Open_NotebookLM_TLDW / config.txt
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[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_key = <kobold api key>
kobold_api_IP = http://127.0.0.1:5001/api/v1/generate
llama_api_key = <llama.cpp api key>
llama_api_IP = http://127.0.0.1:8080/completion
ooba_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>
vllm_api_IP = http://127.0.0.1:8000/v1/chat/completions
[Paths]
output_path = Results
logging_file = Logs
[Processing]
processing_choice = cuda
[Settings]
max_tokens = 1000
[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 = media_summary.db
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
[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
[Chunking]
method = words
max_size = 400
overlap = 200
adaptive = false
multi_level = false
language = english