686 lines
24 KiB
Python
686 lines
24 KiB
Python
# ai_client.py
|
|
import tomllib
|
|
import json
|
|
import datetime
|
|
from pathlib import Path
|
|
import file_cache
|
|
|
|
_provider: str = "gemini"
|
|
_model: str = "gemini-2.0-flash"
|
|
|
|
_gemini_client = None
|
|
_gemini_chat = None
|
|
|
|
_anthropic_client = None
|
|
_anthropic_history: list[dict] = []
|
|
|
|
# Injected by gui.py - called when AI wants to run a command.
|
|
# Signature: (script: str, base_dir: str) -> str | None
|
|
confirm_and_run_callback = None
|
|
|
|
# Injected by gui.py - called whenever a comms entry is appended.
|
|
# Signature: (entry: dict) -> None
|
|
comms_log_callback = None
|
|
|
|
# Injected by gui.py - called whenever a tool call completes.
|
|
# Signature: (script: str, result: str) -> None
|
|
tool_log_callback = None
|
|
|
|
MAX_TOOL_ROUNDS = 5
|
|
|
|
# Maximum characters per text chunk sent to Anthropic.
|
|
# Kept well under the ~200k token API limit.
|
|
_ANTHROPIC_CHUNK_SIZE = 180_000
|
|
|
|
_ANTHROPIC_SYSTEM = (
|
|
"You are a helpful coding assistant with access to a PowerShell tool. "
|
|
"When asked to create or edit files, prefer targeted edits over full rewrites. "
|
|
"Always explain what you are doing before invoking the tool.\n\n"
|
|
"When writing or rewriting large files (especially those containing quotes, backticks, or special characters), "
|
|
"avoid python -c with inline strings. Instead: (1) write a .py helper script to disk using a PS here-string "
|
|
"(@'...'@ for literal content), (2) run it with `python <script>`, (3) delete the helper. "
|
|
"For small targeted edits, use PowerShell's (Get-Content) / .Replace() / Set-Content or Add-Content directly.\n\n"
|
|
"When making function calls using tools that accept array or object parameters "
|
|
"ensure those are structured using JSON. For example:\n"
|
|
"When you need to verify a change, rely on the exit code and stdout/stderr from the tool \u2014 "
|
|
"the user's context files are automatically refreshed after every tool call, so you do NOT "
|
|
"need to re-read files that are already provided in the <context> block."
|
|
)
|
|
|
|
# ------------------------------------------------------------------ comms log
|
|
|
|
_comms_log: list[dict] = []
|
|
|
|
COMMS_CLAMP_CHARS = 300
|
|
|
|
|
|
def _append_comms(direction: str, kind: str, payload: dict):
|
|
entry = {
|
|
"ts": datetime.datetime.now().strftime("%H:%M:%S"),
|
|
"direction": direction,
|
|
"kind": kind,
|
|
"provider": _provider,
|
|
"model": _model,
|
|
"payload": payload,
|
|
}
|
|
_comms_log.append(entry)
|
|
if comms_log_callback is not None:
|
|
comms_log_callback(entry)
|
|
|
|
|
|
def get_comms_log() -> list[dict]:
|
|
return list(_comms_log)
|
|
|
|
|
|
def clear_comms_log():
|
|
_comms_log.clear()
|
|
|
|
|
|
def _load_credentials() -> dict:
|
|
with open("credentials.toml", "rb") as f:
|
|
return tomllib.load(f)
|
|
|
|
|
|
# ------------------------------------------------------------------ provider errors
|
|
|
|
class ProviderError(Exception):
|
|
def __init__(self, kind: str, provider: str, original: Exception):
|
|
self.kind = kind
|
|
self.provider = provider
|
|
self.original = original
|
|
super().__init__(str(original))
|
|
|
|
def ui_message(self) -> str:
|
|
labels = {
|
|
"quota": "QUOTA EXHAUSTED",
|
|
"rate_limit": "RATE LIMITED",
|
|
"auth": "AUTH / API KEY ERROR",
|
|
"balance": "BALANCE / BILLING ERROR",
|
|
"network": "NETWORK / CONNECTION ERROR",
|
|
"unknown": "API ERROR",
|
|
}
|
|
label = labels.get(self.kind, "API ERROR")
|
|
return f"[{self.provider.upper()} {label}]\n\n{self.original}"
|
|
|
|
|
|
def _classify_anthropic_error(exc: Exception) -> ProviderError:
|
|
try:
|
|
import anthropic
|
|
if isinstance(exc, anthropic.RateLimitError):
|
|
return ProviderError("rate_limit", "anthropic", exc)
|
|
if isinstance(exc, anthropic.AuthenticationError):
|
|
return ProviderError("auth", "anthropic", exc)
|
|
if isinstance(exc, anthropic.PermissionDeniedError):
|
|
return ProviderError("auth", "anthropic", exc)
|
|
if isinstance(exc, anthropic.APIConnectionError):
|
|
return ProviderError("network", "anthropic", exc)
|
|
if isinstance(exc, anthropic.APIStatusError):
|
|
status = getattr(exc, "status_code", 0)
|
|
body = str(exc).lower()
|
|
if status == 429:
|
|
return ProviderError("rate_limit", "anthropic", exc)
|
|
if status in (401, 403):
|
|
return ProviderError("auth", "anthropic", exc)
|
|
if status == 402:
|
|
return ProviderError("balance", "anthropic", exc)
|
|
if "credit" in body or "balance" in body or "billing" in body:
|
|
return ProviderError("balance", "anthropic", exc)
|
|
if "quota" in body or "limit" in body or "exceeded" in body:
|
|
return ProviderError("quota", "anthropic", exc)
|
|
except ImportError:
|
|
pass
|
|
return ProviderError("unknown", "anthropic", exc)
|
|
|
|
|
|
def _classify_gemini_error(exc: Exception) -> ProviderError:
|
|
body = str(exc).lower()
|
|
try:
|
|
from google.api_core import exceptions as gac
|
|
if isinstance(exc, gac.ResourceExhausted):
|
|
return ProviderError("quota", "gemini", exc)
|
|
if isinstance(exc, gac.TooManyRequests):
|
|
return ProviderError("rate_limit", "gemini", exc)
|
|
if isinstance(exc, (gac.Unauthenticated, gac.PermissionDenied)):
|
|
return ProviderError("auth", "gemini", exc)
|
|
if isinstance(exc, gac.ServiceUnavailable):
|
|
return ProviderError("network", "gemini", exc)
|
|
except ImportError:
|
|
pass
|
|
if "429" in body or "quota" in body or "resource exhausted" in body:
|
|
return ProviderError("quota", "gemini", exc)
|
|
if "rate" in body and "limit" in body:
|
|
return ProviderError("rate_limit", "gemini", exc)
|
|
if "401" in body or "403" in body or "api key" in body or "unauthenticated" in body:
|
|
return ProviderError("auth", "gemini", exc)
|
|
if "402" in body or "billing" in body or "balance" in body or "payment" in body:
|
|
return ProviderError("balance", "gemini", exc)
|
|
if "connection" in body or "timeout" in body or "unreachable" in body:
|
|
return ProviderError("network", "gemini", exc)
|
|
return ProviderError("unknown", "gemini", exc)
|
|
|
|
|
|
# ------------------------------------------------------------------ provider setup
|
|
|
|
def set_provider(provider: str, model: str):
|
|
global _provider, _model
|
|
_provider = provider
|
|
_model = model
|
|
|
|
|
|
def reset_session():
|
|
global _gemini_client, _gemini_chat
|
|
global _anthropic_client, _anthropic_history
|
|
_gemini_client = None
|
|
_gemini_chat = None
|
|
_anthropic_client = None
|
|
_anthropic_history = []
|
|
file_cache.reset_client()
|
|
|
|
|
|
# ------------------------------------------------------------------ model listing
|
|
|
|
def list_models(provider: str) -> list[str]:
|
|
creds = _load_credentials()
|
|
if provider == "gemini":
|
|
return _list_gemini_models(creds["gemini"]["api_key"])
|
|
elif provider == "anthropic":
|
|
return _list_anthropic_models()
|
|
return []
|
|
|
|
|
|
def _list_gemini_models(api_key: str) -> list[str]:
|
|
from google import genai
|
|
try:
|
|
client = genai.Client(api_key=api_key)
|
|
models = []
|
|
for m in client.models.list():
|
|
name = m.name
|
|
if name.startswith("models/"):
|
|
name = name[len("models/"):]
|
|
if "gemini" in name.lower():
|
|
models.append(name)
|
|
return sorted(models)
|
|
except Exception as exc:
|
|
raise _classify_gemini_error(exc) from exc
|
|
|
|
|
|
def _list_anthropic_models() -> list[str]:
|
|
import anthropic
|
|
try:
|
|
creds = _load_credentials()
|
|
client = anthropic.Anthropic(api_key=creds["anthropic"]["api_key"])
|
|
models = []
|
|
for m in client.models.list():
|
|
models.append(m.id)
|
|
return sorted(models)
|
|
except Exception as exc:
|
|
raise _classify_anthropic_error(exc) from exc
|
|
|
|
|
|
# ------------------------------------------------------------------ tool definition
|
|
|
|
TOOL_NAME = "run_powershell"
|
|
|
|
_ANTHROPIC_TOOLS = [
|
|
{
|
|
"name": TOOL_NAME,
|
|
"description": (
|
|
"Run a PowerShell script within the project base_dir. "
|
|
"Use this to create, edit, rename, or delete files and directories. "
|
|
"The working directory is set to base_dir automatically. "
|
|
"Always prefer targeted edits over full rewrites where possible. "
|
|
"stdout and stderr are returned to you as the result."
|
|
),
|
|
"input_schema": {
|
|
"type": "object",
|
|
"properties": {
|
|
"script": {
|
|
"type": "string",
|
|
"description": "The PowerShell script to execute."
|
|
}
|
|
},
|
|
"required": ["script"]
|
|
},
|
|
"cache_control": {"type": "ephemeral"},
|
|
}
|
|
]
|
|
|
|
|
|
def _gemini_tool_declaration():
|
|
from google.genai import types
|
|
return types.Tool(
|
|
function_declarations=[
|
|
types.FunctionDeclaration(
|
|
name=TOOL_NAME,
|
|
description=(
|
|
"Run a PowerShell script within the project base_dir. "
|
|
"Use this to create, edit, rename, or delete files and directories. "
|
|
"The working directory is set to base_dir automatically. "
|
|
"stdout and stderr are returned to you as the result."
|
|
),
|
|
parameters=types.Schema(
|
|
type=types.Type.OBJECT,
|
|
properties={
|
|
"script": types.Schema(
|
|
type=types.Type.STRING,
|
|
description="The PowerShell script to execute."
|
|
)
|
|
},
|
|
required=["script"]
|
|
)
|
|
)
|
|
]
|
|
)
|
|
|
|
|
|
def _run_script(script: str, base_dir: str) -> str:
|
|
if confirm_and_run_callback is None:
|
|
return "ERROR: no confirmation handler registered"
|
|
result = confirm_and_run_callback(script, base_dir)
|
|
if result is None:
|
|
output = "USER REJECTED: command was not executed"
|
|
else:
|
|
output = result
|
|
if tool_log_callback is not None:
|
|
tool_log_callback(script, output)
|
|
return output
|
|
|
|
|
|
# ------------------------------------------------------------------ dynamic file context refresh
|
|
|
|
def _reread_file_items(file_items: list[dict]) -> list[dict]:
|
|
"""
|
|
Re-read every file in file_items from disk, returning a fresh list.
|
|
This is called after tool calls so the AI sees updated file contents.
|
|
"""
|
|
refreshed = []
|
|
for item in file_items:
|
|
path = item.get("path")
|
|
if path is None:
|
|
refreshed.append(item)
|
|
continue
|
|
from pathlib import Path as _P
|
|
p = _P(path) if not isinstance(path, _P) else path
|
|
try:
|
|
content = p.read_text(encoding="utf-8")
|
|
refreshed.append({**item, "content": content, "error": False})
|
|
except Exception as e:
|
|
refreshed.append({**item, "content": f"ERROR re-reading {p}: {e}", "error": True})
|
|
return refreshed
|
|
|
|
|
|
def _build_file_context_text(file_items: list[dict]) -> str:
|
|
"""
|
|
Build a compact text summary of all files from file_items, suitable for
|
|
injecting into a tool_result message so the AI sees current file contents.
|
|
"""
|
|
if not file_items:
|
|
return ""
|
|
parts = []
|
|
for item in file_items:
|
|
path = item.get("path") or item.get("entry", "unknown")
|
|
suffix = str(path).rsplit(".", 1)[-1] if "." in str(path) else "text"
|
|
content = item.get("content", "")
|
|
parts.append(f"### `{path}`\n\n```{suffix}\n{content}\n```")
|
|
return "\n\n---\n\n".join(parts)
|
|
|
|
|
|
# ------------------------------------------------------------------ content block serialisation
|
|
|
|
def _content_block_to_dict(block) -> dict:
|
|
"""
|
|
Convert an Anthropic SDK content block object to a plain dict.
|
|
This ensures history entries are always JSON-serialisable dicts,
|
|
not opaque SDK objects that may fail on re-serialisation.
|
|
"""
|
|
if isinstance(block, dict):
|
|
return block
|
|
if hasattr(block, "model_dump"):
|
|
return block.model_dump()
|
|
if hasattr(block, "to_dict"):
|
|
return block.to_dict()
|
|
# Fallback: manually construct based on type
|
|
block_type = getattr(block, "type", None)
|
|
if block_type == "text":
|
|
return {"type": "text", "text": block.text}
|
|
if block_type == "tool_use":
|
|
return {"type": "tool_use", "id": block.id, "name": block.name, "input": block.input}
|
|
return {"type": "text", "text": str(block)}
|
|
|
|
|
|
# ------------------------------------------------------------------ gemini
|
|
|
|
def _ensure_gemini_client():
|
|
global _gemini_client
|
|
if _gemini_client is None:
|
|
from google import genai
|
|
creds = _load_credentials()
|
|
_gemini_client = genai.Client(api_key=creds["gemini"]["api_key"])
|
|
|
|
|
|
def _send_gemini(md_content: str, user_message: str, base_dir: str, file_items: list[dict] | None = None) -> str:
|
|
global _gemini_chat
|
|
from google import genai
|
|
from google.genai import types
|
|
|
|
try:
|
|
_ensure_gemini_client()
|
|
|
|
if _gemini_chat is None:
|
|
_gemini_chat = _gemini_client.chats.create(
|
|
model=_model,
|
|
config=types.GenerateContentConfig(
|
|
tools=[_gemini_tool_declaration()]
|
|
)
|
|
)
|
|
|
|
full_message = f"<context>\n{md_content}\n</context>\n\n{user_message}"
|
|
|
|
_append_comms("OUT", "request", {
|
|
"message": f"[context {len(md_content)} chars + user message {len(user_message)} chars]",
|
|
})
|
|
|
|
response = _gemini_chat.send_message(full_message)
|
|
|
|
for round_idx in range(MAX_TOOL_ROUNDS):
|
|
text_parts_raw = [
|
|
part.text
|
|
for candidate in response.candidates
|
|
for part in candidate.content.parts
|
|
if hasattr(part, "text") and part.text
|
|
]
|
|
tool_calls = [
|
|
part.function_call
|
|
for candidate in response.candidates
|
|
for part in candidate.content.parts
|
|
if hasattr(part, "function_call") and part.function_call is not None
|
|
]
|
|
|
|
_append_comms("IN", "response", {
|
|
"round": round_idx,
|
|
"text": "\n".join(text_parts_raw),
|
|
"tool_calls": [{"name": fc.name, "args": dict(fc.args)} for fc in tool_calls],
|
|
})
|
|
|
|
if not tool_calls:
|
|
break
|
|
|
|
function_responses = []
|
|
for fc in tool_calls:
|
|
if fc.name == TOOL_NAME:
|
|
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,
|
|
})
|
|
function_responses.append(
|
|
types.Part.from_function_response(
|
|
name=TOOL_NAME,
|
|
response={"output": output}
|
|
)
|
|
)
|
|
|
|
if not function_responses:
|
|
break
|
|
|
|
# Refresh file context after tool calls
|
|
if file_items:
|
|
file_items = _reread_file_items(file_items)
|
|
|
|
response = _gemini_chat.send_message(function_responses)
|
|
|
|
text_parts = [
|
|
part.text
|
|
for candidate in response.candidates
|
|
for part in candidate.content.parts
|
|
if hasattr(part, "text") and part.text
|
|
]
|
|
return "\n".join(text_parts)
|
|
|
|
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()
|
|
|
|
context_blocks = _build_chunked_context_blocks(md_content)
|
|
|
|
user_content = context_blocks + [
|
|
{"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(context_blocks)
|
|
_append_comms("OUT", "request", {
|
|
"message": (
|
|
f"[{n_chunks} chunk(s), {len(md_content)} chars context] "
|
|
f"{user_message[:200]}{'...' if len(user_message) > 200 else ''}"
|
|
),
|
|
})
|
|
|
|
for round_idx in range(MAX_TOOL_ROUNDS):
|
|
response = _anthropic_client.messages.create(
|
|
model=_model,
|
|
max_tokens=8096,
|
|
system=[
|
|
{
|
|
"type": "text",
|
|
"text": _ANTHROPIC_SYSTEM,
|
|
"cache_control": {"type": "ephemeral"},
|
|
}
|
|
],
|
|
tools=_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]
|
|
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":
|
|
break
|
|
|
|
tool_results = []
|
|
for block in response.content:
|
|
if getattr(block, "type", None) == "tool_use" and getattr(block, "name", None) == TOOL_NAME:
|
|
script = block.input.get("script", "")
|
|
_append_comms("OUT", "tool_call", {
|
|
"name": TOOL_NAME,
|
|
"id": block.id,
|
|
"script": script,
|
|
})
|
|
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
|
|
|
|
# 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
|
|
),
|
|
})
|
|
|
|
_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"
|
|
],
|
|
})
|
|
|
|
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 = ".",
|
|
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}")
|