lightrag.py and lightrag_ollama_demo.py dimensions are inconsistent. Make dimensions a global constant for consistency

[s-github-2]

Default values for v1.0.7 are inconsistent so was getting errors.
Fix was to change lightrag.py line 145 "dimensions" to 768 from the default 1536 to run the lightrag_ollama_demo..py on windows 11 machine with nividia rtx gpu.
Suggest making dimensions parameter for default_factory line 145 in lightrag.py a global constant that can be used by the demo files . Was getting errors that went away once I made this change and made default dimensions in the example and the lightrag.py consistent

[blakkd]

Just a global share to everyone: The dimension isn't equal to the context length (I don't say this for you @s-github-2 ;)).

You can find the dimension as [...].embedding_length, n_embd, or even hidden_size probably among others:

• in the config.json (eg. in the HF repo)
• in the Ollama model page (clicking on model to expand all the properties)

Or any other place providing those details :)

For example, Snowflake/snowflake-arctic-embed-l-v2.0 has:
A context length (=max_tokens=num_ctx, etc.) of 8192 and a dimension of 1024.

They aren't linked, as you can see with this other example:
mixedbread-ai/mxbai-embed-large-v1 has:
A context length of 512 (max_position_embeddings here) but a dimension of 1024 too.

To make it tidier, you can already memorize this:

Context Length

• max_tokens
• num_ctx
• max_position_embeddings

Dimension

• embedding_length
• n_embd
• hidden_size