diff --git a/Dockerfile b/Dockerfile index a8f664ada..d95ab986b 100644 --- a/Dockerfile +++ b/Dockerfile @@ -8,8 +8,9 @@ ARG USE_CUDA_VER=cu121 # any sentence transformer model; models to use can be found at https://huggingface.co/models?library=sentence-transformers # Leaderboard: https://huggingface.co/spaces/mteb/leaderboard # for better performance and multilangauge support use "intfloat/multilingual-e5-large" (~2.5GB) or "intfloat/multilingual-e5-base" (~1.5GB) -# 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. +# 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. ARG USE_EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2 +ARG USE_RERANKING_MODEL=BAAI/bge-reranker-base ######## WebUI frontend ######## FROM --platform=$BUILDPLATFORM node:21-alpine3.19 as build @@ -30,6 +31,7 @@ ARG USE_CUDA ARG USE_OLLAMA ARG USE_CUDA_VER ARG USE_EMBEDDING_MODEL +ARG USE_RERANKING_MODEL ## Basis ## ENV ENV=prod \ @@ -38,7 +40,8 @@ ENV ENV=prod \ USE_OLLAMA_DOCKER=${USE_OLLAMA} \ USE_CUDA_DOCKER=${USE_CUDA} \ USE_CUDA_DOCKER_VER=${USE_CUDA_VER} \ - USE_EMBEDDING_MODEL_DOCKER=${USE_EMBEDDING_MODEL} + USE_EMBEDDING_MODEL_DOCKER=${USE_EMBEDDING_MODEL} \ + USE_RERANKING_MODEL_DOCKER=${USE_RERANKING_MODEL} ## Basis URL Config ## ENV OLLAMA_BASE_URL="/ollama" \ @@ -62,7 +65,7 @@ ENV WHISPER_MODEL="base" \ ## RAG Embedding model settings ## ENV RAG_EMBEDDING_MODEL="$USE_EMBEDDING_MODEL_DOCKER" \ - RAG_EMBEDDING_MODEL_DIR="/app/backend/data/cache/embedding/models" \ + RAG_RERANKING_MODEL="$USE_RERANKING_MODEL_DOCKER" \ SENTENCE_TRANSFORMERS_HOME="/app/backend/data/cache/embedding/models" #### Other models ########################################################## @@ -99,11 +102,13 @@ RUN pip3 install uv && \ pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/$USE_CUDA_DOCKER_VER --no-cache-dir && \ uv pip install --system -r requirements.txt --no-cache-dir && \ python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ['RAG_EMBEDDING_MODEL'], device='cpu')" && \ + python -c "import os; from sentence_transformers import CrossEncoder; CrossEncoder(os.environ['RAG_RERANKING_MODEL'], device='cpu')" && \ 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'])"; \ else \ pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir && \ uv pip install --system -r requirements.txt --no-cache-dir && \ python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ['RAG_EMBEDDING_MODEL'], device='cpu')" && \ + python -c "import os; from sentence_transformers import CrossEncoder; CrossEncoder(os.environ['RAG_RERANKING_MODEL'], device='cpu')" && \ 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'])"; \ fi diff --git a/backend/apps/rag/main.py b/backend/apps/rag/main.py index 5da7489f1..38ac5d4b8 100644 --- a/backend/apps/rag/main.py +++ b/backend/apps/rag/main.py @@ -49,8 +49,8 @@ from apps.web.models.documents import ( from apps.rag.utils import ( query_embeddings_doc, + query_embeddings_function, query_embeddings_collection, - generate_openai_embeddings, ) from utils.misc import ( @@ -67,6 +67,8 @@ from config import ( RAG_EMBEDDING_ENGINE, RAG_EMBEDDING_MODEL, RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE, + RAG_RERANKING_MODEL, + RAG_RERANKING_MODEL_TRUST_REMOTE_CODE, RAG_OPENAI_API_BASE_URL, RAG_OPENAI_API_KEY, DEVICE_TYPE, @@ -91,6 +93,7 @@ app.state.CHUNK_OVERLAP = CHUNK_OVERLAP app.state.RAG_EMBEDDING_ENGINE = RAG_EMBEDDING_ENGINE app.state.RAG_EMBEDDING_MODEL = RAG_EMBEDDING_MODEL +app.state.RAG_RERANKING_MODEL = RAG_RERANKING_MODEL app.state.RAG_TEMPLATE = RAG_TEMPLATE app.state.OPENAI_API_BASE_URL = RAG_OPENAI_API_BASE_URL @@ -105,6 +108,12 @@ if app.state.RAG_EMBEDDING_ENGINE == "": trust_remote_code=RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE, ) +app.state.sentence_transformer_rf = sentence_transformers.CrossEncoder( + app.state.RAG_RERANKING_MODEL, + device=DEVICE_TYPE, + trust_remote_code=RAG_RERANKING_MODEL_TRUST_REMOTE_CODE, +) + origins = ["*"] @@ -134,6 +143,7 @@ async def get_status(): "template": app.state.RAG_TEMPLATE, "embedding_engine": app.state.RAG_EMBEDDING_ENGINE, "embedding_model": app.state.RAG_EMBEDDING_MODEL, + "reranking_model": app.state.RAG_RERANKING_MODEL, } @@ -150,6 +160,11 @@ async def get_embedding_config(user=Depends(get_admin_user)): } +@app.get("/reranking") +async def get_reraanking_config(user=Depends(get_admin_user)): + return {"status": True, "reranking_model": app.state.RAG_RERANKING_MODEL} + + class OpenAIConfigForm(BaseModel): url: str key: str @@ -205,6 +220,36 @@ async def update_embedding_config( ) +class RerankingModelUpdateForm(BaseModel): + reranking_model: str + + +@app.post("/reranking/update") +async def update_reranking_config( + form_data: RerankingModelUpdateForm, user=Depends(get_admin_user) +): + log.info( + f"Updating reranking model: {app.state.RAG_RERANKING_MODEL} to {form_data.reranking_model}" + ) + try: + app.state.RAG_RERANKING_MODEL = form_data.reranking_model + app.state.sentence_transformer_rf = sentence_transformers.CrossEncoder( + app.state.RAG_RERANKING_MODEL, + device=DEVICE_TYPE, + ) + + return { + "status": True, + "reranking_model": app.state.RAG_RERANKING_MODEL, + } + except Exception as e: + log.exception(f"Problem updating reranking model: {e}") + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=ERROR_MESSAGES.DEFAULT(e), + ) + + @app.get("/config") async def get_rag_config(user=Depends(get_admin_user)): return { @@ -286,34 +331,21 @@ def query_doc_handler( user=Depends(get_current_user), ): try: - if app.state.RAG_EMBEDDING_ENGINE == "": - query_embeddings = app.state.sentence_transformer_ef.encode( - form_data.query - ).tolist() - elif app.state.RAG_EMBEDDING_ENGINE == "ollama": - query_embeddings = generate_ollama_embeddings( - GenerateEmbeddingsForm( - **{ - "model": app.state.RAG_EMBEDDING_MODEL, - "prompt": form_data.query, - } - ) - ) - elif app.state.RAG_EMBEDDING_ENGINE == "openai": - query_embeddings = generate_openai_embeddings( - model=app.state.RAG_EMBEDDING_MODEL, - text=form_data.query, - key=app.state.OPENAI_API_KEY, - url=app.state.OPENAI_API_BASE_URL, - ) + embeddings_function = 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, + ) return query_embeddings_doc( collection_name=form_data.collection_name, query=form_data.query, - query_embeddings=query_embeddings, k=form_data.k if form_data.k else app.state.TOP_K, + embeddings_function=embeddings_function, + reranking_function=app.state.sentence_transformer_rf, ) - except Exception as e: log.exception(e) raise HTTPException( @@ -334,33 +366,21 @@ def query_collection_handler( user=Depends(get_current_user), ): try: - if app.state.RAG_EMBEDDING_ENGINE == "": - query_embeddings = app.state.sentence_transformer_ef.encode( - form_data.query - ).tolist() - elif app.state.RAG_EMBEDDING_ENGINE == "ollama": - query_embeddings = generate_ollama_embeddings( - GenerateEmbeddingsForm( - **{ - "model": app.state.RAG_EMBEDDING_MODEL, - "prompt": form_data.query, - } - ) - ) - elif app.state.RAG_EMBEDDING_ENGINE == "openai": - query_embeddings = generate_openai_embeddings( - model=app.state.RAG_EMBEDDING_MODEL, - text=form_data.query, - key=app.state.OPENAI_API_KEY, - url=app.state.OPENAI_API_BASE_URL, - ) + embeddings_function = 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, + ) return query_embeddings_collection( collection_names=form_data.collection_names, - query_embeddings=query_embeddings, + query=form_data.query, k=form_data.k if form_data.k else app.state.TOP_K, + embeddings_function=embeddings_function, + reranking_function=app.state.sentence_transformer_rf, ) - except Exception as e: log.exception(e) raise HTTPException( @@ -427,8 +447,6 @@ def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> b log.info(f"store_docs_in_vector_db {docs} {collection_name}") texts = [doc.page_content for doc in docs] - texts = list(map(lambda x: x.replace("\n", " "), texts)) - metadatas = [doc.metadata for doc in docs] try: @@ -440,26 +458,20 @@ def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> b collection = CHROMA_CLIENT.create_collection(name=collection_name) + 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, + ) + + embedding_texts = list(map(lambda x: x.replace("\n", " "), texts)) if app.state.RAG_EMBEDDING_ENGINE == "": - embeddings = app.state.sentence_transformer_ef.encode(texts).tolist() - elif app.state.RAG_EMBEDDING_ENGINE == "ollama": + embeddings = embedding_func(embedding_texts) + else: embeddings = [ - generate_ollama_embeddings( - GenerateEmbeddingsForm( - **{"model": app.state.RAG_EMBEDDING_MODEL, "prompt": text} - ) - ) - for text in texts - ] - elif app.state.RAG_EMBEDDING_ENGINE == "openai": - embeddings = [ - generate_openai_embeddings( - model=app.state.RAG_EMBEDDING_MODEL, - text=text, - key=app.state.OPENAI_API_KEY, - url=app.state.OPENAI_API_BASE_URL, - ) - for text in texts + embedding_func(embedding_texts) for text in texts ] for batch in create_batches( diff --git a/backend/apps/rag/utils.py b/backend/apps/rag/utils.py index 0ce299279..22d5ece8b 100644 --- a/backend/apps/rag/utils.py +++ b/backend/apps/rag/utils.py @@ -1,5 +1,8 @@ import logging import requests +import operator + +import sentence_transformers from typing import List @@ -8,6 +11,11 @@ from apps.ollama.main import ( GenerateEmbeddingsForm, ) +from langchain.retrievers import ( + BM25Retriever, + EnsembleRetriever, +) + from config import SRC_LOG_LEVELS, CHROMA_CLIENT @@ -15,60 +23,96 @@ 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, + 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, + # keyword search + documents = collection.get() # get all documents + bm25_retriever = BM25Retriever.from_texts( + texts=documents.get("documents"), + metadatas=documents.get("metadatas"), + ) + bm25_retriever.k = k + + # semantic search (vector) + chroma_retriever = ChromaRetriever( + collection=collection, + k=k, + embeddings_function=embeddings_function, ) - log.info(f"query_embeddings_doc:result {result}") + # hybrid search (ensemble) + ensemble_retriever = EnsembleRetriever( + retrievers=[bm25_retriever, chroma_retriever], + weights=[0.6, 0.4] + ) + + documents = ensemble_retriever.invoke(query) + result = query_results_rank( + query=query, + documents=documents, + k=k, + reranking_function=reranking_function, + ) + result = { + "distances": [[d[1].item() for d in result]], + "documents": [[d[0].page_content for d in result]], + "metadatas": [[d[0].metadata for d in result]], + } + return result except Exception as e: raise e +def query_results_rank(query: str, documents, k: int, reranking_function): + scores = reranking_function.predict([(query, doc.page_content) for doc in documents]) + docs_with_scores = list(zip(documents, scores)) + result = sorted(docs_with_scores, key=operator.itemgetter(1), reverse=True) + return result[: 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) + # Create a list of tuples (distance, document, metadata) combined = list( - zip(combined_distances, combined_ids, combined_metadatas, combined_documents) + zip(combined_distances, combined_documents, combined_metadatas) ) # Sort the list based on distances combined.sort(key=lambda x: x[0]) # 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], "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)) + ] diff --git a/backend/config.py b/backend/config.py index 17f8f91bf..29284667b 100644 --- a/backend/config.py +++ b/backend/config.py @@ -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") diff --git a/backend/main.py b/backend/main.py index 4b1809a25..655bdb6da 100644 --- a/backend/main.py +++ b/backend/main.py @@ -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, ) diff --git a/src/lib/apis/rag/index.ts b/src/lib/apis/rag/index.ts index 8a63b69ca..481d286a3 100644 --- a/src/lib/apis/rag/index.ts +++ b/src/lib/apis/rag/index.ts @@ -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 ${token}` + }, + body: JSON.stringify({ + ...payload + }) + }) + .then(async (res) => { + if (!res.ok) throw await res.json(); + return res.json(); + }) + .catch((err) => { + console.log(err); + error = err.detail; + return null; + }); + + if (error) { + throw error; + } + + return res; +}; \ No newline at end of file diff --git a/src/lib/components/documents/Settings/General.svelte b/src/lib/components/documents/Settings/General.svelte index 6abdda5af..e3e7543e7 100644 --- a/src/lib/components/documents/Settings/General.svelte +++ b/src/lib/components/documents/Settings/General.svelte @@ -8,7 +8,9 @@ updateQuerySettings, resetVectorDB, getEmbeddingConfig, - updateEmbeddingConfig + updateEmbeddingConfig, + getRerankingConfig, + updateRerankingConfig } from '$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 @@
+
+
{$i18n.t('Update Reranking Model')}
+ +
+
+ +
+ +
+
+ +
+
{$i18n.t('Scan for documents from {{path}}', { path: '/data/docs' })}