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Timothy Jaeryang Baek
2026-07-27 01:59:17 -04:00
parent 8f9e9398f8
commit c4f5ac65ee
4 changed files with 20 additions and 11 deletions
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backend/open_webui/retrieval/external.py
+4 -3
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@@ -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
+2 -2
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@@ -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
+12 -5
View File
@@ -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
+2 -1
View File
@@ -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 = []