LLM - Allocated Requirements¶
This document outlines the requirements allocated to the LLM component within the AgentHeaven architecture.
1. Overview¶
The LLM component provides a unified interface for interacting with various language model providers.
2. Allocated Requirements¶
2.1 Functional Requirements¶
ID |
Requirement |
Implementation Details |
Priority |
|---|---|---|---|
FR-03 |
Unified LLM Interface |
Implements provider-agnostic LLM calls |
High |
FR-04 |
Unified KV Interface |
Caches LLM responses |
High |
FR-09 |
Multi-lingual Prompt Engine |
Executes prompts with LLMs |
High |
FR-35 |
LLM Parsers |
Implements Markdown and Keys parsers for response processing |
High |
FR-36 |
LLM Streaming |
Supports streaming responses from LLM providers |
High |
FR-37 |
LLM Tool Use |
Enables function calling and tool use capabilities |
High |
FR-38 |
LLM Embedding |
Provides text embedding generation |
High |
FR-39 |
LLM Batch Inference |
Supports batch processing of multiple prompts |
High |
FR-40 |
LLM Session Management |
Maintains conversation state and context |
High |
FR-41 |
LLM Chat CLI |
Provides command-line interface for LLM interactions |
Medium |
FR-42 |
LLM Configuration |
Supports flexible provider configuration and API key management |
Medium |
2.2 Non-Functional Requirements¶
ID |
Requirement |
Implementation Details |
Priority |
|---|---|---|---|
NFR-01 |
Performance |
Optimizes LLM call latency |
High |
NFR-14 |
Provider Compatibility |
Ensures compatibility with multiple LLM providers |
High |
NFR-15 |
Error Handling |
Provides robust error handling for API failures |
High |
NFR-16 |
Rate Limiting |
Implements rate limiting to avoid API quota exhaustion |
Medium |
NFR-17 |
Response Quality |
Ensures high-quality response parsing and validation |
Medium |
2.3 Cross-Component Dependencies¶
Component |
Description |
|---|---|
Prompts |
For template rendering |
Cache |
For storing LLM interactions |
KLBase |
For knowledge-augmented generation |
3. Implementation Notes¶
[Add implementation notes here]
4. Open Issues¶
[List any open issues or questions]
Further Exploration¶
Tip: For more information about the LLM system in AgentHeaven, see:
Main Guide (Python) - LLM Integration - LLM implementation and usage in Python
Configuration - LLM - LLM configuration options and settings
CLI Guide - LLM Inference - Command-line LLM inference tools
CLI Guide - LLM Session - Command-line LLM session management
Tip: For more information about AgentHeaven architecture, see:
Agent Component - Agent implementation and architecture
Cache System - System caching and experience management
Base UKF - Base Unified Knowledge Format implementation