Upgraded ui

This commit is contained in:
Saifeddine ALOUI 2024-12-03 00:39:14 +01:00
parent c3a347212f
commit ea9d8b7aa9
10 changed files with 180 additions and 192 deletions

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@ -60,7 +60,7 @@ Thank you for all users who tested this tool and helped making it more user frie
## Installation
### Automatic installation (UI)
If you are using Windows, just visit the release page, download the windows installer and install it.
If you are using Windows, just visit the release page, download the lollms_installer.bat.
### Automatic installation (Console)
Download the installation script from scripts folder and run it.

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app.py
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@ -70,7 +70,7 @@ packages: List[Tuple[str, str]] = [
("freedom_search", "0.1.9"),
("scrapemaster", "0.2.1"),
("lollms_client", "0.7.7"),
("lollmsvectordb", "1.1.6"),
("lollmsvectordb", "1.2.0"),
]
def check_pn_libs():

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@ -1,5 +1,5 @@
# =================== Lord Of Large Language Multimodal Systems Configuration file ===========================
version: 142
version: 143
# video viewing and news recovering
last_viewed_video: null
@ -280,7 +280,7 @@ audio_silenceTimer: 5000
# Data vectorization
rag_databases: [] # This is the list of paths to database sources. Each database is a folder containing data
rag_vectorizer: tfidf # possible values semantic, tfidf, openai
rag_vectorizer: semantic # possible values semantic, tfidf, openai
rag_vectorizer_model: sentence-transformers/bert-base-nli-mean-tokens # The model name if applicable
rag_vectorizer_parameters: null # Parameters of the model in json format
rag_chunk_size: 512 # number of tokens per chunk
@ -309,7 +309,7 @@ data_vectorization_visualize_on_vectorization: false
data_vectorization_activate: true # To activate/deactivate data vectorization
data_vectorization_method: "tfidf_vectorizer" #"model_embedding" or "tfidf_vectorizer"
data_visualization_method: "PCA" #"PCA" or "TSNE"
data_vectorization_sentense_transformer_model: "all-MiniLM-L6-v2" # you can use another model by setting its name here or its path
data_vectorization_sentense_transformer_model: "BAAI/bge-m3" # you can use another model by setting its name here or its path
data_vectorization_save_db: true # For each new session, new files
data_vectorization_chunk_size: 512 # chunk size

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web/dist/index.html vendored
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@ -6,8 +6,8 @@
<script src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-svg.js"></script>
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>LoLLMS WebUI</title>
<script type="module" crossorigin src="/assets/index-BZKF20f-.js"></script>
<link rel="stylesheet" crossorigin href="/assets/index-B_lSsKM1.css">
<script type="module" crossorigin src="/assets/index-BzGsYtS_.js"></script>
<link rel="stylesheet" crossorigin href="/assets/index-BMbzMYXz.css">
</head>
<body>
<div id="app"></div>

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@ -1146,20 +1146,8 @@
:disabled="configFile.rag_vectorizer === 'tfidf'"
>
<!-- Semantic Vectorizer Models -->
<option v-if="configFile.rag_vectorizer === 'semantic'" value="sentence-transformers/bert-base-nli-mean-tokens">sentence-transformers/bert-base-nli-mean-tokens</option>
<option v-if="configFile.rag_vectorizer === 'semantic'" value="bert-base-uncased">bert-base-uncased</option>
<option v-if="configFile.rag_vectorizer === 'semantic'" value="bert-base-multilingual-uncased">bert-base-multilingual-uncased</option>
<option v-if="configFile.rag_vectorizer === 'semantic'" value="bert-large-uncased">bert-large-uncased</option>
<option v-if="configFile.rag_vectorizer === 'semantic'" value="bert-large-uncased-whole-word-masking-finetuned-squad">bert-large-uncased-whole-word-masking-finetuned-squad</option>
<option v-if="configFile.rag_vectorizer === 'semantic'" value="distilbert-base-uncased">distilbert-base-uncased</option>
<option v-if="configFile.rag_vectorizer === 'semantic'" value="roberta-base">roberta-base</option>
<option v-if="configFile.rag_vectorizer === 'semantic'" value="roberta-large">roberta-large</option>
<option v-if="configFile.rag_vectorizer === 'semantic'" value="xlm-roberta-base">xlm-roberta-base</option>
<option v-if="configFile.rag_vectorizer === 'semantic'" value="xlm-roberta-large">xlm-roberta-large</option>
<option v-if="configFile.rag_vectorizer === 'semantic'" value="albert-base-v2">albert-base-v2</option>
<option v-if="configFile.rag_vectorizer === 'semantic'" value="albert-large-v2">albert-large-v2</option>
<option v-if="configFile.rag_vectorizer === 'semantic'" value="albert-xlarge-v2">albert-xlarge-v2</option>
<option v-if="configFile.rag_vectorizer === 'semantic'" value="albert-xxlarge-v2">albert-xxlarge-v2</option>
<option v-if="configFile.rag_vectorizer === 'semantic'" value="BAAI/bge-m3">BAAI/bge-m3</option>
<option v-if="configFile.rag_vectorizer === 'semantic'" value="sentence-transformers/all-MiniLM-L6-v2">sentence-transformers/all-MiniLM-L6-v2</option>
<option v-if="configFile.rag_vectorizer === 'semantic'" value="sentence-transformers/all-MiniLM-L12-v2">sentence-transformers/all-MiniLM-L12-v2</option>
<option v-if="configFile.rag_vectorizer === 'semantic'" value="sentence-transformers/all-distilroberta-v1">sentence-transformers/all-distilroberta-v1</option>

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