Merge pull request #4304 from adenhq/fix/init-config
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model selection + max_tokens in quickstart
This commit is contained in:
Timothy @aden
@@ -1,33 +1,8 @@
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"""Runtime configuration."""
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import json
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from dataclasses import dataclass, field
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from pathlib import Path
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def _load_preferred_model() -> str:
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"""Load preferred model from ~/.hive/configuration.json."""
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config_path = Path.home() / ".hive" / "configuration.json"
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if config_path.exists():
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try:
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with open(config_path) as f:
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config = json.load(f)
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llm = config.get("llm", {})
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if llm.get("provider") and llm.get("model"):
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return f"{llm['provider']}/{llm['model']}"
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except Exception:
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pass
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return "anthropic/claude-sonnet-4-20250514"
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@dataclass
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class RuntimeConfig:
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model: str = field(default_factory=_load_preferred_model)
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temperature: float = 0.7
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max_tokens: int = 40000
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api_key: str | None = None
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api_base: str | None = None
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from dataclasses import dataclass
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from framework.config import RuntimeConfig
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default_config = RuntimeConfig()
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@@ -0,0 +1,64 @@
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"""Shared Hive configuration utilities.
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Centralises reading of ~/.hive/configuration.json so that the runner
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and every agent template share one implementation instead of copy-pasting
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helper functions.
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"""
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import json
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Any
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from framework.graph.edge import DEFAULT_MAX_TOKENS
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# ---------------------------------------------------------------------------
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# Low-level config file access
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# ---------------------------------------------------------------------------
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HIVE_CONFIG_FILE = Path.home() / ".hive" / "configuration.json"
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def get_hive_config() -> dict[str, Any]:
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"""Load hive configuration from ~/.hive/configuration.json."""
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if not HIVE_CONFIG_FILE.exists():
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return {}
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try:
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with open(HIVE_CONFIG_FILE) as f:
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return json.load(f)
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except (json.JSONDecodeError, OSError):
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return {}
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# ---------------------------------------------------------------------------
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# Derived helpers
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# ---------------------------------------------------------------------------
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def get_preferred_model() -> str:
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"""Return the user's preferred LLM model string (e.g. 'anthropic/claude-sonnet-4-20250514')."""
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llm = get_hive_config().get("llm", {})
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if llm.get("provider") and llm.get("model"):
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return f"{llm['provider']}/{llm['model']}"
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return "anthropic/claude-sonnet-4-20250514"
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def get_max_tokens() -> int:
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"""Return the configured max_tokens, falling back to DEFAULT_MAX_TOKENS."""
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return get_hive_config().get("llm", {}).get("max_tokens", DEFAULT_MAX_TOKENS)
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# ---------------------------------------------------------------------------
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# RuntimeConfig – shared across agent templates
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# ---------------------------------------------------------------------------
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@dataclass
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class RuntimeConfig:
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"""Agent runtime configuration loaded from ~/.hive/configuration.json."""
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model: str = field(default_factory=get_preferred_model)
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temperature: float = 0.7
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max_tokens: int = field(default_factory=get_max_tokens)
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api_key: str | None = None
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api_base: str | None = None
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@@ -9,7 +9,7 @@ from framework.graph.client_io import (
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from framework.graph.code_sandbox import CodeSandbox, safe_eval, safe_exec
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from framework.graph.context_handoff import ContextHandoff, HandoffContext
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from framework.graph.conversation import ConversationStore, Message, NodeConversation
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from framework.graph.edge import EdgeCondition, EdgeSpec, GraphSpec
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from framework.graph.edge import DEFAULT_MAX_TOKENS, EdgeCondition, EdgeSpec, GraphSpec
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from framework.graph.event_loop_node import (
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EventLoopNode,
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JudgeProtocol,
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@@ -58,6 +58,7 @@ __all__ = [
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"EdgeSpec",
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"EdgeCondition",
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"GraphSpec",
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"DEFAULT_MAX_TOKENS",
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# Executor (fixed graph)
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"GraphExecutor",
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# Plan (flexible execution)
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@@ -24,10 +24,12 @@ given the current goal, context, and execution state.
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from enum import StrEnum
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from typing import Any
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from pydantic import BaseModel, Field
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from pydantic import BaseModel, Field, model_validator
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from framework.graph.safe_eval import safe_eval
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DEFAULT_MAX_TOKENS = 8192
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class EdgeCondition(StrEnum):
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"""When an edge should be traversed."""
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@@ -424,7 +426,7 @@ class GraphSpec(BaseModel):
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# Default LLM settings
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default_model: str = "claude-haiku-4-5-20251001"
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max_tokens: int = 1024
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max_tokens: int = Field(default=None) # resolved by _resolve_max_tokens validator
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# Cleanup LLM for JSON extraction fallback (fast/cheap model preferred)
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# If not set, uses CEREBRAS_API_KEY -> cerebras/llama-3.3-70b or
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@@ -447,6 +449,16 @@ class GraphSpec(BaseModel):
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model_config = {"extra": "allow"}
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@model_validator(mode="before")
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@classmethod
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def _resolve_max_tokens(cls, values: Any) -> Any:
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"""Resolve max_tokens from the global config store when not explicitly set."""
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if isinstance(values, dict) and values.get("max_tokens") is None:
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from framework.config import get_max_tokens
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values["max_tokens"] = get_max_tokens()
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return values
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def get_node(self, node_id: str) -> Any | None:
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"""Get a node by ID."""
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for node in self.nodes:
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@@ -8,8 +8,15 @@ from dataclasses import dataclass, field
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from pathlib import Path
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from typing import TYPE_CHECKING, Any
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from framework.config import get_hive_config, get_preferred_model
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from framework.graph import Goal
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from framework.graph.edge import AsyncEntryPointSpec, EdgeCondition, EdgeSpec, GraphSpec
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from framework.graph.edge import (
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DEFAULT_MAX_TOKENS,
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AsyncEntryPointSpec,
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EdgeCondition,
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EdgeSpec,
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GraphSpec,
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)
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from framework.graph.executor import ExecutionResult, GraphExecutor
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from framework.graph.node import NodeSpec
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from framework.llm.provider import LLMProvider, Tool
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@@ -28,9 +35,6 @@ if TYPE_CHECKING:
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logger = logging.getLogger(__name__)
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# Configuration paths
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HIVE_CONFIG_FILE = Path.home() / ".hive" / "configuration.json"
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def _ensure_credential_key_env() -> None:
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"""Load HIVE_CREDENTIAL_KEY from shell config if not already in environment.
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@@ -60,17 +64,6 @@ def _ensure_credential_key_env() -> None:
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CLAUDE_CREDENTIALS_FILE = Path.home() / ".claude" / ".credentials.json"
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def get_hive_config() -> dict[str, Any]:
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"""Load hive configuration from ~/.hive/configuration.json."""
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if not HIVE_CONFIG_FILE.exists():
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return {}
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try:
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with open(HIVE_CONFIG_FILE) as f:
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return json.load(f)
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except (json.JSONDecodeError, OSError):
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return {}
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def get_claude_code_token() -> str | None:
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"""
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Get the OAuth token from Claude Code subscription.
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@@ -268,11 +261,7 @@ class AgentRunner:
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@staticmethod
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def _resolve_default_model() -> str:
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"""Resolve the default model from ~/.hive/configuration.json."""
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config = get_hive_config()
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llm = config.get("llm", {})
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if llm.get("provider") and llm.get("model"):
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return f"{llm['provider']}/{llm['model']}"
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return "anthropic/claude-sonnet-4-20250514"
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return get_preferred_model()
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def __init__(
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self,
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@@ -425,7 +414,11 @@ class AgentRunner:
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if agent_config and hasattr(agent_config, "model"):
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model = agent_config.model
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max_tokens = getattr(agent_config, "max_tokens", 1024) if agent_config else 1024
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if agent_config and hasattr(agent_config, "max_tokens"):
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max_tokens = agent_config.max_tokens
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else:
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hive_config = get_hive_config()
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max_tokens = hive_config.get("llm", {}).get("max_tokens", DEFAULT_MAX_TOKENS)
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# Build GraphSpec from module-level variables
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graph = GraphSpec(
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+23
-2
@@ -5,12 +5,31 @@ Aden Hive is a Python-based agent framework. Configuration is handled through en
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## Configuration Overview
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```
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~/.hive/configuration.json (global defaults: provider, model, max_tokens)
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Environment variables (API keys, runtime flags)
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Agent config.py (per-agent settings: model, tools, storage)
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pyproject.toml (package metadata and dependencies)
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.mcp.json (MCP server connections)
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```
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## Global Configuration (~/.hive/configuration.json)
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The `quickstart.sh` script creates this file during setup. It stores the default LLM provider, model, and max_tokens used by all agents unless overridden in an agent's own `config.py`.
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```json
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{
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"llm": {
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"provider": "anthropic",
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"model": "claude-sonnet-4-5-20250929",
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"max_tokens": 8192,
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"api_key_env_var": "ANTHROPIC_API_KEY"
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},
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"created_at": "2026-01-15T12:00:00+00:00"
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}
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```
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The default `max_tokens` value (8192) is defined as `DEFAULT_MAX_TOKENS` in `framework.graph.edge` and re-exported from `framework.graph`. Each agent's `RuntimeConfig` reads from this file at startup. To change defaults, either re-run `quickstart.sh` or edit the file directly.
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## Environment Variables
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### LLM Providers (at least one required for real execution)
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@@ -61,14 +80,16 @@ Each agent package in `exports/` contains its own `config.py`:
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```python
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# exports/my_agent/config.py
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CONFIG = {
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"model": "claude-haiku-4-5-20251001", # Default LLM model
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"max_tokens": 4096,
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"model": "anthropic/claude-sonnet-4-5-20250929", # Default LLM model
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"max_tokens": 8192, # default: DEFAULT_MAX_TOKENS from framework.graph
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"temperature": 0.7,
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"tools": ["web_search", "pdf_read"], # MCP tools to enable
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"storage_path": "/tmp/my_agent", # Runtime data location
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}
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```
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If `model` or `max_tokens` are omitted, the agent loads defaults from `~/.hive/configuration.json`.
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### Agent Graph Specification
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Agent behavior is defined in `agent.json` (or constructed in `agent.py`):
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@@ -1,33 +1,8 @@
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"""Runtime configuration."""
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import json
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from dataclasses import dataclass, field
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from pathlib import Path
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def _load_preferred_model() -> str:
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"""Load preferred model from ~/.hive/configuration.json."""
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config_path = Path.home() / ".hive" / "configuration.json"
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if config_path.exists():
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try:
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with open(config_path) as f:
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config = json.load(f)
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llm = config.get("llm", {})
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if llm.get("provider") and llm.get("model"):
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return f"{llm['provider']}/{llm['model']}"
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except Exception:
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pass
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return "anthropic/claude-sonnet-4-20250514"
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@dataclass
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class RuntimeConfig:
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model: str = field(default_factory=_load_preferred_model)
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temperature: float = 0.7
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max_tokens: int = 40000
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api_key: str | None = None
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api_base: str | None = None
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from dataclasses import dataclass
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from framework.config import RuntimeConfig
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default_config = RuntimeConfig()
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@@ -1,33 +1,8 @@
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"""Runtime configuration."""
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import json
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from dataclasses import dataclass, field
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from pathlib import Path
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def _load_preferred_model() -> str:
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"""Load preferred model from ~/.hive/configuration.json."""
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config_path = Path.home() / ".hive" / "configuration.json"
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if config_path.exists():
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try:
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with open(config_path) as f:
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config = json.load(f)
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llm = config.get("llm", {})
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if llm.get("provider") and llm.get("model"):
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return f"{llm['provider']}/{llm['model']}"
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except Exception:
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pass
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return "anthropic/claude-sonnet-4-20250514"
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@dataclass
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class RuntimeConfig:
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model: str = field(default_factory=_load_preferred_model)
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temperature: float = 0.7
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max_tokens: int = 40000
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api_key: str | None = None
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api_base: str | None = None
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from dataclasses import dataclass
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from framework.config import RuntimeConfig
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default_config = RuntimeConfig()
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@@ -1,33 +1,8 @@
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"""Runtime configuration."""
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import json
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from dataclasses import dataclass, field
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from pathlib import Path
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|
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def _load_preferred_model() -> str:
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"""Load preferred model from ~/.hive/configuration.json."""
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config_path = Path.home() / ".hive" / "configuration.json"
|
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if config_path.exists():
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try:
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with open(config_path) as f:
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config = json.load(f)
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llm = config.get("llm", {})
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if llm.get("provider") and llm.get("model"):
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return f"{llm['provider']}/{llm['model']}"
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except Exception:
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pass
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return "anthropic/claude-sonnet-4-20250514"
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@dataclass
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class RuntimeConfig:
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model: str = field(default_factory=_load_preferred_model)
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temperature: float = 0.7
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max_tokens: int = 40000
|
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api_key: str | None = None
|
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api_base: str | None = None
|
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from dataclasses import dataclass
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from framework.config import RuntimeConfig
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default_config = RuntimeConfig()
|
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+242
-11
@@ -303,9 +303,9 @@ if [ "$USE_ASSOC_ARRAYS" = true ]; then
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)
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declare -A DEFAULT_MODELS=(
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["anthropic"]="claude-sonnet-4-5-20250929"
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["openai"]="gpt-4o"
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["gemini"]="gemini-3.0-flash-preview"
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["anthropic"]="claude-opus-4-6"
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["openai"]="gpt-5.2"
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["gemini"]="gemini-3-flash-preview"
|
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["groq"]="moonshotai/kimi-k2-instruct-0905"
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["cerebras"]="zai-glm-4.7"
|
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["mistral"]="mistral-large-latest"
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@@ -313,6 +313,65 @@ if [ "$USE_ASSOC_ARRAYS" = true ]; then
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["deepseek"]="deepseek-chat"
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)
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# Model choices per provider: composite-key associative arrays
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# Keys: "provider:index" -> value
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declare -A MODEL_CHOICES_ID=(
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["anthropic:0"]="claude-opus-4-6"
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["anthropic:1"]="claude-sonnet-4-5-20250929"
|
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["anthropic:2"]="claude-sonnet-4-20250514"
|
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["anthropic:3"]="claude-haiku-4-5-20251001"
|
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["openai:0"]="gpt-5.2"
|
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["openai:1"]="gpt-5-mini"
|
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["openai:2"]="gpt-5-nano"
|
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["gemini:0"]="gemini-3-flash-preview"
|
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["gemini:1"]="gemini-3-pro-preview"
|
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["groq:0"]="moonshotai/kimi-k2-instruct-0905"
|
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["groq:1"]="openai/gpt-oss-120b"
|
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["cerebras:0"]="zai-glm-4.7"
|
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["cerebras:1"]="qwen3-235b-a22b-instruct-2507"
|
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)
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declare -A MODEL_CHOICES_LABEL=(
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["anthropic:0"]="Opus 4.6 - Most capable (recommended)"
|
||||
["anthropic:1"]="Sonnet 4.5 - Best balance"
|
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["anthropic:2"]="Sonnet 4 - Fast + capable"
|
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["anthropic:3"]="Haiku 4.5 - Fast + cheap"
|
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["openai:0"]="GPT-5.2 - Most capable (recommended)"
|
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["openai:1"]="GPT-5 Mini - Fast + cheap"
|
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["openai:2"]="GPT-5 Nano - Fastest"
|
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["gemini:0"]="Gemini 3 Flash - Fast (recommended)"
|
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["gemini:1"]="Gemini 3 Pro - Best quality"
|
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["groq:0"]="Kimi K2 - Best quality (recommended)"
|
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["groq:1"]="GPT-OSS 120B - Fast reasoning"
|
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["cerebras:0"]="ZAI-GLM 4.7 - Best quality (recommended)"
|
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["cerebras:1"]="Qwen3 235B - Frontier reasoning"
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)
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||||
|
||||
# NOTE: 8192 should match DEFAULT_MAX_TOKENS in core/framework/graph/edge.py
|
||||
declare -A MODEL_CHOICES_MAXTOKENS=(
|
||||
["anthropic:0"]=8192
|
||||
["anthropic:1"]=8192
|
||||
["anthropic:2"]=8192
|
||||
["anthropic:3"]=8192
|
||||
["openai:0"]=16384
|
||||
["openai:1"]=16384
|
||||
["openai:2"]=16384
|
||||
["gemini:0"]=8192
|
||||
["gemini:1"]=8192
|
||||
["groq:0"]=8192
|
||||
["groq:1"]=8192
|
||||
["cerebras:0"]=8192
|
||||
["cerebras:1"]=8192
|
||||
)
|
||||
|
||||
declare -A MODEL_CHOICES_COUNT=(
|
||||
["anthropic"]=4
|
||||
["openai"]=3
|
||||
["gemini"]=2
|
||||
["groq"]=2
|
||||
["cerebras"]=2
|
||||
)
|
||||
|
||||
# Helper functions for Bash 4+
|
||||
get_provider_name() {
|
||||
echo "${PROVIDER_NAMES[$1]}"
|
||||
@@ -325,6 +384,22 @@ if [ "$USE_ASSOC_ARRAYS" = true ]; then
|
||||
get_default_model() {
|
||||
echo "${DEFAULT_MODELS[$1]}"
|
||||
}
|
||||
|
||||
get_model_choice_count() {
|
||||
echo "${MODEL_CHOICES_COUNT[$1]:-0}"
|
||||
}
|
||||
|
||||
get_model_choice_id() {
|
||||
echo "${MODEL_CHOICES_ID[$1:$2]}"
|
||||
}
|
||||
|
||||
get_model_choice_label() {
|
||||
echo "${MODEL_CHOICES_LABEL[$1:$2]}"
|
||||
}
|
||||
|
||||
get_model_choice_maxtokens() {
|
||||
echo "${MODEL_CHOICES_MAXTOKENS[$1:$2]}"
|
||||
}
|
||||
else
|
||||
# Bash 3.2 - use parallel indexed arrays
|
||||
PROVIDER_ENV_VARS=(ANTHROPIC_API_KEY OPENAI_API_KEY GEMINI_API_KEY GOOGLE_API_KEY GROQ_API_KEY CEREBRAS_API_KEY MISTRAL_API_KEY TOGETHER_API_KEY DEEPSEEK_API_KEY)
|
||||
@@ -333,7 +408,7 @@ else
|
||||
|
||||
# Default models by provider id (parallel arrays)
|
||||
MODEL_PROVIDER_IDS=(anthropic openai gemini groq cerebras mistral together_ai deepseek)
|
||||
MODEL_DEFAULTS=("claude-sonnet-4-5-20250929" "gpt-4o" "gemini-3.0-flash-preview" "moonshotai/kimi-k2-instruct-0905" "zai-glm-4.7" "mistral-large-latest" "meta-llama/Llama-3.3-70B-Instruct-Turbo" "deepseek-chat")
|
||||
MODEL_DEFAULTS=("claude-opus-4-6" "gpt-5.2" "gemini-3-flash-preview" "moonshotai/kimi-k2-instruct-0905" "zai-glm-4.7" "mistral-large-latest" "meta-llama/Llama-3.3-70B-Instruct-Turbo" "deepseek-chat")
|
||||
|
||||
# Helper: get provider display name for an env var
|
||||
get_provider_name() {
|
||||
@@ -373,6 +448,82 @@ else
|
||||
i=$((i + 1))
|
||||
done
|
||||
}
|
||||
|
||||
# Model choices per provider - flat parallel arrays with provider offsets
|
||||
# Provider order: anthropic(4), openai(3), gemini(2), groq(2), cerebras(2)
|
||||
MC_PROVIDERS=(anthropic anthropic anthropic anthropic openai openai openai gemini gemini groq groq cerebras cerebras)
|
||||
MC_IDS=("claude-opus-4-6" "claude-sonnet-4-5-20250929" "claude-sonnet-4-20250514" "claude-haiku-4-5-20251001" "gpt-5.2" "gpt-5-mini" "gpt-5-nano" "gemini-3-flash-preview" "gemini-3-pro-preview" "moonshotai/kimi-k2-instruct-0905" "openai/gpt-oss-120b" "zai-glm-4.7" "qwen3-235b-a22b-instruct-2507")
|
||||
MC_LABELS=("Opus 4.6 - Most capable (recommended)" "Sonnet 4.5 - Best balance" "Sonnet 4 - Fast + capable" "Haiku 4.5 - Fast + cheap" "GPT-5.2 - Most capable (recommended)" "GPT-5 Mini - Fast + cheap" "GPT-5 Nano - Fastest" "Gemini 3 Flash - Fast (recommended)" "Gemini 3 Pro - Best quality" "Kimi K2 - Best quality (recommended)" "GPT-OSS 120B - Fast reasoning" "ZAI-GLM 4.7 - Best quality (recommended)" "Qwen3 235B - Frontier reasoning")
|
||||
# NOTE: 8192 should match DEFAULT_MAX_TOKENS in core/framework/graph/edge.py
|
||||
MC_MAXTOKENS=(8192 8192 8192 8192 16384 16384 16384 8192 8192 8192 8192 8192 8192)
|
||||
|
||||
# Helper: get number of model choices for a provider
|
||||
get_model_choice_count() {
|
||||
local provider_id="$1"
|
||||
local count=0
|
||||
local i=0
|
||||
while [ $i -lt ${#MC_PROVIDERS[@]} ]; do
|
||||
if [ "${MC_PROVIDERS[$i]}" = "$provider_id" ]; then
|
||||
count=$((count + 1))
|
||||
fi
|
||||
i=$((i + 1))
|
||||
done
|
||||
echo "$count"
|
||||
}
|
||||
|
||||
# Helper: get model choice id by provider and index (0-based within provider)
|
||||
get_model_choice_id() {
|
||||
local provider_id="$1"
|
||||
local idx="$2"
|
||||
local count=0
|
||||
local i=0
|
||||
while [ $i -lt ${#MC_PROVIDERS[@]} ]; do
|
||||
if [ "${MC_PROVIDERS[$i]}" = "$provider_id" ]; then
|
||||
if [ $count -eq "$idx" ]; then
|
||||
echo "${MC_IDS[$i]}"
|
||||
return
|
||||
fi
|
||||
count=$((count + 1))
|
||||
fi
|
||||
i=$((i + 1))
|
||||
done
|
||||
}
|
||||
|
||||
# Helper: get model choice label by provider and index
|
||||
get_model_choice_label() {
|
||||
local provider_id="$1"
|
||||
local idx="$2"
|
||||
local count=0
|
||||
local i=0
|
||||
while [ $i -lt ${#MC_PROVIDERS[@]} ]; do
|
||||
if [ "${MC_PROVIDERS[$i]}" = "$provider_id" ]; then
|
||||
if [ $count -eq "$idx" ]; then
|
||||
echo "${MC_LABELS[$i]}"
|
||||
return
|
||||
fi
|
||||
count=$((count + 1))
|
||||
fi
|
||||
i=$((i + 1))
|
||||
done
|
||||
}
|
||||
|
||||
# Helper: get model choice max_tokens by provider and index
|
||||
get_model_choice_maxtokens() {
|
||||
local provider_id="$1"
|
||||
local idx="$2"
|
||||
local count=0
|
||||
local i=0
|
||||
while [ $i -lt ${#MC_PROVIDERS[@]} ]; do
|
||||
if [ "${MC_PROVIDERS[$i]}" = "$provider_id" ]; then
|
||||
if [ $count -eq "$idx" ]; then
|
||||
echo "${MC_MAXTOKENS[$i]}"
|
||||
return
|
||||
fi
|
||||
count=$((count + 1))
|
||||
fi
|
||||
i=$((i + 1))
|
||||
done
|
||||
}
|
||||
fi
|
||||
|
||||
# Configuration directory
|
||||
@@ -411,12 +562,74 @@ detect_shell_rc() {
|
||||
SHELL_RC_FILE=$(detect_shell_rc)
|
||||
SHELL_NAME=$(basename "$SHELL")
|
||||
|
||||
# Prompt the user to choose a model for their selected provider.
|
||||
# Sets SELECTED_MODEL and SELECTED_MAX_TOKENS.
|
||||
prompt_model_selection() {
|
||||
local provider_id="$1"
|
||||
local count
|
||||
count="$(get_model_choice_count "$provider_id")"
|
||||
|
||||
if [ "$count" -eq 0 ]; then
|
||||
# No curated choices for this provider (e.g. Mistral, DeepSeek)
|
||||
SELECTED_MODEL="$(get_default_model "$provider_id")"
|
||||
SELECTED_MAX_TOKENS=8192
|
||||
return
|
||||
fi
|
||||
|
||||
if [ "$count" -eq 1 ]; then
|
||||
# Only one choice — auto-select
|
||||
SELECTED_MODEL="$(get_model_choice_id "$provider_id" 0)"
|
||||
SELECTED_MAX_TOKENS="$(get_model_choice_maxtokens "$provider_id" 0)"
|
||||
return
|
||||
fi
|
||||
|
||||
# Multiple choices — show menu
|
||||
echo ""
|
||||
echo -e "${BOLD}Select a model:${NC}"
|
||||
echo ""
|
||||
|
||||
local i=0
|
||||
while [ $i -lt "$count" ]; do
|
||||
local label
|
||||
label="$(get_model_choice_label "$provider_id" "$i")"
|
||||
local mid
|
||||
mid="$(get_model_choice_id "$provider_id" "$i")"
|
||||
local num=$((i + 1))
|
||||
echo -e " ${CYAN}$num)${NC} $label ${DIM}($mid)${NC}"
|
||||
i=$((i + 1))
|
||||
done
|
||||
echo ""
|
||||
|
||||
local choice
|
||||
while true; do
|
||||
read -r -p "Enter choice [1]: " choice
|
||||
choice="${choice:-1}"
|
||||
if [[ "$choice" =~ ^[0-9]+$ ]] && [ "$choice" -ge 1 ] && [ "$choice" -le "$count" ]; then
|
||||
local idx=$((choice - 1))
|
||||
SELECTED_MODEL="$(get_model_choice_id "$provider_id" "$idx")"
|
||||
SELECTED_MAX_TOKENS="$(get_model_choice_maxtokens "$provider_id" "$idx")"
|
||||
echo ""
|
||||
echo -e "${GREEN}⬢${NC} Model: ${DIM}$SELECTED_MODEL${NC}"
|
||||
return
|
||||
fi
|
||||
echo -e "${RED}Invalid choice. Please enter 1-$count${NC}"
|
||||
done
|
||||
}
|
||||
|
||||
# Function to save configuration
|
||||
save_configuration() {
|
||||
local provider_id="$1"
|
||||
local env_var="$2"
|
||||
local model
|
||||
local model="$3"
|
||||
local max_tokens="$4"
|
||||
|
||||
# Fallbacks if not provided
|
||||
if [ -z "$model" ]; then
|
||||
model="$(get_default_model "$provider_id")"
|
||||
fi
|
||||
if [ -z "$max_tokens" ]; then
|
||||
max_tokens=8192
|
||||
fi
|
||||
|
||||
mkdir -p "$HIVE_CONFIG_DIR"
|
||||
|
||||
@@ -426,6 +639,7 @@ config = {
|
||||
'llm': {
|
||||
'provider': '$provider_id',
|
||||
'model': '$model',
|
||||
'max_tokens': $max_tokens,
|
||||
'api_key_env_var': '$env_var'
|
||||
},
|
||||
'created_at': '$(date -u +"%Y-%m-%dT%H:%M:%S+00:00")'
|
||||
@@ -449,6 +663,8 @@ FOUND_PROVIDERS=() # Display names for UI
|
||||
FOUND_ENV_VARS=() # Corresponding env var names
|
||||
SELECTED_PROVIDER_ID="" # Will hold the chosen provider ID
|
||||
SELECTED_ENV_VAR="" # Will hold the chosen env var
|
||||
SELECTED_MODEL="" # Will hold the chosen model ID
|
||||
SELECTED_MAX_TOKENS=8192 # Will hold the chosen max_tokens
|
||||
|
||||
if [ "$USE_ASSOC_ARRAYS" = true ]; then
|
||||
# Bash 4+ - iterate over associative array keys
|
||||
@@ -486,6 +702,8 @@ if [ ${#FOUND_PROVIDERS[@]} -gt 0 ]; then
|
||||
|
||||
echo ""
|
||||
echo -e "${GREEN}⬢${NC} Using ${FOUND_PROVIDERS[0]}"
|
||||
|
||||
prompt_model_selection "$SELECTED_PROVIDER_ID"
|
||||
fi
|
||||
else
|
||||
# Multiple providers found, let user pick one
|
||||
@@ -498,28 +716,34 @@ if [ ${#FOUND_PROVIDERS[@]} -gt 0 ]; then
|
||||
echo -e " ${CYAN}$i)${NC} $provider"
|
||||
i=$((i + 1))
|
||||
done
|
||||
echo -e " ${CYAN}$i)${NC} Other"
|
||||
max_choice=$i
|
||||
echo ""
|
||||
|
||||
while true; do
|
||||
read -r -p "Enter choice (1-${#FOUND_PROVIDERS[@]}): " choice
|
||||
if [[ "$choice" =~ ^[0-9]+$ ]] && [ "$choice" -ge 1 ] && [ "$choice" -le "${#FOUND_PROVIDERS[@]}" ]; then
|
||||
read -r -p "Enter choice (1-$max_choice): " choice
|
||||
if [[ "$choice" =~ ^[0-9]+$ ]] && [ "$choice" -ge 1 ] && [ "$choice" -le "$max_choice" ]; then
|
||||
if [ "$choice" -eq "$max_choice" ]; then
|
||||
# Fall through to the manual provider selection below
|
||||
break
|
||||
fi
|
||||
idx=$((choice - 1))
|
||||
SELECTED_ENV_VAR="${FOUND_ENV_VARS[$idx]}"
|
||||
SELECTED_PROVIDER_ID="$(get_provider_id "$SELECTED_ENV_VAR")"
|
||||
|
||||
echo ""
|
||||
echo -e "${GREEN}⬢${NC} Selected: ${FOUND_PROVIDERS[$idx]}"
|
||||
|
||||
prompt_model_selection "$SELECTED_PROVIDER_ID"
|
||||
break
|
||||
fi
|
||||
echo -e "${RED}Invalid choice. Please enter 1-${#FOUND_PROVIDERS[@]}${NC}"
|
||||
echo -e "${RED}Invalid choice. Please enter 1-$max_choice${NC}"
|
||||
done
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ -z "$SELECTED_PROVIDER_ID" ]; then
|
||||
echo "No API keys found. Let's configure one."
|
||||
echo ""
|
||||
|
||||
prompt_choice "Select your LLM provider:" \
|
||||
"Anthropic (Claude) - Recommended" \
|
||||
"OpenAI (GPT)" \
|
||||
@@ -595,11 +819,16 @@ if [ -z "$SELECTED_PROVIDER_ID" ]; then
|
||||
fi
|
||||
fi
|
||||
|
||||
# Prompt for model if not already selected (manual provider path)
|
||||
if [ -n "$SELECTED_PROVIDER_ID" ] && [ -z "$SELECTED_MODEL" ]; then
|
||||
prompt_model_selection "$SELECTED_PROVIDER_ID"
|
||||
fi
|
||||
|
||||
# Save configuration if a provider was selected
|
||||
if [ -n "$SELECTED_PROVIDER_ID" ]; then
|
||||
echo ""
|
||||
echo -n " Saving configuration... "
|
||||
save_configuration "$SELECTED_PROVIDER_ID" "$SELECTED_ENV_VAR" > /dev/null
|
||||
save_configuration "$SELECTED_PROVIDER_ID" "$SELECTED_ENV_VAR" "$SELECTED_MODEL" "$SELECTED_MAX_TOKENS" > /dev/null
|
||||
echo -e "${GREEN}⬢${NC}"
|
||||
echo -e " ${DIM}~/.hive/configuration.json${NC}"
|
||||
fi
|
||||
@@ -781,7 +1010,9 @@ echo ""
|
||||
|
||||
# Show configured provider
|
||||
if [ -n "$SELECTED_PROVIDER_ID" ]; then
|
||||
if [ -z "$SELECTED_MODEL" ]; then
|
||||
SELECTED_MODEL="$(get_default_model "$SELECTED_PROVIDER_ID")"
|
||||
fi
|
||||
echo -e "${BOLD}Default LLM:${NC}"
|
||||
echo -e " ${CYAN}$SELECTED_PROVIDER_ID${NC} → ${DIM}$SELECTED_MODEL${NC}"
|
||||
echo ""
|
||||
|
||||
Reference in New Issue
Block a user