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feat: hybrid search

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Steven Kreitzer 2024-04-22 15:49:58 -05:00 committed by Steven Kreitzer
parent f3e5700d49
commit 4e0b32b505
7 changed files with 406 additions and 110 deletions
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11
Dockerfile
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@ -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

142
backend/apps/rag/main.py
View file

@ -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(

187
backend/apps/rag/utils.py
View file

@ -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))
]

9
backend/config.py
View file

@ -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")

1
backend/main.py
View file

@ -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,
)

61
src/lib/apis/rag/index.ts
View file

@ -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;
};

105
src/lib/components/documents/Settings/General.svelte
View file

@ -8,7 +8,9 @@
updateQuerySettings,
resetVectorDB,
getEmbeddingConfig,
updateEmbeddingConfig
updateEmbeddingConfig,
getRerankingConfig,
updateRerankingConfig
} from '$lib/apis/rag';
import { documents, models } from '$lib/stores';
@ -23,11 +25,13 @@
let scanDirLoading = false;
let updateEmbeddingModelLoading = false;
let updateRerankingModelLoading = false;
let showResetConfirm = false;
let embeddingEngine = '';
let embeddingModel = '';
let rerankingModel = '';
let OpenAIKey = '';
let OpenAIUrl = '';
@ -115,6 +119,29 @@
}
};
const rerankingModelUpdateHandler = async () => {
console.log('Update reranking model attempt:', rerankingModel);
updateRerankingModelLoading = true;
const res = await updateRerankingConfig(localStorage.token, {
reranking_model: rerankingModel,
}).catch(async (error) => {
toast.error(error);
await setRerankingConfig();
return null;
});
updateRerankingModelLoading = false;
if (res) {
console.log('rerankingModelUpdateHandler:', res);
if (res.status === true) {
toast.success($i18n.t('Reranking model set to "{{reranking_model}}"', res), {
duration: 1000 * 10
});
}
}
};
const submitHandler = async () => {
const res = await updateRAGConfig(localStorage.token, {
pdf_extract_images: pdfExtractImages,
@ -138,6 +165,14 @@
}
};
const setRerankingConfig = async () => {
const rerankingConfig = await getRerankingConfig(localStorage.token);
if (rerankingConfig) {
rerankingModel = rerankingConfig.reranking_model;
}
};
onMount(async () => {
const res = await getRAGConfig(localStorage.token);
@ -149,6 +184,7 @@
}
await setEmbeddingConfig();
await setRerankingConfig();
querySettings = await getQuerySettings(localStorage.token);
});
@ -349,6 +385,73 @@
<hr class=" dark:border-gray-700 my-3" />
<div class=" ">
<div class=" mb-2 text-sm font-medium">{$i18n.t('Update Reranking Model')}</div>
<div class="flex w-full">
<div class="flex-1 mr-2">
<input
class="w-full rounded-lg py-2 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
placeholder={$i18n.t('Update reranking model (e.g. {{model}})', {
model: rerankingModel.slice(-40)
})}
bind:value={rerankingModel}
/>
</div>
<button
class="px-2.5 bg-gray-100 hover:bg-gray-200 text-gray-800 dark:bg-gray-850 dark:hover:bg-gray-800 dark:text-gray-100 rounded-lg transition"
on:click={() => {
rerankingModelUpdateHandler();
}}
disabled={updateRerankingModelLoading}
>
{#if updateRerankingModelLoading}
<div class="self-center">
<svg
class=" w-4 h-4"
viewBox="0 0 24 24"
fill="currentColor"
xmlns="http://www.w3.org/2000/svg"
><style>
.spinner_ajPY {
transform-origin: center;
animation: spinner_AtaB 0.75s infinite linear;
}
@keyframes spinner_AtaB {
100% {
transform: rotate(360deg);
}
}
</style><path
d="M12,1A11,11,0,1,0,23,12,11,11,0,0,0,12,1Zm0,19a8,8,0,1,1,8-8A8,8,0,0,1,12,20Z"
opacity=".25"
/><path
d="M10.14,1.16a11,11,0,0,0-9,8.92A1.59,1.59,0,0,0,2.46,12,1.52,1.52,0,0,0,4.11,10.7a8,8,0,0,1,6.66-6.61A1.42,1.42,0,0,0,12,2.69h0A1.57,1.57,0,0,0,10.14,1.16Z"
class="spinner_ajPY"
/></svg
>
</div>
{:else}
<svg
xmlns="http://www.w3.org/2000/svg"
viewBox="0 0 16 16"
fill="currentColor"
class="w-4 h-4"
>
<path
d="M8.75 2.75a.75.75 0 0 0-1.5 0v5.69L5.03 6.22a.75.75 0 0 0-1.06 1.06l3.5 3.5a.75.75 0 0 0 1.06 0l3.5-3.5a.75.75 0 0 0-1.06-1.06L8.75 8.44V2.75Z"
/>
<path
d="M3.5 9.75a.75.75 0 0 0-1.5 0v1.5A2.75 2.75 0 0 0 4.75 14h6.5A2.75 2.75 0 0 0 14 11.25v-1.5a.75.75 0 0 0-1.5 0v1.5c0 .69-.56 1.25-1.25 1.25h-6.5c-.69 0-1.25-.56-1.25-1.25v-1.5Z"
/>
</svg>
{/if}
</button>
</div>
</div>
<hr class=" dark:border-gray-700 my-3" />
<div class=" flex w-full justify-between">
<div class=" self-center text-xs font-medium">
{$i18n.t('Scan for documents from {{path}}', { path: '/data/docs' })}