MemGPT/letta/interfaces/openai_streaming_interface.py

from datetime import datetime, timezone
from typing import AsyncGenerator, List, Optional

from openai import AsyncStream
from openai.types.chat.chat_completion_chunk import ChatCompletionChunk

from letta.constants import DEFAULT_MESSAGE_TOOL, DEFAULT_MESSAGE_TOOL_KWARG
from letta.schemas.letta_message import AssistantMessage, LettaMessage, ReasoningMessage, ToolCallDelta, ToolCallMessage
from letta.schemas.letta_message_content import TextContent
from letta.schemas.message import Message
from letta.schemas.openai.chat_completion_response import FunctionCall, ToolCall
from letta.server.rest_api.json_parser import OptimisticJSONParser
from letta.streaming_utils import JSONInnerThoughtsExtractor


class OpenAIStreamingInterface:
    """
    Encapsulates the logic for streaming responses from OpenAI.
    This class handles parsing of partial tokens, pre-execution messages,
    and detection of tool call events.
    """

    def __init__(self, use_assistant_message: bool = False, put_inner_thoughts_in_kwarg: bool = False):
        self.use_assistant_message = use_assistant_message
        self.assistant_message_tool_name = DEFAULT_MESSAGE_TOOL
        self.assistant_message_tool_kwarg = DEFAULT_MESSAGE_TOOL_KWARG

        self.optimistic_json_parser: OptimisticJSONParser = OptimisticJSONParser()
        self.function_args_reader = JSONInnerThoughtsExtractor(wait_for_first_key=True)  # TODO: pass in kward
        self.function_name_buffer = None
        self.function_args_buffer = None
        self.function_id_buffer = None
        self.last_flushed_function_name = None

        # Buffer to hold function arguments until inner thoughts are complete
        self.current_function_arguments = ""
        self.current_json_parse_result = {}

        # Premake IDs for database writes
        self.letta_assistant_message_id = Message.generate_id()
        self.letta_tool_message_id = Message.generate_id()

        self.message_id = None
        self.model = None

        # token counters
        self.input_tokens = 0
        self.output_tokens = 0

        self.content_buffer: List[str] = []
        self.tool_call_name: Optional[str] = None
        self.tool_call_id: Optional[str] = None
        self.reasoning_messages = []

    def get_reasoning_content(self) -> List[TextContent]:
        content = "".join(self.reasoning_messages)
        return [TextContent(text=content)]

    def get_tool_call_object(self) -> ToolCall:
        """Useful for agent loop"""
        return ToolCall(
            id=self.letta_tool_message_id,
            function=FunctionCall(arguments=self.current_function_arguments, name=self.last_flushed_function_name),
        )

    async def process(self, stream: AsyncStream[ChatCompletionChunk]) -> AsyncGenerator[LettaMessage, None]:
        """
        Iterates over the OpenAI stream, yielding SSE events.
        It also collects tokens and detects if a tool call is triggered.
        """
        async with stream:
            prev_message_type = None
            message_index = 0
            async for chunk in stream:
                if not self.model or not self.message_id:
                    self.model = chunk.model
                    self.message_id = chunk.id

                # track usage
                if chunk.usage:
                    self.input_tokens += chunk.usage.prompt_tokens
                    self.output_tokens += chunk.usage.completion_tokens

                if chunk.choices:
                    choice = chunk.choices[0]
                    message_delta = choice.delta

                    if message_delta.tool_calls is not None and len(message_delta.tool_calls) > 0:
                        tool_call = message_delta.tool_calls[0]

                        if tool_call.function.name:
                            # If we're waiting for the first key, then we should hold back the name
                            # ie add it to a buffer instead of returning it as a chunk
                            if self.function_name_buffer is None:
                                self.function_name_buffer = tool_call.function.name
                            else:
                                self.function_name_buffer += tool_call.function.name

                        if tool_call.id:
                            # Buffer until next time
                            if self.function_id_buffer is None:
                                self.function_id_buffer = tool_call.id
                            else:
                                self.function_id_buffer += tool_call.id

                        if tool_call.function.arguments:
                            # updates_main_json, updates_inner_thoughts = self.function_args_reader.process_fragment(tool_call.function.arguments)
                            self.current_function_arguments += tool_call.function.arguments
                            updates_main_json, updates_inner_thoughts = self.function_args_reader.process_fragment(
                                tool_call.function.arguments
                            )

                            # If we have inner thoughts, we should output them as a chunk
                            if updates_inner_thoughts:
                                if prev_message_type and prev_message_type != "reasoning_message":
                                    message_index += 1
                                self.reasoning_messages.append(updates_inner_thoughts)
                                reasoning_message = ReasoningMessage(
                                    id=self.letta_tool_message_id,
                                    date=datetime.now(timezone.utc),
                                    reasoning=updates_inner_thoughts,
                                    # name=name,
                                    otid=Message.generate_otid_from_id(self.letta_tool_message_id, message_index),
                                )
                                prev_message_type = reasoning_message.message_type
                                yield reasoning_message

                                # Additionally inner thoughts may stream back with a chunk of main JSON
                                # In that case, since we can only return a chunk at a time, we should buffer it
                                if updates_main_json:
                                    if self.function_args_buffer is None:
                                        self.function_args_buffer = updates_main_json
                                    else:
                                        self.function_args_buffer += updates_main_json

                            # If we have main_json, we should output a ToolCallMessage
                            elif updates_main_json:

                                # If there's something in the function_name buffer, we should release it first
                                # NOTE: we could output it as part of a chunk that has both name and args,
                                #       however the frontend may expect name first, then args, so to be
                                #       safe we'll output name first in a separate chunk
                                if self.function_name_buffer:

                                    # use_assisitant_message means that we should also not release main_json raw, and instead should only release the contents of "message": "..."
                                    if self.use_assistant_message and self.function_name_buffer == self.assistant_message_tool_name:

                                        # Store the ID of the tool call so allow skipping the corresponding response
                                        if self.function_id_buffer:
                                            self.prev_assistant_message_id = self.function_id_buffer

                                    else:
                                        if prev_message_type and prev_message_type != "tool_call_message":
                                            message_index += 1
                                        self.tool_call_name = str(self.function_name_buffer)
                                        tool_call_msg = ToolCallMessage(
                                            id=self.letta_tool_message_id,
                                            date=datetime.now(timezone.utc),
                                            tool_call=ToolCallDelta(
                                                name=self.function_name_buffer,
                                                arguments=None,
                                                tool_call_id=self.function_id_buffer,
                                            ),
                                            otid=Message.generate_otid_from_id(self.letta_tool_message_id, message_index),
                                        )
                                        prev_message_type = tool_call_msg.message_type
                                        yield tool_call_msg

                                    # Record what the last function name we flushed was
                                    self.last_flushed_function_name = self.function_name_buffer
                                    # Clear the buffer
                                    self.function_name_buffer = None
                                    self.function_id_buffer = None
                                    # Since we're clearing the name buffer, we should store
                                    # any updates to the arguments inside a separate buffer

                                    # Add any main_json updates to the arguments buffer
                                    if self.function_args_buffer is None:
                                        self.function_args_buffer = updates_main_json
                                    else:
                                        self.function_args_buffer += updates_main_json

                                # If there was nothing in the name buffer, we can proceed to
                                # output the arguments chunk as a ToolCallMessage
                                else:

                                    # use_assisitant_message means that we should also not release main_json raw, and instead should only release the contents of "message": "..."
                                    if self.use_assistant_message and (
                                        self.last_flushed_function_name is not None
                                        and self.last_flushed_function_name == self.assistant_message_tool_name
                                    ):
                                        # do an additional parse on the updates_main_json
                                        if self.function_args_buffer:
                                            updates_main_json = self.function_args_buffer + updates_main_json
                                            self.function_args_buffer = None

                                            # Pretty gross hardcoding that assumes that if we're toggling into the keywords, we have the full prefix
                                            match_str = '{"' + self.assistant_message_tool_kwarg + '":"'
                                            if updates_main_json == match_str:
                                                updates_main_json = None

                                        else:
                                            # Some hardcoding to strip off the trailing "}"
                                            if updates_main_json in ["}", '"}']:
                                                updates_main_json = None
                                            if updates_main_json and len(updates_main_json) > 0 and updates_main_json[-1:] == '"':
                                                updates_main_json = updates_main_json[:-1]

                                        if not updates_main_json:
                                            # early exit to turn into content mode
                                            continue

                                        # There may be a buffer from a previous chunk, for example
                                        # if the previous chunk had arguments but we needed to flush name
                                        if self.function_args_buffer:
                                            # In this case, we should release the buffer + new data at once
                                            combined_chunk = self.function_args_buffer + updates_main_json

                                            if prev_message_type and prev_message_type != "assistant_message":
                                                message_index += 1
                                            assistant_message = AssistantMessage(
                                                id=self.letta_assistant_message_id,
                                                date=datetime.now(timezone.utc),
                                                content=combined_chunk,
                                                otid=Message.generate_otid_from_id(self.letta_assistant_message_id, message_index),
                                            )
                                            prev_message_type = assistant_message.message_type
                                            yield assistant_message
                                            # Store the ID of the tool call so allow skipping the corresponding response
                                            if self.function_id_buffer:
                                                self.prev_assistant_message_id = self.function_id_buffer
                                            # clear buffer
                                            self.function_args_buffer = None
                                            self.function_id_buffer = None

                                        else:
                                            # If there's no buffer to clear, just output a new chunk with new data
                                            # TODO: THIS IS HORRIBLE
                                            # TODO: WE USE THE OLD JSON PARSER EARLIER (WHICH DOES NOTHING) AND NOW THE NEW JSON PARSER
                                            # TODO: THIS IS TOTALLY WRONG AND BAD, BUT SAVING FOR A LARGER REWRITE IN THE NEAR FUTURE
                                            parsed_args = self.optimistic_json_parser.parse(self.current_function_arguments)

                                            if parsed_args.get(self.assistant_message_tool_kwarg) and parsed_args.get(
                                                self.assistant_message_tool_kwarg
                                            ) != self.current_json_parse_result.get(self.assistant_message_tool_kwarg):
                                                new_content = parsed_args.get(self.assistant_message_tool_kwarg)
                                                prev_content = self.current_json_parse_result.get(self.assistant_message_tool_kwarg, "")
                                                # TODO: Assumes consistent state and that prev_content is subset of new_content
                                                diff = new_content.replace(prev_content, "", 1)
                                                self.current_json_parse_result = parsed_args
                                                if prev_message_type and prev_message_type != "assistant_message":
                                                    message_index += 1
                                                assistant_message = AssistantMessage(
                                                    id=self.letta_assistant_message_id,
                                                    date=datetime.now(timezone.utc),
                                                    content=diff,
                                                    # name=name,
                                                    otid=Message.generate_otid_from_id(self.letta_assistant_message_id, message_index),
                                                )
                                                prev_message_type = assistant_message.message_type
                                                yield assistant_message

                                            # Store the ID of the tool call so allow skipping the corresponding response
                                            if self.function_id_buffer:
                                                self.prev_assistant_message_id = self.function_id_buffer
                                            # clear buffers
                                            self.function_id_buffer = None
                                    else:

                                        # There may be a buffer from a previous chunk, for example
                                        # if the previous chunk had arguments but we needed to flush name
                                        if self.function_args_buffer:
                                            # In this case, we should release the buffer + new data at once
                                            combined_chunk = self.function_args_buffer + updates_main_json
                                            if prev_message_type and prev_message_type != "tool_call_message":
                                                message_index += 1
                                            tool_call_msg = ToolCallMessage(
                                                id=self.letta_tool_message_id,
                                                date=datetime.now(timezone.utc),
                                                tool_call=ToolCallDelta(
                                                    name=None,
                                                    arguments=combined_chunk,
                                                    tool_call_id=self.function_id_buffer,
                                                ),
                                                # name=name,
                                                otid=Message.generate_otid_from_id(self.letta_tool_message_id, message_index),
                                            )
                                            prev_message_type = tool_call_msg.message_type
                                            yield tool_call_msg
                                            # clear buffer
                                            self.function_args_buffer = None
                                            self.function_id_buffer = None
                                        else:
                                            # If there's no buffer to clear, just output a new chunk with new data
                                            if prev_message_type and prev_message_type != "tool_call_message":
                                                message_index += 1
                                            tool_call_msg = ToolCallMessage(
                                                id=self.letta_tool_message_id,
                                                date=datetime.now(timezone.utc),
                                                tool_call=ToolCallDelta(
                                                    name=None,
                                                    arguments=updates_main_json,
                                                    tool_call_id=self.function_id_buffer,
                                                ),
                                                # name=name,
                                                otid=Message.generate_otid_from_id(self.letta_tool_message_id, message_index),
                                            )
                                            prev_message_type = tool_call_msg.message_type
                                            yield tool_call_msg
                                            self.function_id_buffer = None