories> XML tags.
<histories>

4
api/migrations/versions/2024_10_10_0516-bbadea11becb_add_name_and_size_to_tool_files.py
View File

@@ -21,14 +21,14 @@ def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
# Get the database connection
conn = op.get_bind()
# Use SQLAlchemy inspector to get the columns of the 'tool_files' table
inspector = sa.inspect(conn)
columns = [col['name'] for col in inspector.get_columns('tool_files')]
# If 'name' or 'size' columns already exist, exit the upgrade function
if 'name' in columns or 'size' in columns:
return
return
with op.batch_alter_table('tool_files', schema=None) as batch_op:
batch_op.add_column(sa.Column('name', sa.String(), nullable=True))

2
api/migrations/versions/2024_12_20_0628-e1944c35e15e_add_retry_index_field_to_node_execution_.py
View File

@@ -35,4 +35,4 @@ def downgrade():
# batch_op.drop_column('retry_index')
pass
# ### end Alembic commands ###
# ### end Alembic commands ###

2
api/migrations/versions/2024_12_23_1154-d7999dfa4aae_remove_workflow_node_executions_retry_.py
View File

@@ -23,7 +23,7 @@ def upgrade():
conn = op.get_bind()
inspector = inspect(conn)
has_column = 'retry_index' in [col['name'] for col in inspector.get_columns('workflow_node_executions')]
if has_column:
with op.batch_alter_table('workflow_node_executions', schema=None) as batch_op:
batch_op.drop_column('retry_index')

2
api/migrations/versions/64b051264f32_init.py
View File

@@ -1,7 +1,7 @@
"""init
Revision ID: 64b051264f32
Revises:
Revises:
Create Date: 2023-05-13 14:26:59.085018
"""

4
api/migrations/versions/de95f5c77138_migration_serpapi_api_key.py
View File

@@ -99,12 +99,12 @@ def upgrade():
id=id,
tenant_id=tenant_id,
user_id=user_id,
provider='google',
provider='google',
encrypted_credentials=encrypted_credentials,
created_at=created_at,
updated_at=updated_at
)
# ### end Alembic commands ###

16
api/services/plugin/data_migration.py
View File

@@ -86,9 +86,9 @@ limit 1000"""
update_retrieval_model_sql = ", retrieval_model = :retrieval_model"
params["retrieval_model"] = json.dumps(retrieval_model)
sql = f"""update {table_name}
set {provider_column_name} =
concat('{DEFAULT_PLUGIN_ID}/', {provider_column_name}, '/', {provider_column_name})
sql = f"""update {table_name}
set {provider_column_name} =
concat('{DEFAULT_PLUGIN_ID}/', {provider_column_name}, '/', {provider_column_name})
{update_retrieval_model_sql}
where id = :record_id"""
conn.execute(db.text(sql), params)
@@ -131,10 +131,10 @@ limit 1000"""
while True:
sql = f"""
SELECT id, {provider_column_name} AS provider_name
SELECT id, {provider_column_name} AS provider_name
FROM {table_name}
WHERE {provider_column_name} NOT LIKE '%/%'
AND {provider_column_name} IS NOT NULL
WHERE {provider_column_name} NOT LIKE '%/%'
AND {provider_column_name} IS NOT NULL
AND {provider_column_name} != ''
AND id > :last_id
ORDER BY id ASC
@@ -183,8 +183,8 @@ limit 1000"""
if batch_updates:
update_sql = f"""
UPDATE {table_name}
SET {provider_column_name} = :updated_value
UPDATE {table_name}
SET {provider_column_name} = :updated_value
WHERE id = :record_id
"""
conn.execute(db.text(update_sql), [{"updated_value": u, "record_id": r} for u, r in batch_updates])

4
api/templates/clean_document_job_mail_template-US.html
View File

@@ -77,7 +77,7 @@
<h1>Some Documents in Your Knowledge Base Have Been Disabled</h1>
<p>Dear {{userName}},</p>
<p>
We're sorry for the inconvenience. To ensure optimal performance, documents
We're sorry for the inconvenience. To ensure optimal performance, documents
that haven’t been updated or accessed in the past 30 days have been disabled in
your knowledge bases:
</p>
@@ -97,4 +97,4 @@
</div>
</div>
</body>
</html>
</html>

2
api/templates/delete_account_code_email_template_en-US.html
View File

@@ -122,4 +122,4 @@
</div>
</body>
</html>
</html>

2
api/templates/delete_account_success_template_en-US.html
View File

@@ -102,4 +102,4 @@
</div>
</body>
</html>
</html>

2
api/tests/integration_tests/.gitignore vendored
View File

@@ -1 +1 @@
.env.test
.env.test

2
api/tests/unit_tests/.gitignore vendored
View File

@@ -1 +1 @@
.env.test
.env.test