Spaces:
Running
Running
File size: 3,308 Bytes
e03784a bcbb6ed e03784a a2369d8 e03784a a2369d8 e03784a a2369d8 e03784a a2369d8 e03784a a2369d8 e03784a bcbb6ed 3eb7d2f bcbb6ed 3eb7d2f 5981334 bcbb6ed a2369d8 bcbb6ed a2369d8 119c6b7 a2369d8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
[API]
anthropic_api_key = <anthropic_api_key
anthropic_model = claude-3-sonnet-20240229
cohere_api_key = <cohere_api_key>
cohere_model = command-r-plus
groq_api_key = <your_groq_api_key>
groq_model = llama3-70b-8192
openai_api_key = <openai_api_key>
openai_model = gpt-4o
huggingface_api_key = <huggingface_api_token>
huggingface_model = CohereForAI/c4ai-command-r-plus
openrouter_api_key = <openrouter_api_key>
openrouter_model = mistralai/mistral-7b-instruct:free
deepseek_api_key = <deepseek_api_key>
deepseek_model = deepseek-coder
mistral_model = mistral-large-latest
mistral_api_key = <mistral_api_key>
[Local-API]
kobold_api_IP = http://127.0.0.1:5001/api/v1/generate
kobold_api_key =
llama_api_IP = http://127.0.0.1:8080/completion
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 =
vllm_api_IP = http://127.0.0.1:8000/v1/chat/completions
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]
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?"
video_summarize_prompt = "Above is the transcript of a video. Please read through the transcript carefully. Identify the main topics that are discussed over the course of the transcript. Then, summarize the key points about each main topic in bullet points. The bullet points should cover the key information conveyed about each topic in the video, but should be much shorter than the full transcript. Please output your bullet point summary inside <bulletpoints> tags. Do not repeat yourself while writing the summary."
[Database]
type = sqlite
sqlite_path = /Databases/media_summary.db
elasticsearch_host = localhost
elasticsearch_port = 9200
chroma_db_path = chroma_db
backup_path = ./tldw_DB_Backups/
prompts_db_path = Databases/prompts.db
rag_qa_db_path = Databases/RAG_QA_Chat.db
character_db_path = Databases/chatDB.db
[Embeddings]
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
max_size = 400
overlap = 200
adaptive = false
multi_level = false
language = english
# 'method' Can be 'words' / 'sentences' / 'paragraphs' / 'semantic' / 'tokens'
# Use ntlk+punkt to split text into sentences and then ID average sentence length and set that as the chunk size
[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))
|