feat reasoning
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@@ -16,7 +16,7 @@ if _cuda_path:
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from ddgs import DDGS # noqa: E402
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from llama_cpp import Llama # noqa: E402
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MODEL_PATH = Path(__file__).parent / "Qwen2.5-Coder-14B-Instruct-Q5_K_M.gguf"
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MODEL_PATH = Path(__file__).parent / "Qwen3.5-27B-IQ4_XS.gguf"
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SYSTEM_PROMPT = (
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"You are a knowledgeable developer assistant. Answer questions about "
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@@ -56,9 +56,20 @@ WEB_SEARCH_TOOL = {
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},
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}
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# Matches the <tool_call>{...}</tool_call> blocks Qwen's chat template trains
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# it to emit when it wants to invoke a tool.
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TOOL_CALL_RE = re.compile(r"<tool_call>\s*(\{.*?\})\s*</tool_call>", re.DOTALL)
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# Matches the <tool_call><function=name>...</function></tool_call> blocks
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# Qwen3.5's chat template trains it to emit when it wants to invoke a tool -
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# an XML-style syntax, not Qwen2.5's JSON-in-<tool_call> format.
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TOOL_CALL_RE = re.compile(
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r"<tool_call>\s*<function=([^>]+)>(.*?)</function>\s*</tool_call>", re.DOTALL
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)
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# Matches each <parameter=name>value</parameter> inside a tool call's body.
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PARAMETER_RE = re.compile(r"<parameter=([^>]+)>\n?(.*?)\n?</parameter>", re.DOTALL)
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# Qwen3.5 thinks by default, wrapping its reasoning trace in these tags
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# before the actual answer (or tool call). _route_stream_piece splits a
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# streamed response between the two so a caller can render them separately.
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THINK_OPEN = "<think>"
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THINK_CLOSE = "</think>"
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def web_search(query: str, max_results: int = 5) -> str:
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@@ -82,42 +93,15 @@ def web_search(query: str, max_results: int = 5) -> str:
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TOOLS = {"web_search": web_search}
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def _iter_balanced_braces(text: str):
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"""Yield each top-level {...} substring of text, respecting nesting."""
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depth = 0
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start = None
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for i, ch in enumerate(text):
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if ch == "{":
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if depth == 0:
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start = i
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depth += 1
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elif ch == "}" and depth > 0:
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depth -= 1
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if depth == 0 and start is not None:
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yield text[start : i + 1]
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def extract_tool_calls(text: str) -> list[dict]:
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calls = []
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for match in TOOL_CALL_RE.finditer(text):
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try:
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calls.append(json.loads(match.group(1)))
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except json.JSONDecodeError:
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continue
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if calls:
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return calls
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# This model often skips the <tool_call> wrapper it's instructed to use
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# and just emits a bare JSON object instead - sometimes with leading
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# prose alongside it (e.g. "Let's try again. {...}") - so scan for any
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# JSON object in the text rather than requiring the whole message to be one.
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for candidate in _iter_balanced_braces(text):
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try:
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call = json.loads(candidate)
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except json.JSONDecodeError:
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continue
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if isinstance(call, dict) and call.get("name") in TOOLS:
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calls.append(call)
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for call_match in TOOL_CALL_RE.finditer(text):
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name = call_match.group(1).strip()
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arguments = {
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param_match.group(1).strip(): param_match.group(2)
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for param_match in PARAMETER_RE.finditer(call_match.group(2))
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}
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calls.append({"name": name, "arguments": arguments})
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return calls
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@@ -133,9 +117,11 @@ def build_messages(history: list[tuple[str, str]], question: str) -> list[dict[s
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def load_model() -> Llama:
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return Llama(
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model_path=str(MODEL_PATH),
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# the model supports up to 32k context natively; give plenty of room
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# for chat history plus a long generated response
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n_ctx=24000,
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# Qwen3.5-27B's hybrid attention keeps KV cache small (most layers
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# carry a fixed-size state rather than one that grows with context),
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# so this fits alongside the IQ4_XS weights on a 16GB card with room
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# to spare - unlike a same-sized dense-attention model.
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n_ctx=32768,
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# -1 offloads every layer to the GPU
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n_gpu_layers=-1,
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# number of tokens in the prompt that are fed into the model at a time
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@@ -144,14 +130,55 @@ def load_model() -> Llama:
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)
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def _route_stream_piece(
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piece: str,
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state: dict,
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on_token: Callable[[str], None] | None,
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on_thinking_token: Callable[[str], None] | None,
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) -> None:
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"""Forward piece to on_thinking_token while inside a <think>...</think>
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block and to on_token otherwise, using state (a per-call {"in_think",
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"buffer"} dict) to track which side of a tag boundary we're on and to
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hold back text that might be the start of a tag split across pieces."""
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buffer = state["buffer"] + piece
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while True:
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tag = THINK_CLOSE if state["in_think"] else THINK_OPEN
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idx = buffer.find(tag)
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if idx == -1:
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break
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before, buffer = buffer[:idx], buffer[idx + len(tag) :]
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callback = on_thinking_token if state["in_think"] else on_token
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if before and callback is not None:
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callback(before)
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state["in_think"] = not state["in_think"]
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# The buffer may end with a prefix of the tag we're now watching for
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# (e.g. a chunk boundary landing mid-"</think>"), so hold that part back
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# rather than misrouting it as ordinary content.
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tag = THINK_CLOSE if state["in_think"] else THINK_OPEN
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keep = 0
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for length in range(min(len(tag), len(buffer)), 0, -1):
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if tag.startswith(buffer[-length:]):
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keep = length
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break
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emit, state["buffer"] = buffer[: len(buffer) - keep], buffer[len(buffer) - keep :]
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callback = on_thinking_token if state["in_think"] else on_token
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if emit and callback is not None:
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callback(emit)
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def _stream_content(
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llm: Llama,
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messages: list[dict],
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tools: list[dict] | None,
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on_token: Callable[[str], None] | None,
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on_thinking_token: Callable[[str], None] | None = None,
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) -> str:
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"""Run one streaming create_chat_completion call, forwarding each piece
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of generated text to on_token as it arrives, and return the full text."""
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of generated text to on_token (or on_thinking_token, while inside a
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<think> block) as it arrives, and return the full raw text, thinking
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tags included, so the caller's message history matches what the model
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actually produced."""
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kwargs = {}
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if tools is not None:
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kwargs["tools"] = tools
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@@ -165,21 +192,40 @@ def _stream_content(
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**kwargs,
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)
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content = ""
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# Qwen3.5's chat template appends "<think>\n" to the prompt itself before
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# generation starts (see its tokenizer.chat_template's add_generation_prompt
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# block), so the model's own streamed output never contains that opening
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# tag - it starts already "inside" one, and only emits "</think>" once
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# reasoning is done.
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route_state = {"in_think": True, "buffer": ""}
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for chunk in stream:
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piece = chunk["choices"][0]["delta"].get("content")
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if piece:
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content += piece
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if on_token is not None:
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on_token(piece)
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_route_stream_piece(piece, route_state, on_token, on_thinking_token)
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if route_state["buffer"]:
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callback = on_thinking_token if route_state["in_think"] else on_token
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if callback is not None:
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callback(route_state["buffer"])
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return content
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def _strip_thinking(text: str) -> str:
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"""Drop a leading reasoning trace from raw model output, the same way
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the chat template splits it back out when re-rendering a past turn:
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everything before the first </think> is reasoning (there's no opening
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tag to look for - see _stream_content), everything after is the answer."""
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before, sep, after = text.partition(THINK_CLOSE)
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return after.lstrip("\n") if sep else before
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def ask(
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llm: Llama,
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messages: list[dict],
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on_tool_call: Callable[[str], None] | None = None,
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on_usage: Callable[[dict], None] | None = None,
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on_token: Callable[[str], None] | None = None,
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on_thinking_token: Callable[[str], None] | None = None,
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) -> str:
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"""Run the chat loop for one question, letting the model call web_search
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up to MAX_TOOL_ROUNDS times before returning its final answer text.
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@@ -189,14 +235,16 @@ def ask(
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model call, so a caller can track how much of the context window is
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filled. If given, on_token is invoked with each piece of generated text
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as it streams from the model, so a caller can render the answer live
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instead of waiting for it to finish."""
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instead of waiting for it to finish. If given, on_thinking_token is
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invoked the same way but for text inside the model's <think> block,
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letting a caller render its reasoning trace separately from the answer."""
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for _ in range(MAX_TOOL_ROUNDS):
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content = _stream_content(llm, messages, [WEB_SEARCH_TOOL], on_token)
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content = _stream_content(llm, messages, [WEB_SEARCH_TOOL], on_token, on_thinking_token)
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if on_usage is not None:
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on_usage({"total_tokens": llm.n_tokens})
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calls = extract_tool_calls(content)
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if not calls:
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return content
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return _strip_thinking(content)
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messages.append({"role": "assistant", "content": content})
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for call in calls:
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@@ -216,7 +264,7 @@ def ask(
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# Ran out of tool-call rounds without a plain answer; ask one more time
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# without offering the tool, forcing it to answer from what it has
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# instead of returning a leftover tool-call as if it were the answer.
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content = _stream_content(llm, messages, None, on_token)
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content = _stream_content(llm, messages, None, on_token, on_thinking_token)
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if on_usage is not None:
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on_usage({"total_tokens": llm.n_tokens})
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return content
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return _strip_thinking(content)
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@@ -6,9 +6,10 @@ from pygments import highlight
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from pygments.formatters import HtmlFormatter
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from pygments.lexers import TextLexer, get_lexer_by_name, guess_lexer
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from pygments.util import ClassNotFound
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from PyQt6.QtCore import Qt, QThread, QTimer, pyqtSignal
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from PyQt6.QtCore import Qt, QThread, QTimer, QUrl, pyqtSignal
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from PyQt6.QtGui import (
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QColor,
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QDesktopServices,
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QFont,
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QIcon,
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QLinearGradient,
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@@ -87,8 +88,14 @@ ROLE_STYLES = {
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"user": (CORAL_GLOW, "#3a2c26"),
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"assistant": (SILVER, "#2f3342"),
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"system": (BLUE_SLATE, "#262a36"),
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"thinking": (TEXT_SECONDARY, "#282b38"),
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}
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# Prefix for the fake anchor href used to make a thinking block's header
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# clickable (e.g. "think:think-3"), intercepted in _on_chat_anchor_clicked
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# rather than let QTextBrowser try to navigate to it.
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_THINK_ANCHOR_PREFIX = "think:"
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_STYLESHEET = f"""
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QMainWindow {{
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background-color: {BG_APP};
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@@ -376,6 +383,7 @@ class AskThread(QThread):
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tool_call = pyqtSignal(str)
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usage = pyqtSignal(dict)
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token = pyqtSignal(str)
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thinking_token = pyqtSignal(str)
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finished_ok = pyqtSignal(str)
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failed = pyqtSignal(str)
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@@ -392,6 +400,7 @@ class AskThread(QThread):
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on_tool_call=self.tool_call.emit,
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on_usage=self.usage.emit,
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on_token=self.token.emit,
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on_thinking_token=self.thinking_token.emit,
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)
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except Exception as e:
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self.failed.emit(str(e))
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@@ -414,6 +423,22 @@ class ChatWindow(QMainWindow):
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# incoming tokens; both None when no answer is currently streaming.
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self._stream_start_pos: int | None = None
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self._stream_body_cursor: QTextCursor | None = None
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# Whether a real (non-newline) answer character has been inserted
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# yet for the in-progress card - the model's text after </think>
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# typically opens with a blank line or two, which _on_token trims
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# rather than showing as a gap at the top of the answer bubble.
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self._answer_started = False
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# Thinking blocks (one per model reasoning round), keyed by an id
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# referenced from the clickable "think:<id>" anchor in their header.
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# Each holds the full reasoning text, whether it's currently shown
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# expanded, a fixed cursor marking where its content region starts,
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# and a live cursor tracking where that region currently ends - see
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# _render_thinking(). self._stream_thinking_id names whichever block
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# (if any) is still actively streaming in the current round.
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self._thinking_blocks: dict[str, dict] = {}
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self._thinking_seq = 0
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self._stream_thinking_id: str | None = None
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central = QWidget()
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self.setCentralWidget(central)
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@@ -448,7 +473,12 @@ class ChatWindow(QMainWindow):
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self.chat_view = QTextBrowser()
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self.chat_view.setObjectName("chatView")
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self.chat_view.setOpenExternalLinks(True)
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# Links are handled by hand in _on_chat_anchor_clicked (real URLs get
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# opened externally there too) rather than left to QTextBrowser,
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# since its own link handling doesn't know about the fake
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# "think:<id>" anchors used to toggle a thinking block.
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self.chat_view.setOpenLinks(False)
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self.chat_view.anchorClicked.connect(self._on_chat_anchor_clicked)
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self.chat_view.setFont(QFont("Segoe UI Variable Text", 10))
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layout.addWidget(self.chat_view)
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@@ -551,6 +581,8 @@ class ChatWindow(QMainWindow):
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self.statusBar().showMessage("Thinking...")
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self._stream_start_pos = None
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self._stream_body_cursor = None
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self._answer_started = False
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self._stream_thinking_id = None
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messages = chatbot.build_messages(self._history, question)
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self._pending_question = question
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@@ -559,12 +591,14 @@ class ChatWindow(QMainWindow):
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self._ask_thread.tool_call.connect(self._on_tool_call)
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self._ask_thread.usage.connect(self._on_usage)
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self._ask_thread.token.connect(self._on_token)
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self._ask_thread.thinking_token.connect(self._on_thinking_token)
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self._ask_thread.finished_ok.connect(self._on_answer)
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self._ask_thread.failed.connect(self._on_ask_failed)
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self._ask_thread.start()
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def _on_token(self, piece: str) -> None:
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if self._stream_body_cursor is None:
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self._collapse_open_thinking()
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self.statusBar().showMessage("Generating...")
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cursor = self.chat_view.textCursor()
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cursor.movePosition(QTextCursor.MoveOperation.End)
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@@ -574,6 +608,13 @@ class ChatWindow(QMainWindow):
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body.insertHtml(f'<span style="color:{accent}; font-weight:600; font-size:8.5pt;">ASSISTANT</span>')
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body.insertBlock(QTextBlockFormat(), QTextCharFormat())
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self._stream_body_cursor = body
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self._answer_started = False
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if not self._answer_started:
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piece = piece.lstrip("\n")
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if not piece:
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return
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self._answer_started = True
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char_format = QTextCharFormat()
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char_format.setForeground(QColor(TEXT_PRIMARY))
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@@ -593,7 +634,95 @@ class ChatWindow(QMainWindow):
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self._stream_start_pos = None
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self._stream_body_cursor = None
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def _on_thinking_token(self, piece: str) -> None:
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if self._stream_thinking_id is None:
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cursor = self.chat_view.textCursor()
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cursor.movePosition(QTextCursor.MoveOperation.End)
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accent, bg = ROLE_STYLES["thinking"]
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body = _insert_message_card(cursor, accent, bg)
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body.insertHtml(f'<span style="color:{accent}; font-weight:600; font-size:8.5pt;">THINKING</span>')
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body.insertBlock(QTextBlockFormat(), QTextCharFormat())
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self._thinking_seq += 1
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block_id = f"think-{self._thinking_seq}"
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content_start = QTextCursor(body)
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# Without this, inserting the header right at content_start's own
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# position (both here and on every later toggle) would drag it
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# forward along with that insertion, per QTextCursor's default -
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# it needs to stay pinned at the true start of the region instead.
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content_start.setKeepPositionOnInsert(True)
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self._thinking_blocks[block_id] = {
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"text": "",
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"expanded": True,
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"content_start": content_start,
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"cursor": body,
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}
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self._insert_thinking_header(body, block_id, expanded=True)
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body.insertBlock(QTextBlockFormat(), QTextCharFormat())
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self._stream_thinking_id = block_id
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block = self._thinking_blocks[self._stream_thinking_id]
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block["text"] += piece
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italic_format = QTextCharFormat()
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italic_format.setForeground(QColor(TEXT_SECONDARY))
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italic_format.setFontItalic(True)
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block["cursor"].insertText(piece, italic_format)
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self.chat_view.moveCursor(QTextCursor.MoveOperation.End)
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def _insert_thinking_header(self, cursor: QTextCursor, block_id: str, expanded: bool) -> None:
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arrow = "▼" if expanded else "▶"
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cursor.insertHtml(
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f'<a href="{_THINK_ANCHOR_PREFIX}{block_id}" '
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f'style="color:{TEXT_SECONDARY}; text-decoration:none;">{arrow} Thinking</a>'
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)
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def _render_thinking(self, block_id: str, expanded: bool) -> None:
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"""Rewrite a thinking block's header and body to match expanded,
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replacing everything from its fixed content_start cursor through
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wherever its content currently ends (tracked by its own cursor,
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which - like any QTextCursor - keeps up with edits made elsewhere in
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the document, so this stays correct even if other messages have
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since been appended below it)."""
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block = self._thinking_blocks[block_id]
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cursor = block["cursor"]
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old_end = cursor.position()
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cursor.setPosition(block["content_start"].position())
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cursor.setPosition(old_end, QTextCursor.MoveMode.KeepAnchor)
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cursor.removeSelectedText()
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||||
|
||||
self._insert_thinking_header(cursor, block_id, expanded)
|
||||
if expanded:
|
||||
cursor.insertBlock(QTextBlockFormat(), QTextCharFormat())
|
||||
italic_format = QTextCharFormat()
|
||||
italic_format.setForeground(QColor(TEXT_SECONDARY))
|
||||
italic_format.setFontItalic(True)
|
||||
cursor.insertText(block["text"], italic_format)
|
||||
block["expanded"] = expanded
|
||||
self.chat_view.moveCursor(QTextCursor.MoveOperation.End)
|
||||
|
||||
def _collapse_open_thinking(self) -> None:
|
||||
if self._stream_thinking_id is not None:
|
||||
self._render_thinking(self._stream_thinking_id, expanded=False)
|
||||
self._stream_thinking_id = None
|
||||
|
||||
def _on_chat_anchor_clicked(self, url: QUrl) -> None:
|
||||
href = url.toString()
|
||||
if not href.startswith(_THINK_ANCHOR_PREFIX):
|
||||
QDesktopServices.openUrl(url)
|
||||
return
|
||||
# A generation currently in flight may be inserting content through
|
||||
# this same block's cursor further down; leave the block alone
|
||||
# rather than risk racing that live cursor.
|
||||
if self._ask_thread is not None:
|
||||
return
|
||||
block_id = href[len(_THINK_ANCHOR_PREFIX) :]
|
||||
block = self._thinking_blocks.get(block_id)
|
||||
if block is None:
|
||||
return
|
||||
self._render_thinking(block_id, expanded=not block["expanded"])
|
||||
|
||||
def _on_tool_call(self, query: str) -> None:
|
||||
self._collapse_open_thinking()
|
||||
self._discard_stream_card()
|
||||
self.statusBar().showMessage(f"Searching: {query}")
|
||||
self._append_system(f"Searching the web for: {query}")
|
||||
@@ -604,6 +733,10 @@ class ChatWindow(QMainWindow):
|
||||
self.context_status_label.setText(f"Context: {total} / {self._n_ctx} ({pct:.0f}%)")
|
||||
|
||||
def _on_answer(self, answer: str) -> None:
|
||||
# Normally already collapsed by the first answer token (_on_token),
|
||||
# but a response that hit max_tokens mid-<think> would leave this
|
||||
# block open with nothing to trigger the collapse - catch that here.
|
||||
self._collapse_open_thinking()
|
||||
# The streamed card holds the raw, plain-text answer; swap it out for
|
||||
# a fully markdown-rendered and syntax-highlighted one now that the
|
||||
# complete text is available.
|
||||
@@ -615,6 +748,7 @@ class ChatWindow(QMainWindow):
|
||||
self._set_input_enabled(True)
|
||||
|
||||
def _on_ask_failed(self, message: str) -> None:
|
||||
self._collapse_open_thinking()
|
||||
self._discard_stream_card()
|
||||
self._append_system(f"Error: {message}")
|
||||
self.statusBar().showMessage("Ready.")
|
||||
|
||||
Reference in new issue
Block a user