diff --git a/CHANGELOG.md b/CHANGELOG.md index 1eaffc692..2e02cd753 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -10,6 +10,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Added - **🛠️ Improved Embedding Model Support**: You can now use any embedding model `sentence_transformers` supports. +- **🌟 Enhanced RAG Pipeline**: Added `BM25` hybrid searching with reranking model support using `sentence_transformers`. ## [0.1.120] - 2024-04-20 diff --git a/Dockerfile b/Dockerfile index d95ab986b..c43cd8cb3 100644 --- a/Dockerfile +++ b/Dockerfile @@ -10,7 +10,7 @@ ARG USE_CUDA_VER=cu121 # 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 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 +ARG USE_RERANKING_MODEL="" ######## WebUI frontend ######## FROM --platform=$BUILDPLATFORM node:21-alpine3.19 as build @@ -67,6 +67,9 @@ ENV WHISPER_MODEL="base" \ ENV RAG_EMBEDDING_MODEL="$USE_EMBEDDING_MODEL_DOCKER" \ RAG_RERANKING_MODEL="$USE_RERANKING_MODEL_DOCKER" \ SENTENCE_TRANSFORMERS_HOME="/app/backend/data/cache/embedding/models" + +## Hugging Face download cache ## +ENV HF_HOME="/app/backend/data/cache/embedding/models" #### Other models ########################################################## WORKDIR /app/backend @@ -102,13 +105,11 @@ RUN pip3 install uv && \ pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/$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/ollama/main.py b/backend/apps/ollama/main.py index 9258efa66..aeac6622d 100644 --- a/backend/apps/ollama/main.py +++ b/backend/apps/ollama/main.py @@ -92,6 +92,10 @@ async def get_ollama_api_urls(user=Depends(get_admin_user)): return {"OLLAMA_BASE_URLS": app.state.OLLAMA_BASE_URLS} +def get_ollama_endpoint(url_idx: int = 0): + return app.state.OLLAMA_BASE_URLS[url_idx] + + class UrlUpdateForm(BaseModel): urls: List[str] diff --git a/backend/apps/rag/main.py b/backend/apps/rag/main.py index 38ac5d4b8..4a7ff7baf 100644 --- a/backend/apps/rag/main.py +++ b/backend/apps/rag/main.py @@ -64,6 +64,8 @@ from config import ( SRC_LOG_LEVELS, UPLOAD_DIR, DOCS_DIR, + RAG_TOP_K, + RAG_RELEVANCE_THRESHOLD, RAG_EMBEDDING_ENGINE, RAG_EMBEDDING_MODEL, RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE, @@ -86,7 +88,8 @@ log.setLevel(SRC_LOG_LEVELS["RAG"]) app = FastAPI() -app.state.TOP_K = 4 +app.state.TOP_K = RAG_TOP_K +app.state.RELEVANCE_THRESHOLD = RAG_RELEVANCE_THRESHOLD app.state.CHUNK_SIZE = CHUNK_SIZE app.state.CHUNK_OVERLAP = CHUNK_OVERLAP @@ -107,12 +110,17 @@ if app.state.RAG_EMBEDDING_ENGINE == "": device=DEVICE_TYPE, trust_remote_code=RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE, ) +else: + app.state.sentence_transformer_ef = None -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, -) +if not app.state.RAG_RERANKING_MODEL == "": + 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, + ) +else: + app.state.sentence_transformer_rf = None origins = ["*"] @@ -185,22 +193,22 @@ async def update_embedding_config( ) try: app.state.RAG_EMBEDDING_ENGINE = form_data.embedding_engine + app.state.RAG_EMBEDDING_MODEL = form_data.embedding_model if app.state.RAG_EMBEDDING_ENGINE in ["ollama", "openai"]: - app.state.RAG_EMBEDDING_MODEL = form_data.embedding_model - app.state.sentence_transformer_ef = None - if form_data.openai_config != None: app.state.OPENAI_API_BASE_URL = form_data.openai_config.url app.state.OPENAI_API_KEY = form_data.openai_config.key + + app.state.sentence_transformer_ef = None else: - sentence_transformer_ef = sentence_transformers.SentenceTransformer( - app.state.RAG_EMBEDDING_MODEL, - device=DEVICE_TYPE, - trust_remote_code=RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE, + app.state.sentence_transformer_ef = ( + sentence_transformers.SentenceTransformer( + app.state.RAG_EMBEDDING_MODEL, + device=DEVICE_TYPE, + trust_remote_code=RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE, + ) ) - app.state.RAG_EMBEDDING_MODEL = form_data.embedding_model - app.state.sentence_transformer_ef = sentence_transformer_ef return { "status": True, @@ -222,7 +230,7 @@ async def update_embedding_config( class RerankingModelUpdateForm(BaseModel): reranking_model: str - + @app.post("/reranking/update") async def update_reranking_config( @@ -233,10 +241,14 @@ async def update_reranking_config( ) 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, - ) + + if app.state.RAG_RERANKING_MODEL == "": + app.state.sentence_transformer_rf = None + else: + app.state.sentence_transformer_rf = sentence_transformers.CrossEncoder( + app.state.RAG_RERANKING_MODEL, + device=DEVICE_TYPE, + ) return { "status": True, @@ -302,11 +314,13 @@ async def get_query_settings(user=Depends(get_admin_user)): "status": True, "template": app.state.RAG_TEMPLATE, "k": app.state.TOP_K, + "r": app.state.RELEVANCE_THRESHOLD, } class QuerySettingsForm(BaseModel): k: Optional[int] = None + r: Optional[float] = None template: Optional[str] = None @@ -316,6 +330,7 @@ async def update_query_settings( ): app.state.RAG_TEMPLATE = form_data.template if form_data.template else RAG_TEMPLATE app.state.TOP_K = form_data.k if form_data.k else 4 + app.state.RELEVANCE_THRESHOLD = form_data.r if form_data.r else 0.0 return {"status": True, "template": app.state.RAG_TEMPLATE} @@ -323,6 +338,7 @@ class QueryDocForm(BaseModel): collection_name: str query: str k: Optional[int] = None + r: Optional[float] = None @app.post("/query/doc") @@ -343,6 +359,7 @@ def query_doc_handler( collection_name=form_data.collection_name, query=form_data.query, k=form_data.k if form_data.k else app.state.TOP_K, + r=form_data.r if form_data.r else app.state.RELEVANCE_THRESHOLD, embeddings_function=embeddings_function, reranking_function=app.state.sentence_transformer_rf, ) @@ -358,6 +375,7 @@ class QueryCollectionsForm(BaseModel): collection_names: List[str] query: str k: Optional[int] = None + r: Optional[float] = None @app.post("/query/collection") @@ -378,6 +396,7 @@ def query_collection_handler( collection_names=form_data.collection_names, query=form_data.query, k=form_data.k if form_data.k else app.state.TOP_K, + r=form_data.r if form_data.r else app.state.RELEVANCE_THRESHOLD, embeddings_function=embeddings_function, reranking_function=app.state.sentence_transformer_rf, ) @@ -467,12 +486,7 @@ def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> b ) embedding_texts = list(map(lambda x: x.replace("\n", " "), texts)) - if app.state.RAG_EMBEDDING_ENGINE == "": - embeddings = embedding_func(embedding_texts) - else: - embeddings = [ - embedding_func(embedding_texts) for text in texts - ] + embeddings = embedding_func(embedding_texts) for batch in create_batches( api=CHROMA_CLIENT, diff --git a/backend/apps/rag/utils.py b/backend/apps/rag/utils.py index 22d5ece8b..f88335a3a 100644 --- a/backend/apps/rag/utils.py +++ b/backend/apps/rag/utils.py @@ -1,8 +1,5 @@ import logging import requests -import operator - -import sentence_transformers from typing import List @@ -11,8 +8,10 @@ from apps.ollama.main import ( GenerateEmbeddingsForm, ) +from langchain_core.documents import Document +from langchain_community.retrievers import BM25Retriever from langchain.retrievers import ( - BM25Retriever, + ContextualCompressionRetriever, EnsembleRetriever, ) @@ -27,6 +26,7 @@ def query_embeddings_doc( collection_name: str, query: str, k: int, + r: float, embeddings_function, reranking_function, ): @@ -34,38 +34,39 @@ def query_embeddings_doc( # if you use docker use the model from the environment variable collection = CHROMA_CLIENT.get_collection(name=collection_name) - # keyword search - documents = collection.get() # get all documents + 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, + top_n=k, ) - # hybrid search (ensemble) ensemble_retriever = EnsembleRetriever( - retrievers=[bm25_retriever, chroma_retriever], - weights=[0.6, 0.4] + retrievers=[bm25_retriever, chroma_retriever], weights=[0.5, 0.5] ) - documents = ensemble_retriever.invoke(query) - result = query_results_rank( - query=query, - documents=documents, - k=k, + compressor = RerankCompressor( + embeddings_function=embeddings_function, reranking_function=reranking_function, + r_score=r, + top_n=k, ) + + compression_retriever = ContextualCompressionRetriever( + base_compressor=compressor, base_retriever=ensemble_retriever + ) + + result = compression_retriever.invoke(query) result = { - "distances": [[d[1].item() for d in result]], - "documents": [[d[0].page_content for d in result]], - "metadatas": [[d[0].metadata for d in result]], + "distances": [[d.metadata.get("score") for d in result]], + "documents": [[d.page_content for d in result]], + "metadatas": [[d.metadata for d in result]], } return result @@ -73,58 +74,52 @@ def query_embeddings_doc( 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_distances = [] combined_documents = [] combined_metadatas = [] - # Combine data from each dictionary for data in query_results: combined_distances.extend(data["distances"][0]) combined_documents.extend(data["documents"][0]) combined_metadatas.extend(data["metadatas"][0]) # Create a list of tuples (distance, document, metadata) - combined = list( - zip(combined_distances, combined_documents, combined_metadatas) - ) + combined = list(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_documents, sorted_metadatas = zip(*combined) + # We don't have anything :-( + if not combined: + sorted_distances = [] + sorted_documents = [] + sorted_metadatas = [] + else: + # Unzip the sorted list + sorted_distances, sorted_documents, sorted_metadatas = zip(*combined) - # Slicing the lists to include only k elements - sorted_distances = list(sorted_distances)[:k] - sorted_documents = list(sorted_documents)[:k] - sorted_metadatas = list(sorted_metadatas)[:k] + # Slicing the lists to include only k elements + sorted_distances = list(sorted_distances)[:k] + sorted_documents = list(sorted_documents)[:k] + sorted_metadatas = list(sorted_metadatas)[:k] # Create the output dictionary - merged_query_results = { + result = { "distances": [sorted_distances], "documents": [sorted_documents], "metadatas": [sorted_metadatas], - "embeddings": None, - "uris": None, - "data": None, } - return merged_query_results + return result def query_embeddings_collection( collection_names: List[str], query: str, k: int, + r: float, embeddings_function, reranking_function, ): @@ -137,6 +132,7 @@ def query_embeddings_collection( collection_name=collection_name, query=query, k=k, + r=r, embeddings_function=embeddings_function, reranking_function=reranking_function, ) @@ -162,22 +158,31 @@ def query_embeddings_function( ): 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 in ["ollama", "openai"]: + if embedding_engine == "ollama": + func = 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, - ) + elif embedding_engine == "openai": + func = lambda query: generate_openai_embeddings( + model=embedding_model, + text=query, + key=openai_key, + url=openai_url, + ) + + def generate_multiple(query, f): + if isinstance(query, list): + return [f(q) for q in query] + else: + return f(query) + + return lambda query: generate_multiple(query, func) def rag_messages( @@ -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 diff --git a/backend/config.py b/backend/config.py index 269f0eedb..8242941d1 100644 --- a/backend/config.py +++ b/backend/config.py @@ -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 XML tags. +DEFAULT_RAG_TEMPLATE = """Use the following context as your learned knowledge, inside XML tags. [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) diff --git a/backend/main.py b/backend/main.py index 47a9ce310..1b92ae733 100644 --- a/backend/main.py +++ b/backend/main.py @@ -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"] diff --git a/src/lib/apis/rag/index.ts b/src/lib/apis/rag/index.ts index 481d286a3..5dfa3d3aa 100644 --- a/src/lib/apis/rag/index.ts +++ b/src/lib/apis/rag/index.ts @@ -123,6 +123,7 @@ export const getQuerySettings = async (token: string) => { type QuerySettings = { k: number | null; + r: number | null; template: string | null; }; @@ -473,4 +474,4 @@ export const updateRerankingConfig = async (token: string, payload: RerankingMod } return res; -}; \ No newline at end of file +}; diff --git a/src/lib/components/chat/Settings/Chats.svelte b/src/lib/components/chat/Settings/Chats.svelte index 26d263625..ea77e2371 100644 --- a/src/lib/components/chat/Settings/Chats.svelte +++ b/src/lib/components/chat/Settings/Chats.svelte @@ -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, diff --git a/src/lib/components/documents/Settings/General.svelte b/src/lib/components/documents/Settings/General.svelte index e3e7543e7..c6695bb6b 100644 --- a/src/lib/components/documents/Settings/General.svelte +++ b/src/lib/components/documents/Settings/General.svelte @@ -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 @@ +
+ {$i18n.t( + 'Note: If you choose a reranking model, it will use that to score and rerank instead of the embedding model.' + )} +
+
@@ -576,6 +583,26 @@
+
+
+
+ {$i18n.t('Relevance Threshold')} +
+ +
+ +
+
+
+
{$i18n.t('RAG Template')}