Merge pull request #2721 from pipecat-ai/pk/update-persistent-storage-examples-to-use-universal-llmcontext
Update persistent conversation storage examples to use universal `LLM…
This commit is contained in:
kompfner
@@ -12,6 +12,8 @@ from datetime import datetime
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.adapters.schemas.function_schema import FunctionSchema
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
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from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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@@ -20,9 +22,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.openai_llm_context import (
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OpenAILLMContext,
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)
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.cartesia.tts import CartesiaTTSService
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@@ -66,11 +67,11 @@ async def save_conversation(params: FunctionCallParams):
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timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
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filename = f"{BASE_FILENAME}{timestamp}.json"
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logger.debug(
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f"writing conversation to {filename}\n{json.dumps(params.context.messages, indent=4)}"
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f"writing conversation to {filename}\n{json.dumps(params.context.get_messages(), indent=4)}"
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)
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try:
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with open(filename, "w") as file:
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messages = params.context.get_messages_for_persistent_storage()
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messages = params.context.get_messages()
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# remove the last message, which is the instruction we just gave to save the conversation
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messages.pop()
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json.dump(messages, file, indent=2)
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@@ -87,7 +88,7 @@ async def load_conversation(params: FunctionCallParams):
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with open(filename, "r") as file:
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params.context.set_messages(json.load(file))
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logger.debug(
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f"loaded conversation from {filename}\n{json.dumps(params.context.messages, indent=4)}"
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f"loaded conversation from {filename}\n{json.dumps(params.context.get_messages(), indent=4)}"
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)
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await params.llm.queue_frame(TTSSpeakFrame("Ok, I've loaded that conversation."))
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except Exception as e:
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@@ -100,71 +101,58 @@ messages = [
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"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
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},
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]
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_current_weather",
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"description": "Get the current weather",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the users location.",
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},
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},
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"required": ["location", "format"],
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},
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weather_function = FunctionSchema(
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name="get_current_weather",
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description="Get the current weather",
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properties={
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the users location.",
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},
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},
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{
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"type": "function",
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"function": {
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"name": "save_conversation",
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"description": "Save the current conversatione. Use this function to persist the current conversation to external storage.",
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"parameters": {
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"type": "object",
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"properties": {},
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"required": [],
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},
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},
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required=["location", "format"],
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)
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save_conversation_function = FunctionSchema(
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name="save_conversation",
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description="Save the current conversatione. Use this function to persist the current conversation to external storage.",
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properties={},
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required=[],
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)
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get_filenames_function = FunctionSchema(
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name="get_saved_conversation_filenames",
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description="Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
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properties={},
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required=[],
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)
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load_conversation_function = FunctionSchema(
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name="load_conversation",
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description="Load a conversation history. Use this function to load a conversation history into the current session.",
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properties={
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"filename": {
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"type": "string",
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"description": "The filename of the conversation history to load.",
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}
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},
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{
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"type": "function",
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"function": {
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"name": "get_saved_conversation_filenames",
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"description": "Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
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"parameters": {
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"type": "object",
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"properties": {},
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"required": [],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "load_conversation",
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"description": "Load a conversation history. Use this function to load a conversation history into the current session.",
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"parameters": {
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"type": "object",
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"properties": {
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"filename": {
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"type": "string",
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"description": "The filename of the conversation history to load.",
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}
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},
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"required": ["filename"],
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},
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},
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},
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]
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required=["filename"],
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)
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tools = ToolsSchema(
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standard_tools=[
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weather_function,
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save_conversation_function,
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get_filenames_function,
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load_conversation_function,
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]
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)
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# We store functions so objects (e.g. SileroVADAnalyzer) don't get
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@@ -211,8 +199,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
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llm.register_function("load_conversation", load_conversation)
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context = OpenAILLMContext(messages, tools)
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context_aggregator = llm.create_context_aggregator(context)
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context = LLMContext(messages, tools)
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context_aggregator = LLMContextAggregatorPair(context)
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pipeline = Pipeline(
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[
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@@ -12,6 +12,8 @@ from datetime import datetime
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.adapters.schemas.function_schema import FunctionSchema
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
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from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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@@ -20,9 +22,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.openai_llm_context import (
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OpenAILLMContext,
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)
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.anthropic.llm import AnthropicLLMService
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@@ -67,12 +68,12 @@ async def save_conversation(params: FunctionCallParams):
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timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
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filename = f"{BASE_FILENAME}{timestamp}.json"
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logger.debug(
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f"writing conversation to {filename}\n{json.dumps(params.context.messages, indent=4)}"
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f"writing conversation to {filename}\n{json.dumps(params.context.get_messages(), indent=4)}"
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)
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try:
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with open(filename, "w") as file:
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# todo: extract 'system' into the first message in the list
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messages = params.context.get_messages_for_persistent_storage()
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messages = params.context.get_messages()
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# remove the last message, which is the instruction we just gave to save the conversation
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messages.pop()
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json.dump(messages, file, indent=2)
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@@ -89,7 +90,7 @@ async def load_conversation(params: FunctionCallParams):
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with open(filename, "r") as file:
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params.context.set_messages(json.load(file))
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logger.debug(
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f"loaded conversation from {filename}\n{json.dumps(params.context.messages, indent=4)}"
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f"loaded conversation from {filename}\n{json.dumps(params.context.get_messages(), indent=4)}"
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)
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await params.llm.queue_frame(TTSSpeakFrame("Ok, I've loaded that conversation."))
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except Exception as e:
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@@ -108,59 +109,58 @@ messages = [
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# {"role": "user", "content": "Tell me"},
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# {"role": "user", "content": "a joke"},
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]
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tools = [
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{
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"name": "get_current_weather",
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"description": "Get the current weather",
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"input_schema": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the users location.",
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},
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},
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"required": ["location", "format"],
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weather_function = FunctionSchema(
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name="get_current_weather",
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description="Get the current weather",
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properties={
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the users location.",
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},
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},
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{
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"name": "save_conversation",
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"description": "Save the current conversation. Use this function to persist the current conversation to external storage.",
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"input_schema": {
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"type": "object",
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"properties": {},
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"required": [],
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},
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required=["location", "format"],
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)
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save_conversation_function = FunctionSchema(
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name="save_conversation",
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description="Save the current conversation. Use this function to persist the current conversation to external storage.",
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properties={},
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required=[],
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)
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get_filenames_function = FunctionSchema(
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name="get_saved_conversation_filenames",
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description="Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
|
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properties={},
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required=[],
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)
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load_conversation_function = FunctionSchema(
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name="load_conversation",
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description="Load a conversation history. Use this function to load a conversation history into the current session.",
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properties={
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"filename": {
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"type": "string",
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"description": "The filename of the conversation history to load.",
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}
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},
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{
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"name": "get_saved_conversation_filenames",
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"description": "Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
|
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"input_schema": {
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"type": "object",
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"properties": {},
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"required": [],
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},
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},
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{
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"name": "load_conversation",
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"description": "Load a conversation history. Use this function to load a conversation history into the current session.",
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"input_schema": {
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"type": "object",
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"properties": {
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"filename": {
|
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"type": "string",
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"description": "The filename of the conversation history to load.",
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}
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},
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"required": ["filename"],
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},
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},
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]
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required=["filename"],
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)
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tools = ToolsSchema(
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standard_tools=[
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weather_function,
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save_conversation_function,
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get_filenames_function,
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load_conversation_function,
|
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]
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)
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# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
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@@ -211,8 +211,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
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llm.register_function("load_conversation", load_conversation)
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context = OpenAILLMContext(messages, tools)
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context_aggregator = llm.create_context_aggregator(context)
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context = LLMContext(messages, tools)
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context_aggregator = LLMContextAggregatorPair(context)
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pipeline = Pipeline(
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[
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@@ -12,6 +12,8 @@ from datetime import datetime
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from dotenv import load_dotenv
|
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from loguru import logger
|
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|
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from pipecat.adapters.schemas.function_schema import FunctionSchema
|
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
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from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
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from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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@@ -20,9 +22,8 @@ from pipecat.frames.frames import LLMRunFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
|
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from pipecat.pipeline.task import PipelineParams, PipelineTask
|
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from pipecat.processors.aggregators.openai_llm_context import (
|
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OpenAILLMContext,
|
||||
)
|
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from pipecat.processors.aggregators.llm_context import LLMContext
|
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from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
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from pipecat.runner.utils import (
|
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create_transport,
|
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@@ -85,12 +86,12 @@ async def save_conversation(params: FunctionCallParams):
|
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timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
|
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filename = f"{BASE_FILENAME}{timestamp}.json"
|
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logger.debug(
|
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f"writing conversation to {filename}\n{json.dumps(params.context.get_messages_for_logging(), indent=4)}"
|
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f"writing conversation to {filename}\n{json.dumps(params.context.get_messages(), indent=4)}"
|
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)
|
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try:
|
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with open(filename, "w") as file:
|
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# todo: extract 'system' into the first message in the list
|
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messages = params.context.get_messages_for_persistent_storage()
|
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messages = params.context.get_messages()
|
||||
# remove the last message (the instruction to save the context)
|
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messages.pop()
|
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json.dump(messages, file, indent=2)
|
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@@ -151,78 +152,76 @@ indicate you should use the get_image tool are:
|
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# {"role": "user", "content": "Tell me"},
|
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# {"role": "user", "content": "a joke"},
|
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]
|
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tools = [
|
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{
|
||||
"function_declarations": [
|
||||
{
|
||||
"name": "get_current_weather",
|
||||
"description": "Get the current weather",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
"format": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
"description": "The temperature unit to use. Infer this from the users location.",
|
||||
},
|
||||
},
|
||||
"required": ["location", "format"],
|
||||
},
|
||||
},
|
||||
{
|
||||
"name": "save_conversation",
|
||||
"description": "Save the current conversation. Use this function to persist the current conversation to external storage.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"user_request_text": {
|
||||
"type": "string",
|
||||
"description": "The text of the user's request to save the conversation.",
|
||||
}
|
||||
},
|
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"required": ["user_request_text"],
|
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},
|
||||
},
|
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{
|
||||
"name": "get_saved_conversation_filenames",
|
||||
"description": "Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
|
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"parameters": None,
|
||||
},
|
||||
{
|
||||
"name": "load_conversation",
|
||||
"description": "Load a conversation history. Use this function to load a conversation history into the current session.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"filename": {
|
||||
"type": "string",
|
||||
"description": "The filename of the conversation history to load.",
|
||||
}
|
||||
},
|
||||
"required": ["filename"],
|
||||
},
|
||||
},
|
||||
{
|
||||
"name": "get_image",
|
||||
"description": "Get and image from the camera or video stream.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question to to use when running inference on the acquired image.",
|
||||
},
|
||||
},
|
||||
"required": ["question"],
|
||||
},
|
||||
},
|
||||
]
|
||||
|
||||
weather_function = FunctionSchema(
|
||||
name="get_current_weather",
|
||||
description="Get the current weather",
|
||||
properties={
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
"format": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
"description": "The temperature unit to use. Infer this from the users location.",
|
||||
},
|
||||
},
|
||||
]
|
||||
required=["location", "format"],
|
||||
)
|
||||
|
||||
save_conversation_function = FunctionSchema(
|
||||
name="save_conversation",
|
||||
description="Save the current conversation. Use this function to persist the current conversation to external storage.",
|
||||
properties={
|
||||
"user_request_text": {
|
||||
"type": "string",
|
||||
"description": "The text of the user's request to save the conversation.",
|
||||
}
|
||||
},
|
||||
required=["user_request_text"],
|
||||
)
|
||||
|
||||
get_filenames_function = FunctionSchema(
|
||||
name="get_saved_conversation_filenames",
|
||||
description="Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
|
||||
properties={},
|
||||
required=[],
|
||||
)
|
||||
|
||||
load_conversation_function = FunctionSchema(
|
||||
name="load_conversation",
|
||||
description="Load a conversation history. Use this function to load a conversation history into the current session.",
|
||||
properties={
|
||||
"filename": {
|
||||
"type": "string",
|
||||
"description": "The filename of the conversation history to load.",
|
||||
}
|
||||
},
|
||||
required=["filename"],
|
||||
)
|
||||
|
||||
get_image_function = FunctionSchema(
|
||||
name="get_image",
|
||||
description="Get and image from the camera or video stream.",
|
||||
properties={
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question to to use when running inference on the acquired image.",
|
||||
},
|
||||
},
|
||||
required=["question"],
|
||||
)
|
||||
|
||||
tools = ToolsSchema(
|
||||
standard_tools=[
|
||||
weather_function,
|
||||
save_conversation_function,
|
||||
get_filenames_function,
|
||||
load_conversation_function,
|
||||
get_image_function,
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
@@ -266,8 +265,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm.register_function("load_conversation", load_conversation)
|
||||
llm.register_function("get_image", get_image)
|
||||
|
||||
context = OpenAILLMContext(messages, tools)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
context = LLMContext(messages, tools)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
|
||||
Reference in New Issue
Block a user