794 lines
30 KiB
Python
794 lines
30 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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import mcp_client
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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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# Increased to allow thorough code exploration before forcing a summary
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MAX_TOOL_ROUNDS = 10
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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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_SYSTEM_PROMPT = (
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"You are a helpful coding assistant with access to a PowerShell tool and MCP file tools (read_file, list_directory, search_files, get_file_summary). "
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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.\n\n"
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"When writing or rewriting large files (especially those containing quotes, backticks, or special characters), "
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"avoid python -c with inline strings. Instead: (1) write a .py helper script to disk using a PS here-string "
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"(@'...'@ for literal content), (2) run it with `python <script>`, (3) delete the helper. "
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"For small targeted edits, use PowerShell's (Get-Content) / .Replace() / Set-Content or Add-Content directly.\n\n"
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"When making function calls using tools that accept array or object parameters "
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"ensure those are structured using JSON. For example:\n"
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"When you need to verify a change, rely on the exit code and stdout/stderr from the tool \u2014 "
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"the user's context files are automatically refreshed after every tool call, so you do NOT "
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"need to re-read files that are already provided in the <context> block."
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)
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_custom_system_prompt: str = ""
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def set_custom_system_prompt(prompt: str):
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global _custom_system_prompt
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_custom_system_prompt = prompt
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def _get_combined_system_prompt() -> str:
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if _custom_system_prompt.strip():
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return f"{_SYSTEM_PROMPT}\n\n[USER SYSTEM PROMPT]\n{_custom_system_prompt}"
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return _SYSTEM_PROMPT
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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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def _build_anthropic_tools() -> list[dict]:
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"""Build the full Anthropic tools list: run_powershell + MCP file tools."""
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mcp_tools = []
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for spec in mcp_client.MCP_TOOL_SPECS:
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mcp_tools.append({
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"name": spec["name"],
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"description": spec["description"],
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"input_schema": spec["parameters"],
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})
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powershell_tool = {
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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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return mcp_tools + [powershell_tool]
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_ANTHROPIC_TOOLS = _build_anthropic_tools()
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def _gemini_tool_declaration():
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from google.genai import types
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declarations = []
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# MCP file tools
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for spec in mcp_client.MCP_TOOL_SPECS:
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props = {}
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for pname, pdef in spec["parameters"].get("properties", {}).items():
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props[pname] = types.Schema(
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type=types.Type.STRING,
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description=pdef.get("description", ""),
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)
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declarations.append(types.FunctionDeclaration(
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name=spec["name"],
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description=spec["description"],
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parameters=types.Schema(
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type=types.Type.OBJECT,
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properties=props,
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required=spec["parameters"].get("required", []),
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),
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))
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# PowerShell tool
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declarations.append(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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return types.Tool(function_declarations=declarations)
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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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# ------------------------------------------------------------------ dynamic file context refresh
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def _reread_file_items(file_items: list[dict]) -> list[dict]:
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"""
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Re-read every file in file_items from disk, returning a fresh list.
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This is called after tool calls so the AI sees updated file contents.
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"""
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refreshed = []
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for item in file_items:
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path = item.get("path")
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if path is None:
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refreshed.append(item)
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continue
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from pathlib import Path as _P
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p = _P(path) if not isinstance(path, _P) else path
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try:
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content = p.read_text(encoding="utf-8")
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refreshed.append({**item, "content": content, "error": False})
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except Exception as e:
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refreshed.append({**item, "content": f"ERROR re-reading {p}: {e}", "error": True})
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return refreshed
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def _build_file_context_text(file_items: list[dict]) -> str:
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"""
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Build a compact text summary of all files from file_items, suitable for
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injecting into a tool_result message so the AI sees current file contents.
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"""
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if not file_items:
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return ""
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parts = []
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for item in file_items:
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path = item.get("path") or item.get("entry", "unknown")
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suffix = str(path).rsplit(".", 1)[-1] if "." in str(path) else "text"
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content = item.get("content", "")
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parts.append(f"### `{path}`\n\n```{suffix}\n{content}\n```")
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return "\n\n---\n\n".join(parts)
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# ------------------------------------------------------------------ content block serialisation
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def _content_block_to_dict(block) -> dict:
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"""
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Convert an Anthropic SDK content block object to a plain dict.
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This ensures history entries are always JSON-serialisable dicts,
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not opaque SDK objects that may fail on re-serialisation.
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"""
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if isinstance(block, dict):
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return block
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if hasattr(block, "model_dump"):
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return block.model_dump()
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if hasattr(block, "to_dict"):
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return block.to_dict()
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# Fallback: manually construct based on type
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block_type = getattr(block, "type", None)
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if block_type == "text":
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return {"type": "text", "text": block.text}
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if block_type == "tool_use":
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return {"type": "tool_use", "id": block.id, "name": block.name, "input": block.input}
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return {"type": "text", "text": str(block)}
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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, file_items: list[dict] | None = None) -> 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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mcp_client.configure(file_items or [], [base_dir])
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system_text = _get_combined_system_prompt() + f"\n\n<context>\n{md_content}\n</context>"
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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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system_instruction=system_text,
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tools=[_gemini_tool_declaration()]
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)
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)
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else:
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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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system_instruction=system_text,
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tools=[_gemini_tool_declaration()]
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),
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history=_gemini_chat.get_history()
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)
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payload_to_send = 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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all_text_parts = []
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# We allow MAX_TOOL_ROUNDS, plus 1 final loop to get the text synthesis
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for round_idx in range(MAX_TOOL_ROUNDS + 2):
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response = _gemini_chat.send_message(payload_to_send)
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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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if text_parts_raw:
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all_text_parts.append("\n".join(text_parts_raw))
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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 hasattr(part, "function_call") and part.function_call is not None
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]
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usage_dict = {}
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if hasattr(response, "usage_metadata") and response.usage_metadata:
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meta = response.usage_metadata
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if hasattr(meta, "prompt_token_count") and meta.prompt_token_count is not None:
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usage_dict["input_tokens"] = meta.prompt_token_count
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if hasattr(meta, "candidates_token_count") and meta.candidates_token_count is not None:
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usage_dict["output_tokens"] = meta.candidates_token_count
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if hasattr(meta, "cached_content_token_count") and meta.cached_content_token_count:
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usage_dict["cache_read_input_tokens"] = meta.cached_content_token_count
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stop_reason = ""
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if response.candidates and hasattr(response.candidates[0], "finish_reason"):
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fr = response.candidates[0].finish_reason
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stop_reason = str(fr.name) if hasattr(fr, "name") else str(fr)
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_append_comms("IN", "response", {
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"round": round_idx,
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"stop_reason": stop_reason,
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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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"usage": usage_dict,
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})
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if not tool_calls:
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break
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if round_idx > MAX_TOOL_ROUNDS:
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# The model ignored the MAX ROUNDS warning and kept calling tools.
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# Force abort to prevent infinite loop.
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break
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function_responses = []
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sent_results_log = []
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for i, fc in enumerate(tool_calls):
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fc_name = fc.name
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fc_args = dict(fc.args)
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if fc_name in mcp_client.TOOL_NAMES:
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_append_comms("OUT", "tool_call", {"name": fc_name, "args": fc_args})
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output = mcp_client.dispatch(fc_name, fc_args)
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_append_comms("IN", "tool_result", {"name": fc_name, "output": output})
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elif fc_name == TOOL_NAME:
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|
script = fc_args.get("script", "")
|
|
_append_comms("OUT", "tool_call", {"name": TOOL_NAME, "script": script})
|
|
output = _run_script(script, base_dir)
|
|
_append_comms("IN", "tool_result", {"name": TOOL_NAME, "output": output})
|
|
else:
|
|
output = f"ERROR: unknown tool '{fc_name}'"
|
|
|
|
# Inject dynamic updates directly into the LAST tool's output string.
|
|
# Gemini strictly expects function_responses only, so we piggyback on the string.
|
|
if i == len(tool_calls) - 1:
|
|
if file_items:
|
|
file_items = _reread_file_items(file_items)
|
|
refreshed_ctx = _build_file_context_text(file_items)
|
|
if refreshed_ctx:
|
|
output += f"\n\n[SYSTEM: FILES UPDATED — current contents below. Do NOT re-read these files.]\n\n{refreshed_ctx}"
|
|
|
|
if round_idx == MAX_TOOL_ROUNDS:
|
|
output += "\n\n[SYSTEM WARNING: MAX TOOL ROUNDS REACHED. YOU MUST PROVIDE YOUR FINAL ANSWER NOW WITHOUT CALLING ANY MORE TOOLS.]"
|
|
|
|
function_responses.append(
|
|
types.Part.from_function_response(name=fc_name, response={"output": output})
|
|
)
|
|
sent_results_log.append({"tool_use_id": fc_name, "content": output})
|
|
|
|
_append_comms("OUT", "tool_result_send", {"results": sent_results_log})
|
|
payload_to_send = function_responses
|
|
|
|
final_text = "\n\n".join(all_text_parts)
|
|
return final_text if final_text.strip() else "(No text returned by the model)"
|
|
|
|
except ProviderError:
|
|
raise
|
|
except Exception as exc:
|
|
raise _classify_gemini_error(exc) from exc
|
|
|
|
|
|
# ------------------------------------------------------------------ anthropic
|
|
|
|
def _ensure_anthropic_client():
|
|
global _anthropic_client
|
|
if _anthropic_client is None:
|
|
import anthropic
|
|
creds = _load_credentials()
|
|
_anthropic_client = anthropic.Anthropic(api_key=creds["anthropic"]["api_key"])
|
|
|
|
|
|
def _chunk_text(text: str, chunk_size: int) -> list[str]:
|
|
return [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]
|
|
|
|
|
|
def _build_chunked_context_blocks(md_content: str) -> list[dict]:
|
|
"""
|
|
Split md_content into <=_ANTHROPIC_CHUNK_SIZE char chunks.
|
|
cache_control:ephemeral is placed only on the LAST block so the whole
|
|
prefix is cached as one unit.
|
|
"""
|
|
chunks = _chunk_text(md_content, _ANTHROPIC_CHUNK_SIZE)
|
|
blocks = []
|
|
for i, chunk in enumerate(chunks):
|
|
block: dict = {"type": "text", "text": chunk}
|
|
if i == len(chunks) - 1:
|
|
block["cache_control"] = {"type": "ephemeral"}
|
|
blocks.append(block)
|
|
return blocks
|
|
|
|
|
|
def _strip_cache_controls(history: list[dict]):
|
|
"""
|
|
Remove cache_control from all content blocks in message history.
|
|
Anthropic allows max 4 cache_control blocks total across system + tools +
|
|
messages. We reserve those slots for the stable system/tools prefix and
|
|
the current turn's context block, so all older history entries must be clean.
|
|
"""
|
|
for msg in history:
|
|
content = msg.get("content")
|
|
if isinstance(content, list):
|
|
for block in content:
|
|
if isinstance(block, dict):
|
|
block.pop("cache_control", None)
|
|
|
|
def _repair_anthropic_history(history: list[dict]):
|
|
"""
|
|
If history ends with an assistant message that contains tool_use blocks
|
|
without a following user tool_result message, append a synthetic tool_result
|
|
message so the history is valid before the next request.
|
|
"""
|
|
if not history:
|
|
return
|
|
last = history[-1]
|
|
if last.get("role") != "assistant":
|
|
return
|
|
content = last.get("content", [])
|
|
tool_use_ids = []
|
|
for block in content:
|
|
if isinstance(block, dict):
|
|
if block.get("type") == "tool_use":
|
|
tool_use_ids.append(block["id"])
|
|
if not tool_use_ids:
|
|
return
|
|
history.append({
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "tool_result",
|
|
"tool_use_id": tid,
|
|
"content": "Tool call was not completed (session interrupted).",
|
|
}
|
|
for tid in tool_use_ids
|
|
],
|
|
})
|
|
|
|
|
|
def _send_anthropic(md_content: str, user_message: str, base_dir: str, file_items: list[dict] | None = None) -> str:
|
|
try:
|
|
_ensure_anthropic_client()
|
|
mcp_client.configure(file_items or [], [base_dir])
|
|
|
|
system_text = _get_combined_system_prompt() + f"\n\n<context>\n{md_content}\n</context>"
|
|
system_blocks = _build_chunked_context_blocks(system_text)
|
|
|
|
user_content = [{"type": "text", "text": user_message}]
|
|
|
|
_strip_cache_controls(_anthropic_history)
|
|
_repair_anthropic_history(_anthropic_history)
|
|
_anthropic_history.append({"role": "user", "content": user_content})
|
|
|
|
n_chunks = len(system_blocks)
|
|
_append_comms("OUT", "request", {
|
|
"message": (
|
|
f"[system {n_chunks} chunk(s), {len(md_content)} chars context] "
|
|
f"{user_message[:200]}{'...' if len(user_message) > 200 else ''}"
|
|
),
|
|
})
|
|
|
|
all_text_parts = []
|
|
|
|
# We allow MAX_TOOL_ROUNDS, plus 1 final loop to get the text synthesis
|
|
for round_idx in range(MAX_TOOL_ROUNDS + 2):
|
|
response = _anthropic_client.messages.create(
|
|
model=_model,
|
|
max_tokens=8096,
|
|
system=system_blocks,
|
|
tools=_build_anthropic_tools(),
|
|
messages=_anthropic_history,
|
|
)
|
|
|
|
# Convert SDK content block objects to plain dicts before storing in history
|
|
serialised_content = [_content_block_to_dict(b) for b in response.content]
|
|
|
|
_anthropic_history.append({
|
|
"role": "assistant",
|
|
"content": serialised_content,
|
|
})
|
|
|
|
text_blocks = [b.text for b in response.content if hasattr(b, "text") and b.text]
|
|
if text_blocks:
|
|
all_text_parts.append("\n".join(text_blocks))
|
|
|
|
tool_use_blocks = [
|
|
{"id": b.id, "name": b.name, "input": b.input}
|
|
for b in response.content
|
|
if getattr(b, "type", None) == "tool_use"
|
|
]
|
|
|
|
usage_dict: dict = {}
|
|
if response.usage:
|
|
usage_dict["input_tokens"] = response.usage.input_tokens
|
|
usage_dict["output_tokens"] = response.usage.output_tokens
|
|
cache_creation = getattr(response.usage, "cache_creation_input_tokens", None)
|
|
cache_read = getattr(response.usage, "cache_read_input_tokens", None)
|
|
if cache_creation is not None:
|
|
usage_dict["cache_creation_input_tokens"] = cache_creation
|
|
if cache_read is not None:
|
|
usage_dict["cache_read_input_tokens"] = cache_read
|
|
|
|
_append_comms("IN", "response", {
|
|
"round": round_idx,
|
|
"stop_reason": response.stop_reason,
|
|
"text": "\n".join(text_blocks),
|
|
"tool_calls": tool_use_blocks,
|
|
"usage": usage_dict,
|
|
})
|
|
|
|
if response.stop_reason != "tool_use" or not tool_use_blocks:
|
|
break
|
|
|
|
if round_idx > MAX_TOOL_ROUNDS:
|
|
# The model ignored the MAX ROUNDS warning and kept calling tools.
|
|
# Force abort to prevent infinite loop.
|
|
break
|
|
|
|
tool_results = []
|
|
for block in response.content:
|
|
if getattr(block, "type", None) != "tool_use":
|
|
continue
|
|
b_name = getattr(block, "name", None)
|
|
b_id = getattr(block, "id", "")
|
|
b_input = getattr(block, "input", {})
|
|
if b_name in mcp_client.TOOL_NAMES:
|
|
_append_comms("OUT", "tool_call", {"name": b_name, "id": b_id, "args": b_input})
|
|
output = mcp_client.dispatch(b_name, b_input)
|
|
_append_comms("IN", "tool_result", {"name": b_name, "id": b_id, "output": output})
|
|
tool_results.append({
|
|
"type": "tool_result",
|
|
"tool_use_id": b_id,
|
|
"content": output,
|
|
})
|
|
elif b_name == TOOL_NAME:
|
|
script = b_input.get("script", "")
|
|
_append_comms("OUT", "tool_call", {
|
|
"name": TOOL_NAME,
|
|
"id": b_id,
|
|
"script": script,
|
|
})
|
|
output = _run_script(script, base_dir)
|
|
_append_comms("IN", "tool_result", {
|
|
"name": TOOL_NAME,
|
|
"id": b_id,
|
|
"output": output,
|
|
})
|
|
tool_results.append({
|
|
"type": "tool_result",
|
|
"tool_use_id": b_id,
|
|
"content": output,
|
|
})
|
|
|
|
# Refresh file context after tool calls and inject into tool result message
|
|
if file_items:
|
|
file_items = _reread_file_items(file_items)
|
|
refreshed_ctx = _build_file_context_text(file_items)
|
|
if refreshed_ctx:
|
|
tool_results.append({
|
|
"type": "text",
|
|
"text": (
|
|
"[FILES UPDATED — current contents below. "
|
|
"Do NOT re-read these files with PowerShell.]\n\n"
|
|
+ refreshed_ctx
|
|
),
|
|
})
|
|
|
|
if round_idx == MAX_TOOL_ROUNDS:
|
|
tool_results.append({
|
|
"type": "text",
|
|
"text": "SYSTEM WARNING: MAX TOOL ROUNDS REACHED. YOU MUST PROVIDE YOUR FINAL ANSWER NOW WITHOUT CALLING ANY MORE TOOLS."
|
|
})
|
|
|
|
_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 if r.get("type") == "tool_result"
|
|
],
|
|
})
|
|
|
|
final_text = "\n\n".join(all_text_parts)
|
|
return final_text if final_text.strip() else "(No text returned by the model)"
|
|
|
|
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 = ".",
|
|
file_items: list[dict] | None = None,
|
|
) -> 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)
|
|
file_items : list of file dicts from aggregate.build_file_items() for
|
|
dynamic context refresh after tool calls
|
|
"""
|
|
if _provider == "gemini":
|
|
return _send_gemini(md_content, user_message, base_dir, file_items)
|
|
elif _provider == "anthropic":
|
|
return _send_anthropic(md_content, user_message, base_dir, file_items)
|
|
raise ValueError(f"unknown provider: {_provider}")
|