Local LightRAG: A GraphRAG Alternative but Fully Local with Ollama
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Top Comments (10)
I'd like to see a RAG system specifically built for working with large code bases. Most rag examples are optimised for document retrieval and citation, but I think there's a lot of room for advanced code modernisation / rewriting augmented with rag simply to enable working with large code bases (e.g. >100k tokens)
Great Video!!! Kindly make more videos on LightRAG and all the latest cool technologies please. You are my one stop source to learn and know about new technologies. Thank you so much!!
Thanks so much for your tutorials and demos. What if the data is related to products and I already process a txt with 200 products. Then next day the price is updated in 5 products. Do I need to process the whole list again? Does the old price will be remembered or it will be replaced from the rag?
What about an existing knowledge graph in neo4j for example ? Can you enrich an existing graph ?
wow, got it working, thank you so much. it took the better part of one day on my non-GPU laptop. next step is to repeat this with some cloud-based GPU horsepower.
Thank you , your videos are helping me a lot, please keep uploading such videos
Thanks for that. I am confused with the types of queries, what are naive vs local vs global vs hybrid ?
Bro, awesome video. Do you have any idea how Qwen extract entities from the text?
Thanks for the update of lightrag with ollama. I am curious if you feed lightrag with a bunch of documents and how it impact the query/inference performance. In standard rag we store the embeddings in a vectorstore. Is this possible with lightrag? It would be nice to see an example with more complex documents and the embeddings stored in a vectorstore with an open source llm (for cost savings ;-) )
What screen recording tool are you using?
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Top Comments (10)
I'd like to see a RAG system specifically built for working with large code bases. Most rag examples are optimised for document retrieval and citation, but I think there's a lot of room for advanced code modernisation / rewriting augmented with rag simply to enable working with large code bases (e.g. >100k tokens)
Great Video!!! Kindly make more videos on LightRAG and all the latest cool technologies please. You are my one stop source to learn and know about new technologies. Thank you so much!!
Thanks so much for your tutorials and demos. What if the data is related to products and I already process a txt with 200 products. Then next day the price is updated in 5 products. Do I need to process the whole list again? Does the old price will be remembered or it will be replaced from the rag?
What about an existing knowledge graph in neo4j for example ? Can you enrich an existing graph ?
wow, got it working, thank you so much. it took the better part of one day on my non-GPU laptop. next step is to repeat this with some cloud-based GPU horsepower.
Thank you , your videos are helping me a lot, please keep uploading such videos
Thanks for that. I am confused with the types of queries, what are naive vs local vs global vs hybrid ?
Bro, awesome video. Do you have any idea how Qwen extract entities from the text?
Thanks for the update of lightrag with ollama. I am curious if you feed lightrag with a bunch of documents and how it impact the query/inference performance. In standard rag we store the embeddings in a vectorstore. Is this possible with lightrag? It would be nice to see an example with more complex documents and the embeddings stored in a vectorstore with an open source llm (for cost savings ;-) )
What screen recording tool are you using?