Generate language model reply using agent prompts

• ❓ Inputs: 👄 Language Intelligence Provider, 🔑 API Key, 🤖 agent template name, 🧠 model name and options, and 🆎 context variables for that agent
• 🤖 Agent Instruction Templates: LangHub template or custom: question(query, chat_history), summarize-bullets(article), summarize(article), suggest-followups(chat_history, article), answer-cite-sources(context, chat_history, query), query-resolution(chat_history, query), knowledge-graph-nodes(query, article), summary-longtext(summaries)
• 🧠 How Language Models Work: Language models learn from billions of text examples to

identify statistical patterns and structures across diverse sources, converting words into high-dimensional vectors—numerical lists that capture meaning and relationships between concepts. These mathematical representations allow models to understand that "king/queen" share properties and "Paris/France" mirrors "Tokyo/Japan" through their transformer architecture, a neural network backbone that processes information through multiple layers of analysis. The attention mechanism enables the system to dynamically focus on relevant parts of input text when generating each word, maintaining context like humans tracking conversation threads, while calculating probability scores across the entire vocabulary for each word position based on processed context. Rather than retrieving stored responses, models create novel text by selecting the most probable words given learned patterns, maintaining coherence across long passages while adapting to specific prompt nuances through deep pattern recognition.
Self-Attention: Each word creates three representations: Query (what it's looking for), Key (what it offers), and Value (its actual content). For example, in "The cat sat on the mat," the word "cat" has a Query vector that searches for actions, a Key vector that advertises itself as a subject, and a Value vector containing its semantic meaning as an animal. The attention mechanism calculates how much "cat" should focus on other words by comparing its Query with their Keys - finding high similarity with "sat" (the action) - then combines the corresponding Value vectors to create a contextualized representation where "cat" now understands it's the one doing the sitting.

• 📚 Learning Resources: LLM Training Example, LangChain ReactAgent Tools, Hugging Face Tutorials, OpenAI Cookbook, Transformer Overview, Building Transformer Guide, PyTorch Overview

👄 Language Intelligence Providers (LIPs)

curl -X POST "https://loading/agents" \
  -H "Content-Type: application/json" \
  -d '{
    "provider": "groq",
    "key": "string"
  }'

{
  "content": "string",
  "extact": {}
}

{
  "error": "string"
}