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* Update cognify and the networkx client to prepare for running in Neo4j * Fix for openai model * Add the fix to the infra so that the models can be passed to the library. Enable llm_provider to be passed. * Auto graph generation now works with neo4j * Added fixes for both neo4j and networkx * Explicitly name semantic node connections * Added updated docs, readme, chunkers and updates to cognify * Make docs build trigger only when changes on it happen * Update docs, test git actions * Separate cognify logic into tasks * Introduce dspy knowledge graph extraction --------- Co-authored-by: Boris Arzentar <borisarzentar@gmail.com>
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Data Engineering and LLMOps
!!! tip "This is a work in progress and any feedback is welcome"
Table of Contents
Data Engineering
Data Engineering focuses on managing and analyzing big data. It revolves around five key aspects:
Volume
The size and amount of data that companies manage and analyze.
Value
The insights and patterns derived from data that lead to business benefits.
Variety
The diversity of data types, including unstructured, semi-structured, and raw data.
Velocity
The speed at which data is received, stored, and managed.
Veracity
The accuracy or truthfulness of data.
Large Language Model Operations (LLM Ops)
The emerging field of Large Language Model Operations (LLM Ops) inherits many practices from data engineering. LLM Ops involves the deployment, monitoring, and maintenance of systems using LLMs to manage and build new generation of AI powered applications.
For more in-depth information on LLM Ops, see Resource Name.