Chore: update poetry dependency

backend/app/alembic/versions/7bb637385f51_modify_skills_table.py

@@ -5,27 +5,32 @@
 Create Date: 2024-09-26 13:41:33.595237
 
 """
-from alembic import op
+
 import sqlalchemy as sa
 import sqlmodel.sql.sqltypes
-
+from alembic import op
 
 # revision identifiers, used by Alembic.
-revision = '7bb637385f51'
-down_revision = 'b5d6291d6db9'
+revision = "7bb637385f51"
+down_revision = "b5d6291d6db9"
 branch_labels = None
 depends_on = None
 
 
 def upgrade():
     # ### commands auto generated by Alembic - please adjust! ###
-    op.add_column('skill', sa.Column('display_name', sqlmodel.sql.sqltypes.AutoString(), nullable=True))
-    op.drop_column('skill', 'icon')
+    op.add_column(
+        "skill",
+        sa.Column("display_name", sqlmodel.sql.sqltypes.AutoString(), nullable=True),
+    )
+    op.drop_column("skill", "icon")
     # ### end Alembic commands ###
 
 
 def downgrade():
     # ### commands auto generated by Alembic - please adjust! ###
-    op.add_column('skill', sa.Column('icon', sa.VARCHAR(), autoincrement=False, nullable=True))
-    op.drop_column('skill', 'display_name')
+    op.add_column(
+        "skill", sa.Column("icon", sa.VARCHAR(), autoincrement=False, nullable=True)
+    )
+    op.drop_column("skill", "display_name")
     # ### end Alembic commands ###

backend/app/api/routes/providermodel.py

@@ -4,9 +4,9 @@
 from app.curd.models import (
     _create_model,
     _delete_model,
+    _update_model,
     get_all_models,
     get_models_by_provider,
-    _update_model,
 )
 from app.models import Models, ModelsBase, ModelsOut
 

backend/app/core/celery_app.py

@@ -1,10 +1,14 @@
+import logging
 import os
+
 from celery import Celery
+
 from app.core.config import settings
-import logging
 
 # 配置基本日志
-logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logging.basicConfig(
+    level=logging.DEBUG, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
 
 os.environ["HUGGINGFACE_HUB_CACHE"] = os.path.join(os.getcwd(), "fastembed_cache")
 os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
@@ -31,10 +35,11 @@
 # 配置 Celery 日志
 celery_app.conf.update(
     worker_hijack_root_logger=False,
-    worker_log_format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    worker_task_log_format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+    worker_log_format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    worker_task_log_format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
 )
 
+
 @celery_app.task(acks_late=True)
 def test_celery(word: str) -> str:
     logging.info(f"Test task received: {word}")

backend/app/core/config.py

@@ -143,18 +143,11 @@ def _enforce_non_default_secrets(self) -> Self:
 
     # Qdrant
     QDRANT_SERVICE_API_KEY: str | None = "XMj3HXm5GlBKQLwZuStOlkwZiOWTdd_IwZNDJINFh-w"
-    # QDRANT_URL: str = "http://localhost:6333"
-    QDRANT_URL: str = "http://127.0.0.1:6333"
+    QDRANT_URL: str = "http://localhost:6333"
+    # QDRANT_URL: str = "http://127.0.0.1:6333"
 
     QDRANT_COLLECTION: str | None = "kb_uploads"
 
-    # LangSmith
-    # USE_LANGSMITH: bool = True
-    # LANGCHAIN_TRACING_V2: bool = False
-    # LANGCHAIN_ENDPOINT: str | None = None
-    # LANGCHAIN_API_KEY: str | None = None
-    # LANGCHAIN_PROJECT: str | None = None
-
     # Embeddings
     # EMBEDDING_MODEL: str = "local"  # 或者你想使用的其他模型
     EMBEDDING_MODEL: str = "zhipuai"  # 或者你想使用的其他模型
@@ -179,5 +172,12 @@ def _enforce_non_default_secrets(self) -> Self:
 
     OPENAI_API_KEY: str
 
+    # LangSmith
+    # USE_LANGSMITH: bool = True
+    # LANGCHAIN_TRACING_V2: bool = False
+    # LANGCHAIN_ENDPOINT: str | None = None
+    # LANGCHAIN_API_KEY: str | None = None
+    # LANGCHAIN_PROJECT: str | None = None
+
 
 settings = Settings()  # type: ignore

backend/app/core/graph/build.py

@@ -828,7 +828,7 @@ async def generator(
                     formatted_output = f"data: {response.model_dump_json()}\n\n"
                     yield formatted_output
             snapshot = await root.aget_state(config)
-           
+
             if snapshot.next:
                 # Interrupt occured
                 message = snapshot.values["messages"][-1]

backend/app/core/graph/members.py

@@ -1,8 +1,6 @@
 from collections.abc import Mapping, Sequence
 from typing import Annotated, Any
-from app.core.rag.qdrant import QdrantStore
-from langchain_core.tools import BaseTool
-from pydantic import BaseModel, Field
+
 from langchain.chat_models import init_chat_model
 from langchain_core.messages import AIMessage, AnyMessage
 from langchain_core.output_parsers.openai_tools import JsonOutputKeyToolsParser
@@ -42,18 +40,6 @@ def tool(self) -> BaseTool:
             raise ValueError("Skill is not managed and no definition provided.")
 
 
-# class GraphUpload(BaseModel):
-#     name: str = Field(description="Name of the upload")
-#     description: str = Field(description="Description of the upload")
-#     owner_id: int = Field(description="Id of the user that owns this upload")
-#     upload_id: int = Field(description="Id of the upload")
-
-#     @property
-#     def tool(self) -> BaseTool:
-#         retriever = QdrantStore().retriever(self.owner_id, self.upload_id)
-#         return create_retriever_tool(retriever)
-
-
 class GraphUpload(BaseModel):
     name: str = Field(description="Name of the upload")
     description: str = Field(description="Description of the upload")

backend/app/core/rag/embeddings.py

@@ -1,11 +1,13 @@
+import logging
 from typing import List
-from langchain_core.embeddings import Embeddings
-from langchain_openai import OpenAIEmbeddings
+
+import requests
 from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import BaseModel, Extra
-import requests
+from langchain_openai import OpenAIEmbeddings
+
 from app.core.config import settings
-import logging
 
 logger = logging.getLogger(__name__)
 

backend/app/core/rag/qdrant.py

@@ -1,23 +1,24 @@
-from typing import List, Callable
+import logging
+from typing import Callable, List
+
 from langchain_community.document_loaders import PyMuPDFLoader, WebBaseLoader
 from langchain_core.documents import Document
-from langchain_text_splitters import RecursiveCharacterTextSplitter
 from langchain_qdrant import QdrantVectorStore
+from langchain_text_splitters import RecursiveCharacterTextSplitter
 from qdrant_client import QdrantClient
 from qdrant_client.http import models as rest
-from qdrant_client.models import VectorParams, Distance
-import pymupdf
+from qdrant_client.models import Distance, VectorParams
+
 from app.core.config import settings
 from app.core.rag.embeddings import get_embedding_model
 
-import logging
-
 logger = logging.getLogger(__name__)
 
 
 class QdrantStore:
     def __init__(self) -> None:
         self.collection_name = settings.QDRANT_COLLECTION
+        # self.url = "http://localhost:6333"
         self.url = settings.QDRANT_URL
         self.embedding_model = get_embedding_model(settings.EMBEDDING_MODEL)
 
@@ -33,7 +34,9 @@ def __init__(self) -> None:
     def _initialize_vector_store(self):
         try:
             collections = self.client.get_collections().collections
-            if self.collection_name not in [collection.name for collection in collections]:
+            if self.collection_name not in [
+                collection.name for collection in collections
+            ]:
                 logger.info(f"Creating new collection: {self.collection_name}")
                 self.client.create_collection(
                     collection_name=self.collection_name,
@@ -113,7 +116,7 @@ def delete(self, upload_id: int, user_id: int) -> bool:
                             match=rest.MatchValue(value=upload_id),
                         ),
                     ],
-                )
+                ),
             )
             logger.info(f"Delete operation result: {result}")
             return True
@@ -136,46 +139,53 @@ def update(
             callback()
 
     def search(self, user_id: int, upload_ids: List[int], query: str) -> List[Document]:
-        logger.info(f"Searching with query: '{query}' for user_id: {user_id}, upload_ids: {upload_ids}")
-
+        logger.info(
+            f"Searching with query: '{query}' for user_id: {user_id}, upload_ids: {upload_ids}"
+        )
+
         query_vector = self.embedding_model.embed_query(query)
-        
+
         filter_condition = {
             "must": [
                 {"key": "metadata.user_id", "match": {"value": user_id}},
-                {"key": "metadata.upload_id", "match": {"any": upload_ids}}
+                {"key": "metadata.upload_id", "match": {"any": upload_ids}},
             ]
         }
         logger.info(f"Search filter condition: {filter_condition}")
-        
+
         search_results = self.client.search(
             collection_name=self.collection_name,
             query_vector=query_vector,
             query_filter=filter_condition,
-            limit=4
+            limit=4,
         )
-
-        documents = [Document(page_content=result.payload.get('page_content', ''), metadata=result.payload.get('metadata', {})) for result in search_results]
-
+
+        documents = [
+            Document(
+                page_content=result.payload.get("page_content", ""),
+                metadata=result.payload.get("metadata", {}),
+            )
+            for result in search_results
+        ]
+
         logger.info(f"Search results: {len(documents)} documents found")
         for doc in documents:
             logger.info(f"Document metadata: {doc.metadata}")
-        
+
         return documents
 
     def retriever(self, user_id: int, upload_id: int):
-        logger.info(f"Creating retriever for user_id: {user_id}, upload_id: {upload_id}")
+        logger.info(
+            f"Creating retriever for user_id: {user_id}, upload_id: {upload_id}"
+        )
         filter_condition = {
             "must": [
                 {"key": "metadata.user_id", "match": {"value": user_id}},
-                {"key": "metadata.upload_id", "match": {"value": upload_id}}
+                {"key": "metadata.upload_id", "match": {"value": upload_id}},
             ]
         }
         retriever = self.vector_store.as_retriever(
-            search_kwargs={
-                "filter": filter_condition,
-                "k": 5
-            },
+            search_kwargs={"filter": filter_condition, "k": 5},
             search_type="similarity",
         )
         logger.info(f"Retriever created: {retriever}")
@@ -205,4 +215,4 @@ def debug_retriever(self, user_id: int, upload_id: int, query: str):
     def get_collection_info(self):
         collection_info = self.client.get_collection(self.collection_name)
         logger.info(f"Collection info: {collection_info}")
-        return collection_info
+        return collection_info

backend/app/core/rag/qdrant_retriever.py

@@ -56,4 +56,4 @@ def _get_relevant_documents(
                 limit=self.k,
             )
             documents.append(document)
-        return documents
+        return documents

backend/app/core/rag/rag_test_temp.py

@@ -2,8 +2,8 @@
 
 sys.path.append("./")
 
-from app.core.rag.qdrant import QdrantStore
 from app.core.rag.embeddings import get_embedding_model
+from app.core.rag.qdrant import QdrantStore
 
 # 初始化 QdrantStore 和嵌入模型
 qdrant_store = QdrantStore()

backend/app/core/rag/ragtest.py

@@ -1,8 +1,8 @@
+import json
+import logging
+
 from app.core.rag.qdrant import QdrantStore
 from app.core.tools.retriever_tool import create_retriever_tool
-import logging
-import json
-from qdrant_client.http import models as rest
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)

backend/app/core/tools/math/math.py

@@ -1,8 +1,8 @@
 # This is an example showing how to create a simple calculator skill
 
+import numexpr as ne
 from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools import StructuredTool
-import numexpr as ne
 
 
 class MathInput(BaseModel):
@@ -14,7 +14,7 @@ def math_cal(expression: str) -> str:
         result = ne.evaluate(expression)
         result_str = str(result)
         return f"{result_str}"
-    except Exception as e:
+    except Exception:
 
         return f"Error evaluating expression: {expression}"
 

backend/app/core/tools/openweather/openweather.py

@@ -58,5 +58,3 @@ def open_weather_qry(
     args_schema=WeatherSearchInput,
     return_direct=True,
 )
-
-