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Add user related headers when calling an external embedding api

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This commit is contained in:
Didier FOURNOUT 2025-01-29 10:55:52 +00:00
parent b72150c881
commit 6ca295ec59
6 changed files with 70 additions and 32 deletions
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54
backend/open_webui/retrieval/utils.py
View File

@ -15,8 +15,9 @@ from langchain_core.documents import Document
from open_webui.config import VECTOR_DB
from open_webui.retrieval.vector.connector import VECTOR_DB_CLIENT
from open_webui.utils.misc import get_last_user_message
from open_webui.models.users import UserModel
from open_webui.env import SRC_LOG_LEVELS, OFFLINE_MODE
from open_webui.env import SRC_LOG_LEVELS, OFFLINE_MODE, ENABLE_FORWARD_USER_INFO_HEADERS
log = logging.getLogger(__name__)
log.setLevel(SRC_LOG_LEVELS["RAG"])
@ -64,6 +65,7 @@ def query_doc(
collection_name: str,
query_embedding: list[float],
k: int,
user: UserModel=None
):
try:
result = VECTOR_DB_CLIENT.search(
@ -256,29 +258,32 @@ def get_embedding_function(
embedding_function,
url,
key,
embedding_batch_size,
embedding_batch_size
):
if embedding_engine == "":
return lambda query: embedding_function.encode(query).tolist()
return lambda query, user=None: embedding_function.encode(query).tolist()
elif embedding_engine in ["ollama", "openai"]:
func = lambda query: generate_embeddings(
func = lambda query, user=None: generate_embeddings(
engine=embedding_engine,
model=embedding_model,
text=query,
url=url,
key=key,
user=user
)
def generate_multiple(query, func):
def generate_multiple(query, user, func):
if isinstance(query, list):
embeddings = []
for i in range(0, len(query), embedding_batch_size):
embeddings.extend(func(query[i : i + embedding_batch_size]))
embeddings.extend(func(query[i : i + embedding_batch_size], user=user))
return embeddings
else:
return func(query)
return func(query, user)
return lambda query: generate_multiple(query, func)
return lambda query, user=None: generate_multiple(query, user, func)
else:
raise ValueError(f"Unknown embedding engine: {embedding_engine}")
def get_sources_from_files(
@ -423,7 +428,7 @@ def get_model_path(model: str, update_model: bool = False):
def generate_openai_batch_embeddings(
model: str, texts: list[str], url: str = "https://api.openai.com/v1", key: str = ""
model: str, texts: list[str], url: str = "https://api.openai.com/v1", key: str = "", user: UserModel = None
) -> Optional[list[list[float]]]:
try:
r = requests.post(
@ -431,6 +436,16 @@ def generate_openai_batch_embeddings(
headers={
"Content-Type": "application/json",
"Authorization": f"Bearer {key}",
**(
{
"X-OpenWebUI-User-Name": user.name,
"X-OpenWebUI-User-Id": user.id,
"X-OpenWebUI-User-Email": user.email,
"X-OpenWebUI-User-Role": user.role,
}
if ENABLE_FORWARD_USER_INFO_HEADERS and user
else {}
),
},
json={"input": texts, "model": model},
)
@ -446,7 +461,7 @@ def generate_openai_batch_embeddings(
def generate_ollama_batch_embeddings(
model: str, texts: list[str], url: str, key: str = ""
model: str, texts: list[str], url: str, key: str = "", user: UserModel = None
) -> Optional[list[list[float]]]:
try:
r = requests.post(
@ -454,6 +469,16 @@ def generate_ollama_batch_embeddings(
headers={
"Content-Type": "application/json",
"Authorization": f"Bearer {key}",
**(
{
"X-OpenWebUI-User-Name": user.name,
"X-OpenWebUI-User-Id": user.id,
"X-OpenWebUI-User-Email": user.email,
"X-OpenWebUI-User-Role": user.role,
}
if ENABLE_FORWARD_USER_INFO_HEADERS
else {}
),
},
json={"input": texts, "model": model},
)
@ -472,22 +497,23 @@ def generate_ollama_batch_embeddings(
def generate_embeddings(engine: str, model: str, text: Union[str, list[str]], **kwargs):
url = kwargs.get("url", "")
key = kwargs.get("key", "")
user = kwargs.get("user")
if engine == "ollama":
if isinstance(text, list):
embeddings = generate_ollama_batch_embeddings(
**{"model": model, "texts": text, "url": url, "key": key}
**{"model": model, "texts": text, "url": url, "key": key, "user": user}
)
else:
embeddings = generate_ollama_batch_embeddings(
**{"model": model, "texts": [text], "url": url, "key": key}
**{"model": model, "texts": [text], "url": url, "key": key, "user": user}
)
return embeddings[0] if isinstance(text, str) else embeddings
elif engine == "openai":
if isinstance(text, list):
embeddings = generate_openai_batch_embeddings(model, text, url, key)
embeddings = generate_openai_batch_embeddings(model, text, url, key, user)
else:
embeddings = generate_openai_batch_embeddings(model, [text], url, key)
embeddings = generate_openai_batch_embeddings(model, [text], url, key, user)
return embeddings[0] if isinstance(text, str) else embeddings

6
backend/open_webui/routers/files.py
View File

@ -71,7 +71,7 @@ def upload_file(
)
try:
process_file(request, ProcessFileForm(file_id=id))
process_file(request, ProcessFileForm(file_id=id), user=user)
file_item = Files.get_file_by_id(id=id)
except Exception as e:
log.exception(e)
@ -193,7 +193,9 @@ async def update_file_data_content_by_id(
if file and (file.user_id == user.id or user.role == "admin"):
try:
process_file(
request, ProcessFileForm(file_id=id, content=form_data.content)
request,
ProcessFileForm(file_id=id, content=form_data.content),
user=user
)
file = Files.get_file_by_id(id=id)
except Exception as e:

8
backend/open_webui/routers/knowledge.py
View File

@ -285,7 +285,9 @@ def add_file_to_knowledge_by_id(
# Add content to the vector database
try:
process_file(
request, ProcessFileForm(file_id=form_data.file_id, collection_name=id)
request,
ProcessFileForm(file_id=form_data.file_id, collection_name=id),
user=user
)
except Exception as e:
log.debug(e)
@ -363,7 +365,9 @@ def update_file_from_knowledge_by_id(
# Add content to the vector database
try:
process_file(
request, ProcessFileForm(file_id=form_data.file_id, collection_name=id)
request,
ProcessFileForm(file_id=form_data.file_id, collection_name=id),
user=user
)
except Exception as e:
raise HTTPException(

8
backend/open_webui/routers/memories.py
View File

@ -57,7 +57,7 @@ async def add_memory(
{
"id": memory.id,
"text": memory.content,
"vector": request.app.state.EMBEDDING_FUNCTION(memory.content),
"vector": request.app.state.EMBEDDING_FUNCTION(memory.content, user),
"metadata": {"created_at": memory.created_at},
}
],
@ -82,7 +82,7 @@ async def query_memory(
):
results = VECTOR_DB_CLIENT.search(
collection_name=f"user-memory-{user.id}",
vectors=[request.app.state.EMBEDDING_FUNCTION(form_data.content)],
vectors=[request.app.state.EMBEDDING_FUNCTION(form_data.content, user)],
limit=form_data.k,
)
@ -105,7 +105,7 @@ async def reset_memory_from_vector_db(
{
"id": memory.id,
"text": memory.content,
"vector": request.app.state.EMBEDDING_FUNCTION(memory.content),
"vector": request.app.state.EMBEDDING_FUNCTION(memory.content, user),
"metadata": {
"created_at": memory.created_at,
"updated_at": memory.updated_at,
@ -160,7 +160,7 @@ async def update_memory_by_id(
{
"id": memory.id,
"text": memory.content,
"vector": request.app.state.EMBEDDING_FUNCTION(memory.content),
"vector": request.app.state.EMBEDDING_FUNCTION(memory.content, user),
"metadata": {
"created_at": memory.created_at,
"updated_at": memory.updated_at,

24
backend/open_webui/routers/retrieval.py
View File

@ -660,6 +660,7 @@ def save_docs_to_vector_db(
overwrite: bool = False,
split: bool = True,
add: bool = False,
user = None,
) -> bool:
def _get_docs_info(docs: list[Document]) -> str:
docs_info = set()
@ -775,7 +776,8 @@ def save_docs_to_vector_db(
)
embeddings = embedding_function(
list(map(lambda x: x.replace("\n", " "), texts))
list(map(lambda x: x.replace("\n", " "), texts)),
user = user
)
items = [
@ -933,6 +935,7 @@ def process_file(
"hash": hash,
},
add=(True if form_data.collection_name else False),
user=user
)
if result:
@ -990,7 +993,7 @@ def process_text(
text_content = form_data.content
log.debug(f"text_content: {text_content}")
result = save_docs_to_vector_db(request, docs, collection_name)
result = save_docs_to_vector_db(request, docs, collection_name, user=user)
if result:
return {
"status": True,
@ -1023,7 +1026,7 @@ def process_youtube_video(
content = " ".join([doc.page_content for doc in docs])
log.debug(f"text_content: {content}")
save_docs_to_vector_db(request, docs, collection_name, overwrite=True)
save_docs_to_vector_db(request, docs, collection_name, overwrite=True, user=user)
return {
"status": True,
@ -1064,7 +1067,7 @@ def process_web(
content = " ".join([doc.page_content for doc in docs])
log.debug(f"text_content: {content}")
save_docs_to_vector_db(request, docs, collection_name, overwrite=True)
save_docs_to_vector_db(request, docs, collection_name, overwrite=True, user=user)
return {
"status": True,
@ -1272,7 +1275,7 @@ def process_web_search(
requests_per_second=request.app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
)
docs = loader.load()
save_docs_to_vector_db(request, docs, collection_name, overwrite=True)
save_docs_to_vector_db(request, docs, collection_name, overwrite=True, user=user)
return {
"status": True,
@ -1306,7 +1309,7 @@ def query_doc_handler(
return query_doc_with_hybrid_search(
collection_name=form_data.collection_name,
query=form_data.query,
embedding_function=request.app.state.EMBEDDING_FUNCTION,
embedding_function=lambda query: request.app.state.EMBEDDING_FUNCTION(query, user=user),
k=form_data.k if form_data.k else request.app.state.config.TOP_K,
reranking_function=request.app.state.rf,
r=(
@ -1314,12 +1317,14 @@ def query_doc_handler(
if form_data.r
else request.app.state.config.RELEVANCE_THRESHOLD
),
user=user
)
else:
return query_doc(
collection_name=form_data.collection_name,
query_embedding=request.app.state.EMBEDDING_FUNCTION(form_data.query),
query_embedding=request.app.state.EMBEDDING_FUNCTION(form_data.query, user=user),
k=form_data.k if form_data.k else request.app.state.config.TOP_K,
user=user
)
except Exception as e:
log.exception(e)
@ -1348,7 +1353,7 @@ def query_collection_handler(
return query_collection_with_hybrid_search(
collection_names=form_data.collection_names,
queries=[form_data.query],
embedding_function=request.app.state.EMBEDDING_FUNCTION,
embedding_function=lambda query: request.app.state.EMBEDDING_FUNCTION(query, user=user),
k=form_data.k if form_data.k else request.app.state.config.TOP_K,
reranking_function=request.app.state.rf,
r=(
@ -1361,7 +1366,7 @@ def query_collection_handler(
return query_collection(
collection_names=form_data.collection_names,
queries=[form_data.query],
embedding_function=request.app.state.EMBEDDING_FUNCTION,
embedding_function=lambda query: request.app.state.EMBEDDING_FUNCTION(query,user=user),
k=form_data.k if form_data.k else request.app.state.config.TOP_K,
)
@ -1509,6 +1514,7 @@ def process_files_batch(
docs=all_docs,
collection_name=collection_name,
add=True,
user=user,
)
# Update all files with collection name

2
backend/open_webui/utils/middleware.py
View File

@ -630,7 +630,7 @@ async def chat_completion_files_handler(
lambda: get_sources_from_files(
files=files,
queries=queries,
embedding_function=request.app.state.EMBEDDING_FUNCTION,
embedding_function=lambda query: request.app.state.EMBEDDING_FUNCTION(query,user=user),
k=request.app.state.config.TOP_K,
reranking_function=request.app.state.rf,
r=request.app.state.config.RELEVANCE_THRESHOLD,