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chore: apply ruff E501 line-too-long linter rule (#8275)

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Co-authored-by: -LAN- <laipz8200@outlook.com>
This commit is contained in:
Bowen Liang
2024-09-12 14:00:36 +08:00
committed by GitHub
parent 56c90e212a
commit c69f5b07ba
85 changed files with 459 additions and 324 deletions
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6
api/core/rag/datasource/vdb/oracle/oraclevector.py
View File

@@ -195,7 +195,8 @@ class OracleVector(BaseVector):
top_k = kwargs.get("top_k", 5)
with self._get_cursor() as cur:
cur.execute(
f"SELECT meta, text, vector_distance(embedding,:1) AS distance FROM {self.table_name} ORDER BY distance fetch first {top_k} rows only",
f"SELECT meta, text, vector_distance(embedding,:1) AS distance FROM {self.table_name}"
f" ORDER BY distance fetch first {top_k} rows only",
[numpy.array(query_vector)],
)
docs = []
@@ -254,7 +255,8 @@ class OracleVector(BaseVector):
entities.append(token)
with self._get_cursor() as cur:
cur.execute(
f"select meta, text, embedding FROM {self.table_name} WHERE CONTAINS(text, :1, 1) > 0 order by score(1) desc fetch first {top_k} rows only",
f"select meta, text, embedding FROM {self.table_name}"
f" WHERE CONTAINS(text, :1, 1) > 0 order by score(1) desc fetch first {top_k} rows only",
[" ACCUM ".join(entities)],
)
docs = []

3
api/core/rag/datasource/vdb/pgvector/pgvector.py
View File

@@ -139,7 +139,8 @@ class PGVector(BaseVector):
with self._get_cursor() as cur:
cur.execute(
f"SELECT meta, text, embedding <=> %s AS distance FROM {self.table_name} ORDER BY distance LIMIT {top_k}",
f"SELECT meta, text, embedding <=> %s AS distance FROM {self.table_name}"
f" ORDER BY distance LIMIT {top_k}",
(json.dumps(query_vector),),
)
docs = []

5
api/core/rag/extractor/extract_processor.py
View File

@@ -30,7 +30,10 @@ from extensions.ext_storage import storage
from models.model import UploadFile
SUPPORT_URL_CONTENT_TYPES = ["application/pdf", "text/plain", "application/json"]
USER_AGENT = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
USER_AGENT = (
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124"
" Safari/537.36"
)
class ExtractProcessor:

9
api/core/rag/retrieval/router/multi_dataset_react_route.py
View File

@@ -14,7 +14,7 @@ from core.workflow.nodes.llm.llm_node import LLMNode
PREFIX = """Respond to the human as helpfully and accurately as possible. You have access to the following tools:"""
SUFFIX = """Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
Thought:"""
Thought:""" # noqa: E501
FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
The nouns in the format of "Thought", "Action", "Action Input", "Final Answer" must be expressed in English.
@@ -46,7 +46,7 @@ Action:
"action": "Final Answer",
"action_input": "Final response to human"
}}
```"""
```""" # noqa: E501
class ReactMultiDatasetRouter:
@@ -204,7 +204,8 @@ class ReactMultiDatasetRouter:
tool_strings = []
for tool in tools:
tool_strings.append(
f"{tool.name}: {tool.description}, args: {{'query': {{'title': 'Query', 'description': 'Query for the dataset to be used to retrieve the dataset.', 'type': 'string'}}}}"
f"{tool.name}: {tool.description}, args: {{'query': {{'title': 'Query',"
f" 'description': 'Query for the dataset to be used to retrieve the dataset.', 'type': 'string'}}}}"
)
formatted_tools = "\n".join(tool_strings)
unique_tool_names = {tool.name for tool in tools}
@@ -236,7 +237,7 @@ class ReactMultiDatasetRouter:
suffix = """Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
Question: {input}
Thought: {agent_scratchpad}
"""
""" # noqa: E501
tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
tool_names = ", ".join([tool.name for tool in tools])