quantumalchemy

using config @ https://github.com/chrisdiana/cms.js/wiki/Server-Mode fo NGINX not picking up the urls pages or posts ie /#/posts nothing shown using example

re: hit-test - Gltf2Node - anything other than sunflower.gltf not working

4

re: using webxr-samples/hit-test.html Is there some sort of special specifications one needs to swap another gltf file? only works with "sunflower.gltf " size, scale, vert ? Thanks

RE FAIL: Downloading precompiled headless Chromium binary (stable channel) for AWS Lambda.

4

Failed at the @serverless-chrome/[email protected] postinstall script. >> { Error: read ECONNRESET at TLSWrap.onStreamRead (internal/stream_base_commons.js:111:27) errno: 'ECONNRESET', code: 'ECONNRESET', syscall: 'read' } npm WARN enoent ENOENT: no such file or directory,...

Colab ?

5

Anyone get a ipynb setup to run this? I don't have enough ram on my laptop to run .. Tried installing on colab but no luck .. wouldnt load the...

cpu: No Errors But No Answer

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what are the min req for cpu ? I ran this on i7 32g ram but ran for over 20min with no answer / then killed it used the .env...

colab?

1

can this be installed in a colab? anyone get this working?

Integration with a vector db for rag ?

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is there a plan to integrate this tool -- for a vector database ie chroma db?

NoSuchElementException XPATH

2

Love this project! .. scraping OP is like the holy grail of scraping! Its a cat and mouse game.. OP always changing their dom -- will see if I can...

### System Info docker run --name hf-embed -p 8083:80 --network AI -v /home/jec/Docker/python_AI/memgpt/hf_emb_data:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.1 --model-id BAAI/bge-large-en-v1.5 --hostname AI docker run --name hf-embed -p 8083:80 --network host -v /home/jec/Docker/python_AI/memgpt/hf_emb_data:/data...

How to return json only in results? from ragatouille import RAGPretrainedModel query = ["query?"] RAG = RAGPretrainedModel.from_index(index_path) results = RAG.search(query,k=2) print(results) >>> Searcher loaded! #> QueryTokenizer.tensorize(batch_text[0], batch_background[0], bsize) == #>...