feat: hybrid search and reranking support

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
 

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
 

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]
 

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
 
+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.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,
@@ -233,6 +241,10 @@ async def update_reranking_config(
     )
     try:
         app.state.RAG_RERANKING_MODEL = form_data.reranking_model
+
+        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,
@@ -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
-            ]
 
         for batch in create_batches(
             api=CHROMA_CLIENT,

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,
 ):
@@ -34,7 +34,6 @@ 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
         bm25_retriever = BM25Retriever.from_texts(
             texts=documents.get("documents"),
@@ -42,30 +41,32 @@ def query_embeddings_doc(
         )
         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],
+            retrievers=[bm25_retriever, chroma_retriever], weights=[0.5, 0.5]
-            weights=[0.6, 0.4]
         )
 
-        documents = ensemble_retriever.invoke(query)
+        compressor = RerankCompressor(
-        result = query_results_rank(
+            embeddings_function=embeddings_function,
-            query=query,
-            documents=documents,
-            k=k,
             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]],
+            "distances": [[d.metadata.get("score") for d in result]],
-            "documents": [[d[0].page_content for d in result]],
+            "documents": [[d.page_content for d in result]],
-            "metadatas": [[d[0].metadata for d in result]],
+            "metadatas": [[d.metadata for d in result]],
         }
 
         return result
@@ -73,33 +74,29 @@ 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(
+    combined = list(zip(combined_distances, combined_documents, combined_metadatas))
-        zip(combined_distances, combined_documents, combined_metadatas)
-    )
 
     # Sort the list based on distances
     combined.sort(key=lambda x: x[0])
 
+    # We don't have anything :-(
+    if not combined:
+        sorted_distances = []
+        sorted_documents = []
+        sorted_metadatas = []
+    else:
         # Unzip the sorted list
         sorted_distances, sorted_documents, sorted_metadatas = zip(*combined)
 
@@ -109,22 +106,20 @@ def merge_and_sort_query_results(query_results, 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,
             )
@@ -162,8 +158,9 @@ def query_embeddings_function(
 ):
     if embedding_engine == "":
         return lambda query: embedding_function.encode(query).tolist()
-    elif embedding_engine == "ollama":
+    elif embedding_engine in ["ollama", "openai"]:
-        return lambda query: generate_ollama_embeddings(
+        if embedding_engine == "ollama":
+            func = lambda query: generate_ollama_embeddings(
                 GenerateEmbeddingsForm(
                     **{
                         "model": embedding_model,
@@ -172,19 +169,28 @@ def query_embeddings_function(
                 )
             )
         elif embedding_engine == "openai":
-        return lambda query: generate_openai_embeddings(
+            func = lambda query: generate_openai_embeddings(
                 model=embedding_model,
                 text=query,
                 key=openai_key,
                 url=openai_url,
             )
 
+        def generate_multiple(query, f):
+            if isinstance(query, list):
+                return [f(q) for q in query]
+            else:
+                return f(query)
+
+        return lambda query: generate_multiple(query, func)
+
 
 def rag_messages(
     docs,
     messages,
     template,
     k,
+    r,
     embedding_engine,
     embedding_model,
     embedding_function,
@@ -221,16 +227,6 @@ def rag_messages(
         content_type = None
         query = ""
 
-    relevant_contexts = []
-
-    for doc in docs:
-        context = None
-
-        try:
-
-            if doc["type"] == "text":
-                context = doc["content"]
-            else:
     embeddings_function = query_embeddings_function(
         embedding_engine,
         embedding_model,
@@ -239,11 +235,32 @@ def rag_messages(
         openai_url,
     )
 
-                if doc["type"] == "collection":
+    extracted_collections = []
+    relevant_contexts = []
+
+    for doc in docs:
+        context = None
+
+        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"]
+            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,
                 )
@@ -252,22 +269,26 @@ def rag_messages(
                     collection_name=doc["collection_name"],
                     query=query,
                     k=k,
+                    r=r,
                     embeddings_function=embeddings_function,
                     reranking_function=reranking_function,
                 )
-
         except Exception as e:
             log.exception(e)
             context = None
 
+        if context:
             relevant_contexts.append(context)
 
+        extracted_collections.extend(collection)
+
     log.debug(f"relevant_contexts: {relevant_contexts}")
 
     context_string = ""
     for context in relevant_contexts:
-        if context:
+        items = context["documents"][0]
-            context_string += " ".join(context["documents"][0]) + "\n"
+        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

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(
@@ -431,9 +434,8 @@ 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 = os.environ.get("RAG_RERANKING_MODEL", "")
-    "RAG_RERANKING_MODEL", "BAAI/bge-reranker-v2-m3"
+if not RAG_RERANKING_MODEL == "":
-)
     log.info(f"Reranking model set: {RAG_RERANKING_MODEL}"),
 
 RAG_RERANKING_MODEL_TRUST_REMOTE_CODE = (
@@ -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)
 

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.OPENAI_API_KEY,
-                    rag_app.state.RAG_OPENAI_API_BASE_URL,
+                    rag_app.state.OPENAI_API_BASE_URL,
                 )
                 del data["docs"]
 

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;
 };
 

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,

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 @@
 					</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