Erika Shorten
@eshorten300 • 4,809 subscribers
Partnerships @weaviate_io | Diary about agents, LLM frameworks, and vector databases 🤪
Videos
Build a RAG Application from Scratch 🐕 This video covers the architecture behind Verba 0.3.0! The design behind Verba is to have an explicit manager for each core component of RAG pipelines (Read, Chunk, Embed, Retrieve, Generate) 1. ReaderManager: Load in your data (GitHubReader, PDFReader, SimpleReader, etc.) 2. ChunkManager: Chunk up your data into smaller bits (avoids hitting the token limit and results in better retrieval) 3. EmbeddingManager: Convert your data into embeddings (use OpenAI, Cohere, SentenceTransformer, etc.) 4. RetrieverManager: Retrieve the relevant context from your query 5. GenerationManager: Generate an answer from the retrieved chunks Test out Verba on the Weaviate AI Database docs/blogs here: Verba repo:
Erika Shorten155,273 Aufrufe • vor 2 Jahren
I'm excited to share my talk on building Agentic RAG systems at last week's event with Arize AI and Google! 🥳 My talk covered: 1. The differences between Vanilla RAG and Agentic RAG 2. The agent ecosystem and how you can build agents today 3. How Weaviate AI Database is building agents with Generative Feedback Loops (GFLs) This snippet presents the multi-agent paradigm for Agentic RAG. I hope you find this talk interesting and would love to know what you think! The slide deck and full video links are below.
Erika Shorten61,317 Aufrufe • vor 1 Jahr
Chunking your text data is a crucial step when building a RAG app ✂️ 1. You avoid hitting the token limit 2. Smaller chunks make the retriever more accurate I cover a few chunking methods and suggest a few frameworks that offer this (LlamaIndex 🦙, , deepset, makers of Haystack)
Erika Shorten28,451 Aufrufe • vor 2 Jahren
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