Update config.txt

config.txt

@@ -17,26 +17,33 @@ mistral_model = mistral-large-latest
 mistral_api_key = <mistral_api_key>
 
 [Local-API]
-kobold_api_key = <kobold api key>
 kobold_api_IP = http://127.0.0.1:5001/api/v1/generate
-llama_api_key = <llama.cpp api key>
+kobold_api_key =
 llama_api_IP = http://127.0.0.1:8080/completion
-ooba_api_key = <ooba api key>
+llama_api_key =
+ooba_api_key =
 ooba_api_IP = http://127.0.0.1:5000/v1/chat/completions
 tabby_api_IP = http://127.0.0.1:5000/v1/chat/completions
-tabby_api_key = <tabbyapi key>
+tabby_api_key =
 vllm_api_IP = http://127.0.0.1:8000/v1/chat/completions
-
-[Paths]
-output_path = Results
-logging_file = Logs
-
+vllm_model =
+ollama_api_IP = http://127.0.0.1:11434/v1/chat/completions
+ollama_api_key =
+ollama_model = llama3
+aphrodite_api_IP = http://127.0.0.1:8080/completion
+aphrodite_api_key =
 
 [Processing]
 processing_choice = cuda
 
 [Settings]
-max_tokens = 1000
+chunk_duration = 30
+words_per_second = 3
+
+[Auto-Save]
+save_character_chats = False
+save_rag_chats = False
+
 
 [Prompts]
 prompt_sample = "What is the meaning of life?"
@@ -44,25 +51,45 @@ video_summarize_prompt = "Above is the transcript of a video. Please read throug
 
 [Database]
 type = sqlite
-sqlite_path = media_summary.db
+sqlite_path = Databases/media_summary.db
+backup_path = ./tldw_DB_Backups/
+#Path to the backup location for the database. If the path does not exist, the backup will not be created.
 elasticsearch_host = localhost
 elasticsearch_port = 9200
 # 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.
-chroma_db_path = chroma_db
+chroma_db_path = Databases/chroma_db
+prompts_db_path = Databases/prompts.db
+rag_qa_db_path = Databases/RAG_QA_Chat.db
+character_db_path = Databases/chatDB.db
 
 [Embeddings]
-provider = openai
-# Can be 'openai', 'local', or 'huggingface'
-model = text-embedding-3-small
-# Model name or path
-api_key = your_api_key_here
-api_url = http://localhost:8080/v1/embeddings
-# Only needed for 'local' provider
+embedding_provider = openai
+embedding_model = text-embedding-3-small
+onnx_model_path = ./App_Function_Libraries/models/onnx_models/
+model_dir = ./App_Function_Libraries/models/embedding_models
+embedding_api_url = http://localhost:8080/v1/embeddings
+embedding_api_key = your_api_key_here
+chunk_size = 400
+overlap = 200
+# 'embedding_provider' Can be 'openai', 'local', or 'huggingface'
+# `embedding_model` Set to the model name you want to use for embeddings. For OpenAI, this can be 'text-embedding-3-small', or 'text-embedding-3-large'.
+# huggingface: model = dunzhang/stella_en_400M_v5
 
 [Chunking]
 method = words
+# 'method' Can be 'words' / 'sentences' / 'paragraphs' / 'semantic' / 'tokens'
 max_size = 400
 overlap = 200
 adaptive = false
+# Use ntlk+punkt to split text into sentences and then ID average sentence length and set that as the chunk size
 multi_level = false
-language = english
+language = english
+
+[Metrics]
+log_file_path =
+#os.getenv("tldw_LOG_FILE_PATH", "tldw_app_logs.json")
+max_bytes =
+#int(os.getenv("tldw_LOG_MAX_BYTES", 10 * 1024 * 1024))  # 10 MB
+backup_count = 5
+#int(os.getenv("tldw_LOG_BACKUP_COUNT", 5))
+