from __future__ import annotations import json import logging import threading from collections.abc import Callable from typing import Any, Literal, Union from openai.types.realtime import ConversationItem from openai.types.realtime.conversation_item import ( RealtimeConversationItemAssistantMessage, RealtimeConversationItemFunctionCall, RealtimeConversationItemFunctionCallOutput, RealtimeConversationItemSystemMessage, RealtimeConversationItemUserMessage, ) from openai.types.realtime.realtime_conversation_item_assistant_message import ( Content as AssistantContent, ) from openai.types.realtime.realtime_conversation_item_system_message import Content as SystemContent from openai.types.realtime.realtime_conversation_item_user_message import Content as UserContent from openai.types.realtime.realtime_response_create_params import RealtimeResponseCreateParams from openai.types.responses.response_input_image_param import ResponseInputImageParam from openai.types.responses.response_input_message_content_list_param import ( ResponseInputMessageContentListParam, ) from openai.types.responses.response_input_param import ( FunctionCallOutput, ResponseFunctionToolCallParam, ResponseInputItemParam, ResponseInputParam, ResponseOutputMessageParam, ) from openai.types.responses.response_input_param import ( Message as ResponseMessage, ) from openai.types.responses.response_input_text_param import ResponseInputTextParam from openai.types.responses.response_output_text_param import ResponseOutputTextParam from pydantic import BaseModel from speech_to_speech.utils.utils import _generate_id logger = logging.getLogger(__name__) class ChatItemError(Exception): """Raised when a conversation item fails validation in :meth:`Chat.add_item`.""" class CompactionResult(BaseModel): """Output of a :data:`CompactFn` summarization run.""" user_summary: str assistant_summary: str def _ensure_id(value: str | None, prefix: str) -> str: if value is None: return _generate_id(prefix) if not value.startswith(f"{prefix}_"): raise ChatItemError(f"ID must start with '{prefix}_', got {value!r}") return value SupportedItem = Union[ RealtimeConversationItemSystemMessage, RealtimeConversationItemUserMessage, RealtimeConversationItemAssistantMessage, RealtimeConversationItemFunctionCall, RealtimeConversationItemFunctionCallOutput, ] CompactFn = Callable[[ResponseInputParam], CompactionResult] class Chat: """Manages conversation history with bounded size to avoid OOM issues. The buffer stores ``ConversationItem`` objects (user messages, assistant messages, function calls, function call outputs). System messages are stored separately in ``init_chat_message`` and never placed in the buffer. History bounding is decided per ``add_item`` call via the ``compactor`` argument: - ``compactor=None``: when the user-turn count exceeds ``size`` the oldest complete turn is evicted in place. Synchronous, lossy, no LLM involvement. - ``compactor=``: when ``size`` is exceeded, ``fn`` is invoked in a background thread to summarize older turns into a single user/assistant pair (with pending function calls preserved). Single-flight: while a compaction is running, additional triggers are silently bypassed. """ def __init__(self, size: int) -> None: self.size = size self.init_chat_message: RealtimeConversationItemSystemMessage | None = None # ``size`` is the number of user turns to keep. When exceeded the # oldest complete turn (everything up to the next user message) # is evicted -- or, with a compactor, summarized in the background. self.buffer: list[SupportedItem] = [] self._pending_tool_calls: dict[str, RealtimeConversationItemFunctionCall] = {} self._user_turn_count: int = 0 # All state mutations and serializations go through _lock. Public methods # acquire it once; internal callers that already hold it use the # ``_locked`` helpers, so no reentry is needed (regular Lock is safe). self._lock = threading.Lock() self._compact_in_flight: bool = False self._compact_thread: threading.Thread | None = None self._shutdown = threading.Event() self._gen_counter = 0 # ── Internal mutators (caller holds _lock) ───────────────── def _evict_oldest_turn(self) -> None: """Remove items from the front until the next user message boundary.""" if not self.buffer: return first = self.buffer.pop(0) if isinstance(first, RealtimeConversationItemUserMessage): self._user_turn_count -= 1 while self.buffer and not isinstance(self.buffer[0], RealtimeConversationItemUserMessage): self.buffer.pop(0) def _has_call_id_in_buffer(self, call_id: str) -> bool: for entry in self.buffer: if isinstance(entry, RealtimeConversationItemFunctionCall) and entry.call_id == call_id: return True return False def _mark_call_completed( self, call_id: str, status: Literal["completed", "incomplete", "in_progress"] | None = None ) -> None: """Set ``status`` to ``"completed"`` on the matching function_call.""" for entry in self.buffer: if isinstance(entry, RealtimeConversationItemFunctionCall) and entry.call_id == call_id: entry.status = "completed" if status is None else status return def append_tool_output(self, call_id: str, output_item: RealtimeConversationItemFunctionCallOutput) -> None: """Append a ``function_call_output``, re-injecting its ``function_call`` if evicted. Also marks the paired ``function_call`` as ``"completed"`` if its status was ``None``. Raises :class:`ChatItemError` if *call_id* is unknown. """ with self._lock: self._append_tool_output_locked(call_id, output_item) def _append_tool_output_locked(self, call_id: str, output_item: RealtimeConversationItemFunctionCallOutput) -> None: """Body of :meth:`append_tool_output`. Caller must hold ``_lock``.""" if self._has_call_id_in_buffer(call_id): self._pending_tool_calls.pop(call_id, None) self._mark_call_completed(call_id, output_item.status) self.buffer.append(output_item) return if call_id in self._pending_tool_calls: logger.info("Re-injecting evicted function_call for call_id=%s", call_id) fc = self._pending_tool_calls.pop(call_id) fc.status = "completed" if output_item.status is None else output_item.status self.buffer.append(fc) self.buffer.append(output_item) return raise ChatItemError(f"No function_call with call_id '{call_id}' found in conversation history.") def init_chat(self, message: RealtimeConversationItemSystemMessage) -> None: with self._lock: self.init_chat_message = message def add_item(self, item: SupportedItem) -> SupportedItem: """Validate and route a conversation item into the chat buffer. Does not enforce the soft size limit — call :meth:`trim_if_needed` explicitly after each successful generation to evict or compact old turns. A hard upper bound at ``2 * size`` is enforced inline as a runaway-client safety net: if the user-turn count exceeds it, the oldest complete turn is evicted (lossy, no compaction). Raises :class:`ChatItemError` if the item fails validation. """ with self._lock: if isinstance(item, RealtimeConversationItemSystemMessage): item.id = _ensure_id(item.id, "sys") self.init_chat_message = item logger.debug("Set system message via conversation item") elif isinstance(item, RealtimeConversationItemUserMessage): item.id = _ensure_id(item.id, "msg") item.content = [ p for p in item.content if (p.type == "input_text" and p.text) or (p.type == "input_image" and p.image_url) ] if not item.content: raise ChatItemError( "Message has no supported content. Supported modalities: input_text, input_image." ) self.buffer.append(item) self._user_turn_count += 1 logger.debug("Added user message to chat (%d parts)", len(item.content)) elif isinstance(item, RealtimeConversationItemAssistantMessage): item.id = _ensure_id(item.id, "msg") item.content = [p for p in item.content if p.type == "output_text" and p.text] if not item.content: return item self.buffer.append(item) logger.debug("Added assistant message to chat (%d parts)", len(item.content)) elif isinstance(item, RealtimeConversationItemFunctionCall): item.id = _ensure_id(item.id, "fc") item.call_id = _ensure_id(item.call_id, "call") self._pending_tool_calls[item.call_id] = item logger.debug("Added function_call to chat (call_id=%s)", item.call_id) elif isinstance(item, RealtimeConversationItemFunctionCallOutput): item.id = _ensure_id(item.id, "fco") self._append_tool_output_locked(item.call_id, item) logger.debug("Added function_call_output to chat (call_id=%s)", item.call_id) else: raise ChatItemError(f"Unsupported item type: {getattr(item, 'type', None)}") if self.size > 0 and self._user_turn_count > 2 * self.size: logger.warning( "Chat buffer exceeded hard cap (%d > 2 * size=%d); evicting oldest turn", self._user_turn_count, self.size, ) while self._user_turn_count > 2 * self.size: self._evict_oldest_turn() return item def trim_if_needed(self, compactor: CompactFn | None = None) -> None: """Enforce the size limit after a generation completes. Fires when ``user_turn_count > size``. - ``compactor=None``: synchronous eviction of the oldest complete turn. - ``compactor=``: launch a background compaction (single-flight). Call once after each successful generation, not inside :meth:`add_item`. """ with self._lock: if self._user_turn_count <= self.size: return if compactor is not None: self._maybe_trigger_compaction(compactor) else: while self._user_turn_count > self.size: self._evict_oldest_turn() def replace_user_message_text(self, item_id: str, text: str) -> bool: """Replace the text content of an existing user message. Used by speculative turn revisions: the conversation turn remains the same, but the STT transcript is superseded by a transcription of a longer raw-audio buffer. """ with self._lock: for item in self.buffer: if not isinstance(item, RealtimeConversationItemUserMessage) or item.id != item_id: continue item.content = [UserContent(type="input_text", text=text)] logger.debug("Replaced speculative user message %s", item_id) return True return False def remove_user_message(self, item_id: str) -> bool: """Remove an existing user message from the bounded chat buffer.""" with self._lock: for index, item in enumerate(self.buffer): if not isinstance(item, RealtimeConversationItemUserMessage) or item.id != item_id: continue del self.buffer[index] self._user_turn_count -= 1 logger.debug("Removed speculative user message %s", item_id) return True return False def to_responses_api_chat(self, items: list[SupportedItem] | None = None) -> ResponseInputParam: """Serialize the chat (system prompt + buffer) for the OpenAI Responses API. If *items* is provided, serialize that slice instead of the live buffer (used by the compaction snapshot). """ with self._lock: return self._to_responses_api_chat_locked(items if items is not None else self.buffer) def _to_responses_api_chat_locked(self, items: list[SupportedItem]) -> ResponseInputParam: """Body of :meth:`to_responses_api_chat`. Caller must hold ``_lock``.""" buffer_items = list(items) result: list[ResponseInputItemParam] = [] if self.init_chat_message: result.append( ResponseMessage( content=[ ResponseInputTextParam(text=p.text or "A helpful AI assistant.", type="input_text") for p in self.init_chat_message.content ], role="system", type="message", ) ) for item in buffer_items: assert item.id is not None and item.id != "", f"item.id is {item.id}" if isinstance(item, RealtimeConversationItemUserMessage): content: ResponseInputMessageContentListParam = [] for user_part in item.content: if user_part.type == "input_text" and user_part.text is not None: content.append(ResponseInputTextParam(text=user_part.text or "", type="input_text")) elif user_part.type == "input_image" and user_part.image_url is not None: img = ResponseInputImageParam(type="input_image", detail=user_part.detail or "auto") if user_part.image_url is not None: img["image_url"] = user_part.image_url content.append(img) if content: result.append(ResponseMessage(content=content, role="user", type="message")) elif isinstance(item, RealtimeConversationItemAssistantMessage): assistant_content: list[ResponseOutputTextParam] = [] for assistant_part in item.content: if assistant_part.type == "output_text" and assistant_part.text is not None: assistant_content.append( ResponseOutputTextParam(text=assistant_part.text, type="output_text", annotations=[]) ) if assistant_content: result.append( ResponseOutputMessageParam( id=item.id, content=assistant_content, role="assistant", status=item.status or "completed", type="message", ) ) elif isinstance(item, RealtimeConversationItemFunctionCall) and item.call_id is not None: assert item.call_id is not None and item.call_id != "" function_call = ResponseFunctionToolCallParam( arguments=item.arguments, call_id=item.call_id, name=item.name, type="function_call", id=item.id, ) if item.id is not None: function_call["id"] = item.id if item.status is not None: function_call["status"] = item.status result.append(function_call) elif isinstance(item, RealtimeConversationItemFunctionCallOutput): function_call_output = FunctionCallOutput( call_id=item.call_id, output=item.output, type="function_call_output", ) if item.id is not None: function_call_output["id"] = item.id if item.status is not None: function_call_output["status"] = item.status result.append(function_call_output) return result def to_transformers_chat(self) -> list[dict[str, Any]]: """Serialize the full chat for HuggingFace transformers ``apply_chat_template``. User messages with only text produce a plain string ``content`` value. User messages containing images keep ``content`` as a list of dicts so VLM pipelines can process them. """ with self._lock: messages: list[TransformersChatMessage] = [] if self.init_chat_message: text = " ".join(p.text for p in self.init_chat_message.content if p.text) messages.append(TransformersSystemMessage(content=text)) for item in self.buffer: if isinstance(item, RealtimeConversationItemUserMessage): has_images = any(p.type == "input_image" for p in item.content) if has_images: messages.append( TransformersUserMessage(content=[p.model_dump(exclude_none=True) for p in item.content]) ) else: text = " ".join(p.text for p in item.content if p.type == "input_text" and p.text) messages.append(TransformersUserMessage(content=text)) elif isinstance(item, RealtimeConversationItemAssistantMessage): text = " ".join(p.text for p in item.content if p.text) messages.append(TransformersAssistantMessage(content=text)) elif isinstance(item, RealtimeConversationItemFunctionCall): assert item.call_id is not None and item.call_id != "" args: Any = item.arguments try: args = json.loads(args) if isinstance(args, str) else args except (json.JSONDecodeError, TypeError): args = {} messages.append( TransformersFunctionCallMessage( tool_calls=[ TransformersToolCall( id=item.call_id, function=TransformersToolCallFunction(name=item.name, arguments=args), ) ] ) ) elif isinstance(item, RealtimeConversationItemFunctionCallOutput): name = "" for prev in reversed(messages): if isinstance(prev, TransformersFunctionCallMessage): for tc in prev.tool_calls: if tc.id == item.call_id: name = tc.function.name break if name: break messages.append( TransformersToolMessage( tool_call_id=item.call_id, name=name, content=item.output, ) ) return [m.model_dump() for m in messages] def copy(self) -> Chat: """Return a shallow snapshot safe for concurrent read access.""" with self._lock: clone = Chat(self.size) clone.init_chat_message = self.init_chat_message clone.buffer = list(self.buffer) clone._pending_tool_calls = dict(self._pending_tool_calls) clone._user_turn_count = self._user_turn_count return clone def reset(self) -> None: """Clear all conversation state. Cancels any in-flight compaction splice.""" with self._lock: self._gen_counter += 1 self._compact_in_flight = False self.buffer = [] self.init_chat_message = None self._pending_tool_calls = {} self._user_turn_count = 0 def close(self) -> None: """Permanently shut down the chat. In-flight compaction splice is suppressed. The compaction worker (a daemon thread) is not joined: it may be blocked in an LLM call. Process exit reaps it. """ self._shutdown.set() with self._lock: self._gen_counter += 1 self._compact_in_flight = False def strip_images(self) -> None: """Remove all image content parts from user messages in the buffer. Called after appending the assistant response so images don't persist across turns. """ with self._lock: for item in self.buffer: if isinstance(item, RealtimeConversationItemUserMessage): item.content = [p for p in item.content if p.type != "input_image"] # ── Compaction internals ────────────────────────────────── def _snapshot_for_compaction( self, ) -> tuple[ResponseInputParam, set[str], int]: """Compute the snapshot of items eligible for compaction. Caller must hold ``_lock``. Returns ``(serialized_snapshot, marker_ids, n_turns)``. ``marker_ids`` identifies the buffer items that may be removed when the splice runs. Always leaves the most recent user turn untouched (it may be in-flight). Returns an empty result if there are fewer than 2 compactable turns. """ n_turns = max(0, self._user_turn_count - 1) if n_turns < 2: return [], set(), n_turns # Slice up to (but not including) the (n_turns + 1)-th user message. user_seen = 0 end_idx = len(self.buffer) for i, entry in enumerate(self.buffer): if isinstance(entry, RealtimeConversationItemUserMessage): user_seen += 1 if user_seen == n_turns + 1: end_idx = i break items_to_compact = self.buffer[:end_idx] marker_ids = {entry.id for entry in items_to_compact if entry.id is not None} snapshot = self._to_responses_api_chat_locked(items=items_to_compact) # Strip image parts so the summarizer doesn't have to handle them. for raw in snapshot: if not isinstance(raw, dict) or raw.get("role") != "user": continue msg: dict[str, Any] = raw # type: ignore[assignment] content = msg.get("content") if isinstance(content, list): msg["content"] = [c for c in content if not (isinstance(c, dict) and c.get("type") == "input_image")] return snapshot, marker_ids, n_turns def _maybe_trigger_compaction(self, compactor: CompactFn) -> None: """Start a background compaction worker. Bypass silently if one is running. Caller must hold ``_lock``. """ if self._shutdown.is_set() or self._compact_in_flight: return snapshot, marker_ids, n_turns = self._snapshot_for_compaction() if n_turns < 2 or not marker_ids: return gen = self._gen_counter self._compact_in_flight = True thread = threading.Thread( target=self._compact_worker, args=(compactor, snapshot, marker_ids, gen), daemon=True, name="chat-compact", ) self._compact_thread = thread logger.info( "Chat compaction triggered: compacting %d turn(s) (%d item(s)), buffer size=%d", n_turns, len(marker_ids), len(self.buffer), ) thread.start() def _compact_worker( self, compactor: CompactFn, snapshot: ResponseInputParam, marker_ids: set[str], gen: int, ) -> None: """Worker thread entry point.""" try: if self._shutdown.is_set() or self._gen_counter != gen: return try: result = compactor(snapshot) except Exception: logger.exception("Chat compaction failed; chat unchanged") return if not isinstance(result, CompactionResult): logger.error("Compactor must return a CompactionResult, got %r", type(result).__name__) return if self._shutdown.is_set() or self._gen_counter != gen: return self._apply_compaction(result, marker_ids, gen) finally: # Don't clobber the flag if reset/close has advanced the gen. with self._lock: if self._gen_counter == gen: self._compact_in_flight = False def _apply_compaction( self, result: CompactionResult, marker_ids: set[str], gen: int, ) -> None: """Splice the summary in front of items not consumed by compaction. FC/FCO pairing is left entirely to :meth:`add_item` / :meth:`append_tool_output`. Compaction only drops items; it never inserts an FC into the buffer. Pending FCs (no FCO yet) stay in ``_pending_tool_calls`` and will be appended adjacent to their FCO when it arrives. """ with self._lock: if self._shutdown.is_set() or self._gen_counter != gen: return # Keep FC if its FCO is outside the compacted range -- otherwise # the FCO in `remaining` would be orphaned. fco_call_ids_in_range = { x.call_id for x in self.buffer if isinstance(x, RealtimeConversationItemFunctionCallOutput) and x.id in marker_ids } fc_ids_to_keep = { x.id for x in self.buffer if x.id in marker_ids and isinstance(x, RealtimeConversationItemFunctionCall) and x.call_id not in fco_call_ids_in_range } drop_ids = marker_ids - fc_ids_to_keep remaining = [x for x in self.buffer if x.id not in drop_ids] user_msg = make_user_message(result.user_summary) user_msg.id = _generate_id("msg") asst_msg = make_assistant_message(result.assistant_summary) asst_msg.id = _generate_id("msg") self.buffer = [user_msg, asst_msg, *remaining] self._user_turn_count = sum(1 for x in self.buffer if isinstance(x, RealtimeConversationItemUserMessage)) logger.info( "Chat compaction applied: buffer now %d item(s), %d user turn(s)", len(self.buffer), self._user_turn_count, ) # --------------------------------------------------------------------------- # Transformers chat message models # --------------------------------------------------------------------------- class TransformersToolCallFunction(BaseModel): name: str arguments: dict[str, Any] class TransformersToolCall(BaseModel): type: Literal["function"] = "function" id: str function: TransformersToolCallFunction class TransformersSystemMessage(BaseModel): role: Literal["system"] = "system" content: str class TransformersUserMessage(BaseModel): role: Literal["user"] = "user" content: str | list[dict[str, Any]] class TransformersAssistantMessage(BaseModel): role: Literal["assistant"] = "assistant" content: str class TransformersFunctionCallMessage(BaseModel): role: Literal["assistant"] = "assistant" tool_calls: list[TransformersToolCall] class TransformersToolMessage(BaseModel): role: Literal["tool"] = "tool" tool_call_id: str name: str content: str TransformersChatMessage = Union[ TransformersSystemMessage, TransformersUserMessage, TransformersAssistantMessage, TransformersFunctionCallMessage, TransformersToolMessage, ] # --------------------------------------------------------------------------- # Factory helpers -- hide verbose constructors behind simple calls # --------------------------------------------------------------------------- def make_user_message(text: str) -> RealtimeConversationItemUserMessage: return RealtimeConversationItemUserMessage( type="message", role="user", content=[UserContent(type="input_text", text=text)], ) def make_assistant_message(text: str) -> RealtimeConversationItemAssistantMessage: return RealtimeConversationItemAssistantMessage( type="message", role="assistant", content=[AssistantContent(type="output_text", text=text)], ) def make_system_message(text: str) -> RealtimeConversationItemSystemMessage: return RealtimeConversationItemSystemMessage( type="message", role="system", content=[SystemContent(type="input_text", text=text)], ) def add_supported_item(chat: Chat, item: ConversationItem) -> None: """Narrow a protocol ``ConversationItem`` to a :data:`SupportedItem` and add it to *chat*. Raises :class:`ChatItemError` on validation failure or unsupported type. Shared by the conversation handler (in-band item injection) and the language-model handlers (seeding an out-of-band response's throwaway chat from ``response.input``). """ # call_id on function_call items must be client-supplied: it is referenced later by # function_call_output items, so we cannot silently generate one here. if isinstance(item, RealtimeConversationItemFunctionCall) and ( item.call_id is None or not item.call_id.startswith("call_") ): raise ChatItemError("function_call item is missing a call_id. The call_id should start with 'call_'.") if isinstance( item, ( RealtimeConversationItemSystemMessage, RealtimeConversationItemUserMessage, RealtimeConversationItemAssistantMessage, RealtimeConversationItemFunctionCall, RealtimeConversationItemFunctionCallOutput, ), ): chat.add_item(item) return raise ChatItemError(f"Unsupported item type: {getattr(item, 'type', None)}") def build_active_chat(original_chat: Chat, response: RealtimeResponseCreateParams | None) -> Chat: """Build the chat an *out-of-band* response generates against (caller ensures out-of-band). Mirrors the OpenAI realtime semantics for ``input``: - ``input is None`` -> a read-only **copy of the default conversation** (the out-of-band response reads history but never commits back). - ``input == []`` -> a **fresh, empty chat** (context cleared; only the system prompt, added later by the handler, will be present). - ``input == [...]`` -> a **fresh chat seeded** with those items. Raises :class:`ChatItemError` if an ``input`` item fails validation. """ if response is not None and response.input is not None: fresh = Chat(original_chat.size) for item in response.input: add_supported_item(fresh, item) return fresh return original_chat.copy()