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#4023 Keto Foods that Taste Good; +Visceral Fat Reduction

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#4023 Keto Foods that Taste Good; +Visceral Fat Reduction #4023  List the Keto foods that "taste good" (i.e. are not just healthy) While Keto has a reputation for being all about leafy greens and oil, the high-fat requirement actually makes it one of the most "flavor-forward" diets out there. Since fat carries flavor, many Keto-compliant foods are naturally indulgent. Here is a list of Keto foods that genuinely taste like a "cheat meal": 🥩 The Savory & Rich These are the heavy hitters that make the diet feel sustainable. Ribeye Steak: The king of keto meats. High fat content means it's tender, juicy, and flavorful without needing much more than salt and butter. Crispy Chicken Thighs: Swapping breasts for skin-on thighs gives you a much richer flavor and a satisfying crunch. Bacon: It’s the universal "flavor enhancer." Use it for snacks, wrapping asparagus, or topping salads. Smoked Brisket: Pure fat and protein. When smoked low and s...
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#4022 Docling/LangExtract of NASA-STD-8739.8B

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#4022 Docling/LangExtract of NASA-STD-8739.8B #4022  Provide a Python application using Docling and LangExtract to convert PDF file NASA-STD-8739.8B to text, then extract Section 4.3.1, Table 1 data To accomplish this task, we will use Docling (by IBM Research) for high-fidelity PDF parsing and LangExtract (or similar Pydantic-based extraction patterns) to isolate the specific table data from NASA-STD-8739.8B (Software Assurance and Software Safety Standard). Prerequisites You will need to install the necessary libraries. Docling handles the heavy lifting of converting complex PDF structures (like nested tables) into Markdown or JSON. Bash pip install docling langchain-core pydantic Python Implementation This script initializes the Docling converter, processes the PDF, and uses a schema-based approach to pull Table 1 from Section 4.3.1. import json from docling.document_converter import DocumentConverter from pydantic import BaseModel, Field from typing import List, Option...
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#4021 LangExtract

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#4021 LangExtract LangExtract is a Python library that uses LLMs to extract structured information from unstructured text documents https://github.com/google/langextract #4021  Tell me about LangExtract LangExtract is a sophisticated data extraction framework designed to bridge the gap between unstructured text and structured, machine-readable data.   While several tools exist in the "ETL for LLMs" space, LangExtract focuses specifically on precision and schema adherence using Large Language Models (LLMs). It is primarily used to pull specific entities, relationships, or attributes out of messy documents (like PDFs, emails, or logs) and format them into JSON, Pydantic objects, or database entries. ### Key Capabilities LangExtract is built on the philosophy that extraction should be predictable and verifiable.  Its core features include: Schema-Driven Extraction: You define a target structure (e.g., a Pydantic model or a JSON schema), and the tool ensures the LLM output...
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