refac

backend/open_webui/retrieval/external.py

@@ -4,6 +4,7 @@ import re
 import time
 from typing import Any, Optional
 
+from open_webui.config import RAG_EMBEDDING_QUERY_PREFIX
 from open_webui.models.config import Config
 from open_webui.models.knowledge import KnowledgeModel
 
@@ -103,7 +104,7 @@ async def _retrieve_qdrant(connection, auth_config, knowledge, query, count, emb
     source_config = _source_config(knowledge)
     vector_field = source_config.get('vector_field') or None
 
-    vector = await embedding_function(query)
+    vector = await embedding_function(query, prefix=RAG_EMBEDDING_QUERY_PREFIX)
 
     def _search():
         client = QdrantClient(
@@ -152,7 +153,7 @@ async def _retrieve_milvus(connection, auth_config, knowledge, query, count, emb
     content_field = source_config.get('content_field') or 'data.text'
     metadata_field = source_config.get('metadata_field') or 'metadata'
 
-    vector = await embedding_function(query)
+    vector = await embedding_function(query, prefix=RAG_EMBEDDING_QUERY_PREFIX)
 
     def _search():
         client_kwargs = {
@@ -229,7 +230,7 @@ async def _retrieve_pgvector(connection, auth_config, knowledge, query, count, e
     metadata_field = source_config.get('metadata_field') or 'vmetadata'
     document_id_field = source_config.get('document_id_field') or 'id'
 
-    vector = await embedding_function(query)
+    vector = await embedding_function(query, prefix=RAG_EMBEDDING_QUERY_PREFIX)
 
     def _search():
         from psycopg import sql

backend/open_webui/routers/knowledge.py

@@ -12,7 +12,7 @@ from urllib.parse import quote
 
 from fastapi import APIRouter, Depends, HTTPException, Query, Request, status
 from fastapi.responses import StreamingResponse
-from open_webui.config import BYPASS_ADMIN_ACCESS_CONTROL
+from open_webui.config import BYPASS_ADMIN_ACCESS_CONTROL, RAG_EMBEDDING_CONTENT_PREFIX
 from open_webui.constants import ERROR_MESSAGES
 from open_webui.events import EVENTS, publish_event
 from open_webui.internal.db import get_async_session
@@ -74,7 +74,7 @@ async def embed_knowledge_base_metadata(
     """Generate and store embedding for knowledge base."""
     try:
         content = f'{name}\n\n{description}' if description else name
-        embedding = await request.app.state.EMBEDDING_FUNCTION(content)
+        embedding = await request.app.state.EMBEDDING_FUNCTION(content, prefix=RAG_EMBEDDING_CONTENT_PREFIX)
         await ASYNC_VECTOR_DB_CLIENT.upsert(
             collection_name=KNOWLEDGE_BASES_COLLECTION,
             items=[

backend/open_webui/routers/memories.py

@@ -5,13 +5,13 @@ import logging
 from typing import Literal
 
 from fastapi import APIRouter, Depends, HTTPException, Request, status
+from open_webui.config import RAG_EMBEDDING_CONTENT_PREFIX, RAG_EMBEDDING_QUERY_PREFIX
 from open_webui.constants import ERROR_MESSAGES
 from open_webui.events import EVENTS, publish_event
 from open_webui.internal.db import get_async_session
 from open_webui.models.config import Config
 from open_webui.models.memories import Memories, MemoryModel
 from open_webui.retrieval.vector.async_client import ASYNC_VECTOR_DB_CLIENT
-from open_webui.config import RAG_EMBEDDING_QUERY_PREFIX
 from open_webui.utils.access_control import has_permission
 from open_webui.utils.auth import get_verified_user
 from open_webui.utils.memory import (
@@ -148,7 +148,9 @@ async def add_memory(
         meta={'created_by': 'manual'},
     )
 
-    vector = await request.app.state.EMBEDDING_FUNCTION(memory_vector_text(memory.content, memory.path), user=user)
+    vector = await request.app.state.EMBEDDING_FUNCTION(
+        memory_vector_text(memory.content, memory.path), prefix=RAG_EMBEDDING_CONTENT_PREFIX, user=user
+    )
 
     await ASYNC_VECTOR_DB_CLIENT.upsert(
         collection_name=f'user-memory-{user.id}',
@@ -208,6 +210,7 @@ async def update_memories(
             if result.get('status') in {'created', 'updated'}:
                 vector = await request.app.state.EMBEDDING_FUNCTION(
                     memory_vector_text(memory.content, memory.path),
+                    prefix=RAG_EMBEDDING_CONTENT_PREFIX,
                     user=user,
                 )
                 upsert_items.append(
@@ -284,7 +287,7 @@ async def query_memory(
     if not memories:
         raise HTTPException(status_code=404, detail='No memories found for user')
 
-    vector = await request.app.state.EMBEDDING_FUNCTION(form_data.content, RAG_EMBEDDING_QUERY_PREFIX, user=user)
+    vector = await request.app.state.EMBEDDING_FUNCTION(form_data.content, prefix=RAG_EMBEDDING_QUERY_PREFIX, user=user)
 
     results = await ASYNC_VECTOR_DB_CLIENT.search(
         collection_name=f'user-memory-{user.id}',
@@ -407,7 +410,9 @@ async def reset_memory_from_vector_db(
     # Generate vectors in parallel
     vectors = await asyncio.gather(
         *[
-            request.app.state.EMBEDDING_FUNCTION(memory_vector_text(memory.content, memory.path), user=user)
+            request.app.state.EMBEDDING_FUNCTION(
+                memory_vector_text(memory.content, memory.path), prefix=RAG_EMBEDDING_CONTENT_PREFIX, user=user
+            )
             for memory in memories
         ]
     )
@@ -503,7 +508,9 @@ async def update_memory_by_id(
         raise HTTPException(status_code=404, detail=ERROR_MESSAGES.NOT_FOUND)
 
     if form_data.content is not None or form_data.path is not None:
-        vector = await request.app.state.EMBEDDING_FUNCTION(memory_vector_text(memory.content, memory.path), user=user)
+        vector = await request.app.state.EMBEDDING_FUNCTION(
+            memory_vector_text(memory.content, memory.path), prefix=RAG_EMBEDDING_CONTENT_PREFIX, user=user
+        )
 
         await ASYNC_VECTOR_DB_CLIENT.upsert(
             collection_name=f'user-memory-{user.id}',

backend/open_webui/tools/builtin.py

@@ -16,6 +16,7 @@ from typing import Literal, Optional
 
 from fastapi import HTTPException, Request
 
+from open_webui.config import RAG_EMBEDDING_QUERY_PREFIX
 from open_webui.models.channels import Channel, ChannelMember, Channels
 from open_webui.models.chats import Chats
 from open_webui.models.config import Config
@@ -3237,7 +3238,7 @@ async def query_knowledge_bases(
 
         user_id = __user__.get('id')
         user_group_ids = [group.id for group in await Groups.get_groups_by_member_id(user_id)]
-        query_embedding = await __request__.app.state.EMBEDDING_FUNCTION(query)
+        query_embedding = await __request__.app.state.EMBEDDING_FUNCTION(query, prefix=RAG_EMBEDDING_QUERY_PREFIX)
 
         # Min-heap of (distance, knowledge_base_id) - only holds top `count` results
         top_results_heap = []