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Unverified Commit 85175470 authored by Timothy Jaeryang Baek's avatar Timothy Jaeryang Baek Committed by GitHub
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Merge pull request #1555 from open-webui/dev 

0.1.119
parents 0399a69b 375056f8
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CHANGELOG.md
View file @ 85175470
......@@ -5,6 +5,26 @@ All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [0.1.119] - 2024-04-16
### Added
- **🌟 Enhanced RAG Embedding Support**: Ollama, and OpenAI models can now be used for RAG embedding model.
- **🔄 Seamless Integration**: Copy 'ollama run <model name>' directly from Ollama page to easily select and pull models.
- **🏷️ Tagging Feature**: Add tags to chats directly via the sidebar chat menu.
- **📱 Mobile Accessibility**: Swipe left and right on mobile to effortlessly open and close the sidebar.
- **🔍 Improved Navigation**: Admin panel now supports pagination for user list.
- **🌍 Additional Language Support**: Added Polish language support.
### Fixed
- **🌍 Language Enhancements**: Vietnamese and Spanish translations have been improved.
- **🔧 Helm Fixes**: Resolved issues with Helm trailing slash and manifest.json.
### Changed
- **🐳 Docker Optimization**: Updated docker image build process to utilize 'uv' for significantly faster builds compared to 'pip3'.
## [0.1.118] - 2024-04-10
### Added
......
Dockerfile
View file @ 85175470
......@@ -93,15 +93,16 @@ RUN if [ "$USE_OLLAMA" = "true" ]; then \
# install python dependencies
COPY ./backend/requirements.txt ./requirements.txt
RUN if [ "$USE_CUDA" = "true" ]; then \
RUN pip3 install uv && \
if [ "$USE_CUDA" = "true" ]; then \
# If you use CUDA the whisper and embedding model will be downloaded on first use
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/$USE_CUDA_DOCKER_VER --no-cache-dir && \
pip3 install -r requirements.txt --no-cache-dir && \
uv pip install --system -r requirements.txt --no-cache-dir && \
python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])" && \
python -c "import os; from chromadb.utils import embedding_functions; sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=os.environ['RAG_EMBEDDING_MODEL'], device='cpu')"; \
else \
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir && \
pip3 install -r requirements.txt --no-cache-dir && \
uv pip install --system -r requirements.txt --no-cache-dir && \
python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])" && \
python -c "import os; from chromadb.utils import embedding_functions; sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=os.environ['RAG_EMBEDDING_MODEL'], device='cpu')"; \
fi
......
backend/apps/audio/main.py
View file @ 85175470
......@@ -28,6 +28,7 @@ from config import (
UPLOAD_DIR,
WHISPER_MODEL,
WHISPER_MODEL_DIR,
WHISPER_MODEL_AUTO_UPDATE,
DEVICE_TYPE,
)
......@@ -69,12 +70,24 @@ def transcribe(
f.write(contents)
f.close()
model = WhisperModel(
WHISPER_MODEL,
device=whisper_device_type,
compute_type="int8",
download_root=WHISPER_MODEL_DIR,
whisper_kwargs = {
"model_size_or_path": WHISPER_MODEL,
"device": whisper_device_type,
"compute_type": "int8",
"download_root": WHISPER_MODEL_DIR,
"local_files_only": not WHISPER_MODEL_AUTO_UPDATE,
}
log.debug(f"whisper_kwargs: {whisper_kwargs}")
try:
model = WhisperModel(**whisper_kwargs)
except:
log.warning(
"WhisperModel initialization failed, attempting download with local_files_only=False"
)
whisper_kwargs["local_files_only"] = False
model = WhisperModel(**whisper_kwargs)
segments, info = model.transcribe(file_path, beam_size=5)
log.info(
......
backend/apps/images/main.py
View file @ 85175470
......@@ -29,7 +29,13 @@ import base64
import json
import logging
from config import SRC_LOG_LEVELS, CACHE_DIR, AUTOMATIC1111_BASE_URL, COMFYUI_BASE_URL
from config import (
SRC_LOG_LEVELS,
CACHE_DIR,
ENABLE_IMAGE_GENERATION,
AUTOMATIC1111_BASE_URL,
COMFYUI_BASE_URL,
)
log = logging.getLogger(__name__)
......@@ -48,7 +54,7 @@ app.add_middleware(
)
app.state.ENGINE = ""
app.state.ENABLED = False
app.state.ENABLED = ENABLE_IMAGE_GENERATION
app.state.OPENAI_API_KEY = ""
app.state.MODEL = ""
......
backend/apps/ollama/main.py
View file @ 85175470
......@@ -612,8 +612,13 @@ async def generate_embeddings(
user=Depends(get_current_user),
):
if url_idx == None:
if form_data.model in app.state.MODELS:
url_idx = random.choice(app.state.MODELS[form_data.model]["urls"])
model = form_data.model
if ":" not in model:
model = f"{model}:latest"
if model in app.state.MODELS:
url_idx = random.choice(app.state.MODELS[model]["urls"])
else:
raise HTTPException(
status_code=400,
......@@ -649,6 +654,60 @@ async def generate_embeddings(
)
def generate_ollama_embeddings(
form_data: GenerateEmbeddingsForm,
url_idx: Optional[int] = None,
):
log.info(f"generate_ollama_embeddings {form_data}")
if url_idx == None:
model = form_data.model
if ":" not in model:
model = f"{model}:latest"
if model in app.state.MODELS:
url_idx = random.choice(app.state.MODELS[model]["urls"])
else:
raise HTTPException(
status_code=400,
detail=ERROR_MESSAGES.MODEL_NOT_FOUND(form_data.model),
)
url = app.state.OLLAMA_BASE_URLS[url_idx]
log.info(f"url: {url}")
try:
r = requests.request(
method="POST",
url=f"{url}/api/embeddings",
data=form_data.model_dump_json(exclude_none=True).encode(),
)
r.raise_for_status()
data = r.json()
log.info(f"generate_ollama_embeddings {data}")
if "embedding" in data:
return data["embedding"]
else:
raise "Something went wrong :/"
except Exception as e:
log.exception(e)
error_detail = "Open WebUI: Server Connection Error"
if r is not None:
try:
res = r.json()
if "error" in res:
error_detail = f"Ollama: {res['error']}"
except:
error_detail = f"Ollama: {e}"
raise error_detail
class GenerateCompletionForm(BaseModel):
model: str
prompt: str
......@@ -672,8 +731,13 @@ async def generate_completion(
):
if url_idx == None:
if form_data.model in app.state.MODELS:
url_idx = random.choice(app.state.MODELS[form_data.model]["urls"])
model = form_data.model
if ":" not in model:
model = f"{model}:latest"
if model in app.state.MODELS:
url_idx = random.choice(app.state.MODELS[model]["urls"])
else:
raise HTTPException(
status_code=400,
......@@ -770,8 +834,13 @@ async def generate_chat_completion(
):
if url_idx == None:
if form_data.model in app.state.MODELS:
url_idx = random.choice(app.state.MODELS[form_data.model]["urls"])
model = form_data.model
if ":" not in model:
model = f"{model}:latest"
if model in app.state.MODELS:
url_idx = random.choice(app.state.MODELS[model]["urls"])
else:
raise HTTPException(
status_code=400,
......@@ -874,8 +943,13 @@ async def generate_openai_chat_completion(
):
if url_idx == None:
if form_data.model in app.state.MODELS:
url_idx = random.choice(app.state.MODELS[form_data.model]["urls"])
model = form_data.model
if ":" not in model:
model = f"{model}:latest"
if model in app.state.MODELS:
url_idx = random.choice(app.state.MODELS[model]["urls"])
else:
raise HTTPException(
status_code=400,
......
backend/apps/rag/main.py
View file @ 85175470
......@@ -39,13 +39,22 @@ import uuid
import json
from apps.ollama.main import generate_ollama_embeddings, GenerateEmbeddingsForm
from apps.web.models.documents import (
Documents,
DocumentForm,
DocumentResponse,
)
from apps.rag.utils import query_doc, query_collection, get_embedding_model_path
from apps.rag.utils import (
query_doc,
query_embeddings_doc,
query_collection,
query_embeddings_collection,
get_embedding_model_path,
generate_openai_embeddings,
)
from utils.misc import (
calculate_sha256,
......@@ -58,6 +67,7 @@ from config import (
SRC_LOG_LEVELS,
UPLOAD_DIR,
DOCS_DIR,
RAG_EMBEDDING_ENGINE,
RAG_EMBEDDING_MODEL,
RAG_EMBEDDING_MODEL_AUTO_UPDATE,
DEVICE_TYPE,
......@@ -74,16 +84,21 @@ log.setLevel(SRC_LOG_LEVELS["RAG"])
app = FastAPI()
app.state.PDF_EXTRACT_IMAGES = False
app.state.TOP_K = 4
app.state.CHUNK_SIZE = CHUNK_SIZE
app.state.CHUNK_OVERLAP = CHUNK_OVERLAP
app.state.RAG_TEMPLATE = RAG_TEMPLATE
app.state.RAG_EMBEDDING_ENGINE = RAG_EMBEDDING_ENGINE
app.state.RAG_EMBEDDING_MODEL = RAG_EMBEDDING_MODEL
app.state.RAG_TEMPLATE = RAG_TEMPLATE
app.state.RAG_OPENAI_API_BASE_URL = "https://api.openai.com"
app.state.RAG_OPENAI_API_KEY = ""
app.state.PDF_EXTRACT_IMAGES = False
app.state.TOP_K = 4
app.state.sentence_transformer_ef = (
embedding_functions.SentenceTransformerEmbeddingFunction(
......@@ -121,45 +136,72 @@ async def get_status():
"chunk_size": app.state.CHUNK_SIZE,
"chunk_overlap": app.state.CHUNK_OVERLAP,
"template": app.state.RAG_TEMPLATE,
"embedding_engine": app.state.RAG_EMBEDDING_ENGINE,
"embedding_model": app.state.RAG_EMBEDDING_MODEL,
}
@app.get("/embedding/model")
async def get_embedding_model(user=Depends(get_admin_user)):
@app.get("/embedding")
async def get_embedding_config(user=Depends(get_admin_user)):
return {
"status": True,
"embedding_engine": app.state.RAG_EMBEDDING_ENGINE,
"embedding_model": app.state.RAG_EMBEDDING_MODEL,
"openai_config": {
"url": app.state.RAG_OPENAI_API_BASE_URL,
"key": app.state.RAG_OPENAI_API_KEY,
},
}
class OpenAIConfigForm(BaseModel):
url: str
key: str
class EmbeddingModelUpdateForm(BaseModel):
openai_config: Optional[OpenAIConfigForm] = None
embedding_engine: str
embedding_model: str
@app.post("/embedding/model/update")
async def update_embedding_model(
@app.post("/embedding/update")
async def update_embedding_config(
form_data: EmbeddingModelUpdateForm, user=Depends(get_admin_user)
):
log.info(
f"Updating embedding model: {app.state.RAG_EMBEDDING_MODEL} to {form_data.embedding_model}"
)
try:
app.state.RAG_EMBEDDING_ENGINE = form_data.embedding_engine
if app.state.RAG_EMBEDDING_ENGINE in ["ollama", "openai"]:
app.state.RAG_EMBEDDING_MODEL = form_data.embedding_model
app.state.sentence_transformer_ef = None
if form_data.openai_config != None:
app.state.RAG_OPENAI_API_BASE_URL = form_data.openai_config.url
app.state.RAG_OPENAI_API_KEY = form_data.openai_config.key
else:
sentence_transformer_ef = (
embedding_functions.SentenceTransformerEmbeddingFunction(
model_name=get_embedding_model_path(form_data.embedding_model, True),
model_name=get_embedding_model_path(
form_data.embedding_model, True
),
device=DEVICE_TYPE,
)
)
app.state.RAG_EMBEDDING_MODEL = form_data.embedding_model
app.state.sentence_transformer_ef = sentence_transformer_ef
return {
"status": True,
"embedding_engine": app.state.RAG_EMBEDDING_ENGINE,
"embedding_model": app.state.RAG_EMBEDDING_MODEL,
"openai_config": {
"url": app.state.RAG_OPENAI_API_BASE_URL,
"key": app.state.RAG_OPENAI_API_KEY,
},
}
except Exception as e:
......@@ -252,12 +294,37 @@ def query_doc_handler(
):
try:
if app.state.RAG_EMBEDDING_ENGINE == "":
return query_doc(
collection_name=form_data.collection_name,
query=form_data.query,
k=form_data.k if form_data.k else app.state.TOP_K,
embedding_function=app.state.sentence_transformer_ef,
)
else:
if app.state.RAG_EMBEDDING_ENGINE == "ollama":
query_embeddings = generate_ollama_embeddings(
GenerateEmbeddingsForm(
**{
"model": app.state.RAG_EMBEDDING_MODEL,
"prompt": form_data.query,
}
)
)
elif app.state.RAG_EMBEDDING_ENGINE == "openai":
query_embeddings = generate_openai_embeddings(
model=app.state.RAG_EMBEDDING_MODEL,
text=form_data.query,
key=app.state.RAG_OPENAI_API_KEY,
url=app.state.RAG_OPENAI_API_BASE_URL,
)
return query_embeddings_doc(
collection_name=form_data.collection_name,
query_embeddings=query_embeddings,
k=form_data.k if form_data.k else app.state.TOP_K,
)
except Exception as e:
log.exception(e)
raise HTTPException(
......@@ -277,12 +344,45 @@ def query_collection_handler(
form_data: QueryCollectionsForm,
user=Depends(get_current_user),
):
try:
if app.state.RAG_EMBEDDING_ENGINE == "":
return query_collection(
collection_names=form_data.collection_names,
query=form_data.query,
k=form_data.k if form_data.k else app.state.TOP_K,
embedding_function=app.state.sentence_transformer_ef,
)
else:
if app.state.RAG_EMBEDDING_ENGINE == "ollama":
query_embeddings = generate_ollama_embeddings(
GenerateEmbeddingsForm(
**{
"model": app.state.RAG_EMBEDDING_MODEL,
"prompt": form_data.query,
}
)
)
elif app.state.RAG_EMBEDDING_ENGINE == "openai":
query_embeddings = generate_openai_embeddings(
model=app.state.RAG_EMBEDDING_MODEL,
text=form_data.query,
key=app.state.RAG_OPENAI_API_KEY,
url=app.state.RAG_OPENAI_API_BASE_URL,
)
return query_embeddings_collection(
collection_names=form_data.collection_names,
query_embeddings=query_embeddings,
k=form_data.k if form_data.k else app.state.TOP_K,
)
except Exception as e:
log.exception(e)
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=ERROR_MESSAGES.DEFAULT(e),
)
@app.post("/web")
......@@ -317,9 +417,11 @@ def store_data_in_vector_db(data, collection_name, overwrite: bool = False) -> b
chunk_overlap=app.state.CHUNK_OVERLAP,
add_start_index=True,
)
docs = text_splitter.split_documents(data)
if len(docs) > 0:
log.info(f"store_data_in_vector_db {docs}")
return store_docs_in_vector_db(docs, collection_name, overwrite), None
else:
raise ValueError(ERROR_MESSAGES.EMPTY_CONTENT)
......@@ -338,6 +440,7 @@ def store_text_in_vector_db(
def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> bool:
log.info(f"store_docs_in_vector_db {docs} {collection_name}")
texts = [doc.page_content for doc in docs]
metadatas = [doc.metadata for doc in docs]
......@@ -349,6 +452,8 @@ def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> b
log.info(f"deleting existing collection {collection_name}")
CHROMA_CLIENT.delete_collection(name=collection_name)
if app.state.RAG_EMBEDDING_ENGINE == "":
collection = CHROMA_CLIENT.create_collection(
name=collection_name,
embedding_function=app.state.sentence_transformer_ef,
......@@ -362,6 +467,38 @@ def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> b
):
collection.add(*batch)
else:
collection = CHROMA_CLIENT.create_collection(name=collection_name)
if app.state.RAG_EMBEDDING_ENGINE == "ollama":
embeddings = [
generate_ollama_embeddings(
GenerateEmbeddingsForm(
**{"model": app.state.RAG_EMBEDDING_MODEL, "prompt": text}
)
)
for text in texts
]
elif app.state.RAG_EMBEDDING_ENGINE == "openai":
embeddings = [
generate_openai_embeddings(
model=app.state.RAG_EMBEDDING_MODEL,
text=text,
key=app.state.RAG_OPENAI_API_KEY,
url=app.state.RAG_OPENAI_API_BASE_URL,
)
for text in texts
]
for batch in create_batches(
api=CHROMA_CLIENT,
ids=[str(uuid.uuid1()) for _ in texts],
metadatas=metadatas,
embeddings=embeddings,
documents=texts,
):
collection.add(*batch)
return True
except Exception as e:
log.exception(e)
......
backend/apps/rag/utils.py
View file @ 85175470
......@@ -2,10 +2,16 @@ import os
import re
import logging
from typing import List
import requests
from huggingface_hub import snapshot_download
from apps.ollama.main import generate_ollama_embeddings, GenerateEmbeddingsForm
from config import SRC_LOG_LEVELS, CHROMA_CLIENT
log = logging.getLogger(__name__)
log.setLevel(SRC_LOG_LEVELS["RAG"])
......@@ -26,6 +32,24 @@ def query_doc(collection_name: str, query: str, k: int, embedding_function):
raise e
def query_embeddings_doc(collection_name: str, query_embeddings, k: int):
try:
# if you use docker use the model from the environment variable
log.info(f"query_embeddings_doc {query_embeddings}")
collection = CHROMA_CLIENT.get_collection(
name=collection_name,
)
result = collection.query(
query_embeddings=[query_embeddings],
n_results=k,
)
log.info(f"query_embeddings_doc:result {result}")
return result
except Exception as e:
raise e
def merge_and_sort_query_results(query_results, k):
# Initialize lists to store combined data
combined_ids = []
......@@ -96,14 +120,46 @@ def query_collection(
return merge_and_sort_query_results(results, k)
def query_embeddings_collection(collection_names: List[str], query_embeddings, k: int):
results = []
log.info(f"query_embeddings_collection {query_embeddings}")
for collection_name in collection_names:
try:
collection = CHROMA_CLIENT.get_collection(name=collection_name)
result = collection.query(
query_embeddings=[query_embeddings],
n_results=k,
)
results.append(result)
except:
pass
return merge_and_sort_query_results(results, k)
def rag_template(template: str, context: str, query: str):
template = template.replace("[context]", context)
template = template.replace("[query]", query)
return template
def rag_messages(docs, messages, template, k, embedding_function):
log.debug(f"docs: {docs}")
def rag_messages(
docs,
messages,
template,
k,
embedding_engine,
embedding_model,
embedding_function,
openai_key,
openai_url,
):
log.debug(
f"docs: {docs} {messages} {embedding_engine} {embedding_model} {embedding_function} {openai_key} {openai_url}"
)
last_user_message_idx = None
for i in range(len(messages) - 1, -1, -1):
......@@ -136,6 +192,11 @@ def rag_messages(docs, messages, template, k, embedding_function):
context = None
try:
if doc["type"] == "text":
context = doc["content"]
else:
if embedding_engine == "":
if doc["type"] == "collection":
context = query_collection(
collection_names=doc["collection_names"],
......@@ -143,8 +204,6 @@ def rag_messages(docs, messages, template, k, embedding_function):
k=k,
embedding_function=embedding_function,
)
elif doc["type"] == "text":
context = doc["content"]
else:
context = query_doc(
collection_name=doc["collection_name"],
......@@ -152,6 +211,38 @@ def rag_messages(docs, messages, template, k, embedding_function):
k=k,
embedding_function=embedding_function,
)
else:
if embedding_engine == "ollama":
query_embeddings = generate_ollama_embeddings(
GenerateEmbeddingsForm(
**{
"model": embedding_model,
"prompt": query,
}
)
)
elif embedding_engine == "openai":
query_embeddings = generate_openai_embeddings(
model=embedding_model,
text=query,
key=openai_key,
url=openai_url,
)
if doc["type"] == "collection":
context = query_embeddings_collection(
collection_names=doc["collection_names"],
query_embeddings=query_embeddings,
k=k,
)
else:
context = query_embeddings_doc(
collection_name=doc["collection_name"],
query_embeddings=query_embeddings,
k=k,
)
except Exception as e:
log.exception(e)
context = None
......@@ -230,3 +321,26 @@ def get_embedding_model_path(
except Exception as e:
log.exception(f"Cannot determine embedding model snapshot path: {e}")
return embedding_model
def generate_openai_embeddings(
model: str, text: str, key: str, url: str = "https://api.openai.com"
):
try:
r = requests.post(
f"{url}/v1/embeddings",
headers={
"Content-Type": "application/json",
"Authorization": f"Bearer {key}",
},
json={"input": text, "model": model},
)
r.raise_for_status()
data = r.json()
if "data" in data:
return data["data"][0]["embedding"]
else:
raise "Something went wrong :/"
except Exception as e:
print(e)
return None
backend/config.py
View file @ 85175470
......@@ -18,6 +18,51 @@ from secrets import token_bytes
from constants import ERROR_MESSAGES
####################################
# LOGGING
####################################
log_levels = ["CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG"]
GLOBAL_LOG_LEVEL = os.environ.get("GLOBAL_LOG_LEVEL", "").upper()
if GLOBAL_LOG_LEVEL in log_levels:
logging.basicConfig(stream=sys.stdout, level=GLOBAL_LOG_LEVEL, force=True)
else:
GLOBAL_LOG_LEVEL = "INFO"
log = logging.getLogger(__name__)
log.info(f"GLOBAL_LOG_LEVEL: {GLOBAL_LOG_LEVEL}")
log_sources = [
"AUDIO",
"COMFYUI",
"CONFIG",
"DB",
"IMAGES",
"LITELLM",
"MAIN",
"MODELS",
"OLLAMA",
"OPENAI",
"RAG",
"WEBHOOK",
]
SRC_LOG_LEVELS = {}
for source in log_sources:
log_env_var = source + "_LOG_LEVEL"
SRC_LOG_LEVELS[source] = os.environ.get(log_env_var, "").upper()
if SRC_LOG_LEVELS[source] not in log_levels:
SRC_LOG_LEVELS[source] = GLOBAL_LOG_LEVEL
log.info(f"{log_env_var}: {SRC_LOG_LEVELS[source]}")
log.setLevel(SRC_LOG_LEVELS["CONFIG"])
####################################
# Load .env file
####################################
try:
from dotenv import load_dotenv, find_dotenv
......@@ -122,47 +167,6 @@ STATIC_DIR = str(Path(os.getenv("STATIC_DIR", "./static")).resolve())
shutil.copyfile(f"{FRONTEND_BUILD_DIR}/favicon.png", f"{STATIC_DIR}/favicon.png")
####################################
# LOGGING
####################################
log_levels = ["CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG"]
GLOBAL_LOG_LEVEL = os.environ.get("GLOBAL_LOG_LEVEL", "").upper()
if GLOBAL_LOG_LEVEL in log_levels:
logging.basicConfig(stream=sys.stdout, level=GLOBAL_LOG_LEVEL, force=True)
else:
GLOBAL_LOG_LEVEL = "INFO"
log = logging.getLogger(__name__)
log.info(f"GLOBAL_LOG_LEVEL: {GLOBAL_LOG_LEVEL}")
log_sources = [
"AUDIO",
"COMFYUI",
"CONFIG",
"DB",
"IMAGES",
"LITELLM",
"MAIN",
"MODELS",
"OLLAMA",
"OPENAI",
"RAG",
"WEBHOOK",
]
SRC_LOG_LEVELS = {}
for source in log_sources:
log_env_var = source + "_LOG_LEVEL"
SRC_LOG_LEVELS[source] = os.environ.get(log_env_var, "").upper()
if SRC_LOG_LEVELS[source] not in log_levels:
SRC_LOG_LEVELS[source] = GLOBAL_LOG_LEVEL
log.info(f"{log_env_var}: {SRC_LOG_LEVELS[source]}")
log.setLevel(SRC_LOG_LEVELS["CONFIG"])
####################################
# CUSTOM_NAME
####################################
......@@ -401,6 +405,9 @@ if WEBUI_AUTH and WEBUI_SECRET_KEY == "":
CHROMA_DATA_PATH = f"{DATA_DIR}/vector_db"
# this uses the model defined in the Dockerfile ENV variable. If you dont use docker or docker based deployments such as k8s, the default embedding model will be used (all-MiniLM-L6-v2)
RAG_EMBEDDING_ENGINE = os.environ.get("RAG_EMBEDDING_ENGINE", "")
RAG_EMBEDDING_MODEL = os.environ.get("RAG_EMBEDDING_MODEL", "all-MiniLM-L6-v2")
log.info(f"Embedding model set: {RAG_EMBEDDING_MODEL}"),
......@@ -409,7 +416,7 @@ RAG_EMBEDDING_MODEL_AUTO_UPDATE = (
)
# device type ebbeding models - "cpu" (default), "cuda" (nvidia gpu required) or "mps" (apple silicon) - choosing this right can lead to better performance
# device type embedding models - "cpu" (default), "cuda" (nvidia gpu required) or "mps" (apple silicon) - choosing this right can lead to better performance
USE_CUDA = os.environ.get("USE_CUDA_DOCKER", "false")
if USE_CUDA.lower() == "true":
......@@ -446,11 +453,17 @@ Query: [query]"""
WHISPER_MODEL = os.getenv("WHISPER_MODEL", "base")
WHISPER_MODEL_DIR = os.getenv("WHISPER_MODEL_DIR", f"{CACHE_DIR}/whisper/models")
WHISPER_MODEL_AUTO_UPDATE = (
os.environ.get("WHISPER_MODEL_AUTO_UPDATE", "").lower() == "true"
)
####################################
# Images
####################################
ENABLE_IMAGE_GENERATION = (
os.environ.get("ENABLE_IMAGE_GENERATION", "").lower() == "true"
)
AUTOMATIC1111_BASE_URL = os.getenv("AUTOMATIC1111_BASE_URL", "")
COMFYUI_BASE_URL = os.getenv("COMFYUI_BASE_URL", "")
backend/main.py
View file @ 85175470
......@@ -114,7 +114,11 @@ class RAGMiddleware(BaseHTTPMiddleware):
data["messages"],
rag_app.state.RAG_TEMPLATE,
rag_app.state.TOP_K,
rag_app.state.RAG_EMBEDDING_ENGINE,
rag_app.state.RAG_EMBEDDING_MODEL,
rag_app.state.sentence_transformer_ef,
rag_app.state.RAG_OPENAI_API_KEY,
rag_app.state.RAG_OPENAI_API_BASE_URL,
)
del data["docs"]
......
kubernetes/helm/templates/_helpers.tpl
View file @ 85175470
......@@ -10,7 +10,7 @@ ollama
{{- if .Values.ollama.externalHost }}
{{- printf .Values.ollama.externalHost }}
{{- else }}
{{- printf "http://%s.%s.svc.cluster.local:%d/" (include "ollama.name" .) (.Release.Namespace) (.Values.ollama.service.port | int) }}
{{- printf "http://%s.%s.svc.cluster.local:%d" (include "ollama.name" .) (.Release.Namespace) (.Values.ollama.service.port | int) }}
{{- end }}
{{- end }}
......
package-lock.json
View file @ 85175470
{
"name": "open-webui",
"version": "0.1.118",
"version": "0.1.119",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "open-webui",
"version": "0.1.118",
"version": "0.1.119",
"dependencies": {
"@sveltejs/adapter-node": "^1.3.1",
"async": "^3.2.5",
......
package.json
View file @ 85175470
{
"name": "open-webui",
"version": "0.1.118",
"version": "0.1.119",
"private": true,
"scripts": {
"dev": "vite dev --host",
......
src/app.html
View file @ 85175470
......@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8" />
<link rel="icon" href="%sveltekit.assets%/favicon.png" />
<link rel="manifest" href="%sveltekit.assets%/manifest.json" />
<link rel="manifest" href="%sveltekit.assets%/manifest.json" crossorigin="use-credentials" />
<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
<meta name="robots" content="noindex,nofollow" />
<script>
......
src/lib/apis/ollama/index.ts
View file @ 85175470
import { OLLAMA_API_BASE_URL } from '$lib/constants';
import { promptTemplate } from '$lib/utils';
export const getOllamaUrls = async (token: string = '') => {
let error = null;
......@@ -144,7 +145,7 @@ export const generateTitle = async (
) => {
let error = null;
template = template.replace(/{{prompt}}/g, prompt);
template = promptTemplate(template, prompt);
console.log(template);
......@@ -219,6 +220,32 @@ export const generatePrompt = async (token: string = '', model: string, conversa
return res;
};
export const generateEmbeddings = async (token: string = '', model: string, text: string) => {
let error = null;
const res = await fetch(`${OLLAMA_API_BASE_URL}/api/embeddings`, {
method: 'POST',
headers: {
Accept: 'application/json',
'Content-Type': 'application/json',
Authorization: `Bearer ${token}`
},
body: JSON.stringify({
model: model,
prompt: text
})
}).catch((err) => {
error = err;
return null;
});
if (error) {
throw error;
}
return res;
};
export const generateTextCompletion = async (token: string = '', model: string, text: string) => {
let error = null;
......
src/lib/apis/openai/index.ts
View file @ 85175470
import { OPENAI_API_BASE_URL } from '$lib/constants';
import { promptTemplate } from '$lib/utils';
export const getOpenAIUrls = async (token: string = '') => {
let error = null;
......@@ -273,7 +274,7 @@ export const generateTitle = async (
) => {
let error = null;
template = template.replace(/{{prompt}}/g, prompt);
template = promptTemplate(template, prompt);
console.log(template);
......
src/lib/apis/rag/index.ts
View file @ 85175470
......@@ -346,10 +346,10 @@ export const resetVectorDB = async (token: string) => {
return res;
};
export const getEmbeddingModel = async (token: string) => {
export const getEmbeddingConfig = async (token: string) => {
let error = null;
const res = await fetch(`${RAG_API_BASE_URL}/embedding/model`, {
const res = await fetch(`${RAG_API_BASE_URL}/embedding`, {
method: 'GET',
headers: {
'Content-Type': 'application/json',
......@@ -373,14 +373,21 @@ export const getEmbeddingModel = async (token: string) => {
return res;
};
type OpenAIConfigForm = {
key: string;
url: string;
};
type EmbeddingModelUpdateForm = {
openai_config?: OpenAIConfigForm;
embedding_engine: string;
embedding_model: string;
};
export const updateEmbeddingModel = async (token: string, payload: EmbeddingModelUpdateForm) => {
export const updateEmbeddingConfig = async (token: string, payload: EmbeddingModelUpdateForm) => {
let error = null;
const res = await fetch(`${RAG_API_BASE_URL}/embedding/model/update`, {
const res = await fetch(`${RAG_API_BASE_URL}/embedding/update`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
......
src/lib/components/ChangelogModal.svelte
View file @ 85175470
......@@ -32,6 +32,7 @@
<button
class="self-center"
on:click={() => {
localStorage.version = $config.version;
show = false;
}}
>
......
src/lib/components/chat/Messages/ResponseMessage.svelte
View file @ 85175470
......@@ -18,6 +18,7 @@
import { synthesizeOpenAISpeech } from '$lib/apis/openai';
import { imageGenerations } from '$lib/apis/images';
import {
approximateToHumanReadable,
extractSentences,
revertSanitizedResponseContent,
sanitizeResponseContent
......@@ -122,7 +123,10 @@
eval_count: ${message.info.eval_count ?? 'N/A'}<br/>
eval_duration: ${
Math.round(((message.info.eval_duration ?? 0) / 1000000) * 100) / 100 ?? 'N/A'
}ms</span>`,
}ms<br/>
approximate_total: ${approximateToHumanReadable(
message.info.total_duration
)}</span>`,
allowHTML: true
});
}
......
src/lib/components/chat/ModelSelector/Selector.svelte
View file @ 85175470
......@@ -33,7 +33,7 @@
: items;
const pullModelHandler = async () => {
const sanitizedModelTag = searchValue.trim();
const sanitizedModelTag = searchValue.trim().replace(/^ollama\s+(run|pull)\s+/, '');
console.log($MODEL_DOWNLOAD_POOL);
if ($MODEL_DOWNLOAD_POOL[sanitizedModelTag]) {
......
src/lib/components/chat/Settings/Models.svelte
View file @ 85175470
......@@ -139,7 +139,7 @@
};
const pullModelHandler = async () => {
const sanitizedModelTag = modelTag.trim();
const sanitizedModelTag = modelTag.trim().replace(/^ollama\s+(run|pull)\s+/, '');
if (modelDownloadStatus[sanitizedModelTag]) {
toast.error(
$i18n.t(`Model '{{modelTag}}' is already in queue for downloading.`, {
......
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