Data & Research · GitHub ·58 ★

Building-Natural-Language-and-LLM-Pipelines

Building RAG and Agentic Applications with Haystack 2.0, RAGAS and LangGraph 1.0 published by Packt

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Details

Author
PacktPublishing
Category
Data & Research
Platform
GitHub
Framework
haystack
Language
jupyter notebook
Stars
58
First indexed
2026-05-15
Last active
2026-01-01
Directory sync
2026-05-15
@PacktPublishing

Overview

Building RAG and Agentic Applications with Haystack 2.0, RAGAS and LangGraph 1.0 published by Packt

Quick start

git

git clone https://github.com/PacktPublishing/Building-Natural-Language-and-LLM-Pipelines

Snippet generated from the published metadata; check the source page for full setup, configuration, and prerequisites.

What Building-Natural-Language-and-LLM-Pipelines can do

llmsentimentraganaly
  • Llm — llm task automation.
  • Sentiment — sentiment task automation.
  • Rag — Retrieves grounded context before answering.
  • Analy — analy task automation.

Frequently asked questions

What is Building-Natural-Language-and-LLM-Pipelines?
Building RAG and Agentic Applications with Haystack 2.0, RAGAS and LangGraph 1.0 published by Packt
How do I install Building-Natural-Language-and-LLM-Pipelines?
Use git: `git clone https://github.com/PacktPublishing/Building-Natural-Language-and-LLM-Pipelines`. Full setup details on the source page linked above.
Is Building-Natural-Language-and-LLM-Pipelines open source?
Building-Natural-Language-and-LLM-Pipelines is published on GitHub.
What are alternatives to Building-Natural-Language-and-LLM-Pipelines?
Comparable agents include ragflow, autoresearch, OpenBB. Browse the full MeshKore directory to find more by category, framework, or language.

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Source & freshness

Profile data for Building-Natural-Language-and-LLM-Pipelines is sourced from GitHub, published by PacktPublishing.

Last scraped: · First indexed:

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