# src/llm_core.py
import httpx
import asyncio
import time
import json
import logging
import hashlib
import threading
from fastapi import HTTPException
from typing import Optional, Dict, List
from src.model_context import get_context_length, DEFAULT_CONTEXT
from urllib.parse import urlparse

logger = logging.getLogger(__name__)

class LLMConfig:
    """Configuration constants for LLM operations."""
    DEFAULT_TIMEOUT = 30
    DEFAULT_TEMPERATURE = 1.0
    DEFAULT_MAX_TOKENS = 0
    MAX_RETRIES = 3
    RETRY_DELAY = 0.5
    STREAM_TIMEOUT = 300


# Cache for LLM responses
def _get_cache_key(url: str, model: str, messages: List[Dict], 
                   temperature: float, max_tokens: int) -> str:
    """Generate cache key for LLM requests."""
    hashable_messages = []
    for msg in messages:
        sorted_items = tuple(sorted(msg.items()))
        hashable_messages.append(sorted_items)
    
    content = json.dumps({
        'url': url,
        'model': model, 
        'messages': hashable_messages,
        'temp': temperature,
        'max_tokens': max_tokens
    }, sort_keys=True)
    return hashlib.sha256(content.encode()).hexdigest()

_response_cache = {}

# Dead-host cooldown: maps host (scheme://host:port) -> unix ts when cooldown expires.
# When a connect to a host fails, we mark it dead for DEAD_HOST_COOLDOWN seconds so
# subsequent calls fail instantly instead of waiting on the connect timeout. Keeps
# one unreachable upstream from jamming chat across the rest of the app.
#
# But a SINGLE transient blip (local model briefly busy, a momentary
# Tailscale hiccup) used to trip a full 60s lockout — the user saw a
# 503 and thought the model died when it was fine a second later. So:
#   - require FAIL_THRESHOLD consecutive failures before cooling
#   - shorter cooldown so recovery is quick
#   - any success resets the failure counter immediately
DEAD_HOST_COOLDOWN = 20.0
_HOST_FAIL_THRESHOLD = 2
_dead_hosts: Dict[str, float] = {}
_host_fails: Dict[str, int] = {}
# Guards the two maps above. The synchronous llm_call() runs inside FastAPI's
# threadpool (sync routes such as /sessions/auto-sort) while llm_call_async()
# runs on the event loop, so these maps are mutated from multiple OS threads.
# Without the lock the get()+1+set on _host_fails is a read-modify-write that
# loses failure counts under concurrent connect errors (issue #659).
_host_health_lock = threading.Lock()
_model_activity: Dict[str, float] = {}

def _model_activity_key(url: str, model: str) -> str:
    return f"{(url or '').strip().rstrip()}|{(model or '').strip()}"

def note_model_activity(url: str, model: str):
    """Record that a real upstream request used this endpoint/model."""
    if not url or not model:
        return
    _model_activity[_model_activity_key(url, model)] = time.time()

def seconds_since_model_activity(url: str, model: str) -> Optional[float]:
    """Seconds since the endpoint/model was last used in this process."""
    ts = _model_activity.get(_model_activity_key(url, model))
    if not ts:
        return None
    return max(0.0, time.time() - ts)

def _host_key(url: str) -> str:
    from urllib.parse import urlsplit
    s = urlsplit(url)
    return f"{s.scheme}://{s.netloc}" if s.scheme and s.netloc else url

def _is_host_dead(url: str) -> bool:
    key = _host_key(url)
    with _host_health_lock:
        exp = _dead_hosts.get(key)
        if exp is None:
            return False
        if time.time() >= exp:
            _dead_hosts.pop(key, None)
            return False
        return True

def _mark_host_dead(url: str) -> bool:
    """Record a connect failure. Only actually cools the host after
    _HOST_FAIL_THRESHOLD consecutive failures. Returns True if the host
    is now cooled (so callers can log accurately), False if it's still
    within its allowed-failure grace."""
    key = _host_key(url)
    with _host_health_lock:
        n = _host_fails.get(key, 0) + 1
        _host_fails[key] = n
        if n >= _HOST_FAIL_THRESHOLD:
            _dead_hosts[key] = time.time() + DEAD_HOST_COOLDOWN
            return True
        return False

def _clear_host_dead(url: str) -> None:
    key = _host_key(url)
    with _host_health_lock:
        _dead_hosts.pop(key, None)
        _host_fails.pop(key, None)


# Shared async HTTP client. Reusing one client keeps connections warm:
# repeat calls to api.anthropic.com / api.openai.com / openrouter skip the
# 100-500ms TCP+TLS handshake. Lazy init so we bind to the running event loop.
_http_client: Optional[httpx.AsyncClient] = None
_http_limits = httpx.Limits(max_connections=100, max_keepalive_connections=30, keepalive_expiry=30.0)

def _get_http_client() -> httpx.AsyncClient:
    """Return process-wide AsyncClient. Per-request timeout is passed at call time."""
    global _http_client
    if _http_client is None or _http_client.is_closed:
        _http_client = httpx.AsyncClient(limits=_http_limits, http2=False)
    return _http_client

def _get_cached_response(cache_key: str) -> Optional[str]:
    """Get cached response if it exists."""
    return _response_cache.get(cache_key)

def _set_cached_response(cache_key: str, response: str) -> None:
    """Store response in cache."""
    if len(_response_cache) > 128:
        keys_to_remove = list(_response_cache.keys())[:64]
        for key in keys_to_remove:
            # pop(), not del: another thread (sync llm_call runs in FastAPI's
            # threadpool) may have already evicted the same snapshotted key,
            # and del would raise KeyError mid-eviction (issue #659).
            _response_cache.pop(key, None)
    _response_cache[cache_key] = response

# ── Anthropic native API adapter ──

ANTHROPIC_MODELS = [
    "claude-opus-4-20250514", "claude-opus-4",
    "claude-sonnet-4-20250514", "claude-sonnet-4", "claude-sonnet-4-5-20250929", "claude-sonnet-4-5",
    "claude-haiku-4-20250514", "claude-haiku-4", "claude-haiku-3-5-20241022", "claude-haiku-3-5",
]


def _is_ollama_native_url(url: str) -> bool:
    """Return True for native Ollama API URLs, including Ollama Cloud."""
    try:
        parsed = urlparse(url or "")
    except Exception:
        return False
    host = parsed.hostname or ""
    path = (parsed.path or "").rstrip("/")
    if _host_match(url, "ollama.com"):
        return True
    local_ollama_host = host in {"localhost", "127.0.0.1", "0.0.0.0", "::1"} or parsed.port == 11434
    return local_ollama_host and (path == "/api" or path.startswith("/api/"))


def _ollama_api_root(url: str) -> str:
    """Return a native Ollama API root such as https://ollama.com/api."""
    url = (url or "").strip().rstrip("/")
    parsed = urlparse(url)
    path = (parsed.path or "").rstrip("/")
    if path.endswith("/api/chat"):
        return url[: -len("/chat")]
    if path.endswith("/api/tags"):
        return url[: -len("/tags")]
    if path.endswith("/api/generate"):
        return url[: -len("/generate")]
    if path.endswith("/api"):
        return url
    if _host_match(url, "ollama.com"):
        root = f"{parsed.scheme}://{parsed.netloc}" if parsed.scheme and parsed.netloc else "https://ollama.com"
        return root.rstrip("/") + "/api"
    return url


def _normalize_ollama_url(url: str) -> str:
    """Ensure a native Ollama URL points at /api/chat."""
    base = _ollama_api_root(url)
    return base.rstrip("/") + "/chat"


def _ollama_normalize_tool_messages(messages: List[Dict]) -> List[Dict]:
    """Adapt Odysseus' canonical OpenAI-style messages to native Ollama /api/chat.

    Odysseus carries assistant tool calls in the OpenAI shape, where
    `function.arguments` is a JSON *string*. Native Ollama expects it to be a
    JSON *object*; given the string it fails the whole request with HTTP 400
    "Value looks like object, but can't find closing '}' symbol", which aborts
    every follow-up (tool-result) round. Parse the arguments back into an object
    here, on a shallow copy, leaving non-tool messages untouched. The opaque
    Gemini `extra_content` (thought_signature) is dropped — it is meaningless to
    Ollama and only matters when the conversation is replayed to Gemini.
    """
    out: List[Dict] = []
    for m in messages or []:
        tcs = m.get("tool_calls") if isinstance(m, dict) else None
        if not tcs:
            out.append(m)
            continue
        new_calls = []
        for tc in tcs:
            fn = tc.get("function") or {}
            args = fn.get("arguments")
            if isinstance(args, str):
                try:
                    args = json.loads(args) if args.strip() else {}
                except (json.JSONDecodeError, TypeError):
                    args = {}
            call: Dict = {"function": {"name": fn.get("name", ""), "arguments": args or {}}}
            if tc.get("id"):
                call["id"] = tc["id"]
            new_calls.append(call)
        nm = dict(m)
        nm["tool_calls"] = new_calls
        out.append(nm)
    return out


def _build_ollama_payload(
    model: str,
    messages: List[Dict],
    temperature: float,
    max_tokens: int,
    stream: bool = False,
    tools: Optional[List[Dict]] = None,
    num_ctx: Optional[int] = None,
) -> Dict:
    """Build the JSON payload for Ollama's /api/chat endpoint.

    ``num_ctx`` sets the input context window. Ollama defaults to 2048
    when the option is omitted, so a model with a larger advertised
    window is silently truncated there, and a model with a smaller one
    gets an oversized window it can't service. Pass the discovered
    context length through ``num_ctx``; this builder only emits it when
    the value is trusted (not the ``DEFAULT_CONTEXT`` fallback), so we
    don't guess for unknown models but do tell Ollama the real window
    when we know it — even if it's smaller than 2048.
    """
    payload: Dict = {
        "model": model,
        "messages": _ollama_normalize_tool_messages(messages),
        "stream": stream,
    }
    options: Dict = {}
    if temperature is not None:
        options["temperature"] = temperature
    if max_tokens and max_tokens > 0:
        options["num_predict"] = max_tokens
    if num_ctx is not None and num_ctx > 0 and num_ctx != DEFAULT_CONTEXT:
        options["num_ctx"] = num_ctx
    if options:
        payload["options"] = options
    if tools:
        payload["tools"] = tools
    return payload


def _parse_ollama_response(data: dict) -> str:
    message = data.get("message") or {}
    return message.get("content") or data.get("response") or ""


def _host_match(url: str, *domains: str) -> bool:
    """Return True if url's hostname equals any of `domains` or is a subdomain of one.

    Used by helpers that want "is this Anthropic?" / "is this OpenRouter?"
    style checks. Prefer this over substring matching on the URL: the
    substring form gives wrong answers for unrelated paths or query strings
    that happen to contain the domain text.
    """
    if not url:
        return False
    try:
        # rstrip(".") so a fully-qualified host with a trailing dot
        # ("api.anthropic.com.") still matches "anthropic.com".
        host = (urlparse(url).hostname or "").lower().rstrip(".")
    except Exception:
        return False
    if not host:
        return False
    return any(host == d or host.endswith("." + d) for d in domains)


def _detect_provider(url: str) -> str:
    """Detect the API provider from a configured endpoint URL.

    Matches on hostname (exact or subdomain) rather than substring, so a URL
    that merely contains a provider's domain in its path or query — or a
    look-alike host such as ``anthropic.com.example`` — is not misclassified.
    Unknown hosts fall back to the OpenAI-compatible default, which the
    majority of providers implement.
    """
    if _is_ollama_native_url(url):
        return "ollama"
    if _host_match(url, "anthropic.com"):
        return "anthropic"
    if _host_match(url, "openrouter.ai"):
        return "openrouter"
    if _host_match(url, "groq.com"):
        return "groq"
    return "openai"


def _provider_headers(provider: str, headers: Optional[Dict] = None) -> Dict[str, str]:
    h = {"Content-Type": "application/json"}
    if isinstance(headers, dict):
        h.update(headers)
    if provider == "openrouter":
        h.setdefault("HTTP-Referer", "https://github.com/pewdiepie-archdaemon/odysseus")
        h.setdefault("X-OpenRouter-Title", "Odysseus")
    return h


def _provider_label(url: str) -> str:
    """Human-friendly provider name for error messages."""
    if not url:
        return "provider"
    if _host_match(url, "anthropic.com"): return "Anthropic"
    if _host_match(url, "ollama.com"): return "Ollama Cloud"
    if _host_match(url, "x.ai"): return "xAI"
    if _host_match(url, "openai.com"): return "OpenAI"
    if _host_match(url, "openrouter.ai"): return "OpenRouter"
    if _host_match(url, "groq.com"): return "Groq"
    if _host_match(url, "mistral.ai"): return "Mistral"
    if _host_match(url, "deepseek.com"): return "DeepSeek"
    if _host_match(url, "googleapis.com"): return "Google"
    if _host_match(url, "together.xyz", "together.ai"): return "Together"
    if _host_match(url, "fireworks.ai"): return "Fireworks"
    if _is_ollama_native_url(url): return "Ollama"
    try:
        host = (urlparse(url).hostname or "").lower()
    except Exception:
        return "provider"
    if host in {"localhost", "127.0.0.1", "::1", "0.0.0.0"}:
        return "local endpoint"
    return host or "provider"


def _format_upstream_error(status: int, body: bytes | str, url: str) -> str:
    """Turn an upstream HTTP error into a user-readable sentence.

    Auth failures (401/403) become 'xAI rejected the API key' etc., so the UI
    stops showing raw JSON like '{"error":{"message":"User not found."}}'.
    """
    if isinstance(body, bytes):
        try:
            body = body.decode("utf-8", errors="replace")
        except Exception:
            body = str(body)
    provider = _provider_label(url)
    # Try to pull a message out of the body
    detail = ""
    try:
        j = json.loads(body) if body else {}
        if isinstance(j, dict):
            err = j.get("error") or j
            if isinstance(err, dict):
                detail = (err.get("message") or err.get("detail") or "").strip()
            elif isinstance(err, str):
                detail = err.strip()
    except Exception:
        detail = (body or "").strip()[:240]

    if status in (401, 403):
        msg = f"{provider} rejected the API key"
        if status == 403:
            msg = f"{provider} denied access (403)"
        if detail:
            msg += f" — {detail}"
        msg += ". Check Model Endpoints → {} and re-paste the key.".format(provider)
        return msg
    if status == 404:
        return f"{provider} returned 404 — check the base URL and model name." + (f" ({detail})" if detail else "")
    if status == 429:
        return f"{provider} rate-limited the request (429)." + (f" {detail}" if detail else "")
    if status >= 500:
        return f"{provider} is having an outage (HTTP {status})." + (f" {detail}" if detail else "")
    return f"{provider} returned HTTP {status}" + (f": {detail}" if detail else "")

# Models that require max_completion_tokens instead of max_tokens
_MAX_COMPLETION_TOKENS_MODELS = {"o1", "o3", "o4", "gpt-4.5", "gpt-5"}

def _uses_max_completion_tokens(model: str) -> bool:
    """Check if a model requires max_completion_tokens instead of max_tokens."""
    if not model:
        return False
    m = model.lower()
    return any(m.startswith(p) or f"/{p}" in m for p in _MAX_COMPLETION_TOKENS_MODELS)

# OpenAI reasoning models (o1, o3, o4, gpt-5 families) only accept the default
# temperature. Sending any explicit value — even 0.0 — returns HTTP 400
# ("Only the default (1) value is supported"). That otherwise breaks chat when a
# preset sets a non-default temperature, and makes endpoint probing report a
# perfectly good model as failing. For these models we omit the field and let
# the API use its required default. (gpt-4.5 is intentionally excluded — it is
# not a reasoning model and accepts temperature normally.)
_FIXED_TEMPERATURE_MODELS = ("o1", "o3", "o4", "gpt-5")

def _restricts_temperature(model: str) -> bool:
    """Check if a model rejects any non-default temperature."""
    if not model:
        return False
    m = model.lower()
    return any(m.startswith(p) or f"/{p}" in m for p in _FIXED_TEMPERATURE_MODELS)

# Models that support structured thinking — may output </think> without opening tag
_THINKING_MODEL_PATTERNS = ("qwen3", "qwq", "deepseek-r1", "deepseek-reasoner", "minimax", "m2-reap")

def _supports_thinking(model: str) -> bool:
    """Check if model supports structured thinking output."""
    if not model:
        return False
    m = model.lower()
    return any(p in m for p in _THINKING_MODEL_PATTERNS)

def _convert_openai_content_to_anthropic(content):
    """Convert OpenAI multimodal content blocks to Anthropic format.

    Converts image_url blocks (data URI) → Anthropic image blocks.
    Passes text blocks through unchanged.
    """
    if not isinstance(content, list):
        return content
    converted = []
    for block in content:
        if not isinstance(block, dict):
            converted.append(block)
            continue
        if block.get("type") == "image_url":
            url = (block.get("image_url") or {}).get("url", "")
            # Parse data URI: data:image/<fmt>;base64,<data>
            if url.startswith("data:"):
                try:
                    header, b64_data = url.split(",", 1)
                    media_type = header.split(";")[0].replace("data:", "")
                except (ValueError, IndexError):
                    continue
                converted.append({
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": media_type,
                        "data": b64_data,
                    },
                })
            else:
                # External URL — use Anthropic's URL source
                converted.append({
                    "type": "image",
                    "source": {"type": "url", "url": url},
                })
        elif block.get("type") == "text":
            converted.append(block)
        else:
            converted.append(block)
    return converted


def _build_anthropic_payload(model, messages, temperature, max_tokens, stream=False, tools=None):
    """Convert OpenAI-style messages to Anthropic format."""
    system_parts = []
    chat_messages = []
    for m in messages:
        if m.get("role") == "system":
            system_parts.append(m["content"])
        elif m.get("role") == "tool":
            # Convert OpenAI tool result to Anthropic format
            chat_messages.append({
                "role": "user",
                "content": [{
                    "type": "tool_result",
                    "tool_use_id": m.get("tool_call_id", ""),
                    "content": m.get("content", ""),
                }],
            })
        elif m.get("role") == "assistant" and isinstance(m.get("tool_calls"), list):
            # Convert OpenAI assistant tool_calls to Anthropic format
            content = []
            if m.get("content"):
                content.append({"type": "text", "text": m["content"]})
            for tc in m["tool_calls"]:
                fn = tc.get("function") or {}
                args_str = fn.get("arguments") or "{}"
                try:
                    args = json.loads(args_str) if isinstance(args_str, str) else args_str
                except (json.JSONDecodeError, TypeError):
                    args = {}
                content.append({
                    "type": "tool_use",
                    "id": tc.get("id", ""),
                    "name": fn.get("name", ""),
                    "input": args,
                })
            chat_messages.append({"role": "assistant", "content": content})
        else:
            # Convert multimodal content (image_url → image) for Anthropic
            content = _convert_openai_content_to_anthropic(m["content"])
            chat_messages.append({"role": m["role"], "content": content})
    payload = {
        "model": model,
        "messages": chat_messages,
        "max_tokens": max_tokens if max_tokens and max_tokens > 0 else 4096,
        "temperature": temperature,
    }
    if system_parts:
        system_text = "\n\n".join(system_parts)
        # Send `system` as a structured text block so we can attach a prompt-cache
        # breakpoint. The agent loop re-sends this same large prefix every round;
        # caching it makes Anthropic re-read it from cache (~90% cheaper, lower TTFB)
        # instead of re-billing it. Skip caching tiny one-off prompts, where the
        # cache-WRITE premium wouldn't pay back (no reuse). Presence of `tools`
        # means an agentic/multi-round call, where the prefix is always reused.
        system_block = {"type": "text", "text": system_text}
        if tools or len(system_text) > 4000:
            system_block["cache_control"] = {"type": "ephemeral"}
        payload["system"] = [system_block]
    if stream:
        payload["stream"] = True
    # Convert OpenAI-format tools to Anthropic format
    if tools:
        anthropic_tools = []
        for t in tools:
            if t.get("type") == "function":
                fn = t["function"]
                anthropic_tools.append({
                    "name": fn["name"],
                    "description": fn.get("description", ""),
                    "input_schema": fn.get("parameters", {"type": "object", "properties": {}}),
                })
        if anthropic_tools:
            # Cache the tool schemas too — they're stable for the whole agent run.
            # The breakpoint caches all tool defs preceding it in the request.
            anthropic_tools[-1]["cache_control"] = {"type": "ephemeral"}
            payload["tools"] = anthropic_tools
    return payload

def _build_anthropic_headers(headers):
    """Convert Bearer auth to x-api-key for Anthropic."""
    h = {"Content-Type": "application/json", "anthropic-version": "2023-06-01"}
    if headers:
        for k, v in headers.items():
            if k.lower() == "authorization" and isinstance(v, str) and v.startswith("Bearer "):
                h["x-api-key"] = v[7:]
            else:
                h[k] = v
    return h

def _parse_anthropic_response(data: dict) -> str:
    """Extract text from an Anthropic response.

    The Messages API `content` is an array that can hold more than one text
    block (e.g. text split around a tool_use block, or citation-segmented
    text). Concatenate them all instead of returning only the first, which
    silently dropped the rest of the reply.
    """
    return "".join(
        block.get("text", "")
        for block in data.get("content", [])
        if isinstance(block, dict) and block.get("type") == "text"
    )


def _as_content_blocks(content) -> List[Dict]:
    """Coerce a message `content` into a list of content blocks.

    A list (multimodal: text + image parts) passes through; a non-empty string
    becomes a single text block; None/empty yields no blocks. Used when merging
    consecutive user messages so multimodal content isn't str()-ed away.
    """
    if isinstance(content, list):
        return content
    if content:
        return [{"type": "text", "text": str(content)}]
    return []


def _sanitize_llm_messages(messages: List[Dict]) -> List[Dict]:
    """Strip Odysseus-only metadata before sending messages to providers.

    Per the OpenAI chat format: user/system messages must have content; a tool
    message needs content + tool_call_id; an assistant message may carry content,
    tool_calls, or both. The old guard required content on every message, which
    dropped a valid assistant message that has only tool_calls — e.g. the
    follow-up message _append_tool_results builds for a no-prose native tool call
    (content=None, since Gemini/Ollama reject tool_calls alongside ""). Dropping
    it leaves the tool result dangling and breaks the next round.
    """
    allowed = {"role", "content", "name", "tool_call_id", "tool_calls", "function_call"}
    cleaned = []
    for msg in messages or []:
        if not isinstance(msg, dict):
            continue
        item = {k: v for k, v in msg.items() if k in allowed and v is not None}
        role = item.get("role")
        if not role:
            continue
        if role == "assistant":
            # Re-add an explicit content=None when the message is tool-calls-only
            # (the None was stripped above) so the provider gets the spec-correct
            # `content: null`, not an omitted key.
            if "content" not in item and item.get("tool_calls"):
                item["content"] = None
            if "content" in item or item.get("tool_calls"):
                cleaned.append(item)
        elif role == "tool":
            if "content" in item and "tool_call_id" in item:
                cleaned.append(item)
        elif "content" in item:
            cleaned.append(item)

    # Repair tool-call adjacency before sending to any OpenAI-compatible
    # provider. Trimming/compaction/retries can leave `role:"tool"` messages
    # without their immediately-preceding assistant `tool_calls` parent, which
    # DeepSeek rejects with:
    # "Messages with role 'tool' must be a response to a preceding message with
    # 'tool_calls'". Also strip unanswered assistant tool_calls; some providers
    # reject those as incomplete conversations.
    repaired: List[Dict] = []
    i = 0
    while i < len(cleaned):
        msg = cleaned[i]
        role = msg.get("role")

        if role == "tool":
            # Orphan tool result. There is no valid assistant tool_calls parent
            # immediately before this batch, so it cannot be sent.
            logger.debug("Dropping orphan tool message before provider request")
            i += 1
            continue

        tool_calls = msg.get("tool_calls") if role == "assistant" else None
        if not tool_calls:
            repaired.append(msg)
            i += 1
            continue

        call_ids = [
            str(tc.get("id"))
            for tc in tool_calls
            if isinstance(tc, dict) and tc.get("id")
        ]
        expected = set(call_ids)
        answered_ids = []
        tool_batch = []
        j = i + 1
        while j < len(cleaned) and cleaned[j].get("role") == "tool":
            tid = str(cleaned[j].get("tool_call_id") or "")
            if tid in expected and tid not in answered_ids:
                answered_ids.append(tid)
                tool_batch.append(cleaned[j])
            else:
                logger.debug("Dropping unmatched/duplicate tool message before provider request")
            j += 1

        if not tool_batch:
            plain = {k: v for k, v in msg.items() if k != "tool_calls"}
            if (plain.get("content") or "").strip():
                repaired.append(plain)
            else:
                logger.debug("Dropping unanswered assistant tool_calls before provider request")
            i = j
            continue

        answered = set(answered_ids)
        pruned_calls = [
            tc for tc in tool_calls
            if isinstance(tc, dict) and str(tc.get("id")) in answered
        ]
        fixed = dict(msg)
        fixed["tool_calls"] = pruned_calls
        if "content" not in fixed:
            fixed["content"] = None
        repaired.append(fixed)
        repaired.extend(tool_batch)
        if len(pruned_calls) != len(tool_calls):
            logger.debug("Pruned unanswered assistant tool_calls before provider request")
        i = j

    # Merge consecutive user messages to satisfy strict role alternation
    # requirements after invalid tool-call fragments have been removed.
    merged: List[Dict] = []
    for item in repaired:
        if not merged:
            merged.append(item)
            continue

        last = merged[-1]
        if last.get("role") == "user" and item.get("role") == "user":
            last_copy = dict(last)
            lc = last_copy.get("content")
            ic = item.get("content")
            if isinstance(lc, list) or isinstance(ic, list):
                # Preserve multimodal content blocks (e.g. an image part) by
                # concatenating the block lists. str()-ing a list turned an
                # image message into its Python repr and dropped the image.
                merged_blocks = _as_content_blocks(lc) + _as_content_blocks(ic)
                if merged_blocks:
                    last_copy["content"] = merged_blocks
                else:
                    last_copy.pop("content", None)
            else:
                last_str = str(lc) if lc is not None else ""
                item_str = str(ic) if ic is not None else ""
                new_content = "\n\n".join(part for part in (last_str, item_str) if part)
                if new_content:
                    last_copy["content"] = new_content
                else:
                    last_copy.pop("content", None)
            merged[-1] = last_copy
        else:
            merged.append(item)

    return merged

def _normalize_anthropic_url(url: str) -> str:
    """Ensure Anthropic URL points to /v1/messages."""
    url = url.rstrip("/")
    if url.endswith("/v1/messages"):
        return url
    if url.endswith("/v1"):
        return url + "/messages"
    return url + "/v1/messages"

def list_model_ids(base_chat_url: str, timeout: int = LLMConfig.DEFAULT_TIMEOUT, headers: Optional[Dict] = None) -> List[str]:
    """List available model IDs from an endpoint."""
    provider = _detect_provider(base_chat_url)
    if provider == "anthropic":
        return list(ANTHROPIC_MODELS)
    try:
        h = {}
        if headers:
            h.update(headers)
        if provider == "ollama":
            models_url = _ollama_api_root(base_chat_url) + "/tags"
        else:
            models_url = base_chat_url.replace("/chat/completions", "/models")
        r = httpx.get(models_url, headers=h, timeout=timeout)
        r.raise_for_status()
        data = r.json()
        model_ids = [m.get("id") for m in (data.get("data") or []) if m.get("id")]
        if not model_ids:
            model_ids = [
                m.get("name") or m.get("model")
                for m in (data.get("models") or [])
                if m.get("name") or m.get("model")
            ]
        return model_ids
    except Exception:
        try:
            if ":11434" in base_chat_url or "ollama" in base_chat_url.lower():
                root = base_chat_url.replace("/v1/chat/completions", "").replace("/chat/completions", "").rstrip("/")
                r = httpx.get(root + "/api/tags", timeout=timeout)
                r.raise_for_status()
                return [m.get("name") or m.get("model") for m in (r.json().get("models") or []) if m.get("name") or m.get("model")]
        except Exception:
            pass
        return []

def normalize_model_id(endpoint_url: str, requested: str, timeout: int = LLMConfig.DEFAULT_TIMEOUT) -> Optional[str]:
    """Normalize a model ID to match available models."""
    avail = list_model_ids(endpoint_url, timeout)
    if not avail:
        return None
    if requested in avail:
        return requested
    import os as _os
    req_base = _os.path.basename(requested.rstrip("/"))
    for a in avail:
        if _os.path.basename(a.rstrip("/")) == req_base:
            return a
    return None

def llm_call(url: str, model: str, messages: List[Dict], temperature: float = LLMConfig.DEFAULT_TEMPERATURE,
             max_tokens: int = LLMConfig.DEFAULT_MAX_TOKENS, headers: Optional[Dict] = None, 
             timeout: int = LLMConfig.DEFAULT_TIMEOUT, prompt_type: Optional[str] = None) -> str:
    """Synchronous LLM call with optional prompt type enhancement."""
    h = _provider_headers(_detect_provider(url))
    # Tolerate headers that arrive as a JSON string (some sessions stored them
    # double-encoded) — otherwise h.update() throws "dictionary update sequence
    # element #0 has length 1; 2 is required".
    if isinstance(headers, str):
        try:
            headers = json.loads(headers)
        except Exception:
            headers = None
    if isinstance(headers, dict):
        h.update(headers)

    messages_copy = _sanitize_llm_messages(messages)

    # Consolidate multiple system messages into one at the start.
    sys_parts = []
    non_sys = []
    for m in messages_copy:
        if m.get("role") == "system":
            sys_parts.append(m["content"])
        else:
            non_sys.append(m)
    if sys_parts:
        messages_copy = [{"role": "system", "content": "\n\n".join(sys_parts)}] + non_sys
    else:
        messages_copy = non_sys

    provider = _detect_provider(url)
    cache_key = _get_cache_key(url, model, messages_copy, temperature, max_tokens)
    cached_response = _get_cached_response(cache_key)
    if cached_response:
        logger.debug(f"Returning cached response for key: {cache_key}")
        return cached_response

    if provider == "anthropic":
        target_url = _normalize_anthropic_url(url)
        h = _build_anthropic_headers(headers)
        payload = _build_anthropic_payload(model, messages_copy, temperature, max_tokens)
    elif provider == "ollama":
        target_url = _normalize_ollama_url(url)
        payload = _build_ollama_payload(
            model, messages_copy, temperature, max_tokens,
            stream=False, num_ctx=get_context_length(url, model),
        )
    else:
        target_url = url
        payload = {
            "model": model,
            "messages": messages_copy,
            "temperature": temperature,
        }
        if _restricts_temperature(model):
            payload.pop("temperature", None)
        if max_tokens and max_tokens > 0:
            tok_key = "max_completion_tokens" if _uses_max_completion_tokens(model) else "max_tokens"
            payload[tok_key] = max_tokens
    try:
        note_model_activity(target_url, model)
        r = httpx.post(target_url, headers=h, json=payload, timeout=timeout)
    except Exception as e:
        raise HTTPException(502, f"POST {target_url} failed: {e}")
    if not r.is_success:
        raise HTTPException(502, f"Upstream {target_url} -> {r.status_code}: {r.text}")
    data = r.json()
    try:
        if provider == "anthropic":
            response = _parse_anthropic_response(data)
        elif provider == "ollama":
            response = _parse_ollama_response(data)
        else:
            msg = data["choices"][0]["message"]
            response = msg.get("content") or msg.get("reasoning_content") or ""
        _set_cached_response(cache_key, response)
        return response
    except Exception:
        raise HTTPException(502, f"Unexpected schema from {target_url}: {str(data)[:400]}")


def llm_call_with_fallback(candidates, messages, **kwargs) -> str:
    """Sync `llm_call` with an ordered fallback chain.

    `candidates` is a list of (url, model, headers). The first one that returns
    without an exception wins. Connection / 5xx-style failures fall through to
    the next candidate. The dead-host cooldown inside `llm_call` makes repeat
    attempts at an offline primary effectively free.
    """
    cands = [c for c in (candidates or []) if c and c[0] and c[1]]
    if not cands:
        raise HTTPException(503, "No model endpoint configured")
    last_err = None
    for i, (url, model, headers) in enumerate(cands):
        try:
            return llm_call(url, model, messages, headers=headers, **kwargs)
        except Exception as e:
            last_err = e
            tag = "primary" if i == 0 else "candidate"
            logger.warning(f"[fallback] {tag} {model} failed ({type(e).__name__}); trying next")
            continue
    raise last_err if last_err else HTTPException(503, "All fallback candidates failed")


async def llm_call_async_with_fallback(candidates, messages, **kwargs) -> str:
    """Async variant of `llm_call_with_fallback` — same semantics."""
    cands = [c for c in (candidates or []) if c and c[0] and c[1]]
    if not cands:
        raise HTTPException(503, "No model endpoint configured")
    last_err = None
    for i, (url, model, headers) in enumerate(cands):
        try:
            return await llm_call_async(url, model, messages, headers=headers, **kwargs)
        except Exception as e:
            last_err = e
            tag = "primary" if i == 0 else "candidate"
            logger.warning(f"[fallback] {tag} {model} failed ({type(e).__name__}); trying next")
            continue
    raise last_err if last_err else HTTPException(503, "All fallback candidates failed")


async def llm_call_async(
    url: str,
    model: str,
    messages: List[Dict],
    temperature: float = LLMConfig.DEFAULT_TEMPERATURE,
    max_tokens: int = LLMConfig.DEFAULT_MAX_TOKENS,
    headers: Optional[Dict] = None,
    timeout: int = LLMConfig.STREAM_TIMEOUT,
    max_retries: int = LLMConfig.MAX_RETRIES,
    prompt_type: Optional[str] = None
) -> str:
    """Asynchronous LLM call using httpx with connection pooling, timeout, retry logic, and performance logging."""
    provider = _detect_provider(url)
    messages_copy = _sanitize_llm_messages(messages)

    # Consolidate multiple system messages into one at the start.
    sys_parts = []
    non_sys = []
    for m in messages_copy:
        if m.get("role") == "system":
            sys_parts.append(m["content"])
        else:
            non_sys.append(m)
    if sys_parts:
        messages_copy = [{"role": "system", "content": "\n\n".join(sys_parts)}] + non_sys
    else:
        messages_copy = non_sys

    cache_key = _get_cache_key(url, model, messages_copy, temperature, max_tokens)
    cached_response = _get_cached_response(cache_key)
    if cached_response:
        logger.debug(f"Returning cached response for key: {cache_key}")
        return cached_response

    if provider == "anthropic":
        target_url = _normalize_anthropic_url(url)
        h = _build_anthropic_headers(headers)
        payload = _build_anthropic_payload(model, messages_copy, temperature, max_tokens)
    elif provider == "ollama":
        target_url = _normalize_ollama_url(url)
        h = {"Content-Type": "application/json"}
        if headers:
            h.update(headers)
        payload = _build_ollama_payload(
            model, messages_copy, temperature, max_tokens,
            stream=False, num_ctx=get_context_length(url, model),
        )
    else:
        target_url = url
        h = _provider_headers(provider, headers)
        payload = {
            "model": model,
            "messages": messages_copy,
            "temperature": temperature,
        }
        if _restricts_temperature(model):
            payload.pop("temperature", None)
        if max_tokens and max_tokens > 0:
            tok_key = "max_completion_tokens" if _uses_max_completion_tokens(model) else "max_tokens"
            payload[tok_key] = max_tokens

    if _is_host_dead(target_url):
        raise HTTPException(503, f"Upstream {_host_key(target_url)} marked unreachable (cooldown active)")

    call_timeout = httpx.Timeout(connect=3.0, read=float(timeout), write=10.0, pool=5.0)
    attempt = 0
    while attempt < max_retries:
        attempt += 1
        start = time.time()
        try:
            note_model_activity(target_url, model)
            client = _get_http_client()
            r = await client.post(target_url, headers=h, json=payload, timeout=call_timeout)
            duration = time.time() - start
            if not r.is_success:
                friendly = _format_upstream_error(r.status_code, r.text, target_url)
                logger.warning(
                    f"LLM async call to {target_url} failed in {duration:.2f}s "
                    f"(attempt {attempt}): HTTP {r.status_code} {friendly}"
                )
                raise HTTPException(r.status_code, friendly)
            logger.info(f"LLM async call to {target_url} succeeded in {duration:.2f}s (attempt {attempt})")
            _clear_host_dead(target_url)
            data = r.json()
            try:
                if provider == "anthropic":
                    response = _parse_anthropic_response(data)
                elif provider == "ollama":
                    response = _parse_ollama_response(data)
                else:
                    msg = data["choices"][0]["message"]
                    response = msg.get("content") or msg.get("reasoning_content") or ""
                _set_cached_response(cache_key, response)
                return response
            except Exception:
                raise HTTPException(502, f"Unexpected schema from {target_url}: {str(data)[:400]}")
        except (httpx.ConnectError, httpx.ConnectTimeout) as e:
            _cooled = _mark_host_dead(target_url)
            duration = time.time() - start
            _tail = f" — host cooled for {DEAD_HOST_COOLDOWN:.0f}s" if _cooled else " — transient, will retry"
            logger.warning(f"LLM async connect to {target_url} failed after {duration:.2f}s: {e}{_tail}")
            raise HTTPException(503, f"Cannot reach {_host_key(target_url)}: {e}")
        except (httpx.RequestError, httpx.HTTPStatusError) as e:
            duration = time.time() - start
            logger.warning(f"LLM async call attempt {attempt} failed after {duration:.2f}s: {e}")
            if attempt >= max_retries:
                raise HTTPException(502, f"POST {target_url} failed after {max_retries} attempts: {e}")
            await asyncio.sleep(LLMConfig.RETRY_DELAY)

async def stream_llm(url: str, model: str, messages: List[Dict], temperature: float = LLMConfig.DEFAULT_TEMPERATURE,
                     max_tokens: int = LLMConfig.DEFAULT_MAX_TOKENS, headers: Optional[Dict] = None,
                     timeout: int = LLMConfig.STREAM_TIMEOUT, prompt_type: Optional[str] = None,
                     tools: Optional[List[Dict]] = None):
    """Stream LLM responses with improved error handling.

    Yields SSE chunks:
      - data: {"delta": "text"}           — text content
      - data: {"type": "tool_calls", ...}  — accumulated native tool calls (before DONE)
      - event: error                       — errors
      - data: [DONE]                       — end of stream
    """
    provider = _detect_provider(url)
    messages_copy = _sanitize_llm_messages(messages)

    # Consolidate multiple system messages into one at the start.
    # Some models (e.g. Qwen3.5) reject system messages that aren't first.
    sys_parts = []
    non_sys = []
    for m in messages_copy:
        if m.get("role") == "system":
            sys_parts.append(m["content"])
        else:
            non_sys.append(m)
    if sys_parts:
        messages_copy = [{"role": "system", "content": "\n\n".join(sys_parts)}] + non_sys
    else:
        messages_copy = non_sys

    if provider == "anthropic":
        target_url = _normalize_anthropic_url(url)
        h = _build_anthropic_headers(headers)
        payload = _build_anthropic_payload(model, messages_copy, temperature, max_tokens, stream=True, tools=tools)
    elif provider == "ollama":
        target_url = _normalize_ollama_url(url)
        h = {"Content-Type": "application/json"}
        if headers:
            h.update(headers)
        payload = _build_ollama_payload(
            model, messages_copy, temperature, max_tokens,
            stream=True, tools=tools, num_ctx=get_context_length(url, model),
        )
    else:
        target_url = url
        payload = {
            "model": model,
            "messages": messages_copy,
            "temperature": temperature,
            "stream": True,
        }
        if _restricts_temperature(model):
            payload.pop("temperature", None)
        if provider not in {"openrouter", "groq"}:
            payload["stream_options"] = {"include_usage": True}
        if max_tokens and max_tokens > 0:
            tok_key = "max_completion_tokens" if _uses_max_completion_tokens(model) else "max_tokens"
            payload[tok_key] = max_tokens
        if tools:
            payload["tools"] = tools
        h = _provider_headers(provider, headers)

    # Short connect timeout: a reachable peer answers SYN in <100ms even on
    # Tailscale. 3s is plenty; 30s let one dead upstream wedge the UI.
    stream_timeout = httpx.Timeout(connect=3.0, read=float(timeout), write=30.0, pool=5.0)

    if _is_host_dead(target_url):
        yield f'event: error\ndata: {json.dumps({"error": f"Upstream {_host_key(target_url)} unreachable (cooldown active)", "status": 503})}\n\n'
        return
    note_model_activity(target_url, model)

    # ── Native Ollama streaming ──
    if provider == "ollama":
        _ollama_tool_calls: List[Dict] = []
        try:
            client = _get_http_client()
            async with client.stream('POST', target_url, json=payload, headers=h, timeout=stream_timeout) as r:
                _clear_host_dead(target_url)
                if r.status_code != 200:
                    raw = (await r.aread()).decode(errors="replace")
                    friendly = _format_upstream_error(r.status_code, raw, target_url)
                    yield f'event: error\ndata: {json.dumps({"status": r.status_code, "text": friendly, "raw": raw[:500]})}\n\n'
                    return
                async for line in r.aiter_lines():
                    if not line:
                        continue
                    try:
                        j = json.loads(line)
                    except json.JSONDecodeError:
                        continue
                    message = j.get("message") or {}
                    thinking = message.get("thinking") or ""
                    if thinking:
                        yield f'data: {json.dumps({"delta": thinking, "thinking": True})}\n\n'
                    content = message.get("content") or ""
                    if content:
                        yield f'data: {json.dumps({"delta": content})}\n\n'
                    for tc in message.get("tool_calls") or []:
                        fn = tc.get("function") or {}
                        if fn.get("name"):
                            _ollama_tool_calls.append({
                                "id": tc.get("id") or f"call_{len(_ollama_tool_calls)}",
                                "name": fn.get("name") or "",
                                "arguments": json.dumps(fn.get("arguments") or {}),
                            })
                    if j.get("done"):
                        if _ollama_tool_calls:
                            yield f'data: {json.dumps({"type": "tool_calls", "calls": _ollama_tool_calls})}\n\n'
                        if j.get("prompt_eval_count") is not None or j.get("eval_count") is not None:
                            yield f'data: {json.dumps({"type": "usage", "data": {"input_tokens": j.get("prompt_eval_count", 0), "output_tokens": j.get("eval_count", 0)}})}\n\n'
                        yield "data: [DONE]\n\n"
                        return
                yield "data: [DONE]\n\n"
        except (httpx.ConnectError, httpx.ConnectTimeout) as e:
            _cooled = _mark_host_dead(target_url)
            _tail = f" — host cooled for {DEAD_HOST_COOLDOWN:.0f}s" if _cooled else " — transient, will retry"
            logger.warning(f"Ollama stream connect to {target_url} failed: {e}{_tail}")
            yield f'event: error\ndata: {json.dumps({"error": f"Cannot reach {_host_key(target_url)}", "status": 503})}\n\n'
        except httpx.ReadTimeout:
            yield f'event: error\ndata: {json.dumps({"error": "Read timeout", "status": 504})}\n\n'
        except httpx.NetworkError:
            yield f'event: error\ndata: {json.dumps({"error": "Network error", "status": 502})}\n\n'
        except Exception as e:
            logger.error(f"Ollama stream error: {e}")
            yield f'event: error\ndata: {json.dumps({"error": str(e), "status": 502})}\n\n'
        return

    # ── Anthropic streaming ──
    if provider == "anthropic":
        _anth_input_tokens = 0
        _anth_output_tokens = 0
        # Track tool_use blocks: {index: {id, name, arguments_json}}
        _anth_tool_blocks: Dict[int, Dict] = {}
        _anth_block_idx = -1
        _anth_block_type = ""
        try:
            client = _get_http_client()
            async with client.stream('POST', target_url, json=payload, headers=h, timeout=stream_timeout) as r:
                _clear_host_dead(target_url)
                if r.status_code != 200:
                    raw = (await r.aread()).decode(errors="replace")
                    friendly = _format_upstream_error(r.status_code, raw, target_url)
                    yield f'event: error\ndata: {json.dumps({"status": r.status_code, "text": friendly, "raw": raw[:500]})}\n\n'
                    return
                async for line in r.aiter_lines():
                    if not line or not line.startswith("data: "):
                        continue
                    data = line[6:].strip()
                    if not data or not data.startswith("{"):
                        continue
                    try:
                        j = json.loads(data)
                        evt = j.get("type", "")
                        if evt == "content_block_start":
                            _anth_block_idx = j.get("index", _anth_block_idx + 1)
                            cb = j.get("content_block") or {}
                            _anth_block_type = cb.get("type", "text")
                            if _anth_block_type == "tool_use":
                                _anth_tool_blocks[_anth_block_idx] = {
                                    "id": cb.get("id") or f"call_{_anth_block_idx}",
                                    "name": cb.get("name") or "",
                                    "arguments": "",
                                }
                        elif evt == "content_block_delta":
                            delta = j.get("delta") or {}
                            delta_type = delta.get("type", "")
                            if delta_type == "text_delta":
                                text = delta.get("text") or ""
                                if text:
                                    yield f'data: {json.dumps({"delta": text})}\n\n'
                            elif delta_type == "input_json_delta":
                                # Accumulate tool arguments JSON
                                idx = j.get("index", _anth_block_idx)
                                if idx in _anth_tool_blocks:
                                    partial = delta.get("partial_json") or ""
                                    _anth_tool_blocks[idx]["arguments"] += partial
                                    # Stream tool arg deltas for doc tools
                                    if partial and _anth_tool_blocks[idx].get("name") in ("create_document", "update_document", "edit_document"):
                                        yield f'data: {json.dumps({"type": "tool_call_delta", "index": idx, "name": _anth_tool_blocks[idx]["name"], "arg_delta": partial})}\n\n'
                        elif evt == "message_start":
                            _u = j.get("message", {}).get("usage", {})
                            _anth_input_tokens = _u.get("input_tokens", 0)
                            # Surface prompt-cache effectiveness: cache_read > 0 means the
                            # stable system+tools prefix was served from cache this round.
                            _c_read = _u.get("cache_read_input_tokens", 0)
                            _c_write = _u.get("cache_creation_input_tokens", 0)
                            if _c_read or _c_write:
                                logger.info(
                                    "[anthropic-cache] read=%s write=%s fresh_input=%s",
                                    _c_read, _c_write, _anth_input_tokens,
                                )
                        elif evt == "message_delta":
                            _anth_output_tokens = j.get("usage", {}).get("output_tokens", 0)
                        elif evt == "message_stop":
                            # Emit accumulated tool calls in OpenAI-compatible format
                            if _anth_tool_blocks:
                                calls = []
                                for idx in sorted(_anth_tool_blocks):
                                    tb = _anth_tool_blocks[idx]
                                    calls.append({
                                        "id": tb["id"],
                                        "name": tb["name"],
                                        "arguments": tb["arguments"],
                                    })
                                yield f'data: {json.dumps({"type": "tool_calls", "calls": calls})}\n\n'
                            if _anth_input_tokens or _anth_output_tokens:
                                yield f'data: {json.dumps({"type": "usage", "data": {"input_tokens": _anth_input_tokens, "output_tokens": _anth_output_tokens}})}\n\n'
                            yield "data: [DONE]\n\n"
                            return
                        elif evt == "error":
                            err_msg = j.get("error", {}).get("message", "Unknown error")
                            yield f'event: error\ndata: {json.dumps({"error": err_msg, "status": 400})}\n\n'
                            return
                    except json.JSONDecodeError:
                        continue
                yield "data: [DONE]\n\n"
        except (httpx.ConnectError, httpx.ConnectTimeout) as e:
            _cooled = _mark_host_dead(target_url)
            _tail = f" — host cooled for {DEAD_HOST_COOLDOWN:.0f}s" if _cooled else " — transient, will retry"
            logger.warning(f"Anthropic stream connect to {target_url} failed: {e}{_tail}")
            yield f'event: error\ndata: {json.dumps({"error": f"Cannot reach {_host_key(target_url)}", "status": 503})}\n\n'
        except httpx.ReadTimeout:
            yield f'event: error\ndata: {json.dumps({"error": "Read timeout", "status": 504})}\n\n'
        except httpx.NetworkError:
            yield f'event: error\ndata: {json.dumps({"error": "Network error", "status": 502})}\n\n'
        except Exception as e:
            logger.error(f"Anthropic stream error: {e}")
            yield f'event: error\ndata: {json.dumps({"error": str(e), "status": 502})}\n\n'
        return

    # ── OpenAI-compatible streaming ──
    # Accumulate native tool_calls across streaming chunks
    _tc_acc: Dict[int, Dict] = {}  # index -> {id, name, arguments}
    _tc_last_idx = [-1]  # most-recently-touched slot, for providers that omit `index`
    # For thinking models: prepend <think> to first content delta so frontend
    # can detect thinking-in-progress (some models output </think> but no <think>)
    _thinking_model = _supports_thinking(model)
    _first_content_sent = False

    def _emit_tool_calls():
        """Build the tool_calls event string if any were accumulated."""
        if not _tc_acc:
            return None
        calls = [_tc_acc[i] for i in sorted(_tc_acc)]
        return f'data: {json.dumps({"type": "tool_calls", "calls": calls})}\n\n'

    try:
        client = _get_http_client()
        async with client.stream('POST', target_url, json=payload, headers=h, timeout=stream_timeout) as r:
            _clear_host_dead(target_url)
            if r.status_code != 200:
                raw = (await r.aread()).decode(errors="replace")
                friendly = _format_upstream_error(r.status_code, raw, target_url)
                yield f'event: error\ndata: {json.dumps({"status": r.status_code, "text": friendly, "raw": raw[:500]})}\n\n'
                return

            async for line in r.aiter_lines():
                if not line:
                    continue

                if line.startswith("data: "):
                    data = line[6:].strip()
                    if data == "[DONE]":
                        tc_event = _emit_tool_calls()
                        if tc_event:
                            yield tc_event
                        yield "data: [DONE]\n\n"
                        return

                    try:
                        if data.strip():
                            if data.startswith("{"):
                                j = json.loads(data)
                                # Usage chunk (from stream_options)
                                _choices = j.get("choices") or []
                                _delta0 = _choices[0].get("delta") if _choices else None
                                if "usage" in j and _delta0 in (None, {}, {"content": None}):
                                    u = j["usage"]
                                    _usage_data = {"input_tokens": u.get("prompt_tokens", 0), "output_tokens": u.get("completion_tokens", 0)}
                                    # llama.cpp puts a `timings` block alongside `usage` with the
                                    # TRUE generation speed (predicted_per_second) — pure decode,
                                    # excluding prefill/network. Pass it through so the UI shows the
                                    # real gen t/s instead of recomputing tokens/wall-clock (which
                                    # includes prefill and reads ~20-40% low). Prefill speed too.
                                    _tm = j.get("timings")
                                    if isinstance(_tm, dict):
                                        if _tm.get("predicted_per_second"):
                                            _usage_data["gen_tps"] = round(_tm["predicted_per_second"], 2)
                                        if _tm.get("prompt_per_second"):
                                            _usage_data["prefill_tps"] = round(_tm["prompt_per_second"], 2)
                                    yield f'data: {json.dumps({"type": "usage", "data": _usage_data})}\n\n'
                                elif "choices" in j:
                                    delta = j["choices"][0].get("delta") or {}
                                    if isinstance(delta, dict):
                                        # Text content
                                        # Reasoning tokens (VLLM --reasoning-parser, e.g. Qwen3/DeepSeek-R1, Nemotron). vLLM 0.20.2 / NIM emit the field as `reasoning`; older builds use `reasoning_content`. Accept either.
                                        reasoning = delta.get("reasoning_content") or delta.get("reasoning") or ""
                                        if reasoning:
                                            yield f'data: {json.dumps({"delta": reasoning, "thinking": True})}\n\n'
                                        content = delta.get("content") or ""
                                        if content:
                                            # Some thinking backends start normal content with a
                                            # stray closing tag. Repair only that shape; do not
                                            # wrap every first token for model families like
                                            # MiniMax, which often stream ordinary answers.
                                            if _thinking_model and not _first_content_sent and content.lstrip().lower().startswith("</think"):
                                                content = "<think>" + content
                                            _first_content_sent = True
                                            yield f'data: {json.dumps({"delta": content})}\n\n'
                                        # Native tool calls — accumulate across chunks
                                        for tc in delta.get("tool_calls") or []:
                                            func = tc.get("function") or {}
                                            raw_idx = tc.get("index")
                                            if raw_idx is None:
                                                # Gemini's OpenAI-compat layer omits `index` on
                                                # parallel tool calls (every delta arrives as
                                                # index=None) and sends each call complete in one
                                                # delta. Without this, all parallel calls collide
                                                # into slot 0 — later calls overwrite the first's
                                                # name and CORRUPT its arguments by concatenation,
                                                # so only one malformed call survives and the
                                                # follow-up round 400s. A function name marks the
                                                # start of a new call → allocate a fresh slot;
                                                # an arg-only continuation attaches to the last.
                                                if func.get("name") or _tc_last_idx[0] < 0:
                                                    # Next free slot ABOVE any existing key (not
                                                    # len()), so a provider mixing integer indices
                                                    # with index=None can never collide.
                                                    idx = max(_tc_acc, default=-1) + 1
                                                else:
                                                    idx = _tc_last_idx[0]
                                            else:
                                                idx = raw_idx
                                            _tc_last_idx[0] = idx
                                            if idx not in _tc_acc:
                                                _tc_acc[idx] = {"id": "", "name": "", "arguments": ""}
                                            if tc.get("id"):
                                                _tc_acc[idx]["id"] = tc["id"]
                                            # Gemini 3 returns an opaque thought_signature in
                                            # extra_content on the function-call delta. It MUST be
                                            # echoed back on the assistant tool_call next round or the
                                            # follow-up request 400s ("Function call is missing a
                                            # thought_signature"). Preserve it verbatim; other
                                            # providers never send it, so this is a no-op for them.
                                            if tc.get("extra_content"):
                                                _tc_acc[idx]["extra_content"] = tc["extra_content"]
                                            if func.get("name"):
                                                _tc_acc[idx]["name"] = func["name"]
                                            if "arguments" in func:
                                                _tc_acc[idx]["arguments"] += func["arguments"]
                                                # Stream tool arg deltas for doc tools
                                                if func["arguments"] and _tc_acc[idx].get("name") in ("create_document", "update_document", "edit_document"):
                                                    yield f'data: {json.dumps({"type": "tool_call_delta", "index": idx, "name": _tc_acc[idx]["name"], "arg_delta": func["arguments"]})}\n\n'
                                elif "text" in j:
                                    if j["text"]:
                                        yield f'data: {json.dumps({"delta": j["text"]})}\n\n'
                            else:
                                if data.strip():
                                    yield f'data: {json.dumps({"delta": data})}\n\n'
                    except Exception as e:
                        logger.error(f"Error parsing stream data: {e}")
                        continue

            # End of stream (no explicit [DONE] received)
            tc_event = _emit_tool_calls()
            if tc_event:
                yield tc_event
            yield "data: [DONE]\n\n"

    except (httpx.ConnectError, httpx.ConnectTimeout) as e:
        _cooled = _mark_host_dead(target_url)
        _tail = f" — host cooled for {DEAD_HOST_COOLDOWN:.0f}s" if _cooled else " — transient, will retry"
        logger.warning(f"Stream connect to {target_url} failed: {e}{_tail}")
        yield f'event: error\ndata: {json.dumps({"error": f"Cannot reach {_host_key(target_url)}", "status": 503})}\n\n'
    except httpx.ReadTimeout:
        yield f'event: error\ndata: {json.dumps({"error": "Read timeout", "status": 504})}\n\n'
    except httpx.NetworkError:
        yield f'event: error\ndata: {json.dumps({"error": "Network error", "status": 502})}\n\n'
    except Exception as e:
        logger.error(f"Stream error: {e}")
        yield f'event: error\ndata: {json.dumps({"error": str(e), "status": 502})}\n\n'


def _summarize_stream_error(err_chunk: Optional[str]) -> str:
    """Pull a short human reason out of an `event: error` SSE chunk for the
    fallback notice. Returns a generic message if it can't be parsed."""
    if not err_chunk:
        return "primary model failed"
    try:
        for line in err_chunk.split("\n"):
            if line.startswith("data: "):
                j = json.loads(line[6:])
                txt = j.get("text") or j.get("error") or ""
                status = j.get("status")
                msg = (f"HTTP {status}: " if status else "") + str(txt)
                return msg[:200].strip() or "primary model failed"
    except Exception:
        pass
    return "primary model failed"


async def stream_llm_with_fallback(candidates, messages, **kwargs):
    """Wrap stream_llm with an ordered fallback chain.

    `candidates` is a list of (url, model, headers). Each is tried in order,
    but only retried on a *pre-content* failure — i.e. an ``event: error``
    that arrives before any assistant text / tool-call data has been yielded.
    Once a candidate has emitted real output we never switch (that would
    duplicate streamed tokens); a later error from that candidate passes
    through unchanged. The dead-host cooldown in stream_llm makes repeat
    attempts at an offline primary effectively instant.

    Yields the same SSE chunk protocol as stream_llm.
    """
    cands = [c for c in (candidates or []) if c and c[0] and c[1]]
    if not cands:
        yield f'event: error\ndata: {json.dumps({"error": "No model endpoint configured", "status": 503})}\n\n'
        return

    primary_model = cands[0][1]
    last_error = None
    for i, (url, model, headers) in enumerate(cands):
        is_last = (i == len(cands) - 1)
        emitted = False
        retried = False
        async for chunk in stream_llm(url, model, messages, headers=headers, **kwargs):
            if chunk.startswith("event: error"):
                if not emitted and not is_last:
                    # Pre-content failure with fallbacks left — swallow and
                    # move to the next candidate.
                    last_error = chunk
                    retried = True
                    if i == 0:
                        logger.warning(f"[fallback] primary {model} failed before output; trying fallback")
                    else:
                        logger.warning(f"[fallback] candidate {model} failed; trying next")
                    break
                yield chunk
                continue
            # Any data chunk other than the terminal [DONE] means real output.
            if chunk.startswith("data: ") and not chunk.startswith("data: [DONE]"):
                # First real output from a NON-primary candidate: tell the client
                # the selected model failed and another answered. Without this the
                # fallback is invisible — a misconfigured provider looks like it
                # works because the reply is shown under the originally selected
                # model's name (e.g. a Bedrock/Claude endpoint that 400s every
                # request but appears fine because another model silently answered).
                if not emitted and i > 0:
                    yield ('data: ' + json.dumps({
                        "type": "fallback",
                        "selected_model": primary_model,
                        "answered_by": model,
                        "reason": _summarize_stream_error(last_error),
                    }) + '\n\n')
                emitted = True
            yield chunk
        if not retried:
            return  # candidate finished (success, or terminal error already sent)
    # Every candidate failed pre-content — surface the last error.
    if last_error:
        yield last_error