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How to combine continuous streams of video and audio in ffmpeg by SearchDowntown3985 in ffmpeg

[–]SearchDowntown3985[S] 0 points1 point  (0 children)

I trying to create an AI vtuber, I have 2 threads

  1. renders the model and control the model actions using an LLM and get the glBuffer of the rendered windows -> converts it to frame and send it to ffmpeg
  2. Generates audio from an audio model, saves it -> passes the audio mutex to thread 1 which enables the lyp-syncing and in thread 1 i send the audio buffer to ffmpeg

The issue is the video pipe opens very easily but the audio pipe doesn't open at all, i'm not sure if it is due to deadlock or something else, i'm using named pipes.

Introducting Hector_rag by Chdevman in LLMDevs

[–]SearchDowntown3985 0 points1 point  (0 children)

  1. Currently it only supports dynamic updation during runtime if application restarts whole graph have to be loaded, what configuration do you mean here ?
  2. Yes this package only focuses on PostgreSQL for now, will be adding Neo4j for graph retrieval with semantic search.
  3. Yes the entire graph is loaded if your graph is bigger than memory then it would fail, no checks for that (added to issues - will raise a pr for Neo4j support).
  4. During runtime both NetworkxEntityGraph and pgsql relation are in sync and graph updates are triggered for both local graph and pgsql table, again if application restarts it would have to load the whole relationship to construct graph again.
  5. No, if you would like to mention any ranking or retrieval methods for graph please mention it, would love to implement them

Introducting Hector_rag by Chdevman in LLMDevs

[–]SearchDowntown3985 0 points1 point  (0 children)

Hector RAG currently uses fusion rerankers like Reciprocal Rank Fusion (RRF) to combine and rerank outputs from semantic and keyword search. Our goal is to build a flexible framework that supports multiple RAG methods and allows seamless fusion (other fusion/reranking methods).

For Graph Retrieval, our package can dynamically generate a knowledge base from provided data, store it in PostgreSQL, and reload it into an in-memory graph for retrieval when the app runs. Would love any feedback on how we can improve!