Building a RAG chatbot with LangChain, Chroma, Hugging Face, and Arcee Conductor

In this video, we build a retrieval-augmented generation chatbot to query PDF files (research articles in this case). We use LangChain for orchestration, a Hugging Face model for embeddings, Chroma for vector search, Gradio for the user interface, and Arcee Conductor to optimize inference. We first run a local version and then push it to a Hugging Face Space.

Arcee Conductor (https://www.arcee.ai/product/arcee-conductor) is an inference platform that intelligently routes any query to the best model, efficiently delivering precise and cost-effective results for any task.

If you’d like to understand how Arcee AI can help your organization build scalable and cost-efficient AI solutions, don't hesitate to contact sales@arcee.ai or book a demo at https://www.arcee.ai/book-a-demo.

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* Arcee Conductor: https://conductor.arcee.ai
* Arcee Conductor product page: https://www.arcee.ai/product/arcee-conductor
* Code: https://gitlab.com/juliensimon/arcee-demos/-/tree/main/conductor-rag

Видео Building a RAG chatbot with LangChain, Chroma, Hugging Face, and Arcee Conductor канала Julien Simon