mirror of
https://github.com/open-webui/open-webui
synced 2024-11-25 13:29:53 +00:00
feat: hybrid search
This commit is contained in:
Steven Kreitzer
Steven Kreitzer
parent
f3e5700d49
commit
4e0b32b505
11
Dockerfile
11
Dockerfile
@ -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=BAAI/bge-reranker-base
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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=${USE_RERANKING_MODEL}
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## Basis URL Config ##
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ENV OLLAMA_BASE_URL="/ollama" \
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@ -62,7 +65,7 @@ ENV WHISPER_MODEL="base" \
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## RAG Embedding model settings ##
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ENV RAG_EMBEDDING_MODEL="$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="$USE_RERANKING_MODEL_DOCKER" \
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SENTENCE_TRANSFORMERS_HOME="/app/backend/data/cache/embedding/models"
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#### Other models ##########################################################
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@ -99,11 +102,13 @@ RUN pip3 install uv && \
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/$USE_CUDA_DOCKER_VER --no-cache-dir && \
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uv pip install --system -r requirements.txt --no-cache-dir && \
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python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ['RAG_EMBEDDING_MODEL'], device='cpu')" && \
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python -c "import os; from sentence_transformers import CrossEncoder; CrossEncoder(os.environ['RAG_RERANKING_MODEL'], device='cpu')" && \
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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'])"; \
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else \
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir && \
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uv pip install --system -r requirements.txt --no-cache-dir && \
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python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ['RAG_EMBEDDING_MODEL'], device='cpu')" && \
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python -c "import os; from sentence_transformers import CrossEncoder; CrossEncoder(os.environ['RAG_RERANKING_MODEL'], device='cpu')" && \
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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'])"; \
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fi
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@ -49,8 +49,8 @@ from apps.web.models.documents import (
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from apps.rag.utils import (
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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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@ -67,6 +67,8 @@ from config import (
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RAG_EMBEDDING_ENGINE,
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RAG_EMBEDDING_MODEL,
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RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE,
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RAG_RERANKING_MODEL,
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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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@ -91,6 +93,7 @@ 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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@ -105,6 +108,12 @@ if app.state.RAG_EMBEDDING_ENGINE == "":
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trust_remote_code=RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE,
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)
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app.state.sentence_transformer_rf = sentence_transformers.CrossEncoder(
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app.state.RAG_RERANKING_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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origins = ["*"]
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@ -134,6 +143,7 @@ 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 +160,11 @@ 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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@ -205,6 +220,36 @@ 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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app.state.sentence_transformer_rf = sentence_transformers.CrossEncoder(
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app.state.RAG_RERANKING_MODEL,
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device=DEVICE_TYPE,
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)
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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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@ -286,34 +331,21 @@ 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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)
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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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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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@ -334,33 +366,21 @@ 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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)
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embeddings_function = 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_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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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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@ -427,8 +447,6 @@ 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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texts = [doc.page_content for doc in docs]
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texts = list(map(lambda x: x.replace("\n", " "), texts))
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metadatas = [doc.metadata for doc in docs]
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try:
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@ -440,26 +458,20 @@ def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> b
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collection = CHROMA_CLIENT.create_collection(name=collection_name)
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embedding_func = 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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embedding_texts = list(map(lambda x: x.replace("\n", " "), texts))
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if app.state.RAG_EMBEDDING_ENGINE == "":
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embeddings = app.state.sentence_transformer_ef.encode(texts).tolist()
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elif app.state.RAG_EMBEDDING_ENGINE == "ollama":
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embeddings = embedding_func(embedding_texts)
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else:
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embeddings = [
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generate_ollama_embeddings(
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GenerateEmbeddingsForm(
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**{"model": app.state.RAG_EMBEDDING_MODEL, "prompt": text}
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)
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)
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for text in texts
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]
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elif app.state.RAG_EMBEDDING_ENGINE == "openai":
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embeddings = [
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generate_openai_embeddings(
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model=app.state.RAG_EMBEDDING_MODEL,
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text=text,
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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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)
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for text in texts
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embedding_func(embedding_texts) for text in texts
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]
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for batch in create_batches(
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@ -1,5 +1,8 @@
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import logging
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import requests
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import operator
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import sentence_transformers
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from typing import List
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@ -8,6 +11,11 @@ from apps.ollama.main import (
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GenerateEmbeddingsForm,
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)
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from langchain.retrievers import (
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BM25Retriever,
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EnsembleRetriever,
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)
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from config import SRC_LOG_LEVELS, CHROMA_CLIENT
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@ -15,60 +23,96 @@ log = logging.getLogger(__name__)
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log.setLevel(SRC_LOG_LEVELS["RAG"])
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def query_embeddings_doc(collection_name: str, query: str, query_embeddings, k: int):
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def query_embeddings_doc(
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collection_name: str,
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query: str,
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k: int,
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embeddings_function,
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reranking_function,
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):
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try:
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# if you use docker use the model from the environment variable
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log.info(f"query_embeddings_doc {query_embeddings}")
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collection = CHROMA_CLIENT.get_collection(name=collection_name)
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result = collection.query(
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query_embeddings=[query_embeddings],
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n_results=k,
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# keyword search
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documents = collection.get() # get all documents
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bm25_retriever = BM25Retriever.from_texts(
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texts=documents.get("documents"),
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metadatas=documents.get("metadatas"),
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)
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bm25_retriever.k = k
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# semantic search (vector)
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chroma_retriever = ChromaRetriever(
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collection=collection,
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k=k,
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embeddings_function=embeddings_function,
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)
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log.info(f"query_embeddings_doc:result {result}")
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# hybrid search (ensemble)
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ensemble_retriever = EnsembleRetriever(
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retrievers=[bm25_retriever, chroma_retriever],
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weights=[0.6, 0.4]
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)
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documents = ensemble_retriever.invoke(query)
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result = query_results_rank(
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query=query,
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documents=documents,
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k=k,
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reranking_function=reranking_function,
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)
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result = {
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"distances": [[d[1].item() for d in result]],
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"documents": [[d[0].page_content for d in result]],
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"metadatas": [[d[0].metadata for d in result]],
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}
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return result
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except Exception as e:
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raise e
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def query_results_rank(query: str, documents, k: int, reranking_function):
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scores = reranking_function.predict([(query, doc.page_content) for doc in documents])
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docs_with_scores = list(zip(documents, scores))
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result = sorted(docs_with_scores, key=operator.itemgetter(1), reverse=True)
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return result[: k]
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def merge_and_sort_query_results(query_results, k):
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# Initialize lists to store combined data
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combined_ids = []
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combined_distances = []
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combined_metadatas = []
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combined_documents = []
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combined_metadatas = []
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# Combine data from each dictionary
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for data in query_results:
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combined_ids.extend(data["ids"][0])
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combined_distances.extend(data["distances"][0])
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combined_metadatas.extend(data["metadatas"][0])
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combined_documents.extend(data["documents"][0])
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combined_metadatas.extend(data["metadatas"][0])
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# Create a list of tuples (distance, id, metadata, document)
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# Create a list of tuples (distance, document, metadata)
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combined = list(
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zip(combined_distances, combined_ids, combined_metadatas, combined_documents)
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zip(combined_distances, combined_documents, combined_metadatas)
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)
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# Sort the list based on distances
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combined.sort(key=lambda x: x[0])
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# Unzip the sorted list
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sorted_distances, sorted_ids, sorted_metadatas, sorted_documents = zip(*combined)
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sorted_distances, sorted_documents, sorted_metadatas = zip(*combined)
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# Slicing the lists to include only k elements
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sorted_distances = list(sorted_distances)[:k]
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sorted_ids = list(sorted_ids)[:k]
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sorted_metadatas = list(sorted_metadatas)[:k]
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sorted_documents = list(sorted_documents)[:k]
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sorted_metadatas = list(sorted_metadatas)[:k]
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# Create the output dictionary
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merged_query_results = {
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"ids": [sorted_ids],
|
||||
"distances": [sorted_distances],
|
||||
"metadatas": [sorted_metadatas],
|
||||
"documents": [sorted_documents],
|
||||
"metadatas": [sorted_metadatas],
|
||||
"embeddings": None,
|
||||
"uris": None,
|
||||
"data": None,
|
||||
@ -78,19 +122,23 @@ def merge_and_sort_query_results(query_results, k):
|
||||
|
||||
|
||||
def query_embeddings_collection(
|
||||
collection_names: List[str], query: str, query_embeddings, k: int
|
||||
collection_names: List[str],
|
||||
query: str,
|
||||
k: int,
|
||||
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,
|
||||
embeddings_function=embeddings_function,
|
||||
reranking_function=reranking_function,
|
||||
)
|
||||
results.append(result)
|
||||
except:
|
||||
@ -105,6 +153,33 @@ def rag_template(template: str, context: str, query: str):
|
||||
return template
|
||||
|
||||
|
||||
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 == "ollama":
|
||||
return lambda query: generate_ollama_embeddings(
|
||||
GenerateEmbeddingsForm(
|
||||
**{
|
||||
"model": embedding_model,
|
||||
"prompt": query,
|
||||
}
|
||||
)
|
||||
)
|
||||
elif embedding_engine == "openai":
|
||||
return lambda query: generate_openai_embeddings(
|
||||
model=embedding_model,
|
||||
text=query,
|
||||
key=openai_key,
|
||||
url=openai_url,
|
||||
)
|
||||
|
||||
|
||||
def rag_messages(
|
||||
docs,
|
||||
messages,
|
||||
@ -113,11 +188,12 @@ def rag_messages(
|
||||
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
|
||||
@ -155,38 +231,29 @@ def rag_messages(
|
||||
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,
|
||||
)
|
||||
embeddings_function = query_embeddings_function(
|
||||
embedding_engine,
|
||||
embedding_model,
|
||||
embedding_function,
|
||||
openai_key,
|
||||
openai_url,
|
||||
)
|
||||
|
||||
if doc["type"] == "collection":
|
||||
context = query_embeddings_collection(
|
||||
collection_names=doc["collection_names"],
|
||||
query=query,
|
||||
query_embeddings=query_embeddings,
|
||||
k=k,
|
||||
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,
|
||||
embeddings_function=embeddings_function,
|
||||
reranking_function=reranking_function,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
@ -250,3 +317,41 @@ def generate_openai_embeddings(
|
||||
except Exception as e:
|
||||
print(e)
|
||||
return None
|
||||
|
||||
|
||||
from typing import Any
|
||||
|
||||
from langchain_core.callbacks import CallbackManagerForRetrieverRun
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.retrievers import BaseRetriever
|
||||
|
||||
|
||||
class ChromaRetriever(BaseRetriever):
|
||||
collection: Any
|
||||
k: int
|
||||
embeddings_function: Any
|
||||
|
||||
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.k,
|
||||
)
|
||||
|
||||
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))
|
||||
]
|
||||
|
||||
@ -424,6 +424,15 @@ 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", "BAAI/bge-reranker-v2-m3"
|
||||
)
|
||||
log.info(f"Reranking model set: {RAG_RERANKING_MODEL}"),
|
||||
|
||||
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")
|
||||
|
||||
|
||||
@ -117,6 +117,7 @@ class RAGMiddleware(BaseHTTPMiddleware):
|
||||
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.RAG_OPENAI_API_KEY,
|
||||
rag_app.state.RAG_OPENAI_API_BASE_URL,
|
||||
)
|
||||
|
||||
@ -413,3 +413,64 @@ 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 '$lib/stores';
|
||||
@ -23,11 +25,13 @@
|
||||
|
||||
let scanDirLoading = false;
|
||||
let updateEmbeddingModelLoading = false;
|
||||
let updateRerankingModelLoading = false;
|
||||
|
||||
let showResetConfirm = false;
|
||||
|
||||
let embeddingEngine = '';
|
||||
let embeddingModel = '';
|
||||
let rerankingModel = '';
|
||||
|
||||
let OpenAIKey = '';
|
||||
let OpenAIUrl = '';
|
||||
@ -115,6 +119,29 @@
|
||||
}
|
||||
};
|
||||
|
||||
const rerankingModelUpdateHandler = async () => {
|
||||
console.log('Update reranking model attempt:', rerankingModel);
|
||||
|
||||
updateRerankingModelLoading = true;
|
||||
const res = await updateRerankingConfig(localStorage.token, {
|
||||
reranking_model: rerankingModel,
|
||||
}).catch(async (error) => {
|
||||
toast.error(error);
|
||||
await setRerankingConfig();
|
||||
return null;
|
||||
});
|
||||
updateRerankingModelLoading = false;
|
||||
|
||||
if (res) {
|
||||
console.log('rerankingModelUpdateHandler:', res);
|
||||
if (res.status === true) {
|
||||
toast.success($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 +165,14 @@
|
||||
}
|
||||
};
|
||||
|
||||
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 +184,7 @@
|
||||
}
|
||||
|
||||
await setEmbeddingConfig();
|
||||
await setRerankingConfig();
|
||||
|
||||
querySettings = await getQuerySettings(localStorage.token);
|
||||
});
|
||||
@ -349,6 +385,73 @@
|
||||
|
||||
<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>
|
||||
|
||||
<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' })}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user