forked from open-webui/open-webui
Merge pull request #1693 from buroa/buroa/hybrid-search
feat: hybrid search with reranking
This commit is contained in:
Timothy Jaeryang Baek
GitHub
commit
5ee2f1729a
8 changed files with 655 additions and 176 deletions
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@ -5,6 +5,10 @@ All notable changes to this project will be documented in this file.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [0.1.122] - 2024-04-24
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- **🌟 Enhanced RAG Pipeline**: Added hybrid searching with `BM25`, reranking using `CrossEncoder`, and relevance score thresholds.
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## [0.1.121] - 2024-04-24
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### Fixed
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12
Dockerfile
12
Dockerfile
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@ -8,8 +8,9 @@ ARG USE_CUDA_VER=cu121
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# any sentence transformer model; models to use can be found at https://huggingface.co/models?library=sentence-transformers
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# Leaderboard: https://huggingface.co/spaces/mteb/leaderboard
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# for better performance and multilangauge support use "intfloat/multilingual-e5-large" (~2.5GB) or "intfloat/multilingual-e5-base" (~1.5GB)
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# IMPORTANT: If you change the default model (sentence-transformers/all-MiniLM-L6-v2) and vice versa, you aren't able to use RAG Chat with your previous documents loaded in the WebUI! You need to re-embed them.
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# IMPORTANT: If you change the embedding model (sentence-transformers/all-MiniLM-L6-v2) and vice versa, you aren't able to use RAG Chat with your previous documents loaded in the WebUI! You need to re-embed them.
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ARG USE_EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
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ARG USE_RERANKING_MODEL=""
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######## WebUI frontend ########
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FROM --platform=$BUILDPLATFORM node:21-alpine3.19 as build
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@ -30,6 +31,7 @@ ARG USE_CUDA
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ARG USE_OLLAMA
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ARG USE_CUDA_VER
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ARG USE_EMBEDDING_MODEL
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ARG USE_RERANKING_MODEL
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## Basis ##
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ENV ENV=prod \
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@ -38,7 +40,8 @@ ENV ENV=prod \
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USE_OLLAMA_DOCKER=${USE_OLLAMA} \
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USE_CUDA_DOCKER=${USE_CUDA} \
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USE_CUDA_DOCKER_VER=${USE_CUDA_VER} \
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USE_EMBEDDING_MODEL_DOCKER=${USE_EMBEDDING_MODEL}
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USE_EMBEDDING_MODEL_DOCKER=${USE_EMBEDDING_MODEL} \
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USE_RERANKING_MODEL_DOCKER=${USE_RERANKING_MODEL}
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## Basis URL Config ##
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ENV OLLAMA_BASE_URL="/ollama" \
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@ -62,8 +65,11 @@ ENV WHISPER_MODEL="base" \
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## RAG Embedding model settings ##
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ENV RAG_EMBEDDING_MODEL="$USE_EMBEDDING_MODEL_DOCKER" \
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RAG_EMBEDDING_MODEL_DIR="/app/backend/data/cache/embedding/models" \
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RAG_RERANKING_MODEL="$USE_RERANKING_MODEL_DOCKER" \
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SENTENCE_TRANSFORMERS_HOME="/app/backend/data/cache/embedding/models"
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## Hugging Face download cache ##
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ENV HF_HOME="/app/backend/data/cache/embedding/models"
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#### Other models ##########################################################
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WORKDIR /app/backend
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@ -39,8 +39,6 @@ import json
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import sentence_transformers
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from apps.ollama.main import generate_ollama_embeddings, GenerateEmbeddingsForm
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from apps.web.models.documents import (
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Documents,
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DocumentForm,
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@ -48,9 +46,10 @@ from apps.web.models.documents import (
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)
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from apps.rag.utils import (
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get_model_path,
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query_embeddings_doc,
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query_embeddings_function,
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query_embeddings_collection,
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generate_openai_embeddings,
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)
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from utils.misc import (
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@ -60,13 +59,20 @@ from utils.misc import (
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extract_folders_after_data_docs,
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)
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from utils.utils import get_current_user, get_admin_user
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from config import (
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SRC_LOG_LEVELS,
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UPLOAD_DIR,
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DOCS_DIR,
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RAG_TOP_K,
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RAG_RELEVANCE_THRESHOLD,
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RAG_EMBEDDING_ENGINE,
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RAG_EMBEDDING_MODEL,
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RAG_EMBEDDING_MODEL_AUTO_UPDATE,
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RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE,
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RAG_RERANKING_MODEL,
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RAG_RERANKING_MODEL_AUTO_UPDATE,
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RAG_RERANKING_MODEL_TRUST_REMOTE_CODE,
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RAG_OPENAI_API_BASE_URL,
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RAG_OPENAI_API_KEY,
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DEVICE_TYPE,
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@ -83,14 +89,14 @@ log.setLevel(SRC_LOG_LEVELS["RAG"])
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app = FastAPI()
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app.state.TOP_K = 4
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app.state.TOP_K = RAG_TOP_K
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app.state.RELEVANCE_THRESHOLD = RAG_RELEVANCE_THRESHOLD
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app.state.CHUNK_SIZE = CHUNK_SIZE
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app.state.CHUNK_OVERLAP = CHUNK_OVERLAP
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app.state.RAG_EMBEDDING_ENGINE = RAG_EMBEDDING_ENGINE
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app.state.RAG_EMBEDDING_MODEL = RAG_EMBEDDING_MODEL
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app.state.RAG_RERANKING_MODEL = RAG_RERANKING_MODEL
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app.state.RAG_TEMPLATE = RAG_TEMPLATE
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app.state.OPENAI_API_BASE_URL = RAG_OPENAI_API_BASE_URL
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@ -98,16 +104,48 @@ app.state.OPENAI_API_KEY = RAG_OPENAI_API_KEY
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app.state.PDF_EXTRACT_IMAGES = False
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if app.state.RAG_EMBEDDING_ENGINE == "":
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def update_embedding_model(
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embedding_model: str,
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update_model: bool = False,
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):
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if embedding_model and app.state.RAG_EMBEDDING_ENGINE == "":
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app.state.sentence_transformer_ef = sentence_transformers.SentenceTransformer(
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app.state.RAG_EMBEDDING_MODEL,
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get_model_path(embedding_model, update_model),
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device=DEVICE_TYPE,
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trust_remote_code=RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE,
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)
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else:
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app.state.sentence_transformer_ef = None
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def update_reranking_model(
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reranking_model: str,
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update_model: bool = False,
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):
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if reranking_model:
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app.state.sentence_transformer_rf = sentence_transformers.CrossEncoder(
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get_model_path(reranking_model, update_model),
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device=DEVICE_TYPE,
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trust_remote_code=RAG_RERANKING_MODEL_TRUST_REMOTE_CODE,
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)
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else:
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app.state.sentence_transformer_rf = None
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update_embedding_model(
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app.state.RAG_EMBEDDING_MODEL,
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RAG_EMBEDDING_MODEL_AUTO_UPDATE,
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)
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update_reranking_model(
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app.state.RAG_RERANKING_MODEL,
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RAG_RERANKING_MODEL_AUTO_UPDATE,
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)
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origins = ["*"]
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app.add_middleware(
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CORSMiddleware,
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allow_origins=origins,
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@ -134,6 +172,7 @@ async def get_status():
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"template": app.state.RAG_TEMPLATE,
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"embedding_engine": app.state.RAG_EMBEDDING_ENGINE,
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"embedding_model": app.state.RAG_EMBEDDING_MODEL,
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"reranking_model": app.state.RAG_RERANKING_MODEL,
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}
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@ -150,6 +189,11 @@ async def get_embedding_config(user=Depends(get_admin_user)):
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}
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@app.get("/reranking")
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async def get_reraanking_config(user=Depends(get_admin_user)):
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return {"status": True, "reranking_model": app.state.RAG_RERANKING_MODEL}
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class OpenAIConfigForm(BaseModel):
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url: str
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key: str
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@ -170,22 +214,14 @@ async def update_embedding_config(
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)
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try:
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app.state.RAG_EMBEDDING_ENGINE = form_data.embedding_engine
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app.state.RAG_EMBEDDING_MODEL = form_data.embedding_model
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if app.state.RAG_EMBEDDING_ENGINE in ["ollama", "openai"]:
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app.state.RAG_EMBEDDING_MODEL = form_data.embedding_model
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app.state.sentence_transformer_ef = None
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if form_data.openai_config != None:
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app.state.OPENAI_API_BASE_URL = form_data.openai_config.url
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app.state.OPENAI_API_KEY = form_data.openai_config.key
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else:
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sentence_transformer_ef = sentence_transformers.SentenceTransformer(
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app.state.RAG_EMBEDDING_MODEL,
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device=DEVICE_TYPE,
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trust_remote_code=RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE,
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)
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app.state.RAG_EMBEDDING_MODEL = form_data.embedding_model
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app.state.sentence_transformer_ef = sentence_transformer_ef
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update_embedding_model(app.state.RAG_EMBEDDING_MODEL, True)
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return {
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"status": True,
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@ -196,7 +232,6 @@ async def update_embedding_config(
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"key": app.state.OPENAI_API_KEY,
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},
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}
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except Exception as e:
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log.exception(f"Problem updating embedding model: {e}")
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raise HTTPException(
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@ -205,6 +240,34 @@ async def update_embedding_config(
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)
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class RerankingModelUpdateForm(BaseModel):
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reranking_model: str
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@app.post("/reranking/update")
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async def update_reranking_config(
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form_data: RerankingModelUpdateForm, user=Depends(get_admin_user)
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):
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log.info(
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f"Updating reranking model: {app.state.RAG_RERANKING_MODEL} to {form_data.reranking_model}"
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)
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try:
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app.state.RAG_RERANKING_MODEL = form_data.reranking_model
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update_reranking_model(app.state.RAG_RERANKING_MODEL, True)
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return {
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"status": True,
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"reranking_model": app.state.RAG_RERANKING_MODEL,
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}
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except Exception as e:
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log.exception(f"Problem updating reranking model: {e}")
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail=ERROR_MESSAGES.DEFAULT(e),
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)
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@app.get("/config")
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async def get_rag_config(user=Depends(get_admin_user)):
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return {
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@ -257,11 +320,13 @@ async def get_query_settings(user=Depends(get_admin_user)):
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"status": True,
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"template": app.state.RAG_TEMPLATE,
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"k": app.state.TOP_K,
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"r": app.state.RELEVANCE_THRESHOLD,
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}
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class QuerySettingsForm(BaseModel):
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k: Optional[int] = None
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r: Optional[float] = None
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template: Optional[str] = None
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@ -271,6 +336,7 @@ async def update_query_settings(
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):
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app.state.RAG_TEMPLATE = form_data.template if form_data.template else RAG_TEMPLATE
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app.state.TOP_K = form_data.k if form_data.k else 4
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app.state.RELEVANCE_THRESHOLD = form_data.r if form_data.r else 0.0
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return {"status": True, "template": app.state.RAG_TEMPLATE}
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@ -278,6 +344,7 @@ class QueryDocForm(BaseModel):
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collection_name: str
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query: str
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k: Optional[int] = None
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r: Optional[float] = None
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@app.post("/query/doc")
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@ -286,34 +353,22 @@ def query_doc_handler(
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user=Depends(get_current_user),
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):
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try:
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if app.state.RAG_EMBEDDING_ENGINE == "":
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query_embeddings = app.state.sentence_transformer_ef.encode(
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form_data.query
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).tolist()
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elif app.state.RAG_EMBEDDING_ENGINE == "ollama":
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query_embeddings = generate_ollama_embeddings(
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GenerateEmbeddingsForm(
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**{
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"model": app.state.RAG_EMBEDDING_MODEL,
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"prompt": form_data.query,
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}
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)
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)
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elif app.state.RAG_EMBEDDING_ENGINE == "openai":
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query_embeddings = generate_openai_embeddings(
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model=app.state.RAG_EMBEDDING_MODEL,
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text=form_data.query,
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key=app.state.OPENAI_API_KEY,
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url=app.state.OPENAI_API_BASE_URL,
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embeddings_function = query_embeddings_function(
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app.state.RAG_EMBEDDING_ENGINE,
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app.state.RAG_EMBEDDING_MODEL,
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app.state.sentence_transformer_ef,
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app.state.OPENAI_API_KEY,
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app.state.OPENAI_API_BASE_URL,
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)
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return query_embeddings_doc(
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collection_name=form_data.collection_name,
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query=form_data.query,
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query_embeddings=query_embeddings,
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k=form_data.k if form_data.k else app.state.TOP_K,
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r=form_data.r if form_data.r else app.state.RELEVANCE_THRESHOLD,
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embeddings_function=embeddings_function,
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reranking_function=app.state.sentence_transformer_rf,
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)
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except Exception as e:
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log.exception(e)
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raise HTTPException(
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@ -326,6 +381,7 @@ class QueryCollectionsForm(BaseModel):
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collection_names: List[str]
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query: str
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k: Optional[int] = None
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r: Optional[float] = None
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@app.post("/query/collection")
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@ -334,33 +390,22 @@ def query_collection_handler(
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user=Depends(get_current_user),
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):
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try:
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if app.state.RAG_EMBEDDING_ENGINE == "":
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query_embeddings = app.state.sentence_transformer_ef.encode(
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form_data.query
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).tolist()
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elif app.state.RAG_EMBEDDING_ENGINE == "ollama":
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query_embeddings = generate_ollama_embeddings(
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GenerateEmbeddingsForm(
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**{
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"model": app.state.RAG_EMBEDDING_MODEL,
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"prompt": form_data.query,
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}
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)
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)
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elif app.state.RAG_EMBEDDING_ENGINE == "openai":
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query_embeddings = generate_openai_embeddings(
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model=app.state.RAG_EMBEDDING_MODEL,
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text=form_data.query,
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key=app.state.OPENAI_API_KEY,
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url=app.state.OPENAI_API_BASE_URL,
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embeddings_function = query_embeddings_function(
|
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app.state.RAG_EMBEDDING_ENGINE,
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app.state.RAG_EMBEDDING_MODEL,
|
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app.state.sentence_transformer_ef,
|
||||
app.state.OPENAI_API_KEY,
|
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app.state.OPENAI_API_BASE_URL,
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)
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return query_embeddings_collection(
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collection_names=form_data.collection_names,
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query_embeddings=query_embeddings,
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query=form_data.query,
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k=form_data.k if form_data.k else app.state.TOP_K,
|
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r=form_data.r if form_data.r else app.state.RELEVANCE_THRESHOLD,
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embeddings_function=embeddings_function,
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reranking_function=app.state.sentence_transformer_rf,
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||||
)
|
||||
|
||||
except Exception as e:
|
||||
log.exception(e)
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||||
raise HTTPException(
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||||
|
|
@ -427,8 +472,6 @@ def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> b
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log.info(f"store_docs_in_vector_db {docs} {collection_name}")
|
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|
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texts = [doc.page_content for doc in docs]
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texts = list(map(lambda x: x.replace("\n", " "), texts))
|
||||
|
||||
metadatas = [doc.metadata for doc in docs]
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|
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try:
|
||||
|
|
@ -440,27 +483,16 @@ def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> b
|
|||
|
||||
collection = CHROMA_CLIENT.create_collection(name=collection_name)
|
||||
|
||||
if app.state.RAG_EMBEDDING_ENGINE == "":
|
||||
embeddings = app.state.sentence_transformer_ef.encode(texts).tolist()
|
||||
elif app.state.RAG_EMBEDDING_ENGINE == "ollama":
|
||||
embeddings = [
|
||||
generate_ollama_embeddings(
|
||||
GenerateEmbeddingsForm(
|
||||
**{"model": app.state.RAG_EMBEDDING_MODEL, "prompt": text}
|
||||
embedding_func = query_embeddings_function(
|
||||
app.state.RAG_EMBEDDING_ENGINE,
|
||||
app.state.RAG_EMBEDDING_MODEL,
|
||||
app.state.sentence_transformer_ef,
|
||||
app.state.OPENAI_API_KEY,
|
||||
app.state.OPENAI_API_BASE_URL,
|
||||
)
|
||||
)
|
||||
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.OPENAI_API_KEY,
|
||||
url=app.state.OPENAI_API_BASE_URL,
|
||||
)
|
||||
for text in texts
|
||||
]
|
||||
|
||||
embedding_texts = list(map(lambda x: x.replace("\n", " "), texts))
|
||||
embeddings = embedding_func(embedding_texts)
|
||||
|
||||
for batch in create_batches(
|
||||
api=CHROMA_CLIENT,
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
import os
|
||||
import logging
|
||||
import requests
|
||||
|
||||
|
|
@ -8,6 +9,15 @@ from apps.ollama.main import (
|
|||
GenerateEmbeddingsForm,
|
||||
)
|
||||
|
||||
from huggingface_hub import snapshot_download
|
||||
|
||||
from langchain_core.documents import Document
|
||||
from langchain_community.retrievers import BM25Retriever
|
||||
from langchain.retrievers import (
|
||||
ContextualCompressionRetriever,
|
||||
EnsembleRetriever,
|
||||
)
|
||||
|
||||
from config import SRC_LOG_LEVELS, CHROMA_CLIENT
|
||||
|
||||
|
||||
|
|
@ -15,18 +25,53 @@ log = logging.getLogger(__name__)
|
|||
log.setLevel(SRC_LOG_LEVELS["RAG"])
|
||||
|
||||
|
||||
def query_embeddings_doc(collection_name: str, query: str, query_embeddings, k: int):
|
||||
def query_embeddings_doc(
|
||||
collection_name: str,
|
||||
query: str,
|
||||
k: int,
|
||||
r: float,
|
||||
embeddings_function,
|
||||
reranking_function,
|
||||
):
|
||||
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,
|
||||
documents = collection.get() # get all documents
|
||||
bm25_retriever = BM25Retriever.from_texts(
|
||||
texts=documents.get("documents"),
|
||||
metadatas=documents.get("metadatas"),
|
||||
)
|
||||
bm25_retriever.k = k
|
||||
|
||||
chroma_retriever = ChromaRetriever(
|
||||
collection=collection,
|
||||
embeddings_function=embeddings_function,
|
||||
top_n=k,
|
||||
)
|
||||
|
||||
log.info(f"query_embeddings_doc:result {result}")
|
||||
ensemble_retriever = EnsembleRetriever(
|
||||
retrievers=[bm25_retriever, chroma_retriever], weights=[0.5, 0.5]
|
||||
)
|
||||
|
||||
compressor = RerankCompressor(
|
||||
embeddings_function=embeddings_function,
|
||||
reranking_function=reranking_function,
|
||||
r_score=r,
|
||||
top_n=k,
|
||||
)
|
||||
|
||||
compression_retriever = ContextualCompressionRetriever(
|
||||
base_compressor=compressor, base_retriever=ensemble_retriever
|
||||
)
|
||||
|
||||
result = compression_retriever.invoke(query)
|
||||
result = {
|
||||
"distances": [[d.metadata.get("score") for d in result]],
|
||||
"documents": [[d.page_content for d in result]],
|
||||
"metadatas": [[d.metadata for d in result]],
|
||||
}
|
||||
|
||||
return result
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
|
@ -34,63 +79,65 @@ def query_embeddings_doc(collection_name: str, query: str, query_embeddings, k:
|
|||
|
||||
def merge_and_sort_query_results(query_results, k):
|
||||
# Initialize lists to store combined data
|
||||
combined_ids = []
|
||||
combined_distances = []
|
||||
combined_metadatas = []
|
||||
combined_documents = []
|
||||
combined_metadatas = []
|
||||
|
||||
# Combine data from each dictionary
|
||||
for data in query_results:
|
||||
combined_ids.extend(data["ids"][0])
|
||||
combined_distances.extend(data["distances"][0])
|
||||
combined_metadatas.extend(data["metadatas"][0])
|
||||
combined_documents.extend(data["documents"][0])
|
||||
combined_metadatas.extend(data["metadatas"][0])
|
||||
|
||||
# Create a list of tuples (distance, id, metadata, document)
|
||||
combined = list(
|
||||
zip(combined_distances, combined_ids, combined_metadatas, combined_documents)
|
||||
)
|
||||
# Create a list of tuples (distance, document, metadata)
|
||||
combined = list(zip(combined_distances, combined_documents, combined_metadatas))
|
||||
|
||||
# Sort the list based on distances
|
||||
combined.sort(key=lambda x: x[0])
|
||||
|
||||
# We don't have anything :-(
|
||||
if not combined:
|
||||
sorted_distances = []
|
||||
sorted_documents = []
|
||||
sorted_metadatas = []
|
||||
else:
|
||||
# Unzip the sorted list
|
||||
sorted_distances, sorted_ids, sorted_metadatas, sorted_documents = zip(*combined)
|
||||
sorted_distances, sorted_documents, sorted_metadatas = zip(*combined)
|
||||
|
||||
# Slicing the lists to include only k elements
|
||||
sorted_distances = list(sorted_distances)[:k]
|
||||
sorted_ids = list(sorted_ids)[:k]
|
||||
sorted_metadatas = list(sorted_metadatas)[:k]
|
||||
sorted_documents = list(sorted_documents)[:k]
|
||||
sorted_metadatas = list(sorted_metadatas)[:k]
|
||||
|
||||
# Create the output dictionary
|
||||
merged_query_results = {
|
||||
"ids": [sorted_ids],
|
||||
result = {
|
||||
"distances": [sorted_distances],
|
||||
"metadatas": [sorted_metadatas],
|
||||
"documents": [sorted_documents],
|
||||
"embeddings": None,
|
||||
"uris": None,
|
||||
"data": None,
|
||||
"metadatas": [sorted_metadatas],
|
||||
}
|
||||
|
||||
return merged_query_results
|
||||
return result
|
||||
|
||||
|
||||
def query_embeddings_collection(
|
||||
collection_names: List[str], query: str, query_embeddings, k: int
|
||||
collection_names: List[str],
|
||||
query: str,
|
||||
k: int,
|
||||
r: float,
|
||||
embeddings_function,
|
||||
reranking_function,
|
||||
):
|
||||
|
||||
results = []
|
||||
log.info(f"query_embeddings_collection {query_embeddings}")
|
||||
|
||||
for collection_name in collection_names:
|
||||
try:
|
||||
result = query_embeddings_doc(
|
||||
collection_name=collection_name,
|
||||
query=query,
|
||||
query_embeddings=query_embeddings,
|
||||
k=k,
|
||||
r=r,
|
||||
embeddings_function=embeddings_function,
|
||||
reranking_function=reranking_function,
|
||||
)
|
||||
results.append(result)
|
||||
except:
|
||||
|
|
@ -105,19 +152,57 @@ def rag_template(template: str, context: str, query: str):
|
|||
return template
|
||||
|
||||
|
||||
def rag_messages(
|
||||
docs,
|
||||
messages,
|
||||
template,
|
||||
k,
|
||||
def query_embeddings_function(
|
||||
embedding_engine,
|
||||
embedding_model,
|
||||
embedding_function,
|
||||
openai_key,
|
||||
openai_url,
|
||||
):
|
||||
if embedding_engine == "":
|
||||
return lambda query: embedding_function.encode(query).tolist()
|
||||
elif embedding_engine in ["ollama", "openai"]:
|
||||
if embedding_engine == "ollama":
|
||||
func = lambda query: generate_ollama_embeddings(
|
||||
GenerateEmbeddingsForm(
|
||||
**{
|
||||
"model": embedding_model,
|
||||
"prompt": query,
|
||||
}
|
||||
)
|
||||
)
|
||||
elif embedding_engine == "openai":
|
||||
func = lambda query: generate_openai_embeddings(
|
||||
model=embedding_model,
|
||||
text=query,
|
||||
key=openai_key,
|
||||
url=openai_url,
|
||||
)
|
||||
|
||||
def generate_multiple(query, f):
|
||||
if isinstance(query, list):
|
||||
return [f(q) for q in query]
|
||||
else:
|
||||
return f(query)
|
||||
|
||||
return lambda query: generate_multiple(query, func)
|
||||
|
||||
|
||||
def rag_messages(
|
||||
docs,
|
||||
messages,
|
||||
template,
|
||||
k,
|
||||
r,
|
||||
embedding_engine,
|
||||
embedding_model,
|
||||
embedding_function,
|
||||
reranking_function,
|
||||
openai_key,
|
||||
openai_url,
|
||||
):
|
||||
log.debug(
|
||||
f"docs: {docs} {messages} {embedding_engine} {embedding_model} {embedding_function} {openai_key} {openai_url}"
|
||||
f"docs: {docs} {messages} {embedding_engine} {embedding_model} {embedding_function} {reranking_function} {openai_key} {openai_url}"
|
||||
)
|
||||
|
||||
last_user_message_idx = None
|
||||
|
|
@ -145,62 +230,66 @@ def rag_messages(
|
|||
content_type = None
|
||||
query = ""
|
||||
|
||||
embeddings_function = query_embeddings_function(
|
||||
embedding_engine,
|
||||
embedding_model,
|
||||
embedding_function,
|
||||
openai_key,
|
||||
openai_url,
|
||||
)
|
||||
|
||||
extracted_collections = []
|
||||
relevant_contexts = []
|
||||
|
||||
for doc in docs:
|
||||
context = None
|
||||
|
||||
try:
|
||||
collection = doc.get("collection_name")
|
||||
if collection:
|
||||
collection = [collection]
|
||||
else:
|
||||
collection = doc.get("collection_names", [])
|
||||
|
||||
collection = set(collection).difference(extracted_collections)
|
||||
if not collection:
|
||||
log.debug(f"skipping {doc} as it has already been extracted")
|
||||
continue
|
||||
|
||||
try:
|
||||
if doc["type"] == "text":
|
||||
context = doc["content"]
|
||||
else:
|
||||
if embedding_engine == "":
|
||||
query_embeddings = embedding_function.encode(query).tolist()
|
||||
elif 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":
|
||||
elif doc["type"] == "collection":
|
||||
context = query_embeddings_collection(
|
||||
collection_names=doc["collection_names"],
|
||||
query=query,
|
||||
query_embeddings=query_embeddings,
|
||||
k=k,
|
||||
r=r,
|
||||
embeddings_function=embeddings_function,
|
||||
reranking_function=reranking_function,
|
||||
)
|
||||
else:
|
||||
context = query_embeddings_doc(
|
||||
collection_name=doc["collection_name"],
|
||||
query=query,
|
||||
query_embeddings=query_embeddings,
|
||||
k=k,
|
||||
r=r,
|
||||
embeddings_function=embeddings_function,
|
||||
reranking_function=reranking_function,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
log.exception(e)
|
||||
context = None
|
||||
|
||||
if context:
|
||||
relevant_contexts.append(context)
|
||||
|
||||
log.debug(f"relevant_contexts: {relevant_contexts}")
|
||||
extracted_collections.extend(collection)
|
||||
|
||||
context_string = ""
|
||||
for context in relevant_contexts:
|
||||
if context:
|
||||
context_string += " ".join(context["documents"][0]) + "\n"
|
||||
items = context["documents"][0]
|
||||
context_string += "\n\n".join(items)
|
||||
context_string = context_string.strip()
|
||||
|
||||
ra_content = rag_template(
|
||||
template=template,
|
||||
|
|
@ -208,6 +297,8 @@ def rag_messages(
|
|||
query=query,
|
||||
)
|
||||
|
||||
log.debug(f"ra_content: {ra_content}")
|
||||
|
||||
if content_type == "list":
|
||||
new_content = []
|
||||
for content_item in user_message["content"]:
|
||||
|
|
@ -229,6 +320,44 @@ def rag_messages(
|
|||
return messages
|
||||
|
||||
|
||||
def get_model_path(model: str, update_model: bool = False):
|
||||
# Construct huggingface_hub kwargs with local_files_only to return the snapshot path
|
||||
cache_dir = os.getenv("SENTENCE_TRANSFORMERS_HOME")
|
||||
|
||||
local_files_only = not update_model
|
||||
|
||||
snapshot_kwargs = {
|
||||
"cache_dir": cache_dir,
|
||||
"local_files_only": local_files_only,
|
||||
}
|
||||
|
||||
log.debug(f"model: {model}")
|
||||
log.debug(f"snapshot_kwargs: {snapshot_kwargs}")
|
||||
|
||||
# Inspiration from upstream sentence_transformers
|
||||
if (
|
||||
os.path.exists(model)
|
||||
or ("\\" in model or model.count("/") > 1)
|
||||
and local_files_only
|
||||
):
|
||||
# If fully qualified path exists, return input, else set repo_id
|
||||
return model
|
||||
elif "/" not in model:
|
||||
# Set valid repo_id for model short-name
|
||||
model = "sentence-transformers" + "/" + model
|
||||
|
||||
snapshot_kwargs["repo_id"] = model
|
||||
|
||||
# Attempt to query the huggingface_hub library to determine the local path and/or to update
|
||||
try:
|
||||
model_repo_path = snapshot_download(**snapshot_kwargs)
|
||||
log.debug(f"model_repo_path: {model_repo_path}")
|
||||
return model_repo_path
|
||||
except Exception as e:
|
||||
log.exception(f"Cannot determine model snapshot path: {e}")
|
||||
return model
|
||||
|
||||
|
||||
def generate_openai_embeddings(
|
||||
model: str, text: str, key: str, url: str = "https://api.openai.com/v1"
|
||||
):
|
||||
|
|
@ -250,3 +379,97 @@ def generate_openai_embeddings(
|
|||
except Exception as e:
|
||||
print(e)
|
||||
return None
|
||||
|
||||
|
||||
from typing import Any
|
||||
|
||||
from langchain_core.retrievers import BaseRetriever
|
||||
from langchain_core.callbacks import CallbackManagerForRetrieverRun
|
||||
|
||||
|
||||
class ChromaRetriever(BaseRetriever):
|
||||
collection: Any
|
||||
embeddings_function: Any
|
||||
top_n: int
|
||||
|
||||
def _get_relevant_documents(
|
||||
self,
|
||||
query: str,
|
||||
*,
|
||||
run_manager: CallbackManagerForRetrieverRun,
|
||||
) -> List[Document]:
|
||||
query_embeddings = self.embeddings_function(query)
|
||||
|
||||
results = self.collection.query(
|
||||
query_embeddings=[query_embeddings],
|
||||
n_results=self.top_n,
|
||||
)
|
||||
|
||||
ids = results["ids"][0]
|
||||
metadatas = results["metadatas"][0]
|
||||
documents = results["documents"][0]
|
||||
|
||||
return [
|
||||
Document(
|
||||
metadata=metadatas[idx],
|
||||
page_content=documents[idx],
|
||||
)
|
||||
for idx in range(len(ids))
|
||||
]
|
||||
|
||||
|
||||
import operator
|
||||
|
||||
from typing import Optional, Sequence
|
||||
|
||||
from langchain_core.documents import BaseDocumentCompressor, Document
|
||||
from langchain_core.callbacks import Callbacks
|
||||
from langchain_core.pydantic_v1 import Extra
|
||||
|
||||
from sentence_transformers import util
|
||||
|
||||
|
||||
class RerankCompressor(BaseDocumentCompressor):
|
||||
embeddings_function: Any
|
||||
reranking_function: Any
|
||||
r_score: float
|
||||
top_n: int
|
||||
|
||||
class Config:
|
||||
extra = Extra.forbid
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
def compress_documents(
|
||||
self,
|
||||
documents: Sequence[Document],
|
||||
query: str,
|
||||
callbacks: Optional[Callbacks] = None,
|
||||
) -> Sequence[Document]:
|
||||
if self.reranking_function:
|
||||
scores = self.reranking_function.predict(
|
||||
[(query, doc.page_content) for doc in documents]
|
||||
)
|
||||
else:
|
||||
query_embedding = self.embeddings_function(query)
|
||||
document_embedding = self.embeddings_function(
|
||||
[doc.page_content for doc in documents]
|
||||
)
|
||||
scores = util.cos_sim(query_embedding, document_embedding)[0]
|
||||
|
||||
docs_with_scores = list(zip(documents, scores.tolist()))
|
||||
if self.r_score:
|
||||
docs_with_scores = [
|
||||
(d, s) for d, s in docs_with_scores if s >= self.r_score
|
||||
]
|
||||
|
||||
result = sorted(docs_with_scores, key=operator.itemgetter(1), reverse=True)
|
||||
final_results = []
|
||||
for doc, doc_score in result[: self.top_n]:
|
||||
metadata = doc.metadata
|
||||
metadata["score"] = doc_score
|
||||
doc = Document(
|
||||
page_content=doc.page_content,
|
||||
metadata=metadata,
|
||||
)
|
||||
final_results.append(doc)
|
||||
return final_results
|
||||
|
|
|
|||
|
|
@ -420,6 +420,9 @@ 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 (sentence-transformers/all-MiniLM-L6-v2)
|
||||
|
||||
RAG_TOP_K = int(os.environ.get("RAG_TOP_K", "5"))
|
||||
RAG_RELEVANCE_THRESHOLD = float(os.environ.get("RAG_RELEVANCE_THRESHOLD", "0.0"))
|
||||
|
||||
RAG_EMBEDDING_ENGINE = os.environ.get("RAG_EMBEDDING_ENGINE", "")
|
||||
|
||||
RAG_EMBEDDING_MODEL = os.environ.get(
|
||||
|
|
@ -427,10 +430,26 @@ RAG_EMBEDDING_MODEL = os.environ.get(
|
|||
)
|
||||
log.info(f"Embedding model set: {RAG_EMBEDDING_MODEL}"),
|
||||
|
||||
RAG_EMBEDDING_MODEL_AUTO_UPDATE = (
|
||||
os.environ.get("RAG_EMBEDDING_MODEL_AUTO_UPDATE", "").lower() == "true"
|
||||
)
|
||||
|
||||
RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE = (
|
||||
os.environ.get("RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE", "").lower() == "true"
|
||||
)
|
||||
|
||||
RAG_RERANKING_MODEL = os.environ.get("RAG_RERANKING_MODEL", "")
|
||||
if not RAG_RERANKING_MODEL == "":
|
||||
log.info(f"Reranking model set: {RAG_RERANKING_MODEL}"),
|
||||
|
||||
RAG_RERANKING_MODEL_AUTO_UPDATE = (
|
||||
os.environ.get("RAG_RERANKING_MODEL_AUTO_UPDATE", "").lower() == "true"
|
||||
)
|
||||
|
||||
RAG_RERANKING_MODEL_TRUST_REMOTE_CODE = (
|
||||
os.environ.get("RAG_RERANKING_MODEL_TRUST_REMOTE_CODE", "").lower() == "true"
|
||||
)
|
||||
|
||||
# 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")
|
||||
|
||||
|
|
@ -439,16 +458,15 @@ if USE_CUDA.lower() == "true":
|
|||
else:
|
||||
DEVICE_TYPE = "cpu"
|
||||
|
||||
|
||||
CHROMA_CLIENT = chromadb.PersistentClient(
|
||||
path=CHROMA_DATA_PATH,
|
||||
settings=Settings(allow_reset=True, anonymized_telemetry=False),
|
||||
)
|
||||
CHUNK_SIZE = 1500
|
||||
CHUNK_OVERLAP = 100
|
||||
|
||||
CHUNK_SIZE = int(os.environ.get("CHUNK_SIZE", "1500"))
|
||||
CHUNK_OVERLAP = int(os.environ.get("CHUNK_OVERLAP", "100"))
|
||||
|
||||
RAG_TEMPLATE = """Use the following context as your learned knowledge, inside <context></context> XML tags.
|
||||
DEFAULT_RAG_TEMPLATE = """Use the following context as your learned knowledge, inside <context></context> XML tags.
|
||||
<context>
|
||||
[context]
|
||||
</context>
|
||||
|
|
@ -462,6 +480,8 @@ And answer according to the language of the user's question.
|
|||
Given the context information, answer the query.
|
||||
Query: [query]"""
|
||||
|
||||
RAG_TEMPLATE = os.environ.get("RAG_TEMPLATE", DEFAULT_RAG_TEMPLATE)
|
||||
|
||||
RAG_OPENAI_API_BASE_URL = os.getenv("RAG_OPENAI_API_BASE_URL", OPENAI_API_BASE_URL)
|
||||
RAG_OPENAI_API_KEY = os.getenv("RAG_OPENAI_API_KEY", OPENAI_API_KEY)
|
||||
|
||||
|
|
|
|||
|
|
@ -120,9 +120,11 @@ class RAGMiddleware(BaseHTTPMiddleware):
|
|||
data["messages"],
|
||||
rag_app.state.RAG_TEMPLATE,
|
||||
rag_app.state.TOP_K,
|
||||
rag_app.state.RELEVANCE_THRESHOLD,
|
||||
rag_app.state.RAG_EMBEDDING_ENGINE,
|
||||
rag_app.state.RAG_EMBEDDING_MODEL,
|
||||
rag_app.state.sentence_transformer_ef,
|
||||
rag_app.state.sentence_transformer_rf,
|
||||
rag_app.state.OPENAI_API_KEY,
|
||||
rag_app.state.OPENAI_API_BASE_URL,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -123,6 +123,7 @@ export const getQuerySettings = async (token: string) => {
|
|||
|
||||
type QuerySettings = {
|
||||
k: number | null;
|
||||
r: number | null;
|
||||
template: string | null;
|
||||
};
|
||||
|
||||
|
|
@ -413,3 +414,64 @@ export const updateEmbeddingConfig = async (token: string, payload: EmbeddingMod
|
|||
|
||||
return res;
|
||||
};
|
||||
|
||||
export const getRerankingConfig = async (token: string) => {
|
||||
let error = null;
|
||||
|
||||
const res = await fetch(`${RAG_API_BASE_URL}/reranking`, {
|
||||
method: 'GET',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
Authorization: `Bearer ${token}`
|
||||
}
|
||||
})
|
||||
.then(async (res) => {
|
||||
if (!res.ok) throw await res.json();
|
||||
return res.json();
|
||||
})
|
||||
.catch((err) => {
|
||||
console.log(err);
|
||||
error = err.detail;
|
||||
return null;
|
||||
});
|
||||
|
||||
if (error) {
|
||||
throw error;
|
||||
}
|
||||
|
||||
return res;
|
||||
};
|
||||
|
||||
type RerankingModelUpdateForm = {
|
||||
reranking_model: string;
|
||||
};
|
||||
|
||||
export const updateRerankingConfig = async (token: string, payload: RerankingModelUpdateForm) => {
|
||||
let error = null;
|
||||
|
||||
const res = await fetch(`${RAG_API_BASE_URL}/reranking/update`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
Authorization: `Bearer ${token}`
|
||||
},
|
||||
body: JSON.stringify({
|
||||
...payload
|
||||
})
|
||||
})
|
||||
.then(async (res) => {
|
||||
if (!res.ok) throw await res.json();
|
||||
return res.json();
|
||||
})
|
||||
.catch((err) => {
|
||||
console.log(err);
|
||||
error = err.detail;
|
||||
return null;
|
||||
});
|
||||
|
||||
if (error) {
|
||||
throw error;
|
||||
}
|
||||
|
||||
return res;
|
||||
};
|
||||
|
|
|
|||
|
|
@ -8,7 +8,9 @@
|
|||
updateQuerySettings,
|
||||
resetVectorDB,
|
||||
getEmbeddingConfig,
|
||||
updateEmbeddingConfig
|
||||
updateEmbeddingConfig,
|
||||
getRerankingConfig,
|
||||
updateRerankingConfig
|
||||
} from '$lib/apis/rag';
|
||||
|
||||
import { documents, models } from '$lib/stores';
|
||||
|
|
@ -23,11 +25,13 @@
|
|||
|
||||
let scanDirLoading = false;
|
||||
let updateEmbeddingModelLoading = false;
|
||||
let updateRerankingModelLoading = false;
|
||||
|
||||
let showResetConfirm = false;
|
||||
|
||||
let embeddingEngine = '';
|
||||
let embeddingModel = '';
|
||||
let rerankingModel = '';
|
||||
|
||||
let OpenAIKey = '';
|
||||
let OpenAIUrl = '';
|
||||
|
|
@ -38,6 +42,7 @@
|
|||
|
||||
let querySettings = {
|
||||
template: '',
|
||||
r: 0.0,
|
||||
k: 4
|
||||
};
|
||||
|
||||
|
|
@ -115,6 +120,29 @@
|
|||
}
|
||||
};
|
||||
|
||||
const rerankingModelUpdateHandler = async () => {
|
||||
console.log('Update reranking model attempt:', rerankingModel);
|
||||
|
||||
updateRerankingModelLoading = true;
|
||||
const res = await updateRerankingConfig(localStorage.token, {
|
||||
reranking_model: rerankingModel
|
||||
}).catch(async (error) => {
|
||||
toast.error(error);
|
||||
await setRerankingConfig();
|
||||
return null;
|
||||
});
|
||||
updateRerankingModelLoading = false;
|
||||
|
||||
if (res) {
|
||||
console.log('rerankingModelUpdateHandler:', res);
|
||||
if (res.status === true) {
|
||||
toast.success($i18n.t('Reranking model set to "{{reranking_model}}"', res), {
|
||||
duration: 1000 * 10
|
||||
});
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
const submitHandler = async () => {
|
||||
const res = await updateRAGConfig(localStorage.token, {
|
||||
pdf_extract_images: pdfExtractImages,
|
||||
|
|
@ -138,6 +166,14 @@
|
|||
}
|
||||
};
|
||||
|
||||
const setRerankingConfig = async () => {
|
||||
const rerankingConfig = await getRerankingConfig(localStorage.token);
|
||||
|
||||
if (rerankingConfig) {
|
||||
rerankingModel = rerankingConfig.reranking_model;
|
||||
}
|
||||
};
|
||||
|
||||
onMount(async () => {
|
||||
const res = await getRAGConfig(localStorage.token);
|
||||
|
||||
|
|
@ -149,6 +185,7 @@
|
|||
}
|
||||
|
||||
await setEmbeddingConfig();
|
||||
await setRerankingConfig();
|
||||
|
||||
querySettings = await getQuerySettings(localStorage.token);
|
||||
});
|
||||
|
|
@ -349,6 +386,79 @@
|
|||
|
||||
<hr class=" dark:border-gray-700 my-3" />
|
||||
|
||||
<div class=" ">
|
||||
<div class=" mb-2 text-sm font-medium">{$i18n.t('Update Reranking Model')}</div>
|
||||
|
||||
<div class="flex w-full">
|
||||
<div class="flex-1 mr-2">
|
||||
<input
|
||||
class="w-full rounded-lg py-2 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
|
||||
placeholder={$i18n.t('Update reranking model (e.g. {{model}})', {
|
||||
model: rerankingModel.slice(-40)
|
||||
})}
|
||||
bind:value={rerankingModel}
|
||||
/>
|
||||
</div>
|
||||
<button
|
||||
class="px-2.5 bg-gray-100 hover:bg-gray-200 text-gray-800 dark:bg-gray-850 dark:hover:bg-gray-800 dark:text-gray-100 rounded-lg transition"
|
||||
on:click={() => {
|
||||
rerankingModelUpdateHandler();
|
||||
}}
|
||||
disabled={updateRerankingModelLoading}
|
||||
>
|
||||
{#if updateRerankingModelLoading}
|
||||
<div class="self-center">
|
||||
<svg
|
||||
class=" w-4 h-4"
|
||||
viewBox="0 0 24 24"
|
||||
fill="currentColor"
|
||||
xmlns="http://www.w3.org/2000/svg"
|
||||
><style>
|
||||
.spinner_ajPY {
|
||||
transform-origin: center;
|
||||
animation: spinner_AtaB 0.75s infinite linear;
|
||||
}
|
||||
@keyframes spinner_AtaB {
|
||||
100% {
|
||||
transform: rotate(360deg);
|
||||
}
|
||||
}
|
||||
</style><path
|
||||
d="M12,1A11,11,0,1,0,23,12,11,11,0,0,0,12,1Zm0,19a8,8,0,1,1,8-8A8,8,0,0,1,12,20Z"
|
||||
opacity=".25"
|
||||
/><path
|
||||
d="M10.14,1.16a11,11,0,0,0-9,8.92A1.59,1.59,0,0,0,2.46,12,1.52,1.52,0,0,0,4.11,10.7a8,8,0,0,1,6.66-6.61A1.42,1.42,0,0,0,12,2.69h0A1.57,1.57,0,0,0,10.14,1.16Z"
|
||||
class="spinner_ajPY"
|
||||
/></svg
|
||||
>
|
||||
</div>
|
||||
{:else}
|
||||
<svg
|
||||
xmlns="http://www.w3.org/2000/svg"
|
||||
viewBox="0 0 16 16"
|
||||
fill="currentColor"
|
||||
class="w-4 h-4"
|
||||
>
|
||||
<path
|
||||
d="M8.75 2.75a.75.75 0 0 0-1.5 0v5.69L5.03 6.22a.75.75 0 0 0-1.06 1.06l3.5 3.5a.75.75 0 0 0 1.06 0l3.5-3.5a.75.75 0 0 0-1.06-1.06L8.75 8.44V2.75Z"
|
||||
/>
|
||||
<path
|
||||
d="M3.5 9.75a.75.75 0 0 0-1.5 0v1.5A2.75 2.75 0 0 0 4.75 14h6.5A2.75 2.75 0 0 0 14 11.25v-1.5a.75.75 0 0 0-1.5 0v1.5c0 .69-.56 1.25-1.25 1.25h-6.5c-.69 0-1.25-.56-1.25-1.25v-1.5Z"
|
||||
/>
|
||||
</svg>
|
||||
{/if}
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="mt-2 mb-1 text-xs text-gray-400 dark:text-gray-500">
|
||||
{$i18n.t(
|
||||
'Note: If you choose a reranking model, it will use that to score and rerank instead of the embedding model.'
|
||||
)}
|
||||
</div>
|
||||
|
||||
<hr class=" dark:border-gray-700 my-3" />
|
||||
|
||||
<div class=" flex w-full justify-between">
|
||||
<div class=" self-center text-xs font-medium">
|
||||
{$i18n.t('Scan for documents from {{path}}', { path: '/data/docs' })}
|
||||
|
|
@ -473,6 +583,26 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<div class=" flex">
|
||||
<div class=" flex w-full justify-between">
|
||||
<div class="self-center text-xs font-medium flex-1">
|
||||
{$i18n.t('Relevance Threshold')}
|
||||
</div>
|
||||
|
||||
<div class="self-center p-3">
|
||||
<input
|
||||
class=" w-full rounded-lg py-1.5 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
|
||||
type="number"
|
||||
step="0.01"
|
||||
placeholder={$i18n.t('Enter Relevance Threshold')}
|
||||
bind:value={querySettings.r}
|
||||
autocomplete="off"
|
||||
min="0.0"
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div>
|
||||
<div class=" mb-2.5 text-sm font-medium">{$i18n.t('RAG Template')}</div>
|
||||
<textarea
|
||||
|
|
|
|||
Loading…
Reference in a new issue