[ezra] Deep Dive keywords configuration (#830)
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# Deep Dive Relevance Keywords
# Define keywords and their weights for scoring entries
# Weight tiers: High (3.0x), Medium (1.5x), Low (0.5x)
weights:
high: 3.0
medium: 1.5
low: 0.5
# High-priority keywords (critical to Hermes/Timmy work)
high:
# Framework specific
- hermes
- timmy
- timmy foundation
- langchain
- langgraph
- crewai
- autogen
- autogpt
- babyagi
# Agent concepts
- llm agent
- llm agents
- agent framework
- agent frameworks
- multi-agent
- multi agent
- agent orchestration
- agentic
- agentic workflow
- agent system
# Tool use
- tool use
- tool calling
- function calling
- mcp
- model context protocol
- toolformer
- gorilla
# Reasoning
- chain-of-thought
- chain of thought
- reasoning
- planning
- reflection
- self-reflection
# RL and training
- reinforcement learning
- RLHF
- DPO
- GRPO
- PPO
- preference optimization
- alignment
# Fine tuning
- fine-tuning
- finetuning
- instruction tuning
- supervised fine-tuning
- sft
- peft
- lora
# Safety
- ai safety
- constitutional ai
- red teaming
- adversarial
# Medium-priority keywords (relevant to AI work)
medium:
# Core concepts
- llm
- large language model
- foundation model
- transformer
- attention mechanism
- prompting
- prompt engineering
- few-shot
- zero-shot
- in-context learning
# Architecture
- mixture of experts
- MoE
- retrieval augmented generation
- RAG
- vector database
- embeddings
- semantic search
# Inference
- inference optimization
- quantization
- model distillation
- knowledge distillation
- KV cache
- speculative decoding
- vLLM
# Open research
- open source
- open weight
- llama
- mistral
- qwen
- deepseek
# Companies
- openai
- anthropic
- claude
- gpt
- gemini
- deepmind
- google ai
# Low-priority keywords (general AI)
low:
- artificial intelligence
- machine learning
- deep learning
- neural network
- natural language processing
- NLP
- computer vision
# Source-specific bonuses (points added based on source)
source_bonuses:
arxiv_ai: 0.5
arxiv_cl: 0.5
arxiv_lg: 0.5
openai_blog: 0.3
anthropic_news: 0.4
deepmind_news: 0.3
# Filter settings
filter:
min_relevance_score: 2.0
max_entries_per_briefing: 15
embedding_model: "all-MiniLM-L6-v2"
use_embeddings: true