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Merge pull request #5565 from open-webui/dev

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0.3.23
This commit is contained in:
Timothy Jaeryang Baek
2024-09-21 04:55:29 +02:00
committed by GitHub
parent 578d52b89d 2f028b45fe
commit ff8a2da751
17 changed files with 552 additions and 291 deletions
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11
backend/open_webui/apps/openai/main.py
View File

@@ -405,14 +405,19 @@ async def generate_chat_completion(
"role": user.role,
}
url = app.state.config.OPENAI_API_BASE_URLS[idx]
key = app.state.config.OPENAI_API_KEYS[idx]
# Change max_completion_tokens to max_tokens (Backward compatible)
if "api.openai.com" not in url and not payload["model"].lower().startswith("o1-"):
if "max_completion_tokens" in payload:
payload["max_tokens"] = payload.pop("max_completion_tokens")
# Convert the modified body back to JSON
payload = json.dumps(payload)
log.debug(payload)
url = app.state.config.OPENAI_API_BASE_URLS[idx]
key = app.state.config.OPENAI_API_KEYS[idx]
headers = {}
headers["Authorization"] = f"Bearer {key}"
headers["Content-Type"] = "application/json"

52
backend/open_webui/apps/rag/main.py
View File

@@ -1099,35 +1099,35 @@ def store_docs_in_vector_db(
log.info(f"deleting existing collection {collection_name}")
VECTOR_DB_CLIENT.delete_collection(collection_name=collection_name)
embedding_function = get_embedding_function(
app.state.config.RAG_EMBEDDING_ENGINE,
app.state.config.RAG_EMBEDDING_MODEL,
app.state.sentence_transformer_ef,
app.state.config.OPENAI_API_KEY,
app.state.config.OPENAI_API_BASE_URL,
app.state.config.RAG_EMBEDDING_OPENAI_BATCH_SIZE,
)
VECTOR_DB_CLIENT.insert(
collection_name=collection_name,
items=[
{
"id": str(uuid.uuid4()),
"text": text,
"vector": embedding_function(text.replace("\n", " ")),
"metadata": metadatas[idx],
}
for idx, text in enumerate(texts)
],
)
return True
except Exception as e:
if e.__class__.__name__ == "UniqueConstraintError":
if VECTOR_DB_CLIENT.has_collection(collection_name=collection_name):
log.info(f"collection {collection_name} already exists")
return True
else:
embedding_function = get_embedding_function(
app.state.config.RAG_EMBEDDING_ENGINE,
app.state.config.RAG_EMBEDDING_MODEL,
app.state.sentence_transformer_ef,
app.state.config.OPENAI_API_KEY,
app.state.config.OPENAI_API_BASE_URL,
app.state.config.RAG_EMBEDDING_OPENAI_BATCH_SIZE,
)
VECTOR_DB_CLIENT.insert(
collection_name=collection_name,
items=[
{
"id": str(uuid.uuid4()),
"text": text,
"vector": embedding_function(text.replace("\n", " ")),
"metadata": metadatas[idx],
}
for idx, text in enumerate(texts)
],
)
return True
except Exception as e:
log.exception(e)
return False

38
backend/open_webui/apps/socket/main.py
View File

@@ -2,16 +2,38 @@ import asyncio
import socketio
from open_webui.apps.webui.models.users import Users
from open_webui.env import ENABLE_WEBSOCKET_SUPPORT
from open_webui.env import (
ENABLE_WEBSOCKET_SUPPORT,
WEBSOCKET_MANAGER,
WEBSOCKET_REDIS_URL,
)
from open_webui.utils.utils import decode_token
sio = socketio.AsyncServer(
cors_allowed_origins=[],
async_mode="asgi",
transports=(["polling", "websocket"] if ENABLE_WEBSOCKET_SUPPORT else ["polling"]),
allow_upgrades=ENABLE_WEBSOCKET_SUPPORT,
always_connect=True,
)
if WEBSOCKET_MANAGER == "redis":
mgr = socketio.AsyncRedisManager(WEBSOCKET_REDIS_URL)
sio = socketio.AsyncServer(
cors_allowed_origins=[],
async_mode="asgi",
transports=(
["polling", "websocket"] if ENABLE_WEBSOCKET_SUPPORT else ["polling"]
),
allow_upgrades=ENABLE_WEBSOCKET_SUPPORT,
always_connect=True,
client_manager=mgr,
)
else:
sio = socketio.AsyncServer(
cors_allowed_origins=[],
async_mode="asgi",
transports=(
["polling", "websocket"] if ENABLE_WEBSOCKET_SUPPORT else ["polling"]
),
allow_upgrades=ENABLE_WEBSOCKET_SUPPORT,
always_connect=True,
)
app = socketio.ASGIApp(sio, socketio_path="/ws/socket.io")
# Dictionary to maintain the user pool

4
backend/open_webui/env.py
View File

@@ -302,3 +302,7 @@ if WEBUI_AUTH and WEBUI_SECRET_KEY == "":
ENABLE_WEBSOCKET_SUPPORT = (
os.environ.get("ENABLE_WEBSOCKET_SUPPORT", "True").lower() == "true"
)
WEBSOCKET_MANAGER = os.environ.get("WEBSOCKET_MANAGER", "")
WEBSOCKET_REDIS_URL = os.environ.get("WEBSOCKET_REDIS_URL", "redis://localhost:6379/0")

52
backend/open_webui/main.py
View File

@@ -19,7 +19,9 @@ from open_webui.apps.audio.main import app as audio_app
from open_webui.apps.images.main import app as images_app
from open_webui.apps.ollama.main import app as ollama_app
from open_webui.apps.ollama.main import (
generate_openai_chat_completion as generate_ollama_chat_completion,
GenerateChatCompletionForm,
generate_chat_completion as generate_ollama_chat_completion,
generate_openai_chat_completion as generate_ollama_openai_chat_completion,
)
from open_webui.apps.ollama.main import get_all_models as get_ollama_models
from open_webui.apps.openai.main import app as openai_app
@@ -135,6 +137,12 @@ from open_webui.utils.utils import (
)
from open_webui.utils.webhook import post_webhook
from open_webui.utils.payload import convert_payload_openai_to_ollama
from open_webui.utils.response import (
convert_response_ollama_to_openai,
convert_streaming_response_ollama_to_openai,
)
if SAFE_MODE:
print("SAFE MODE ENABLED")
Functions.deactivate_all_functions()
@@ -1048,7 +1056,18 @@ async def generate_chat_completions(form_data: dict, user=Depends(get_verified_u
if model.get("pipe"):
return await generate_function_chat_completion(form_data, user=user)
if model["owned_by"] == "ollama":
return await generate_ollama_chat_completion(form_data, user=user)
# Using /ollama/api/chat endpoint
form_data = convert_payload_openai_to_ollama(form_data)
form_data = GenerateChatCompletionForm(**form_data)
response = await generate_ollama_chat_completion(form_data=form_data, user=user)
if form_data.stream:
response.headers["content-type"] = "text/event-stream"
return StreamingResponse(
convert_streaming_response_ollama_to_openai(response),
headers=dict(response.headers),
)
else:
return convert_response_ollama_to_openai(response)
else:
return await generate_openai_chat_completion(form_data, user=user)
@@ -1399,9 +1418,10 @@ async def generate_title(form_data: dict, user=Depends(get_verified_user)):
# Check if the user has a custom task model
# If the user has a custom task model, use that model
task_model_id = get_task_model_id(model_id)
print(task_model_id)
model = app.state.MODELS[task_model_id]
if app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE != "":
template = app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE
else:
@@ -1440,9 +1460,9 @@ Prompt: {{prompt:middletruncate:8000}}"""
"chat_id": form_data.get("chat_id", None),
"metadata": {"task": str(TASKS.TITLE_GENERATION)},
}
log.debug(payload)
# Handle pipeline filters
try:
payload = filter_pipeline(payload, user)
except Exception as e:
@@ -1456,7 +1476,6 @@ Prompt: {{prompt:middletruncate:8000}}"""
status_code=status.HTTP_400_BAD_REQUEST,
content={"detail": str(e)},
)
if "chat_id" in payload:
del payload["chat_id"]
@@ -1484,6 +1503,8 @@ async def generate_search_query(form_data: dict, user=Depends(get_verified_user)
task_model_id = get_task_model_id(model_id)
print(task_model_id)
model = app.state.MODELS[task_model_id]
if app.state.config.SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE != "":
template = app.state.config.SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE
else:
@@ -1516,9 +1537,9 @@ Search Query:"""
),
"metadata": {"task": str(TASKS.QUERY_GENERATION)},
}
log.debug(payload)
print(payload)
# Handle pipeline filters
try:
payload = filter_pipeline(payload, user)
except Exception as e:
@@ -1532,7 +1553,6 @@ Search Query:"""
status_code=status.HTTP_400_BAD_REQUEST,
content={"detail": str(e)},
)
if "chat_id" in payload:
del payload["chat_id"]
@@ -1555,12 +1575,13 @@ async def generate_emoji(form_data: dict, user=Depends(get_verified_user)):
task_model_id = get_task_model_id(model_id)
print(task_model_id)
model = app.state.MODELS[task_model_id]
template = '''
Your task is to reflect the speaker's likely facial expression through a fitting emoji. Interpret emotions from the message and reflect their facial expression using fitting, diverse emojis (e.g., 😊, 😢, 😡, 😱).
Message: """{{prompt}}"""
'''
content = title_generation_template(
template,
form_data["prompt"],
@@ -1584,9 +1605,9 @@ Message: """{{prompt}}"""
"chat_id": form_data.get("chat_id", None),
"metadata": {"task": str(TASKS.EMOJI_GENERATION)},
}
log.debug(payload)
# Handle pipeline filters
try:
payload = filter_pipeline(payload, user)
except Exception as e:
@@ -1600,7 +1621,6 @@ Message: """{{prompt}}"""
status_code=status.HTTP_400_BAD_REQUEST,
content={"detail": str(e)},
)
if "chat_id" in payload:
del payload["chat_id"]
@@ -1620,8 +1640,10 @@ async def generate_moa_response(form_data: dict, user=Depends(get_verified_user)
# Check if the user has a custom task model
# If the user has a custom task model, use that model
model_id = get_task_model_id(model_id)
print(model_id)
task_model_id = get_task_model_id(model_id)
print(task_model_id)
model = app.state.MODELS[task_model_id]
template = """You have been provided with a set of responses from various models to the latest user query: "{{prompt}}"
@@ -1636,13 +1658,12 @@ Responses from models: {{responses}}"""
)
payload = {
"model": model_id,
"model": task_model_id,
"messages": [{"role": "user", "content": content}],
"stream": form_data.get("stream", False),
"chat_id": form_data.get("chat_id", None),
"metadata": {"task": str(TASKS.MOA_RESPONSE_GENERATION)},
}
log.debug(payload)
try:
@@ -1658,7 +1679,6 @@ Responses from models: {{responses}}"""
status_code=status.HTTP_400_BAD_REQUEST,
content={"detail": str(e)},
)
if "chat_id" in payload:
del payload["chat_id"]

16
backend/open_webui/utils/misc.py
View File

@@ -105,17 +105,25 @@ def openai_chat_message_template(model: str):
}
def openai_chat_chunk_message_template(model: str, message: str) -> dict:
def openai_chat_chunk_message_template(
model: str, message: Optional[str] = None
) -> dict:
template = openai_chat_message_template(model)
template["object"] = "chat.completion.chunk"
template["choices"][0]["delta"] = {"content": message}
if message:
template["choices"][0]["delta"] = {"content": message}
else:
template["choices"][0]["finish_reason"] = "stop"
return template
def openai_chat_completion_message_template(model: str, message: str) -> dict:
def openai_chat_completion_message_template(
model: str, message: Optional[str] = None
) -> dict:
template = openai_chat_message_template(model)
template["object"] = "chat.completion"
template["choices"][0]["message"] = {"content": message, "role": "assistant"}
if message:
template["choices"][0]["message"] = {"content": message, "role": "assistant"}
template["choices"][0]["finish_reason"] = "stop"
return template

46
backend/open_webui/utils/payload.py
View File

@@ -86,3 +86,49 @@ def apply_model_params_to_body_ollama(params: dict, form_data: dict) -> dict:
form_data[value] = param
return form_data
def convert_payload_openai_to_ollama(openai_payload: dict) -> dict:
"""
Converts a payload formatted for OpenAI's API to be compatible with Ollama's API endpoint for chat completions.
Args:
openai_payload (dict): The payload originally designed for OpenAI API usage.
Returns:
dict: A modified payload compatible with the Ollama API.
"""
ollama_payload = {}
# Mapping basic model and message details
ollama_payload["model"] = openai_payload.get("model")
ollama_payload["messages"] = openai_payload.get("messages")
ollama_payload["stream"] = openai_payload.get("stream", False)
# If there are advanced parameters in the payload, format them in Ollama's options field
ollama_options = {}
# Handle parameters which map directly
for param in ["temperature", "top_p", "seed"]:
if param in openai_payload:
ollama_options[param] = openai_payload[param]
# Mapping OpenAI's `max_tokens` -> Ollama's `num_predict`
if "max_completion_tokens" in openai_payload:
ollama_options["num_predict"] = openai_payload["max_completion_tokens"]
elif "max_tokens" in openai_payload:
ollama_options["num_predict"] = openai_payload["max_tokens"]
# Handle frequency / presence_penalty, which needs renaming and checking
if "frequency_penalty" in openai_payload:
ollama_options["repeat_penalty"] = openai_payload["frequency_penalty"]
if "presence_penalty" in openai_payload and "penalty" not in ollama_options:
# We are assuming presence penalty uses a similar concept in Ollama, which needs custom handling if exists.
ollama_options["new_topic_penalty"] = openai_payload["presence_penalty"]
# Add options to payload if any have been set
if ollama_options:
ollama_payload["options"] = ollama_options
return ollama_payload

32
backend/open_webui/utils/response.py Normal file
View File

@@ -0,0 +1,32 @@
import json
from open_webui.utils.misc import (
openai_chat_chunk_message_template,
openai_chat_completion_message_template,
)
def convert_response_ollama_to_openai(ollama_response: dict) -> dict:
model = ollama_response.get("model", "ollama")
message_content = ollama_response.get("message", {}).get("content", "")
response = openai_chat_completion_message_template(model, message_content)
return response
async def convert_streaming_response_ollama_to_openai(ollama_streaming_response):
async for data in ollama_streaming_response.body_iterator:
data = json.loads(data)
model = data.get("model", "ollama")
message_content = data.get("message", {}).get("content", "")
done = data.get("done", False)
data = openai_chat_chunk_message_template(
model, message_content if not done else None
)
line = f"data: {json.dumps(data)}\n\n"
if done:
line += "data: [DONE]\n\n"
yield line