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Update __init__.py
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@ -150,33 +150,17 @@ class LoLLMsAPI(LollmsApplication):
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# prepare vectorization
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if self.config.data_vectorization_activate and self.config.use_discussions_history:
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try:
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ASCIIColors.yellow("Loading vectorized discussions")
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ASCIIColors.yellow("Loading long term memory")
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folder = self.lollms_paths.personal_databases_path/"vectorized_dbs"
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folder.mkdir(parents=True, exist_ok=True)
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self.discussions_store = TextVectorizer(
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vectorization_method=VectorizationMethod.TFIDF_VECTORIZER,#=VectorizationMethod.BM25_VECTORIZER,
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database_path=folder/self.config.db_path,
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data_visualization_method=VisualizationMethod.PCA,#VisualizationMethod.PCA,
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save_db=True
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)
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ASCIIColors.yellow("1- Exporting discussions")
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discussions = self.db.export_all_as_markdown_list_for_vectorization()
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ASCIIColors.yellow("2- Adding discussions to vectorizer")
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for (title,discussion) in discussions:
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if discussion!='' and title!='None':
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self.discussions_store.add_document(title, discussion, chunk_size=self.config.data_vectorization_chunk_size, overlap_size=self.config.data_vectorization_overlap_size, force_vectorize=False, add_as_a_bloc=False)
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ASCIIColors.yellow("3- Indexing database")
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self.discussions_store.index()
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ASCIIColors.yellow("4- Saving database")
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self.discussions_store.save_to_json()
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self.build_long_term_skills_memory()
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ASCIIColors.yellow("Ready")
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except Exception as ex:
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trace_exception(ex)
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self.discussions_store = None
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self.long_term_memory = None
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else:
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self.discussions_store = None
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self.long_term_memory = None
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# This is used to keep track of messages
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self.download_infos={}
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@ -1284,12 +1268,12 @@ class LoLLMsAPI(LollmsApplication):
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documentation += f"document chunk:\nchunk path: {infos[0]}\nchunk content:{doc}"
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# Check if there is discussion history to add to the prompt
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if self.config.use_discussions_history and self.discussions_store is not None:
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if self.config.use_discussions_history and self.long_term_memory is not None:
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if history=="":
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documentation="!@>History:\n"
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docs, sorted_similarities = self.discussions_store.recover_text(current_message.content, top_k=self.config.data_vectorization_nb_chunks)
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for doc, infos in zip(docs, sorted_similarities):
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history += f"!@>discussion chunk:\n!@>discussion title: {infos[0]}\nchunk content:{doc}"
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history="!@>previous discussions:\n"
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docs, sorted_similarities = self.long_term_memory.recover_text(current_message.content, top_k=self.config.data_vectorization_nb_chunks)
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for i,(doc, infos) in enumerate(zip(docs, sorted_similarities)):
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history += f"!@>previous discussion {i}:\n!@>discussion title:\n{infos[0]}\ndiscussion content:\n{doc}"
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# Add information about the user
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user_description=""
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