forked from open-webui/open-webui
feat: hybrid search and reranking support
This commit is contained in:
Steven Kreitzer
parent
db801aee79
commit
c0259aad67
10 changed files with 262 additions and 131 deletions
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@ -10,6 +10,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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### Added
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- **🛠️ Improved Embedding Model Support**: You can now use any embedding model `sentence_transformers` supports.
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- **🌟 Enhanced RAG Pipeline**: Added `BM25` hybrid searching with reranking model support using `sentence_transformers`.
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## [0.1.120] - 2024-04-20
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@ -10,7 +10,7 @@ ARG USE_CUDA_VER=cu121
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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 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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ARG USE_RERANKING_MODEL=""
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######## WebUI frontend ########
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FROM --platform=$BUILDPLATFORM node:21-alpine3.19 as build
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@ -67,6 +67,9 @@ ENV WHISPER_MODEL="base" \
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ENV RAG_EMBEDDING_MODEL="$USE_EMBEDDING_MODEL_DOCKER" \
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RAG_RERANKING_MODEL="$USE_RERANKING_MODEL_DOCKER" \
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SENTENCE_TRANSFORMERS_HOME="/app/backend/data/cache/embedding/models"
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## Hugging Face download cache ##
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ENV HF_HOME="/app/backend/data/cache/embedding/models"
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#### Other models ##########################################################
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WORKDIR /app/backend
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@ -102,13 +105,11 @@ RUN pip3 install uv && \
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/$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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@ -92,6 +92,10 @@ async def get_ollama_api_urls(user=Depends(get_admin_user)):
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return {"OLLAMA_BASE_URLS": app.state.OLLAMA_BASE_URLS}
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def get_ollama_endpoint(url_idx: int = 0):
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return app.state.OLLAMA_BASE_URLS[url_idx]
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class UrlUpdateForm(BaseModel):
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urls: List[str]
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@ -64,6 +64,8 @@ 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_TRUST_REMOTE_CODE,
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@ -86,7 +88,8 @@ 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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@ -107,12 +110,17 @@ if app.state.RAG_EMBEDDING_ENGINE == "":
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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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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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if not app.state.RAG_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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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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origins = ["*"]
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@ -185,22 +193,22 @@ 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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app.state.sentence_transformer_ef = None
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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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app.state.sentence_transformer_ef = (
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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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)
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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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return {
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"status": True,
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@ -233,10 +241,14 @@ async def update_reranking_config(
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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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if app.state.RAG_RERANKING_MODEL == "":
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app.state.sentence_transformer_rf = None
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else:
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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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@ -302,11 +314,13 @@ 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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@ -316,6 +330,7 @@ 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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@ -323,6 +338,7 @@ 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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@ -343,6 +359,7 @@ def query_doc_handler(
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collection_name=form_data.collection_name,
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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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)
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@ -358,6 +375,7 @@ 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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@ -378,6 +396,7 @@ def query_collection_handler(
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collection_names=form_data.collection_names,
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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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)
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@ -467,12 +486,7 @@ def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> b
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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 = embedding_func(embedding_texts)
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else:
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embeddings = [
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embedding_func(embedding_texts) for text in texts
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]
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embeddings = embedding_func(embedding_texts)
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for batch in create_batches(
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api=CHROMA_CLIENT,
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@ -1,8 +1,5 @@
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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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@ -11,8 +8,10 @@ from apps.ollama.main import (
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GenerateEmbeddingsForm,
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)
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from langchain_core.documents import Document
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from langchain_community.retrievers import BM25Retriever
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from langchain.retrievers import (
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BM25Retriever,
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ContextualCompressionRetriever,
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EnsembleRetriever,
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)
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@ -27,6 +26,7 @@ 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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r: float,
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embeddings_function,
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reranking_function,
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):
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@ -34,38 +34,39 @@ def query_embeddings_doc(
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# if you use docker use the model from the environment variable
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collection = CHROMA_CLIENT.get_collection(name=collection_name)
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# keyword search
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documents = collection.get() # get all documents
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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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top_n=k,
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)
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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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retrievers=[bm25_retriever, chroma_retriever], weights=[0.5, 0.5]
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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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compressor = RerankCompressor(
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embeddings_function=embeddings_function,
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reranking_function=reranking_function,
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r_score=r,
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top_n=k,
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)
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compression_retriever = ContextualCompressionRetriever(
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base_compressor=compressor, base_retriever=ensemble_retriever
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)
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result = compression_retriever.invoke(query)
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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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"distances": [[d.metadata.get("score") for d in result]],
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"documents": [[d.page_content for d in result]],
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"metadatas": [[d.metadata for d in result]],
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}
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return result
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@ -73,58 +74,52 @@ def query_embeddings_doc(
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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_distances = []
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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_distances.extend(data["distances"][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, document, metadata)
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combined = list(
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zip(combined_distances, combined_documents, combined_metadatas)
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)
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combined = list(zip(combined_distances, combined_documents, combined_metadatas))
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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_documents, sorted_metadatas = zip(*combined)
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# We don't have anything :-(
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if not combined:
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sorted_distances = []
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sorted_documents = []
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sorted_metadatas = []
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else:
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# Unzip the sorted list
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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_documents = list(sorted_documents)[:k]
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sorted_metadatas = list(sorted_metadatas)[:k]
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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_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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result = {
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"distances": [sorted_distances],
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"documents": [sorted_documents],
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"metadatas": [sorted_metadatas],
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"embeddings": None,
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"uris": None,
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"data": None,
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}
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return merged_query_results
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return result
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def query_embeddings_collection(
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collection_names: List[str],
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query: str,
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k: int,
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r: float,
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embeddings_function,
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reranking_function,
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):
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@ -137,6 +132,7 @@ def query_embeddings_collection(
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collection_name=collection_name,
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query=query,
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k=k,
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r=r,
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embeddings_function=embeddings_function,
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reranking_function=reranking_function,
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)
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@ -162,22 +158,31 @@ def query_embeddings_function(
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):
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if embedding_engine == "":
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return lambda query: embedding_function.encode(query).tolist()
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elif embedding_engine == "ollama":
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return lambda query: generate_ollama_embeddings(
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GenerateEmbeddingsForm(
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**{
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"model": embedding_model,
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"prompt": query,
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}
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elif embedding_engine in ["ollama", "openai"]:
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if embedding_engine == "ollama":
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func = lambda query: generate_ollama_embeddings(
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GenerateEmbeddingsForm(
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**{
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"model": embedding_model,
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"prompt": query,
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}
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)
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)
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)
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elif embedding_engine == "openai":
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return lambda query: generate_openai_embeddings(
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model=embedding_model,
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text=query,
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key=openai_key,
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url=openai_url,
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)
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elif embedding_engine == "openai":
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func = lambda query: generate_openai_embeddings(
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model=embedding_model,
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text=query,
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key=openai_key,
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url=openai_url,
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)
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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(
|
||||
|
|
@ -185,6 +190,7 @@ def rag_messages(
|
|||
messages,
|
||||
template,
|
||||
k,
|
||||
r,
|
||||
embedding_engine,
|
||||
embedding_model,
|
||||
embedding_function,
|
||||
|
|
@ -221,53 +227,68 @@ 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:
|
||||
embeddings_function = query_embeddings_function(
|
||||
embedding_engine,
|
||||
embedding_model,
|
||||
embedding_function,
|
||||
openai_key,
|
||||
openai_url,
|
||||
elif doc["type"] == "collection":
|
||||
context = query_embeddings_collection(
|
||||
collection_names=doc["collection_names"],
|
||||
query=query,
|
||||
k=k,
|
||||
r=r,
|
||||
embeddings_function=embeddings_function,
|
||||
reranking_function=reranking_function,
|
||||
)
|
||||
else:
|
||||
context = query_embeddings_doc(
|
||||
collection_name=doc["collection_name"],
|
||||
query=query,
|
||||
k=k,
|
||||
r=r,
|
||||
embeddings_function=embeddings_function,
|
||||
reranking_function=reranking_function,
|
||||
)
|
||||
|
||||
if doc["type"] == "collection":
|
||||
context = query_embeddings_collection(
|
||||
collection_names=doc["collection_names"],
|
||||
query=query,
|
||||
k=k,
|
||||
embeddings_function=embeddings_function,
|
||||
reranking_function=reranking_function,
|
||||
)
|
||||
else:
|
||||
context = query_embeddings_doc(
|
||||
collection_name=doc["collection_name"],
|
||||
query=query,
|
||||
k=k,
|
||||
embeddings_function=embeddings_function,
|
||||
reranking_function=reranking_function,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
log.exception(e)
|
||||
context = None
|
||||
|
||||
relevant_contexts.append(context)
|
||||
if context:
|
||||
relevant_contexts.append(context)
|
||||
|
||||
extracted_collections.extend(collection)
|
||||
|
||||
log.debug(f"relevant_contexts: {relevant_contexts}")
|
||||
|
||||
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,
|
||||
|
|
@ -275,6 +296,8 @@ 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"]:
|
||||
|
|
@ -321,15 +344,14 @@ def generate_openai_embeddings(
|
|||
|
||||
from typing import Any
|
||||
|
||||
from langchain_core.callbacks import CallbackManagerForRetrieverRun
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.retrievers import BaseRetriever
|
||||
from langchain_core.callbacks import CallbackManagerForRetrieverRun
|
||||
|
||||
|
||||
class ChromaRetriever(BaseRetriever):
|
||||
collection: Any
|
||||
k: int
|
||||
embeddings_function: Any
|
||||
top_n: int
|
||||
|
||||
def _get_relevant_documents(
|
||||
self,
|
||||
|
|
@ -341,7 +363,7 @@ class ChromaRetriever(BaseRetriever):
|
|||
|
||||
results = self.collection.query(
|
||||
query_embeddings=[query_embeddings],
|
||||
n_results=self.k,
|
||||
n_results=self.top_n,
|
||||
)
|
||||
|
||||
ids = results["ids"][0]
|
||||
|
|
@ -355,3 +377,60 @@ class ChromaRetriever(BaseRetriever):
|
|||
)
|
||||
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(
|
||||
|
|
@ -431,10 +434,9 @@ 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 = os.environ.get("RAG_RERANKING_MODEL", "")
|
||||
if not RAG_RERANKING_MODEL == "":
|
||||
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"
|
||||
|
|
@ -448,16 +450,15 @@ 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>
|
||||
|
|
@ -471,6 +472,8 @@ 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)
|
||||
|
||||
|
|
|
|||
|
|
@ -120,12 +120,13 @@ 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.RAG_OPENAI_API_KEY,
|
||||
rag_app.state.RAG_OPENAI_API_BASE_URL,
|
||||
rag_app.state.OPENAI_API_KEY,
|
||||
rag_app.state.OPENAI_API_BASE_URL,
|
||||
)
|
||||
del data["docs"]
|
||||
|
||||
|
|
|
|||
|
|
@ -123,6 +123,7 @@ export const getQuerySettings = async (token: string) => {
|
|||
|
||||
type QuerySettings = {
|
||||
k: number | null;
|
||||
r: number | null;
|
||||
template: string | null;
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
import fileSaver from 'file-saver';
|
||||
const { saveAs } = fileSaver;
|
||||
|
||||
import { chats, user } from '$lib/stores';
|
||||
import { config, chats, user } from '$lib/stores';
|
||||
|
||||
import {
|
||||
createNewChat,
|
||||
|
|
|
|||
|
|
@ -42,6 +42,7 @@
|
|||
|
||||
let querySettings = {
|
||||
template: '',
|
||||
r: 0.0,
|
||||
k: 4
|
||||
};
|
||||
|
||||
|
|
@ -124,7 +125,7 @@
|
|||
|
||||
updateRerankingModelLoading = true;
|
||||
const res = await updateRerankingConfig(localStorage.token, {
|
||||
reranking_model: rerankingModel,
|
||||
reranking_model: rerankingModel
|
||||
}).catch(async (error) => {
|
||||
toast.error(error);
|
||||
await setRerankingConfig();
|
||||
|
|
@ -450,6 +451,12 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<div class="mt-2 mb-1 text-xs text-gray-400 dark:text-gray-500">
|
||||
{$i18n.t(
|
||||
'Note: If you choose a reranking model, it will use that to score and rerank instead of the embedding model.'
|
||||
)}
|
||||
</div>
|
||||
|
||||
<hr class=" dark:border-gray-700 my-3" />
|
||||
|
||||
<div class=" flex w-full justify-between">
|
||||
|
|
@ -576,6 +583,26 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<div class=" flex">
|
||||
<div class=" flex w-full justify-between">
|
||||
<div class="self-center text-xs font-medium flex-1">
|
||||
{$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…
Add table
Add a link
Reference in a new issue