feat:精简

backend/open_webui/retrieval/utils.py

@@ -27,7 +27,6 @@ from open_webui.models.files import Files
 from open_webui.models.knowledge import Knowledges
 
 from open_webui.models.chats import Chats
-from open_webui.models.notes import Notes
 
 from open_webui.retrieval.vector.main import GetResult
 from open_webui.utils.access_control import has_access
@@ -46,8 +45,6 @@ from open_webui.config import (
     RAG_EMBEDDING_CONTENT_PREFIX,
     RAG_EMBEDDING_PREFIX_FIELD_NAME,
 )
-from open_webui.utils.credit.usage import CreditDeduct
-from open_webui.utils.credit.utils import check_credit_by_user_id
 
 log = logging.getLogger(__name__)
 
@@ -540,10 +537,6 @@ async def agenerate_openai_batch_embeddings(
     prefix: str = None,
     user: UserModel = None,
 ) -> Optional[list[list[float]]]:
-    # check credit
-    if user:
-        check_credit_by_user_id(user_id=user.id, form_data={}, is_embedding=True)
-
     try:
         log.debug(
             f"agenerate_openai_batch_embeddings:model {model} batch size: {len(texts)}"
@@ -566,26 +559,6 @@ async def agenerate_openai_batch_embeddings(
                 r.raise_for_status()
                 data = await r.json()
 
-                if user:
-                    with CreditDeduct(
-                        user=user,
-                        model_id=model,
-                        body={
-                            "messages": [
-                                {"role": "user", "content": form_data["input"]}
-                            ]
-                        },
-                        is_stream=False,
-                        is_embedding=True,
-                    ) as credit_deduct:
-                        if "usage" in data:
-                            credit_deduct.is_official_usage = True
-                            prompt_tokens = data["usage"]["prompt_tokens"]
-                            credit_deduct.usage.prompt_tokens = prompt_tokens
-                            credit_deduct.usage.total_tokens = prompt_tokens
-                        else:
-                            credit_deduct.run(form_data["input"])
-
                 if "data" in data:
                     return [item["embedding"] for item in data["data"]]
                 else:
@@ -604,10 +577,6 @@ async def agenerate_azure_openai_batch_embeddings(
     prefix: str = None,
     user: UserModel = None,
 ) -> Optional[list[list[float]]]:
-    # check credit
-    if user:
-        check_credit_by_user_id(user_id=user.id, form_data={}, is_embedding=True)
-
     try:
         log.debug(
             f"agenerate_azure_openai_batch_embeddings:deployment {model} batch size: {len(texts)}"
@@ -630,22 +599,6 @@ async def agenerate_azure_openai_batch_embeddings(
                 r.raise_for_status()
                 data = await r.json()
 
-                input_text = str(form_data["input"])
-                with CreditDeduct(
-                    user=user,
-                    model_id=model,
-                    body={"messages": [{"role": "user", "content": input_text}]},
-                    is_stream=False,
-                    is_embedding=True,
-                ) as credit_deduct:
-                    if "usage" in data:
-                        credit_deduct.is_official_usage = True
-                        prompt_tokens = data["usage"]["prompt_tokens"]
-                        credit_deduct.usage.prompt_tokens = prompt_tokens
-                        credit_deduct.usage.total_tokens = prompt_tokens
-                    else:
-                        credit_deduct.run(input_text)
-
                 if "data" in data:
                     return [item["embedding"] for item in data["data"]]
                 else:
@@ -663,10 +616,6 @@ async def agenerate_ollama_batch_embeddings(
     prefix: str = None,
     user: UserModel = None,
 ) -> Optional[list[list[float]]]:
-    # check credit
-    if user:
-        check_credit_by_user_id(user_id=user.id, form_data={}, is_embedding=True)
-
     try:
         log.debug(
             f"agenerate_ollama_batch_embeddings:model {model} batch size: {len(texts)}"
@@ -689,16 +638,6 @@ async def agenerate_ollama_batch_embeddings(
                 r.raise_for_status()
                 data = await r.json()
 
-                input_text = str(form_data["input"])
-                with CreditDeduct(
-                    user=user,
-                    model_id=model,
-                    body={"messages": [{"role": "user", "content": input_text}]},
-                    is_stream=False,
-                    is_embedding=True,
-                ) as credit_deduct:
-                    credit_deduct.run(input_text)
-
                 if "embeddings" in data:
                     return data["embeddings"]
                 else:
@@ -912,21 +851,6 @@ async def get_sources_from_items(
                         ],
                     }
 
-        elif item.get("type") == "note":
-            # Note Attached
-            note = Notes.get_note_by_id(item.get("id"))
-
-            if note and (
-                user.role == "admin"
-                or note.user_id == user.id
-                or has_access(user.id, "read", note.access_control)
-            ):
-                # User has access to the note
-                query_result = {
-                    "documents": [[note.data.get("content", {}).get("md", "")]],
-                    "metadatas": [[{"file_id": note.id, "name": note.title}]],
-                }
-
         elif item.get("type") == "chat":
             # Chat Attached
             chat = Chats.get_chat_by_id(item.get("id"))