594 lines
20 KiB
Python
594 lines
20 KiB
Python
# ai_client.py
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import tomllib
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import json
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import datetime
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from pathlib import Path
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import file_cache
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_provider: str = "gemini"
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_model: str = "gemini-2.0-flash"
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_gemini_client = None
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_gemini_chat = None
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_anthropic_client = None
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_anthropic_history: list[dict] = []
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# Injected by gui.py - called when AI wants to run a command.
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# Signature: (script: str, base_dir: str) -> str | None
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confirm_and_run_callback = None
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# Injected by gui.py - called whenever a comms entry is appended.
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# Signature: (entry: dict) -> None
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comms_log_callback = None
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# Injected by gui.py - called whenever a tool call completes.
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# Signature: (script: str, result: str) -> None
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tool_log_callback = None
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MAX_TOOL_ROUNDS = 5
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# Maximum characters per text chunk sent to Anthropic.
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# Kept well under the ~200k token API limit.
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_ANTHROPIC_CHUNK_SIZE = 180_000
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_ANTHROPIC_SYSTEM = (
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"You are a helpful coding assistant with access to a PowerShell tool. "
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"When asked to create or edit files, prefer targeted edits over full rewrites. "
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"Always explain what you are doing before invoking the tool."
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)
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# ------------------------------------------------------------------ comms log
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_comms_log: list[dict] = []
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COMMS_CLAMP_CHARS = 300
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def _append_comms(direction: str, kind: str, payload: dict):
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entry = {
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"ts": datetime.datetime.now().strftime("%H:%M:%S"),
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"direction": direction,
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"kind": kind,
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"provider": _provider,
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"model": _model,
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"payload": payload,
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}
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_comms_log.append(entry)
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if comms_log_callback is not None:
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comms_log_callback(entry)
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def get_comms_log() -> list[dict]:
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return list(_comms_log)
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def clear_comms_log():
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_comms_log.clear()
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def _load_credentials() -> dict:
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with open("credentials.toml", "rb") as f:
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return tomllib.load(f)
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# ------------------------------------------------------------------ provider errors
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class ProviderError(Exception):
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def __init__(self, kind: str, provider: str, original: Exception):
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self.kind = kind
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self.provider = provider
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self.original = original
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super().__init__(str(original))
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def ui_message(self) -> str:
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labels = {
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"quota": "QUOTA EXHAUSTED",
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"rate_limit": "RATE LIMITED",
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"auth": "AUTH / API KEY ERROR",
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"balance": "BALANCE / BILLING ERROR",
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"network": "NETWORK / CONNECTION ERROR",
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"unknown": "API ERROR",
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}
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label = labels.get(self.kind, "API ERROR")
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return f"[{self.provider.upper()} {label}]\n\n{self.original}"
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def _classify_anthropic_error(exc: Exception) -> ProviderError:
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try:
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import anthropic
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if isinstance(exc, anthropic.RateLimitError):
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return ProviderError("rate_limit", "anthropic", exc)
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if isinstance(exc, anthropic.AuthenticationError):
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return ProviderError("auth", "anthropic", exc)
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if isinstance(exc, anthropic.PermissionDeniedError):
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return ProviderError("auth", "anthropic", exc)
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if isinstance(exc, anthropic.APIConnectionError):
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return ProviderError("network", "anthropic", exc)
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if isinstance(exc, anthropic.APIStatusError):
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status = getattr(exc, "status_code", 0)
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body = str(exc).lower()
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if status == 429:
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return ProviderError("rate_limit", "anthropic", exc)
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if status in (401, 403):
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return ProviderError("auth", "anthropic", exc)
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if status == 402:
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return ProviderError("balance", "anthropic", exc)
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if "credit" in body or "balance" in body or "billing" in body:
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return ProviderError("balance", "anthropic", exc)
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if "quota" in body or "limit" in body or "exceeded" in body:
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return ProviderError("quota", "anthropic", exc)
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except ImportError:
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pass
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return ProviderError("unknown", "anthropic", exc)
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def _classify_gemini_error(exc: Exception) -> ProviderError:
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body = str(exc).lower()
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try:
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from google.api_core import exceptions as gac
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if isinstance(exc, gac.ResourceExhausted):
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return ProviderError("quota", "gemini", exc)
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if isinstance(exc, gac.TooManyRequests):
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return ProviderError("rate_limit", "gemini", exc)
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if isinstance(exc, (gac.Unauthenticated, gac.PermissionDenied)):
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return ProviderError("auth", "gemini", exc)
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if isinstance(exc, gac.ServiceUnavailable):
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return ProviderError("network", "gemini", exc)
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except ImportError:
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pass
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if "429" in body or "quota" in body or "resource exhausted" in body:
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return ProviderError("quota", "gemini", exc)
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if "rate" in body and "limit" in body:
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return ProviderError("rate_limit", "gemini", exc)
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if "401" in body or "403" in body or "api key" in body or "unauthenticated" in body:
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return ProviderError("auth", "gemini", exc)
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if "402" in body or "billing" in body or "balance" in body or "payment" in body:
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return ProviderError("balance", "gemini", exc)
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if "connection" in body or "timeout" in body or "unreachable" in body:
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return ProviderError("network", "gemini", exc)
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return ProviderError("unknown", "gemini", exc)
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# ------------------------------------------------------------------ provider setup
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def set_provider(provider: str, model: str):
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global _provider, _model
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_provider = provider
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_model = model
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def reset_session():
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global _gemini_client, _gemini_chat
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global _anthropic_client, _anthropic_history
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_gemini_client = None
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_gemini_chat = None
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_anthropic_client = None
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_anthropic_history = []
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file_cache.reset_client()
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# ------------------------------------------------------------------ model listing
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def list_models(provider: str) -> list[str]:
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creds = _load_credentials()
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if provider == "gemini":
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return _list_gemini_models(creds["gemini"]["api_key"])
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elif provider == "anthropic":
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return _list_anthropic_models()
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return []
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def _list_gemini_models(api_key: str) -> list[str]:
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from google import genai
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try:
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client = genai.Client(api_key=api_key)
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models = []
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for m in client.models.list():
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name = m.name
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if name.startswith("models/"):
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name = name[len("models/"):]
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if "gemini" in name.lower():
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models.append(name)
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return sorted(models)
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except Exception as exc:
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raise _classify_gemini_error(exc) from exc
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def _list_anthropic_models() -> list[str]:
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import anthropic
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try:
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creds = _load_credentials()
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client = anthropic.Anthropic(api_key=creds["anthropic"]["api_key"])
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models = []
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for m in client.models.list():
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models.append(m.id)
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return sorted(models)
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except Exception as exc:
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raise _classify_anthropic_error(exc) from exc
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# ------------------------------------------------------------------ tool definition
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TOOL_NAME = "run_powershell"
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_ANTHROPIC_TOOLS = [
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{
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"name": TOOL_NAME,
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"description": (
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"Run a PowerShell script within the project base_dir. "
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"Use this to create, edit, rename, or delete files and directories. "
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"The working directory is set to base_dir automatically. "
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"Always prefer targeted edits over full rewrites where possible. "
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"stdout and stderr are returned to you as the result."
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),
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"input_schema": {
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"type": "object",
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"properties": {
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"script": {
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"type": "string",
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"description": "The PowerShell script to execute."
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}
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},
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"required": ["script"]
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},
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"cache_control": {"type": "ephemeral"},
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}
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]
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def _gemini_tool_declaration():
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from google.genai import types
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return types.Tool(
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function_declarations=[
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types.FunctionDeclaration(
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name=TOOL_NAME,
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description=(
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"Run a PowerShell script within the project base_dir. "
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"Use this to create, edit, rename, or delete files and directories. "
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"The working directory is set to base_dir automatically. "
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"stdout and stderr are returned to you as the result."
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),
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parameters=types.Schema(
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type=types.Type.OBJECT,
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properties={
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"script": types.Schema(
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type=types.Type.STRING,
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description="The PowerShell script to execute."
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)
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},
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required=["script"]
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)
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)
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]
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)
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def _run_script(script: str, base_dir: str) -> str:
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if confirm_and_run_callback is None:
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return "ERROR: no confirmation handler registered"
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result = confirm_and_run_callback(script, base_dir)
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if result is None:
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output = "USER REJECTED: command was not executed"
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else:
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output = result
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if tool_log_callback is not None:
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tool_log_callback(script, output)
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return output
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# ------------------------------------------------------------------ gemini
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def _ensure_gemini_client():
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global _gemini_client
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if _gemini_client is None:
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from google import genai
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creds = _load_credentials()
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_gemini_client = genai.Client(api_key=creds["gemini"]["api_key"])
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def _send_gemini(md_content: str, user_message: str, base_dir: str) -> str:
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global _gemini_chat
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from google import genai
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from google.genai import types
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try:
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_ensure_gemini_client()
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if _gemini_chat is None:
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_gemini_chat = _gemini_client.chats.create(
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model=_model,
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config=types.GenerateContentConfig(
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tools=[_gemini_tool_declaration()]
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)
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)
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full_message = f"<context>\n{md_content}\n</context>\n\n{user_message}"
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_append_comms("OUT", "request", {
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"message": f"[context {len(md_content)} chars + user message {len(user_message)} chars]",
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})
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response = _gemini_chat.send_message(full_message)
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for round_idx in range(MAX_TOOL_ROUNDS):
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text_parts_raw = [
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part.text
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for candidate in response.candidates
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for part in candidate.content.parts
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if hasattr(part, "text") and part.text
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]
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tool_calls = [
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part.function_call
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for candidate in response.candidates
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for part in candidate.content.parts
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if part.function_call is not None
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]
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_append_comms("IN", "response", {
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"round": round_idx,
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"text": "\n".join(text_parts_raw),
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"tool_calls": [{"name": fc.name, "args": dict(fc.args)} for fc in tool_calls],
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})
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if not tool_calls:
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break
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function_responses = []
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for fc in tool_calls:
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if fc.name == TOOL_NAME:
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script = fc.args.get("script", "")
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_append_comms("OUT", "tool_call", {
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"name": TOOL_NAME,
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"script": script,
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})
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output = _run_script(script, base_dir)
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_append_comms("IN", "tool_result", {
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"name": TOOL_NAME,
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"output": output,
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})
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function_responses.append(
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types.Part.from_function_response(
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name=TOOL_NAME,
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response={"output": output}
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)
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)
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if not function_responses:
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break
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response = _gemini_chat.send_message(function_responses)
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text_parts = [
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part.text
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for candidate in response.candidates
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for part in candidate.content.parts
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if hasattr(part, "text") and part.text
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]
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return "\n".join(text_parts)
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except ProviderError:
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raise
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except Exception as exc:
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raise _classify_gemini_error(exc) from exc
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# ------------------------------------------------------------------ anthropic
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def _ensure_anthropic_client():
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global _anthropic_client
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if _anthropic_client is None:
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import anthropic
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creds = _load_credentials()
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_anthropic_client = anthropic.Anthropic(api_key=creds["anthropic"]["api_key"])
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def _chunk_text(text: str, chunk_size: int) -> list[str]:
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return [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]
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def _build_chunked_context_blocks(md_content: str) -> list[dict]:
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"""
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Split md_content into <=_ANTHROPIC_CHUNK_SIZE char chunks.
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cache_control:ephemeral is placed only on the LAST block so the whole
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prefix is cached as one unit.
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"""
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chunks = _chunk_text(md_content, _ANTHROPIC_CHUNK_SIZE)
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blocks = []
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for i, chunk in enumerate(chunks):
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block: dict = {"type": "text", "text": chunk}
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if i == len(chunks) - 1:
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block["cache_control"] = {"type": "ephemeral"}
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blocks.append(block)
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return blocks
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def _strip_cache_controls(history: list[dict]):
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"""
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Remove cache_control from all content blocks in message history.
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Anthropic allows max 4 cache_control blocks total across system + tools +
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messages. We reserve those slots for the stable system/tools prefix and
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the current turn's context block, so all older history entries must be clean.
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"""
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for msg in history:
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content = msg.get("content")
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if isinstance(content, list):
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for block in content:
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if isinstance(block, dict):
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block.pop("cache_control", None)
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def _repair_anthropic_history(history: list[dict]):
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"""
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If history ends with an assistant message that contains tool_use blocks
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without a following user tool_result message, append a synthetic tool_result
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message so the history is valid before the next request.
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"""
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if not history:
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return
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last = history[-1]
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if last.get("role") != "assistant":
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return
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content = last.get("content", [])
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tool_use_ids = []
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for block in content:
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if isinstance(block, dict):
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if block.get("type") == "tool_use":
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tool_use_ids.append(block["id"])
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else:
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if getattr(block, "type", None) == "tool_use":
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tool_use_ids.append(block.id)
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if not tool_use_ids:
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return
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history.append({
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"role": "user",
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"content": [
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{
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"type": "tool_result",
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"tool_use_id": tid,
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"content": "Tool call was not completed (session interrupted).",
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}
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for tid in tool_use_ids
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],
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})
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def _send_anthropic(md_content: str, user_message: str, base_dir: str) -> str:
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try:
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_ensure_anthropic_client()
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context_blocks = _build_chunked_context_blocks(md_content)
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user_content = context_blocks + [
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{"type": "text", "text": user_message}
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]
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_strip_cache_controls(_anthropic_history)
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_repair_anthropic_history(_anthropic_history)
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_anthropic_history.append({"role": "user", "content": user_content})
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n_chunks = len(context_blocks)
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_append_comms("OUT", "request", {
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"message": (
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f"[{n_chunks} chunk(s), {len(md_content)} chars context] "
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f"{user_message[:200]}{'...' if len(user_message) > 200 else ''}"
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),
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})
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for round_idx in range(MAX_TOOL_ROUNDS):
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response = _anthropic_client.messages.create(
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model=_model,
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max_tokens=8096,
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system=[
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{
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"type": "text",
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"text": _ANTHROPIC_SYSTEM,
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"cache_control": {"type": "ephemeral"},
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}
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],
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tools=_ANTHROPIC_TOOLS,
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messages=_anthropic_history,
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)
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_anthropic_history.append({
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"role": "assistant",
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"content": response.content,
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})
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text_blocks = [b.text for b in response.content if hasattr(b, "text") and b.text]
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tool_use_blocks = [
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{"id": b.id, "name": b.name, "input": b.input}
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for b in response.content
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if b.type == "tool_use"
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]
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usage_dict: dict = {}
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if response.usage:
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usage_dict["input_tokens"] = response.usage.input_tokens
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usage_dict["output_tokens"] = response.usage.output_tokens
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cache_creation = getattr(response.usage, "cache_creation_input_tokens", None)
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cache_read = getattr(response.usage, "cache_read_input_tokens", None)
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if cache_creation is not None:
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usage_dict["cache_creation_input_tokens"] = cache_creation
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if cache_read is not None:
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usage_dict["cache_read_input_tokens"] = cache_read
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_append_comms("IN", "response", {
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"round": round_idx,
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"stop_reason": response.stop_reason,
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"text": "\n".join(text_blocks),
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"tool_calls": tool_use_blocks,
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"usage": usage_dict,
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})
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if response.stop_reason != "tool_use":
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break
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tool_results = []
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for block in response.content:
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if block.type == "tool_use" and block.name == TOOL_NAME:
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script = block.input.get("script", "")
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_append_comms("OUT", "tool_call", {
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"name": TOOL_NAME,
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"id": block.id,
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"script": script,
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})
|
|
output = _run_script(script, base_dir)
|
|
_append_comms("IN", "tool_result", {
|
|
"name": TOOL_NAME,
|
|
"id": block.id,
|
|
"output": output,
|
|
})
|
|
tool_results.append({
|
|
"type": "tool_result",
|
|
"tool_use_id": block.id,
|
|
"content": output,
|
|
})
|
|
|
|
if not tool_results:
|
|
break
|
|
|
|
_anthropic_history.append({
|
|
"role": "user",
|
|
"content": tool_results,
|
|
})
|
|
|
|
_append_comms("OUT", "tool_result_send", {
|
|
"results": [
|
|
{"tool_use_id": r["tool_use_id"], "content": r["content"]}
|
|
for r in tool_results
|
|
],
|
|
})
|
|
|
|
text_parts = [
|
|
block.text
|
|
for block in response.content
|
|
if hasattr(block, "text") and block.text
|
|
]
|
|
return "\n".join(text_parts)
|
|
|
|
except ProviderError:
|
|
raise
|
|
except Exception as exc:
|
|
raise _classify_anthropic_error(exc) from exc
|
|
|
|
|
|
# ------------------------------------------------------------------ unified send
|
|
|
|
def send(
|
|
md_content: str,
|
|
user_message: str,
|
|
base_dir: str = ".",
|
|
) -> str:
|
|
"""
|
|
Send a message to the active provider.
|
|
|
|
md_content : aggregated markdown string from aggregate.run()
|
|
user_message: the user question / instruction
|
|
base_dir : project base directory (for PowerShell tool calls)
|
|
"""
|
|
if _provider == "gemini":
|
|
return _send_gemini(md_content, user_message, base_dir)
|
|
elif _provider == "anthropic":
|
|
return _send_anthropic(md_content, user_message, base_dir)
|
|
raise ValueError(f"unknown provider: {_provider}")
|