ضمن تشکر از Gemini Advanced 1.5 Pro with Deep Research که این ریپورت رو برامون آماده کرد
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@ai_python

ظاهرن یکی از برتری های فعلی docling نسبت به MarkItDown اینه که داکلینگ توی LlamaIndex و LangChain هم Integrate شده 🔗

یکی از کتاب های مناسب برای مباحث پایه ریاضی مورد نیاز در علوم کامپیوتر

@ai_python

https://courses.csail.mit.edu/6.042/spring18/mcs.pdf


➕ Linear algebra
➕ Series
➕ Logic
➕ Probability
➕ Number theory
➕ Graphs

@ai_python

هر ری اکتی ، ری اکت نیست 🤪

@ai_python

https://cloud.google.com/bigquery/docs/vector-index-text-search-tutorial

Perform semantic search and retrieval-augmented generation

👉 This tutorial guides you through the end-to-end process of creating and using text embeddings, including using vector indexes to improve search performance.

This tutorial covers the following tasks:

🖥 Creating a BigQuery ML remote model over a Vertex AI embedding model.

🖥 Using the remote model with the ML.GENERATE_EMBEDDING function to generate embeddings from text in a BigQuery table.

🖥 Creating a vector index to index the embeddings.

🖥 Using the VECTOR_SEARCH function with the embeddings to search for similar text.

🖥 Perform retrieval-augmented generation (RAG) by generating text with the ML.GENERATE_TEXT function, and using vector search results to augment the prompt input and improve results.

This tutorial uses the BigQuery public table patents-public-data.google_patents_research.publications.