The architecture of today's LLM applications
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# Definition of used LLM
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def graphPrompt(input: str, metadata={}, model="mixtral:latest"):
if model == None:
model = "mixtral:latest"
chunk_id = metadata.get('chunk_id', None)
# model_info = client.show(model_name=m... See more
# Definition of used LLM
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##########################################################################
def graphPrompt(input: str, metadata={}, model="mixtral:latest"):
if model == None:
model = "mixtral:latest"
chunk_id = metadata.get('chunk_id', None)
# model_info = client.show(model_name=m... See more
Knowledge Graph Extraction & Visualization with local LLM from Unstructured Text: a History example
The CoD prompt instructs highly powered LLMs such as GPT-4 to produce an initial sparse, verbose summary of an article containing only a few entities. It then iteratively identifies 1–3 missing entities and fuses them into a rewrite of the previous summary in the same number of words.
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
We found the ML engineering workflow to revolve around the following stages (Figure 1): (1) Data Preparation , which includes scheduled data acquisition, cleaning, labeling, and trans-formation, (2) Experimentation , which includes both data-driven and model-driven changes to increase overall ML performance, and is typically measured by metrics suc... See more
Shreya Shankar • "We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning.
All the recommendation systems you see at Twitter, Facebook, TikTok, YouTube, etc. have a similar high-level architecture.
They have a layered architecture that looks something like the following
They have a layered architecture that looks something like the following
- Retrieval - Narrow down the candidates of what to show a user to thousands of potential items
- First Stage Ranking - Apply a low-level ranking system to