mirror of
https://github.com/ParisNeo/lollms-webui.git
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Upgraded ui
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@ -60,7 +60,7 @@ Thank you for all users who tested this tool and helped making it more user frie
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## Installation
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### Automatic installation (UI)
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If you are using Windows, just visit the release page, download the windows installer and install it.
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If you are using Windows, just visit the release page, download the lollms_installer.bat.
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### Automatic installation (Console)
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Download the installation script from scripts folder and run it.
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app.py
2
app.py
@ -70,7 +70,7 @@ packages: List[Tuple[str, str]] = [
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("freedom_search", "0.1.9"),
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("scrapemaster", "0.2.1"),
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("lollms_client", "0.7.7"),
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("lollmsvectordb", "1.1.6"),
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("lollmsvectordb", "1.2.0"),
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]
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def check_pn_libs():
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@ -1,5 +1,5 @@
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# =================== Lord Of Large Language Multimodal Systems Configuration file ===========================
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version: 142
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version: 143
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# video viewing and news recovering
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last_viewed_video: null
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@ -280,7 +280,7 @@ audio_silenceTimer: 5000
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# Data vectorization
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rag_databases: [] # This is the list of paths to database sources. Each database is a folder containing data
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rag_vectorizer: tfidf # possible values semantic, tfidf, openai
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rag_vectorizer: semantic # possible values semantic, tfidf, openai
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rag_vectorizer_model: sentence-transformers/bert-base-nli-mean-tokens # The model name if applicable
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rag_vectorizer_parameters: null # Parameters of the model in json format
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rag_chunk_size: 512 # number of tokens per chunk
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@ -309,7 +309,7 @@ data_vectorization_visualize_on_vectorization: false
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data_vectorization_activate: true # To activate/deactivate data vectorization
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data_vectorization_method: "tfidf_vectorizer" #"model_embedding" or "tfidf_vectorizer"
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data_visualization_method: "PCA" #"PCA" or "TSNE"
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data_vectorization_sentense_transformer_model: "all-MiniLM-L6-v2" # you can use another model by setting its name here or its path
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data_vectorization_sentense_transformer_model: "BAAI/bge-m3" # you can use another model by setting its name here or its path
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data_vectorization_save_db: true # For each new session, new files
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data_vectorization_chunk_size: 512 # chunk size
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@ -1 +1 @@
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Subproject commit 1e963ab4d490c69475b2de754def66e5b77cef86
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Subproject commit 3b808e65e61133116eba90eb17e19b7f5af79f88
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web/dist/index.html
vendored
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web/dist/index.html
vendored
@ -6,8 +6,8 @@
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<script src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-svg.js"></script>
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>LoLLMS WebUI</title>
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<script type="module" crossorigin src="/assets/index-BZKF20f-.js"></script>
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<link rel="stylesheet" crossorigin href="/assets/index-B_lSsKM1.css">
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<script type="module" crossorigin src="/assets/index-BzGsYtS_.js"></script>
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<link rel="stylesheet" crossorigin href="/assets/index-BMbzMYXz.css">
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</head>
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<body>
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<div id="app"></div>
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@ -1146,20 +1146,8 @@
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:disabled="configFile.rag_vectorizer === 'tfidf'"
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>
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<!-- Semantic Vectorizer Models -->
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<option v-if="configFile.rag_vectorizer === 'semantic'" value="sentence-transformers/bert-base-nli-mean-tokens">sentence-transformers/bert-base-nli-mean-tokens</option>
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<option v-if="configFile.rag_vectorizer === 'semantic'" value="bert-base-uncased">bert-base-uncased</option>
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<option v-if="configFile.rag_vectorizer === 'semantic'" value="bert-base-multilingual-uncased">bert-base-multilingual-uncased</option>
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<option v-if="configFile.rag_vectorizer === 'semantic'" value="bert-large-uncased">bert-large-uncased</option>
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<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>
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<option v-if="configFile.rag_vectorizer === 'semantic'" value="distilbert-base-uncased">distilbert-base-uncased</option>
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<option v-if="configFile.rag_vectorizer === 'semantic'" value="roberta-base">roberta-base</option>
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<option v-if="configFile.rag_vectorizer === 'semantic'" value="roberta-large">roberta-large</option>
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<option v-if="configFile.rag_vectorizer === 'semantic'" value="xlm-roberta-base">xlm-roberta-base</option>
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<option v-if="configFile.rag_vectorizer === 'semantic'" value="xlm-roberta-large">xlm-roberta-large</option>
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<option v-if="configFile.rag_vectorizer === 'semantic'" value="albert-base-v2">albert-base-v2</option>
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<option v-if="configFile.rag_vectorizer === 'semantic'" value="albert-large-v2">albert-large-v2</option>
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<option v-if="configFile.rag_vectorizer === 'semantic'" value="albert-xlarge-v2">albert-xlarge-v2</option>
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<option v-if="configFile.rag_vectorizer === 'semantic'" value="albert-xxlarge-v2">albert-xxlarge-v2</option>
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<option v-if="configFile.rag_vectorizer === 'semantic'" value="BAAI/bge-m3">BAAI/bge-m3</option>
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<option v-if="configFile.rag_vectorizer === 'semantic'" value="sentence-transformers/all-MiniLM-L6-v2">sentence-transformers/all-MiniLM-L6-v2</option>
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<option v-if="configFile.rag_vectorizer === 'semantic'" value="sentence-transformers/all-MiniLM-L12-v2">sentence-transformers/all-MiniLM-L12-v2</option>
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<option v-if="configFile.rag_vectorizer === 'semantic'" value="sentence-transformers/all-distilroberta-v1">sentence-transformers/all-distilroberta-v1</option>
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Subproject commit 98a0c5e95ec93511c00ecea42bf4950645eadf0d
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Subproject commit 41cdcc2b9ce96663e7bc1a72735c5eb9316e4ab6
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Subproject commit e7878ac85c9645d7e2dab640afdc202f29b6343c
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Subproject commit d95dbd4a491bf55295f4f3b2fe95b7cb09773b59
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