Feat/static analysis (#582)

* feat: add static analysis

* chore: update Makefile add static analysis script

* chore: add vscode extensions recommandations

.vscode/extensions.json

@@ -0,0 +1,8 @@
+{
+    "recommendations": [
+        "ms-pyright.pyright",
+        "dbaeumer.vscode-eslint",
+        "ms-python.vscode-pylance",
+        "ms-pyright.pyright"
+    ]
+}

.vscode/settings.json

@@ -12,7 +12,8 @@
     "editor.defaultFormatter": "ms-python.black-formatter",
     "editor.formatOnSave": true,
     "editor.codeActionsOnSave": {
-      "source.organizeImports": true
+      "source.organizeImports": true,
+      "source.fixAll": true
     }
   },
   "[typescriptreact]": {
@@ -41,4 +42,7 @@
     "**/.docusaurus/": true,
     "**/node_modules/": true,
   },
+  "python.linting.pycodestyleCategorySeverity.W": "Error",
+  "python.defaultInterpreterPath": "python3",
+  "python.linting.flake8CategorySeverity.W": "Error",
 }

Makefile

@@ -5,3 +5,10 @@ dev:
 
 prod:
 	docker compose -f docker-compose.yml up --build
+
+test-type:
+	@if command -v python3 &>/dev/null; then \
+		python3 -m pyright; \
+	else \
+		python -m pyright; \
+	fi

backend/auth/auth_bearer.py

@@ -1,12 +1,13 @@
 import os
 from typing import Optional
 
-from auth.api_key_handler import get_user_from_api_key, verify_api_key
-from auth.jwt_token_handler import decode_access_token, verify_token
 from fastapi import Depends, HTTPException, Request
 from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
 from models.users import User
 
+from auth.api_key_handler import get_user_from_api_key, verify_api_key
+from auth.jwt_token_handler import decode_access_token, verify_token
+
 
 class AuthBearer(HTTPBearer):
     def __init__(self, auto_error: bool = True):
@@ -20,7 +21,7 @@ class AuthBearer(HTTPBearer):
             request
         )
         self.check_scheme(credentials)
-        token = credentials.credentials
+        token = credentials.credentials  # pyright: ignore reportPrivateUsage=none
         return await self.authenticate(
             token,
         )
@@ -52,7 +53,7 @@ class AuthBearer(HTTPBearer):
 
     def get_test_user(self) -> User:
         return User(
-            email="test@example.com", id="XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX"
+            email="test@example.com", id="XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX"  # type: ignore
         )  # replace with test user information
 
 

backend/auth/jwt_token_handler.py

@@ -9,6 +9,9 @@ from models.users import User
 SECRET_KEY = os.environ.get("JWT_SECRET_KEY")
 ALGORITHM = "HS256"
 
+if not SECRET_KEY:
+    raise ValueError("JWT_SECRET_KEY environment variable not set")
+
 
 def create_access_token(data: dict, expires_delta: Optional[timedelta] = None):
     to_encode = data.copy()
@@ -27,9 +30,12 @@ def decode_access_token(token: str) -> User:
             token, SECRET_KEY, algorithms=[ALGORITHM], options={"verify_aud": False}
         )
     except JWTError:
-        return None
+        return None  # pyright: ignore reportPrivateUsage=none
 
-    return User(email=payload.get("email"), id=payload.get("sub"))
+    return User(
+        email=payload.get("email"),
+        id=payload.get("sub"),  # pyright: ignore reportPrivateUsage=none
+    )
 
 
 def verify_token(token: str):

backend/crawl/crawler.py

@@ -4,7 +4,6 @@ import tempfile
 import unicodedata
 
 import requests
-from langchain.document_loaders import GitLoader
 from pydantic import BaseModel
 
 
@@ -29,7 +28,7 @@ class CrawlWebsite(BaseModel):
         file_name = slugify(self.url) + ".html"
         temp_file_path = os.path.join(tempfile.gettempdir(), file_name)
         with open(temp_file_path, "w") as temp_file:
-            temp_file.write(content)
+            temp_file.write(content)  # pyright: ignore reportPrivateUsage=none
             # Process the file
 
         if content:

backend/llm/base.py

@@ -20,19 +20,21 @@ class BaseBrainPicking(BaseModel):
     """
 
     # Instantiate settings
-    brain_settings = BrainSettings()
+    brain_settings = BrainSettings()  # type: ignore other parameters are optional
 
     # Default class attributes
-    model: str = None
+    model: str = None  # pyright: ignore reportPrivateUsage=none
     temperature: float = 0.0
-    chat_id: str = None
+    chat_id: str = None  # pyright: ignore reportPrivateUsage=none
-    brain_id: str = None
+    brain_id: str = None  # pyright: ignore reportPrivateUsage=none
     max_tokens: int = 256
-    user_openai_api_key: str = None
+    user_openai_api_key: str = None  # pyright: ignore reportPrivateUsage=none
     streaming: bool = False
 
-    openai_api_key: str = None
+    openai_api_key: str = None  # pyright: ignore reportPrivateUsage=none
-    callbacks: List[AsyncCallbackHandler] = None
+    callbacks: List[
+        AsyncCallbackHandler
+    ] = None  # pyright: ignore reportPrivateUsage=none
 
     def _determine_api_key(self, openai_api_key, user_openai_api_key):
         """If user provided an API key, use it."""
@@ -55,10 +57,12 @@ class BaseBrainPicking(BaseModel):
 
     def _determine_callback_array(
         self, streaming
-    ) -> List[AsyncIteratorCallbackHandler]:
+    ) -> List[AsyncIteratorCallbackHandler]:  # pyright: ignore reportPrivateUsage=none
         """If streaming is set, set the AsyncIteratorCallbackHandler as the only callback."""
         if streaming:
-            return [AsyncIteratorCallbackHandler]
+            return [
+                AsyncIteratorCallbackHandler  # pyright: ignore reportPrivateUsage=none
+            ]
 
     def __init__(self, **data):
         super().__init__(**data)
@@ -66,8 +70,12 @@ class BaseBrainPicking(BaseModel):
         self.openai_api_key = self._determine_api_key(
             self.brain_settings.openai_api_key, self.user_openai_api_key
         )
-        self.streaming = self._determine_streaming(self.model, self.streaming)
+        self.streaming = self._determine_streaming(
-        self.callbacks = self._determine_callback_array(self.streaming)
+            self.model, self.streaming
+        )  # pyright: ignore reportPrivateUsage=none
+        self.callbacks = self._determine_callback_array(
+            self.streaming
+        )  # pyright: ignore reportPrivateUsage=none
 
     class Config:
         """Configuration of the Pydantic Object"""

backend/llm/models/OpenAiAnswer.py

@@ -1,4 +1,5 @@
 from typing import Optional
+
 from .FunctionCall import FunctionCall
 
 
@@ -6,7 +7,7 @@ class OpenAiAnswer:
     def __init__(
         self,
         content: Optional[str] = None,
-        function_call: FunctionCall = None,
+        function_call: FunctionCall = None,  # pyright: ignore reportPrivateUsage=none
     ):
         self.content = content
         self.function_call = function_call

backend/llm/openai.py

@@ -13,11 +13,12 @@ from repository.chat.format_chat_history import format_chat_history
 from repository.chat.get_chat_history import get_chat_history
 from repository.chat.update_chat_history import update_chat_history
 from repository.chat.update_message_by_id import update_message_by_id
-from supabase import Client, create_client
+from supabase.client import Client, create_client
 from vectorstore.supabase import (
     CustomSupabaseVectorStore,
-)  # Custom class for handling vector storage with Supabase
+)
 
+# Custom class for handling vector storage with Supabase
 from .base import BaseBrainPicking
 from .prompts.CONDENSE_PROMPT import CONDENSE_QUESTION_PROMPT
 
@@ -42,7 +43,7 @@ class OpenAIBrainPicking(BaseBrainPicking):
         max_tokens: int,
         user_openai_api_key: str,
         streaming: bool = False,
-    ) -> "OpenAIBrainPicking":
+    ) -> "OpenAIBrainPicking":  # pyright: ignore reportPrivateUsage=none
         """
         Initialize the BrainPicking class by setting embeddings, supabase client, vector store, language model and chains.
         :return: OpenAIBrainPicking instance
@@ -59,7 +60,9 @@ class OpenAIBrainPicking(BaseBrainPicking):
 
     @property
     def embeddings(self) -> OpenAIEmbeddings:
-        return OpenAIEmbeddings(openai_api_key=self.openai_api_key)
+        return OpenAIEmbeddings(
+            openai_api_key=self.openai_api_key
+        )  # pyright: ignore reportPrivateUsage=none
 
     @property
     def supabase_client(self) -> Client:
@@ -92,14 +95,16 @@ class OpenAIBrainPicking(BaseBrainPicking):
 
     @property
     def doc_chain(self) -> LLMChain:
-        return load_qa_chain(llm=self.doc_llm, chain_type="stuff")
+        return load_qa_chain(
+            llm=self.doc_llm, chain_type="stuff"
+        )  # pyright: ignore reportPrivateUsage=none
 
     @property
     def qa(self) -> ConversationalRetrievalChain:
         return ConversationalRetrievalChain(
             retriever=self.vector_store.as_retriever(),
             question_generator=self.question_generator,
-            combine_docs_chain=self.doc_chain,
+            combine_docs_chain=self.doc_chain,  # pyright: ignore reportPrivateUsage=none
             verbose=True,
         )
 
@@ -116,7 +121,7 @@ class OpenAIBrainPicking(BaseBrainPicking):
             model=model,
             streaming=streaming,
             callbacks=callbacks,
-        )
+        )  # pyright: ignore reportPrivateUsage=none
 
     def _call_chain(self, chain, question, history):
         """
@@ -205,8 +210,10 @@ class OpenAIBrainPicking(BaseBrainPicking):
 
         task = asyncio.create_task(
             wrap_done(
-                self.qa._acall_chain(self.qa, question, transformed_history),
+                self.qa._acall_chain(  # pyright: ignore reportPrivateUsage=none
-                callback.done,
+                    self.qa, question, transformed_history
+                ),
+                callback.done,  # pyright: ignore reportPrivateUsage=none
             )
         )
 
@@ -217,7 +224,7 @@ class OpenAIBrainPicking(BaseBrainPicking):
         )
 
         # Use the aiter method of the callback to stream the response with server-sent-events
-        async for token in callback.aiter():
+        async for token in callback.aiter():  # pyright: ignore reportPrivateUsage=none
             logger.info("Token: %s", token)
 
             # Add the token to the response_tokens list

backend/llm/openai_functions.py

@@ -2,15 +2,16 @@ from typing import Any, Dict, List, Optional
 
 from langchain.chat_models import ChatOpenAI
 from langchain.embeddings.openai import OpenAIEmbeddings
-from llm.models.FunctionCall import FunctionCall
-from llm.models.OpenAiAnswer import OpenAiAnswer
 from logger import get_logger
 from models.chat import ChatHistory
 from repository.chat.get_chat_history import get_chat_history
 from repository.chat.update_chat_history import update_chat_history
-from supabase import Client, create_client
+from supabase.client import Client, create_client
 from vectorstore.supabase import CustomSupabaseVectorStore
 
+from llm.models.FunctionCall import FunctionCall
+from llm.models.OpenAiAnswer import OpenAiAnswer
+
 from .base import BaseBrainPicking
 
 logger = get_logger(__name__)
@@ -27,7 +28,10 @@ def format_answer(model_response: Dict[str, Any]) -> OpenAiAnswer:
             answer["function_call"]["arguments"],
         )
 
-    return OpenAiAnswer(content=content, function_call=function_call)
+    return OpenAiAnswer(
+        content=content,
+        function_call=function_call,  # pyright: ignore reportPrivateUsage=none
+    )
 
 
 class OpenAIFunctionsBrainPicking(BaseBrainPicking):
@@ -48,7 +52,7 @@ class OpenAIFunctionsBrainPicking(BaseBrainPicking):
         brain_id: str,
         user_openai_api_key: str,
         # TODO: add streaming
-    ) -> "OpenAIFunctionsBrainPicking":
+    ) -> "OpenAIFunctionsBrainPicking":  # pyright: ignore reportPrivateUsage=none
         super().__init__(
             model=model,
             chat_id=chat_id,
@@ -61,11 +65,15 @@ class OpenAIFunctionsBrainPicking(BaseBrainPicking):
 
     @property
     def openai_client(self) -> ChatOpenAI:
-        return ChatOpenAI(openai_api_key=self.openai_api_key)
+        return ChatOpenAI(
+            openai_api_key=self.openai_api_key
+        )  # pyright: ignore reportPrivateUsage=none
 
     @property
     def embeddings(self) -> OpenAIEmbeddings:
-        return OpenAIEmbeddings(openai_api_key=self.openai_api_key)
+        return OpenAIEmbeddings(
+            openai_api_key=self.openai_api_key
+        )  # pyright: ignore reportPrivateUsage=none
 
     @property
     def supabase_client(self) -> Client:
@@ -125,7 +133,9 @@ class OpenAIFunctionsBrainPicking(BaseBrainPicking):
         """
         logger.info("Getting context")
 
-        return self.vector_store.similarity_search(query=question)
+        return self.vector_store.similarity_search(
+            query=question
+        )  # pyright: ignore reportPrivateUsage=none
 
     def _construct_prompt(
         self, question: str, useContext: bool = False, useHistory: bool = False

backend/llm/private_gpt4all.py

@@ -21,7 +21,7 @@ class PrivateGPT4AllBrainPicking(BaseBrainPicking):
         chat_id: str,
         brain_id: str,
         streaming: bool,
-    ) -> "PrivateGPT4AllBrainPicking":
+    ) -> "PrivateGPT4AllBrainPicking":  # pyright: ignore reportPrivateUsage=none
         """
         Initialize the PrivateBrainPicking class by calling the parent class's initializer.
         :param brain_id: The brain_id in the DB.
@@ -57,4 +57,4 @@ class PrivateGPT4AllBrainPicking(BaseBrainPicking):
             n_batch=model_n_batch,
             backend="gptj",
             verbose=True,
-        )
+        )  # pyright: ignore reportPrivateUsage=none

backend/llm/utils/summarization.py

@@ -31,7 +31,7 @@ Summarize the following text:
 {{/assistant~}}
 """,
         llm=summary_llm,
-    )
+    )  # pyright: ignore reportPrivateUsage=none
 
     summary = summary(document=document)
     logger.info("Summarization: %s", summary)
@@ -78,10 +78,12 @@ Summary
 {{/assistant~}}
 """,
         llm=evaluation_llm,
-    )
+    )  # pyright: ignore reportPrivateUsage=none
     result = evaluation(question=question, summaries=summaries)
     evaluations = {}
-    for evaluation in result["evaluation"].split("\n"):
+    for evaluation in result["evaluation"].split(
+        "\n"
+    ):  # pyright: ignore reportPrivateUsage=none
         if evaluation == "" or not evaluation[0].isdigit():
             continue
         logger.info("Evaluation Row: %s", evaluation)

backend/models/brains.py

@@ -2,11 +2,12 @@ import os
 from typing import Any, List, Optional
 from uuid import UUID
 
-from models.settings import CommonsDep, common_dependencies
-from models.users import User
 from pydantic import BaseModel
 from utils.vectors import get_unique_files_from_vector_ids
 
+from models.settings import CommonsDep, common_dependencies
+from models.users import User
+
 
 class Brain(BaseModel):
     id: Optional[UUID] = None
@@ -15,7 +16,7 @@ class Brain(BaseModel):
     model: Optional[str] = "gpt-3.5-turbo-0613"
     temperature: Optional[float] = 0.0
     max_tokens: Optional[int] = 256
-    brain_size: Optional[float] = 0.0
+    brain_size: Optional[float] = 0.0  # pyright: ignore reportPrivateUsage=none
     max_brain_size: Optional[int] = int(os.getenv("MAX_BRAIN_SIZE", 0))
     files: List[Any] = []
     _commons: Optional[CommonsDep] = None
@@ -27,7 +28,7 @@ class Brain(BaseModel):
     def commons(self) -> CommonsDep:
         if not self._commons:
             self.__class__._commons = common_dependencies()
-        return self._commons
+        return self._commons  # pyright: ignore reportPrivateUsage=none
 
     @property
     def brain_size(self):
@@ -39,12 +40,17 @@ class Brain(BaseModel):
 
     @property
     def remaining_brain_size(self):
-        return float(self.max_brain_size) - self.brain_size
+        return (
+            float(self.max_brain_size)  # pyright: ignore reportPrivateUsage=none
+            - self.brain_size  # pyright: ignore reportPrivateUsage=none
+        )
 
     @classmethod
     def create(cls, *args, **kwargs):
         commons = common_dependencies()
-        return cls(commons=commons, *args, **kwargs)
+        return cls(
+            commons=commons, *args, **kwargs  # pyright: ignore reportPrivateUsage=none
+        )  # pyright: ignore reportPrivateUsage=none
 
     def get_user_brains(self, user_id):
         response = (

backend/models/chat.py

@@ -9,10 +9,18 @@ class Chat:
     chat_name: str
 
     def __init__(self, chat_dict: dict):
-        self.chat_id = chat_dict.get("chat_id")
+        self.chat_id = chat_dict.get(
-        self.user_id = chat_dict.get("user_id")
+            "chat_id"
-        self.creation_time = chat_dict.get("creation_time")
+        )  # pyright: ignore reportPrivateUsage=none
-        self.chat_name = chat_dict.get("chat_name")
+        self.user_id = chat_dict.get(
+            "user_id"
+        )  # pyright: ignore reportPrivateUsage=none
+        self.creation_time = chat_dict.get(
+            "creation_time"
+        )  # pyright: ignore reportPrivateUsage=none
+        self.chat_name = chat_dict.get(
+            "chat_name"
+        )  # pyright: ignore reportPrivateUsage=none
 
 
 @dataclass
@@ -24,11 +32,21 @@ class ChatHistory:
     message_time: str
 
     def __init__(self, chat_dict: dict):
-        self.chat_id = chat_dict.get("chat_id")
+        self.chat_id = chat_dict.get(
-        self.message_id = chat_dict.get("message_id")
+            "chat_id"
-        self.user_message = chat_dict.get("user_message")
+        )  # pyright: ignore reportPrivateUsage=none
-        self.assistant = chat_dict.get("assistant")
+        self.message_id = chat_dict.get(
-        self.message_time = chat_dict.get("message_time")
+            "message_id"
+        )  # pyright: ignore reportPrivateUsage=none
+        self.user_message = chat_dict.get(
+            "user_message"
+        )  # pyright: ignore reportPrivateUsage=none
+        self.assistant = chat_dict.get(
+            "assistant"
+        )  # pyright: ignore reportPrivateUsage=none
+        self.message_time = chat_dict.get(
+            "message_time"
+        )  # pyright: ignore reportPrivateUsage=none
 
     def to_dict(self):
         return asdict(self)

backend/models/files.py

@@ -6,11 +6,12 @@ from uuid import UUID
 from fastapi import UploadFile
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from logger import get_logger
-from models.brains import Brain
-from models.settings import CommonsDep, common_dependencies
 from pydantic import BaseModel
 from utils.file import compute_sha1_from_file
 
+from models.brains import Brain
+from models.settings import CommonsDep, common_dependencies
+
 logger = get_logger(__name__)
 
 
@@ -18,9 +19,9 @@ class File(BaseModel):
     id: Optional[UUID] = None
     file: Optional[UploadFile]
     file_name: Optional[str] = ""
-    file_size: Optional[int] = ""
+    file_size: Optional[int] = ""  # pyright: ignore reportPrivateUsage=none
     file_sha1: Optional[str] = ""
-    vectors_ids: Optional[int] = []
+    vectors_ids: Optional[int] = []  # pyright: ignore reportPrivateUsage=none
     file_extension: Optional[str] = ""
     content: Optional[Any] = None
     chunk_size: int = 500
@@ -33,13 +34,22 @@ class File(BaseModel):
 
         if self.file:
             self.file_name = self.file.filename
-            self.file_size = self.file.file._file.tell() 
+            self.file_size = (
-            self.file_extension = os.path.splitext(self.file.filename)[-1].lower()
+                self.file.file._file.tell()  # pyright: ignore reportPrivateUsage=none
+            )
+            self.file_extension = os.path.splitext(
+                self.file.filename  # pyright: ignore reportPrivateUsage=none
+            )[-1].lower()
 
     async def compute_file_sha1(self):
-        with tempfile.NamedTemporaryFile(delete=False, suffix=self.file.filename) as tmp_file:
+        with tempfile.NamedTemporaryFile(
-            await self.file.seek(0)
+            delete=False,
-            self.content = await self.file.read()
+            suffix=self.file.filename,  # pyright: ignore reportPrivateUsage=none
+        ) as tmp_file:
+            await self.file.seek(0)  # pyright: ignore reportPrivateUsage=none
+            self.content = (
+                await self.file.read()  # pyright: ignore reportPrivateUsage=none
+            )
             tmp_file.write(self.content)
             tmp_file.flush()
             self.file_sha1 = compute_sha1_from_file(tmp_file.name)
@@ -50,8 +60,11 @@ class File(BaseModel):
         logger.info(f"Computing documents from file {self.file_name}")
 
         documents = []
-        with tempfile.NamedTemporaryFile(delete=False, suffix=self.file.filename) as tmp_file:
+        with tempfile.NamedTemporaryFile(
-            tmp_file.write(self.content)
+            delete=False,
+            suffix=self.file.filename,  # pyright: ignore reportPrivateUsage=none
+        ) as tmp_file:
+            tmp_file.write(self.content)  # pyright: ignore reportPrivateUsage=none
             tmp_file.flush()
             loader = loader_class(tmp_file.name)
             documents = loader.load()
@@ -76,7 +89,8 @@ class File(BaseModel):
 
         commons = common_dependencies()
         response = (
-            commons["supabase"].table("vectors")
+            commons["supabase"]
+            .table("vectors")
             .select("id")
             .filter("metadata->>file_sha1", "eq", self.file_sha1)
             .execute()
@@ -92,10 +106,13 @@ class File(BaseModel):
 
         print("file_sha1", self.file_sha1)
         print("vectors_ids", self.vectors_ids)
-        print("len(vectors_ids)", len(self.vectors_ids))
+        print(
+            "len(vectors_ids)",
+            len(self.vectors_ids),  # pyright: ignore reportPrivateUsage=none
+        )
 
         # if the file does not exist in vectors then no need to go check in brains_vectors
-        if len(self.vectors_ids) == 0:
+        if len(self.vectors_ids) == 0:  # pyright: ignore reportPrivateUsage=none
             return False
 
         return True
@@ -105,7 +122,8 @@ class File(BaseModel):
         self.set_file_vectors_ids()
         # Check if file exists in that brain
         response = (
-            commons["supabase"].table("brains_vectors")
+            commons["supabase"]
+            .table("brains_vectors")
             .select("brain_id, vector_id")
             .filter("brain_id", "eq", brain_id)
             .filter("file_sha1", "eq", self.file_sha1)
@@ -118,11 +136,13 @@ class File(BaseModel):
         return True
 
     def file_is_empty(self):
-        return self.file.file._file.tell()  < 1
+        return (
+            self.file.file._file.tell() < 1  # pyright: ignore reportPrivateUsage=none
+        )
 
     def link_file_to_brain(self, brain: Brain):
         self.set_file_vectors_ids()
 
-        for vector_id in self.vectors_ids:
+        for vector_id in self.vectors_ids:  # pyright: ignore reportPrivateUsage=none
-            brain.create_brain_vector(vector_id['id'], self.file_sha1)
+            brain.create_brain_vector(vector_id["id"], self.file_sha1)
         print(f"Successfully linked file {self.file_sha1} to brain {brain.id}")

backend/models/settings.py

@@ -3,7 +3,7 @@ from typing import Annotated
 from fastapi import Depends
 from langchain.embeddings.openai import OpenAIEmbeddings
 from pydantic import BaseSettings
-from supabase import Client, create_client
+from supabase.client import Client, create_client
 from vectorstore.supabase import SupabaseVectorStore
 
 
@@ -22,8 +22,10 @@ class LLMSettings(BaseSettings):
 
 
 def common_dependencies() -> dict:
-    settings = BrainSettings()
+    settings = BrainSettings()  # pyright: ignore reportPrivateUsage=none
-    embeddings = OpenAIEmbeddings(openai_api_key=settings.openai_api_key)
+    embeddings = OpenAIEmbeddings(
+        openai_api_key=settings.openai_api_key
+    )  # pyright: ignore reportPrivateUsage=none
     supabase_client: Client = create_client(
         settings.supabase_url, settings.supabase_service_key
     )

backend/parsers/audio.py

@@ -10,23 +10,33 @@ from models.settings import CommonsDep
 from utils.file import compute_sha1_from_content
 
 
-async def process_audio(commons: CommonsDep, file: File, enable_summarization: bool, user, user_openai_api_key):
+async def process_audio(
-
+    commons: CommonsDep,  # pyright: ignore reportPrivateUsage=none
+    file: File,
+    enable_summarization: bool,
+    user,
+    user_openai_api_key,
+):
     temp_filename = None
     file_sha = ""
     dateshort = time.strftime("%Y%m%d-%H%M%S")
     file_meta_name = f"audiotranscript_{dateshort}.txt"
 
     # use this for whisper
-    openai_api_key = os.environ.get("OPENAI_API_KEY")
+    os.environ.get("OPENAI_API_KEY")
     if user_openai_api_key:
-        openai_api_key = user_openai_api_key
+        pass
 
     try:
         upload_file = file.file
-        with tempfile.NamedTemporaryFile(delete=False, suffix=upload_file.filename) as tmp_file:
+        with tempfile.NamedTemporaryFile(
-            await upload_file.seek(0)
+            delete=False,
-            content = await upload_file.read()
+            suffix=upload_file.filename,  # pyright: ignore reportPrivateUsage=none
+        ) as tmp_file:
+            await upload_file.seek(0)  # pyright: ignore reportPrivateUsage=none
+            content = (
+                await upload_file.read()  # pyright: ignore reportPrivateUsage=none
+            )
             tmp_file.write(content)
             tmp_file.flush()
             tmp_file.close()
@@ -36,21 +46,42 @@ async def process_audio(commons: CommonsDep, file: File, enable_summarization: b
             with open(tmp_file.name, "rb") as audio_file:
                 transcript = openai.Audio.transcribe("whisper-1", audio_file)
 
-        file_sha = compute_sha1_from_content(transcript.text.encode("utf-8"))
+        file_sha = compute_sha1_from_content(
-        file_size = len(transcript.text.encode("utf-8"))
+            transcript.text.encode("utf-8")  # pyright: ignore reportPrivateUsage=none
+        )
+        file_size = len(
+            transcript.text.encode("utf-8")  # pyright: ignore reportPrivateUsage=none
+        )
 
         chunk_size = 500
         chunk_overlap = 0
 
         text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
-            chunk_size=chunk_size, chunk_overlap=chunk_overlap)
+            chunk_size=chunk_size, chunk_overlap=chunk_overlap
-        texts = text_splitter.split_text(transcript.text.encode("utf-8"))
+        )
+        texts = text_splitter.split_text(
+            transcript.text.encode("utf-8")  # pyright: ignore reportPrivateUsage=none
+        )
 
-        docs_with_metadata = [Document(page_content=text, metadata={"file_sha1": file_sha, "file_size": file_size, "file_name": file_meta_name,
+        docs_with_metadata = [
-                                    "chunk_size": chunk_size, "chunk_overlap": chunk_overlap, "date": dateshort}) for text in texts]
+            Document(
+                page_content=text,
+                metadata={
+                    "file_sha1": file_sha,
+                    "file_size": file_size,
+                    "file_name": file_meta_name,
+                    "chunk_size": chunk_size,
+                    "chunk_overlap": chunk_overlap,
+                    "date": dateshort,
+                },
+            )
+            for text in texts
+        ]
 
-        commons.documents_vector_store.add_documents(docs_with_metadata)
+        commons.documents_vector_store.add_documents(  # pyright: ignore reportPrivateUsage=none
+            docs_with_metadata
+        )
 
     finally:
         if temp_filename and os.path.exists(temp_filename):
-             os.remove(temp_filename)
+            os.remove(temp_filename)

backend/parsers/common.py

@@ -19,7 +19,7 @@ async def process_file(
 
     file.compute_documents(loader_class)
 
-    for doc in file.documents:
+    for doc in file.documents:  # pyright: ignore reportPrivateUsage=none
         metadata = {
             "file_sha1": file.file_sha1,
             "file_size": file.file_size,
@@ -29,17 +29,15 @@ async def process_file(
             "date": dateshort,
             "summarization": "true" if enable_summarization else "false",
         }
-        doc_with_metadata = Document(
+        doc_with_metadata = Document(page_content=doc.page_content, metadata=metadata)
-            page_content=doc.page_content, metadata=metadata)
 
         neurons = Neurons(commons=commons)
         created_vector = neurons.create_vector(doc_with_metadata, user_openai_api_key)
         # add_usage(stats_db, "embedding", "audio", metadata={"file_name": file_meta_name,"file_type": ".txt", "chunk_size": chunk_size, "chunk_overlap": chunk_overlap})
 
-        created_vector_id = created_vector[0]
+        created_vector_id = created_vector[0]  # pyright: ignore reportPrivateUsage=none
 
         brain = Brain(id=brain_id)
         brain.create_brain_vector(created_vector_id, file.file_sha1)
 
     return
-

backend/parsers/github.py

@@ -11,7 +11,13 @@ from utils.file import compute_sha1_from_content
 from utils.vectors import Neurons
 
 
-async def process_github(commons: CommonsDep, repo, enable_summarization, brain_id, user_openai_api_key): 
+async def process_github(
+    commons: CommonsDep,  # pyright: ignore reportPrivateUsage=none
+    repo,
+    enable_summarization,
+    brain_id,
+    user_openai_api_key,
+):
     random_dir_name = os.urandom(16).hex()
     dateshort = time.strftime("%Y%m%d")
     loader = GitLoader(
@@ -24,41 +30,60 @@ async def process_github(commons: CommonsDep, repo, enable_summarization, brain_
     chunk_size = 500
     chunk_overlap = 0
     text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
-        chunk_size=chunk_size, chunk_overlap=chunk_overlap)
+        chunk_size=chunk_size, chunk_overlap=chunk_overlap
+    )
 
     documents = text_splitter.split_documents(documents)
     print(documents[:1])
 
     for doc in documents:
-        if doc.metadata["file_type"] in [".pyc",".png",".svg", ".env", ".lock", ".gitignore", ".gitmodules", ".gitattributes", ".gitkeep", ".git", ".json"]:
+        if doc.metadata["file_type"] in [
+            ".pyc",
+            ".png",
+            ".svg",
+            ".env",
+            ".lock",
+            ".gitignore",
+            ".gitmodules",
+            ".gitattributes",
+            ".gitkeep",
+            ".git",
+            ".json",
+        ]:
             continue
         metadata = {
             "file_sha1": compute_sha1_from_content(doc.page_content.encode("utf-8")),
-            "file_size": len(doc.page_content)*8,
+            "file_size": len(doc.page_content) * 8,
             "file_name": doc.metadata["file_name"],
             "chunk_size": chunk_size,
             "chunk_overlap": chunk_overlap,
             "date": dateshort,
-            "summarization": "true" if enable_summarization else "false"
+            "summarization": "true" if enable_summarization else "false",
         }
-        doc_with_metadata = Document(
+        doc_with_metadata = Document(page_content=doc.page_content, metadata=metadata)
-            page_content=doc.page_content, metadata=metadata)
 
-        file = File(file_sha1=compute_sha1_from_content(doc.page_content.encode("utf-8")))
+        file = File(
+            file_sha1=compute_sha1_from_content(doc.page_content.encode("utf-8"))
+        )
 
         file_exists = file.file_already_exists()
 
         if not file_exists:
             print(f"Creating entry for file {file.file_sha1} in vectors...")
-            neurons =  Neurons(commons=commons)
+            neurons = Neurons(commons=commons)
-            created_vector = neurons.create_vector(doc_with_metadata, user_openai_api_key)
+            created_vector = neurons.create_vector(
+                doc_with_metadata, user_openai_api_key
+            )
             print("Created vector sids ", created_vector)
             print("Created vector for ", doc.metadata["file_name"])
 
         file_exists_in_brain = file.file_already_exists_in_brain(brain_id)
 
         if not file_exists_in_brain:
-            file.add_file_to_brain(brain_id)
+            file.add_file_to_brain(brain_id)  # pyright: ignore reportPrivateUsage=none
             brain = Brain(id=brain_id)
             file.link_file_to_brain(brain)
-    return {"message": f"✅ Github with {len(documents)} files has been uploaded.", "type": "success"}
+    return {
+        "message": f"✅ Github with {len(documents)} files has been uploaded.",
+        "type": "success",
+    }

backend/pyrightconfig.json

@@ -0,0 +1,5 @@
+{
+  "exclude": [
+    "supabase"
+  ]
+}

backend/repository/chat/get_chat_history.py

@@ -1,6 +1,7 @@
+from typing import List  # For type hinting
+
 from models.chat import ChatHistory
 from models.settings import common_dependencies
-from typing import List  # For type hinting
 
 
 def get_chat_history(chat_id: str) -> List[ChatHistory]:
@@ -16,4 +17,7 @@ def get_chat_history(chat_id: str) -> List[ChatHistory]:
     if history is None:
         return []
     else:
-        return [ChatHistory(message) for message in history]
+        return [
+            ChatHistory(message)  # pyright: ignore reportPrivateUsage=none
+            for message in history
+        ]

backend/repository/chat/update_chat.py

@@ -21,7 +21,7 @@ def update_chat(chat_id, chat_data: ChatUpdatableProperties) -> Chat:
 
     if not chat_id:
         logger.error("No chat_id provided")
-        return
+        return  # pyright: ignore reportPrivateUsage=none
 
     updates = {}
 
@@ -41,4 +41,4 @@ def update_chat(chat_id, chat_data: ChatUpdatableProperties) -> Chat:
         logger.info(f"Chat {chat_id} updated")
     else:
         logger.info(f"No updates to apply for chat {chat_id}")
-    return updated_chat
+    return updated_chat  # pyright: ignore reportPrivateUsage=none

backend/repository/chat/update_chat_history.py

@@ -23,4 +23,4 @@ def update_chat_history(chat_id: str, user_message: str, assistant: str) -> Chat
         raise HTTPException(
             status_code=500, detail="An exception occurred while updating chat history."
         )
-    return ChatHistory(response[0])
+    return ChatHistory(response[0])  # pyright: ignore reportPrivateUsage=none

backend/repository/chat/update_message_by_id.py

@@ -6,13 +6,15 @@ logger = get_logger(__name__)
 
 
 def update_message_by_id(
-    message_id: str, user_message: str = None, assistant: str = None
+    message_id: str,
+    user_message: str = None,  # pyright: ignore reportPrivateUsage=none
+    assistant: str = None,  # pyright: ignore reportPrivateUsage=none
 ) -> ChatHistory:
     commons = common_dependencies()
 
     if not message_id:
         logger.error("No message_id provided")
-        return
+        return  # pyright: ignore reportPrivateUsage=none
 
     updates = {}
 
@@ -35,4 +37,4 @@ def update_message_by_id(
         logger.info(f"Message {message_id} updated")
     else:
         logger.info(f"No updates to apply for message {message_id}")
-    return ChatHistory(updated_message)
+    return ChatHistory(updated_message)  # pyright: ignore reportPrivateUsage=none

backend/requirements.txt

@@ -23,3 +23,4 @@ flake8==6.0.0
 flake8-black==0.3.6
 sentence_transformers>=2.0.0
 sentry-sdk==1.26.0
+pyright==1.1.316

backend/routes/brain_routes.py

@@ -142,24 +142,27 @@ async def create_brain_endpoint(
     In the brains table & in the brains_users table and put the creator user as 'Owner'
     """
 
-    brain = Brain(name=brain.name)
+    brain = Brain(name=brain.name)  # pyright: ignore reportPrivateUsage=none
 
-    brain.create_brain()
+    brain.create_brain()  # pyright: ignore reportPrivateUsage=none
     default_brain = get_default_user_brain(current_user)
     if default_brain:
         logger.info(f"Default brain already exists for user {current_user.id}")
-        brain.create_brain_user(
+        brain.create_brain_user(  # pyright: ignore reportPrivateUsage=none
             user_id=current_user.id, rights="Owner", default_brain=False
         )
     else:
         logger.info(
             f"Default brain does not exist for user {current_user.id}. It will be created."
         )
-        brain.create_brain_user(
+        brain.create_brain_user(  # pyright: ignore reportPrivateUsage=none
             user_id=current_user.id, rights="Owner", default_brain=True
         )
 
-    return {"id": brain.id, "name": brain.name}
+    return {
+        "id": brain.id,  # pyright: ignore reportPrivateUsage=none
+        "name": brain.name,
+    }
 
 
 # update existing brain
@@ -182,10 +185,12 @@ async def update_brain_endpoint(
     brain = Brain(id=brain_id)
 
     # Add new file to brain , il file_sha1 already exists in brains_vectors -> out (not now)
-    if brain.file_sha1:
+    if brain.file_sha1:  # pyright: ignore reportPrivateUsage=none
         # add all the vector Ids to the brains_vectors  with the given brain.brain_id
-        brain.update_brain_with_file(file_sha1=input_brain.file_sha1)
+        brain.update_brain_with_file(
+            file_sha1=input_brain.file_sha1  # pyright: ignore reportPrivateUsage=none
+        )
         print("brain:", brain)
 
-    brain.update_brain_fields(commons, brain)
+    brain.update_brain_fields(commons, brain)  # pyright: ignore reportPrivateUsage=none
     return {"message": f"Brain {brain_id} has been updated."}

backend/routes/chat_routes.py

@@ -78,8 +78,8 @@ def check_user_limit(
         user.increment_user_request_count(date)
         if int(user.requests_count) >= int(max_requests_number):
             raise HTTPException(
-                status_code=429,
+                status_code=429,  # pyright: ignore reportPrivateUsage=none
-                detail="You have reached the maximum number of requests for today.",
+                detail="You have reached the maximum number of requests for today.",  # pyright: ignore reportPrivateUsage=none
             )
     else:
         pass
@@ -97,7 +97,7 @@ async def get_chats(current_user: User = Depends(get_current_user)):
     This endpoint retrieves all the chats associated with the current authenticated user. It returns a list of chat objects
     containing the chat ID and chat name for each chat.
     """
-    chats = get_user_chats(current_user.id)
+    chats = get_user_chats(current_user.id)  # pyright: ignore reportPrivateUsage=none
     return {"chats": chats}
 
 
@@ -127,10 +127,11 @@ async def update_chat_metadata_handler(
     Update chat attributes
     """
 
-    chat = get_chat_by_id(chat_id)
+    chat = get_chat_by_id(chat_id)  # pyright: ignore reportPrivateUsage=none
     if current_user.id != chat.user_id:
         raise HTTPException(
-            status_code=403, detail="You should be the owner of the chat to update it."
+            status_code=403,  # pyright: ignore reportPrivateUsage=none
+            detail="You should be the owner of the chat to update it.",  # pyright: ignore reportPrivateUsage=none
         )
     return update_chat(chat_id=chat_id, chat_data=chat_data)
 
@@ -181,7 +182,7 @@ async def create_question_handler(
                 temperature=chat_question.temperature,
                 max_tokens=chat_question.max_tokens,
                 brain_id=str(brain_id),
-                user_openai_api_key=current_user.user_openai_api_key,
+                user_openai_api_key=current_user.user_openai_api_key,  # pyright: ignore reportPrivateUsage=none
             )
 
         else:
@@ -191,10 +192,12 @@ async def create_question_handler(
                 max_tokens=chat_question.max_tokens,
                 temperature=chat_question.temperature,
                 brain_id=str(brain_id),
-                user_openai_api_key=current_user.user_openai_api_key,
+                user_openai_api_key=current_user.user_openai_api_key,  # pyright: ignore reportPrivateUsage=none
             )
 
-        chat_answer = gpt_answer_generator.generate_answer(chat_question.question)
+        chat_answer = gpt_answer_generator.generate_answer(  # pyright: ignore reportPrivateUsage=none
+            chat_question.question
+        )
 
         return chat_answer
     except HTTPException as e:
@@ -217,7 +220,10 @@ async def create_stream_question_handler(
     if chat_question.model not in streaming_compatible_models:
         # Forward the request to the none streaming endpoint
         return await create_question_handler(
-            request, chat_question, chat_id, current_user
+            request,
+            chat_question,
+            chat_id,
+            current_user,  # pyright: ignore reportPrivateUsage=none
         )
 
     try:
@@ -238,12 +244,14 @@ async def create_stream_question_handler(
                 max_tokens=chat_question.max_tokens,
                 temperature=chat_question.temperature,
                 brain_id=str(brain_id),
-                user_openai_api_key=user_openai_api_key,
+                user_openai_api_key=user_openai_api_key,  # pyright: ignore reportPrivateUsage=none
                 streaming=True,
             )
 
         return StreamingResponse(
-            gpt_answer_generator.generate_stream(chat_question.question),
+            gpt_answer_generator.generate_stream(  # pyright: ignore reportPrivateUsage=none
+                chat_question.question
+            ),
             media_type="text/event-stream",
         )
 
@@ -259,4 +267,4 @@ async def get_chat_history_handler(
     chat_id: UUID,
 ) -> List[ChatHistory]:
     # TODO: RBAC with current_user
-    return get_chat_history(chat_id)
+    return get_chat_history(chat_id)  # pyright: ignore reportPrivateUsage=none

backend/routes/crawl_routes.py

@@ -35,7 +35,9 @@ async def crawl_endpoint(
     commons = common_dependencies()
 
     if request.headers.get("Openai-Api-Key"):
-        brain.max_brain_size = os.getenv("MAX_BRAIN_SIZE_WITH_KEY", 209715200)
+        brain.max_brain_size = os.getenv(
+            "MAX_BRAIN_SIZE_WITH_KEY", 209715200
+        )  # pyright: ignore reportPrivateUsage=none
 
     file_size = 1000000
     remaining_free_space = brain.remaining_brain_size
@@ -47,14 +49,20 @@ async def crawl_endpoint(
         }
     else:
         if not crawl_website.checkGithub():
-            file_path, file_name = crawl_website.process()
+            (
+                file_path,
+                file_name,
+            ) = crawl_website.process()  # pyright: ignore reportPrivateUsage=none
             # Create a SpooledTemporaryFile from the file_path
             spooled_file = SpooledTemporaryFile()
             with open(file_path, "rb") as f:
                 shutil.copyfileobj(f, spooled_file)
 
             # Pass the SpooledTemporaryFile to UploadFile
-            uploadFile = UploadFile(file=spooled_file, filename=file_name)
+            uploadFile = UploadFile(
+                file=spooled_file,  # pyright: ignore reportPrivateUsage=none
+                filename=file_name,
+            )
             file = File(file=uploadFile)
             #  check remaining free space here !!
             message = await filter_file(

backend/routes/upload_routes.py

@@ -42,7 +42,9 @@ async def upload_file(
     commons = common_dependencies()
 
     if request.headers.get("Openai-Api-Key"):
-        brain.max_brain_size = os.getenv("MAX_BRAIN_SIZE_WITH_KEY", 209715200)
+        brain.max_brain_size = os.getenv(
+            "MAX_BRAIN_SIZE_WITH_KEY", 209715200
+        )  # pyright: ignore reportPrivateUsage=none
     remaining_free_space = brain.remaining_brain_size
 
     file_size = get_file_size(uploadFile)

backend/test_main.py

@@ -10,6 +10,9 @@ client = TestClient(app)
 
 API_KEY = os.getenv("CI_TEST_API_KEY")
 
+if not API_KEY:
+    raise ValueError("CI_TEST_API_KEY environment variable not set. Cannot run tests.")
+
 
 def test_read_main():
     response = client.get("/")
@@ -54,7 +57,8 @@ def test_create_and_delete_api_key():
 def test_retrieve_default_brain():
     # Making a GET request to the /brains/default/ endpoint
     response = client.get(
-        "/brains/default/", headers={"Authorization": "Bearer " + API_KEY}
+        "/brains/default/",
+        headers={"Authorization": "Bearer " + API_KEY},
     )
 
     # Assert that the response status code is 200 (HTTP OK)
@@ -88,7 +92,9 @@ def test_create_brain():
 
     # Making a POST request to the /brains/ endpoint
     response = client.post(
-        "/brains/", json=payload, headers={"Authorization": "Bearer " + API_KEY}
+        "/brains/",
+        json=payload,
+        headers={"Authorization": "Bearer " + API_KEY},
     )
 
     # Assert that the response status code is 200 (HTTP OK)
@@ -106,7 +112,10 @@ def test_create_brain():
 
 def test_retrieve_all_brains():
     # Making a GET request to the /brains/ endpoint to retrieve all brains for the current user
-    response = client.get("/brains/", headers={"Authorization": "Bearer " + API_KEY})
+    response = client.get(
+        "/brains/",
+        headers={"Authorization": "Bearer " + API_KEY},
+    )
 
     # Assert that the response status code is 200 (HTTP OK)
     assert response.status_code == 200
@@ -120,7 +129,10 @@ def test_retrieve_all_brains():
 
 def test_delete_all_brains():
     # First, retrieve all brains for the current user
-    response = client.get("/brains/", headers={"Authorization": "Bearer " + API_KEY})
+    response = client.get(
+        "/brains/",
+        headers={"Authorization": "Bearer " + API_KEY},
+    )
 
     # Assert that the response status code is 200 (HTTP OK)
     assert response.status_code == 200
@@ -133,7 +145,8 @@ def test_delete_all_brains():
 
         # Send a DELETE request to delete the specific brain
         delete_response = client.delete(
-            f"/brains/{brain_id}/", headers={"Authorization": "Bearer " + API_KEY}
+            f"/brains/{brain_id}/",
+            headers={"Authorization": "Bearer " + API_KEY},
         )
 
         # Assert that the DELETE response status code is 200 (HTTP OK)
@@ -142,7 +155,10 @@ def test_delete_all_brains():
 
 def test_get_all_chats():
     # Making a GET request to the /chat endpoint to retrieve all chats
-    response = client.get("/chat", headers={"Authorization": "Bearer " + API_KEY})
+    response = client.get(
+        "/chat",
+        headers={"Authorization": "Bearer " + API_KEY},
+    )
 
     # Assert that the response status code is 200 (HTTP OK)
     assert response.status_code == 200

backend/utils/file.py

@@ -18,8 +18,10 @@ def convert_bytes(bytes, precision=2):
 
 def get_file_size(file: UploadFile):
     # move the cursor to the end of the file
-    file.file._file.seek(0, 2)
+    file.file._file.seek(0, 2)  # pyright: ignore reportPrivateUsage=none
-    file_size = file.file._file.tell()  # Getting the size of the file
+    file_size = (
+        file.file._file.tell()  # pyright: ignore reportPrivateUsage=none
+    )  # Getting the size of the file
     # move the cursor back to the beginning of the file
     file.file.seek(0)
 

backend/utils/processors.py

@@ -54,14 +54,19 @@ async def filter_file(
 
     if file_exists_in_brain:
         return create_response(
-            f"🤔 {file.file.filename} already exists in brain {brain_id}.", "warning"
+            f"🤔 {file.file.filename} already exists in brain {brain_id}.",  # pyright: ignore reportPrivateUsage=none
+            "warning",
         )
     elif file.file_is_empty():
-        return create_response(f"❌ {file.file.filename} is empty.", "error")
+        return create_response(
+            f"❌ {file.file.filename} is empty.",  # pyright: ignore reportPrivateUsage=none
+            "error",  # pyright: ignore reportPrivateUsage=none
+        )
     elif file_exists:
         file.link_file_to_brain(brain=Brain(id=brain_id))
         return create_response(
-            f"✅ {file.file.filename} has been uploaded to brain {brain_id}.", "success"
+            f"✅ {file.file.filename} has been uploaded to brain {brain_id}.",  # pyright: ignore reportPrivateUsage=none
+            "success",
         )
 
     if file.file_extension in file_processors:
@@ -70,14 +75,18 @@ async def filter_file(
                 commons, file, enable_summarization, brain_id, openai_api_key
             )
             return create_response(
-                f"✅ {file.file.filename} has been uploaded to brain {brain_id}.",
+                f"✅ {file.file.filename} has been uploaded to brain {brain_id}.",  # pyright: ignore reportPrivateUsage=none
                 "success",
             )
         except Exception as e:
             # Add more specific exceptions as needed.
             print(f"Error processing file: {e}")
             return create_response(
-                f"⚠️ An error occurred while processing {file.file.filename}.", "error"
+                f"⚠️ An error occurred while processing {file.file.filename}.",  # pyright: ignore reportPrivateUsage=none
+                "error",
             )
 
-    return create_response(f"❌ {file.file.filename} is not supported.", "error")
+    return create_response(
+        f"❌ {file.file.filename} is not supported.",  # pyright: ignore reportPrivateUsage=none
+        "error",
+    )

backend/utils/vectors.py

@@ -13,7 +13,7 @@ logger = get_logger(__name__)
 
 class Neurons(BaseModel):
     commons: CommonsDep
-    settings = BrainSettings()
+    settings = BrainSettings()  # pyright: ignore reportPrivateUsage=none
 
     def create_vector(self, doc, user_openai_api_key=None):
         logger.info("Creating vector for document")
@@ -21,7 +21,7 @@ class Neurons(BaseModel):
         if user_openai_api_key:
             self.commons["documents_vector_store"]._embedding = OpenAIEmbeddings(
                 openai_api_key=user_openai_api_key
-            )
+            )  # pyright: ignore reportPrivateUsage=none
         try:
             sids = self.commons["documents_vector_store"].add_documents([doc])
             if sids and len(sids) > 0:
@@ -64,7 +64,7 @@ def create_summary(commons: CommonsDep, document_id, content, metadata):
 
 
 def error_callback(exception):
-    print('An exception occurred:', exception)
+    print("An exception occurred:", exception)
 
 
 def process_batch(batch_ids):
@@ -106,7 +106,7 @@ def get_unique_files_from_vector_ids(vectors_ids: List[int]):
     with ThreadPoolExecutor() as executor:
         futures = []
         for i in range(0, len(vectors_ids), BATCH_SIZE):
-            batch_ids = vectors_ids[i:i + BATCH_SIZE]
+            batch_ids = vectors_ids[i : i + BATCH_SIZE]
             future = executor.submit(process_batch, batch_ids)
             futures.append(future)
 
@@ -114,6 +114,6 @@ def get_unique_files_from_vector_ids(vectors_ids: List[int]):
         vectors_responses = [future.result() for future in futures]
 
     documents = [item for sublist in vectors_responses for item in sublist]
-    print('document', documents)
+    print("document", documents)
     unique_files = [dict(t) for t in set(tuple(d.items()) for d in documents)]
     return unique_files

backend/vectorstore/supabase.py

@@ -3,8 +3,7 @@ from typing import Any, List
 from langchain.docstore.document import Document
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.vectorstores import SupabaseVectorStore
-
+from supabase.client import Client
-from supabase import Client
 
 
 class CustomSupabaseVectorStore(SupabaseVectorStore):