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Update config.txt

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  1. config.txt +4 -4
config.txt CHANGED
@@ -53,14 +53,14 @@ video_summarize_prompt = "Above is the transcript of a video. Please read throug
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  type = sqlite
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  sqlite_path = Databases/media_summary.db
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  backup_path = ./tldw_DB_Backups/
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- #Path to the backup location for the database. If the path does not exist, the backup will not be created.
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  elasticsearch_host = localhost
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  elasticsearch_port = 9200
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- # Additionally you can use elasticsearch as the database type, just replace `sqlite` with `elasticsearch` for `type` and provide the `elasticsearch_host` and `elasticsearch_port` of your configured ES instance.
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  chroma_db_path = Databases/chroma_db
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  prompts_db_path = Databases/prompts.db
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  rag_qa_db_path = Databases/RAG_QA_Chat.db
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  character_db_path = Databases/chatDB.db
 
 
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  [Embeddings]
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  embedding_provider = openai
@@ -77,13 +77,13 @@ overlap = 200
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  [Chunking]
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  method = words
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- # 'method' Can be 'words' / 'sentences' / 'paragraphs' / 'semantic' / 'tokens'
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  max_size = 400
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  overlap = 200
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  adaptive = false
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- # Use ntlk+punkt to split text into sentences and then ID average sentence length and set that as the chunk size
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  multi_level = false
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  language = english
 
 
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  [Metrics]
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  log_file_path =
 
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  type = sqlite
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  sqlite_path = Databases/media_summary.db
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  backup_path = ./tldw_DB_Backups/
 
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  elasticsearch_host = localhost
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  elasticsearch_port = 9200
 
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  chroma_db_path = Databases/chroma_db
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  prompts_db_path = Databases/prompts.db
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  rag_qa_db_path = Databases/RAG_QA_Chat.db
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  character_db_path = Databases/chatDB.db
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+ #Path to the backup location for the database. If the path does not exist, the backup will not be created.
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+ # Additionally you can use elasticsearch as the database type, just replace `sqlite` with `elasticsearch` for `type` and provide the `elasticsearch_host` and `elasticsearch_port` of your configured ES instance.
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  [Embeddings]
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  embedding_provider = openai
 
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  [Chunking]
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  method = words
 
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  max_size = 400
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  overlap = 200
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  adaptive = false
 
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  multi_level = false
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  language = english
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+ # 'method' Can be 'words' / 'sentences' / 'paragraphs' / 'semantic' / 'tokens'
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+ # Use ntlk+punkt to split text into sentences and then ID average sentence length and set that as the chunk size
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  [Metrics]
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  log_file_path =