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Merge pull request #2721 from pipecat-ai/pk/update-persistent-storage-examples-to-use-universal-llmcontext

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Update persistent conversation storage examples to use universal `LLM…
This commit is contained in:
kompfner
2025-09-23 15:18:21 -04:00
committed by GitHub
parent 667569ef47 780e91eb91
commit 0bba02028d
3 changed files with 193 additions and 206 deletions
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128
examples/foundational/20a-persistent-context-openai.py
View File

@@ -12,6 +12,8 @@ from datetime import datetime
from dotenv import load_dotenv from dotenv import load_dotenv
from loguru import logger from loguru import logger
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
from pipecat.audio.vad.silero import SileroVADAnalyzer from pipecat.audio.vad.silero import SileroVADAnalyzer
@@ -20,9 +22,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import ( from pipecat.processors.aggregators.llm_context import LLMContext
OpenAILLMContext, from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
)
from pipecat.runner.types import RunnerArguments from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport from pipecat.runner.utils import create_transport
from pipecat.services.cartesia.tts import CartesiaTTSService from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -66,11 +67,11 @@ async def save_conversation(params: FunctionCallParams):
timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S") timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
filename = f"{BASE_FILENAME}{timestamp}.json" filename = f"{BASE_FILENAME}{timestamp}.json"
logger.debug( logger.debug(
f"writing conversation to {filename}\n{json.dumps(params.context.messages, indent=4)}" f"writing conversation to {filename}\n{json.dumps(params.context.get_messages(), indent=4)}"
) )
try: try:
with open(filename, "w") as file: with open(filename, "w") as file:
messages = params.context.get_messages_for_persistent_storage() messages = params.context.get_messages()
# remove the last message, which is the instruction we just gave to save the conversation # remove the last message, which is the instruction we just gave to save the conversation
messages.pop() messages.pop()
json.dump(messages, file, indent=2) json.dump(messages, file, indent=2)
@@ -87,7 +88,7 @@ async def load_conversation(params: FunctionCallParams):
with open(filename, "r") as file: with open(filename, "r") as file:
params.context.set_messages(json.load(file)) params.context.set_messages(json.load(file))
logger.debug( logger.debug(
f"loaded conversation from {filename}\n{json.dumps(params.context.messages, indent=4)}" f"loaded conversation from {filename}\n{json.dumps(params.context.get_messages(), indent=4)}"
) )
await params.llm.queue_frame(TTSSpeakFrame("Ok, I've loaded that conversation.")) await params.llm.queue_frame(TTSSpeakFrame("Ok, I've loaded that conversation."))
except Exception as e: except Exception as e:
@@ -100,71 +101,58 @@ messages = [
"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.", "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.",
}, },
] ]
tools = [
{ weather_function = FunctionSchema(
"type": "function", name="get_current_weather",
"function": { description="Get the current weather",
"name": "get_current_weather", properties={
"description": "Get the current weather", "location": {
"parameters": { "type": "string",
"type": "object", "description": "The city and state, e.g. San Francisco, CA",
"properties": { },
"location": { "format": {
"type": "string", "type": "string",
"description": "The city and state, e.g. San Francisco, CA", "enum": ["celsius", "fahrenheit"],
}, "description": "The temperature unit to use. Infer this from the users location.",
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use. Infer this from the users location.",
},
},
"required": ["location", "format"],
},
}, },
}, },
{ required=["location", "format"],
"type": "function", )
"function": {
"name": "save_conversation", save_conversation_function = FunctionSchema(
"description": "Save the current conversatione. Use this function to persist the current conversation to external storage.", name="save_conversation",
"parameters": { description="Save the current conversatione. Use this function to persist the current conversation to external storage.",
"type": "object", properties={},
"properties": {}, required=[],
"required": [], )
},
}, 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"],
"type": "function", )
"function": {
"name": "get_saved_conversation_filenames", tools = ToolsSchema(
"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.", standard_tools=[
"parameters": { weather_function,
"type": "object", save_conversation_function,
"properties": {}, get_filenames_function,
"required": [], load_conversation_function,
}, ]
}, )
},
{
"type": "function",
"function": {
"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"],
},
},
},
]
# We store functions so objects (e.g. SileroVADAnalyzer) don't get # We store functions so objects (e.g. SileroVADAnalyzer) don't get
@@ -211,8 +199,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames) llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
llm.register_function("load_conversation", load_conversation) llm.register_function("load_conversation", load_conversation)
context = OpenAILLMContext(messages, tools) context = LLMContext(messages, tools)
context_aggregator = llm.create_context_aggregator(context) context_aggregator = LLMContextAggregatorPair(context)
pipeline = Pipeline( pipeline = Pipeline(
[ [

116
examples/foundational/20c-persistent-context-anthropic.py
View File

@@ -12,6 +12,8 @@ from datetime import datetime
from dotenv import load_dotenv from dotenv import load_dotenv
from loguru import logger from loguru import logger
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
from pipecat.audio.vad.silero import SileroVADAnalyzer from pipecat.audio.vad.silero import SileroVADAnalyzer
@@ -20,9 +22,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import ( from pipecat.processors.aggregators.llm_context import LLMContext
OpenAILLMContext, from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
)
from pipecat.runner.types import RunnerArguments from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport from pipecat.runner.utils import create_transport
from pipecat.services.anthropic.llm import AnthropicLLMService from pipecat.services.anthropic.llm import AnthropicLLMService
@@ -67,12 +68,12 @@ async def save_conversation(params: FunctionCallParams):
timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S") timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
filename = f"{BASE_FILENAME}{timestamp}.json" filename = f"{BASE_FILENAME}{timestamp}.json"
logger.debug( logger.debug(
f"writing conversation to {filename}\n{json.dumps(params.context.messages, indent=4)}" f"writing conversation to {filename}\n{json.dumps(params.context.get_messages(), indent=4)}"
) )
try: try:
with open(filename, "w") as file: with open(filename, "w") as file:
# todo: extract 'system' into the first message in the list # todo: extract 'system' into the first message in the list
messages = params.context.get_messages_for_persistent_storage() messages = params.context.get_messages()
# remove the last message, which is the instruction we just gave to save the conversation # remove the last message, which is the instruction we just gave to save the conversation
messages.pop() messages.pop()
json.dump(messages, file, indent=2) json.dump(messages, file, indent=2)
@@ -89,7 +90,7 @@ async def load_conversation(params: FunctionCallParams):
with open(filename, "r") as file: with open(filename, "r") as file:
params.context.set_messages(json.load(file)) params.context.set_messages(json.load(file))
logger.debug( logger.debug(
f"loaded conversation from {filename}\n{json.dumps(params.context.messages, indent=4)}" f"loaded conversation from {filename}\n{json.dumps(params.context.get_messages(), indent=4)}"
) )
await params.llm.queue_frame(TTSSpeakFrame("Ok, I've loaded that conversation.")) await params.llm.queue_frame(TTSSpeakFrame("Ok, I've loaded that conversation."))
except Exception as e: except Exception as e:
@@ -108,59 +109,58 @@ messages = [
# {"role": "user", "content": "Tell me"}, # {"role": "user", "content": "Tell me"},
# {"role": "user", "content": "a joke"}, # {"role": "user", "content": "a joke"},
] ]
tools = [
{ weather_function = FunctionSchema(
"name": "get_current_weather", name="get_current_weather",
"description": "Get the current weather", description="Get the current weather",
"input_schema": { properties={
"type": "object", "location": {
"properties": { "type": "string",
"location": { "description": "The city and state, e.g. San Francisco, CA",
"type": "string", },
"description": "The city and state, e.g. San Francisco, CA", "format": {
}, "type": "string",
"format": { "enum": ["celsius", "fahrenheit"],
"type": "string", "description": "The temperature unit to use. Infer this from the users location.",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use. Infer this from the users location.",
},
},
"required": ["location", "format"],
}, },
}, },
{ required=["location", "format"],
"name": "save_conversation", )
"description": "Save the current conversation. Use this function to persist the current conversation to external storage.",
"input_schema": { save_conversation_function = FunctionSchema(
"type": "object", name="save_conversation",
"properties": {}, description="Save the current conversation. Use this function to persist the current conversation to external storage.",
"required": [], properties={},
}, required=[],
)
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"],
"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.",
"input_schema": { tools = ToolsSchema(
"type": "object", standard_tools=[
"properties": {}, weather_function,
"required": [], save_conversation_function,
}, get_filenames_function,
}, load_conversation_function,
{ ]
"name": "load_conversation", )
"description": "Load a conversation history. Use this function to load a conversation history into the current session.",
"input_schema": {
"type": "object",
"properties": {
"filename": {
"type": "string",
"description": "The filename of the conversation history to load.",
}
},
"required": ["filename"],
},
},
]
# We store functions so objects (e.g. SileroVADAnalyzer) don't get # We store functions so objects (e.g. SileroVADAnalyzer) don't get
@@ -211,8 +211,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames) llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
llm.register_function("load_conversation", load_conversation) llm.register_function("load_conversation", load_conversation)
context = OpenAILLMContext(messages, tools) context = LLMContext(messages, tools)
context_aggregator = llm.create_context_aggregator(context) context_aggregator = LLMContextAggregatorPair(context)
pipeline = Pipeline( pipeline = Pipeline(
[ [

155
examples/foundational/20d-persistent-context-gemini.py
View File

@@ -12,6 +12,8 @@ from datetime import datetime
from dotenv import load_dotenv from dotenv import load_dotenv
from loguru import logger from loguru import logger
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
from pipecat.audio.vad.silero import SileroVADAnalyzer from pipecat.audio.vad.silero import SileroVADAnalyzer
@@ -20,9 +22,8 @@ from pipecat.frames.frames import LLMRunFrame
from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import ( from pipecat.processors.aggregators.llm_context import LLMContext
OpenAILLMContext, from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
)
from pipecat.runner.types import RunnerArguments from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import ( from pipecat.runner.utils import (
create_transport, create_transport,
@@ -85,12 +86,12 @@ async def save_conversation(params: FunctionCallParams):
timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S") timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
filename = f"{BASE_FILENAME}{timestamp}.json" filename = f"{BASE_FILENAME}{timestamp}.json"
logger.debug( logger.debug(
f"writing conversation to {filename}\n{json.dumps(params.context.get_messages_for_logging(), indent=4)}" f"writing conversation to {filename}\n{json.dumps(params.context.get_messages(), indent=4)}"
) )
try: try:
with open(filename, "w") as file: with open(filename, "w") as file:
# todo: extract 'system' into the first message in the list # todo: extract 'system' into the first message in the list
messages = params.context.get_messages_for_persistent_storage() messages = params.context.get_messages()
# remove the last message (the instruction to save the context) # remove the last message (the instruction to save the context)
messages.pop() messages.pop()
json.dump(messages, file, indent=2) json.dump(messages, file, indent=2)
@@ -151,78 +152,76 @@ indicate you should use the get_image tool are:
# {"role": "user", "content": "Tell me"}, # {"role": "user", "content": "Tell me"},
# {"role": "user", "content": "a joke"}, # {"role": "user", "content": "a joke"},
] ]
tools = [
{ weather_function = FunctionSchema(
"function_declarations": [ name="get_current_weather",
{ description="Get the current weather",
"name": "get_current_weather", properties={
"description": "Get the current weather", "location": {
"parameters": { "type": "string",
"type": "object", "description": "The city and state, e.g. San Francisco, CA",
"properties": { },
"location": { "format": {
"type": "string", "type": "string",
"description": "The city and state, e.g. San Francisco, CA", "enum": ["celsius", "fahrenheit"],
}, "description": "The temperature unit to use. Infer this from the users location.",
"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.",
}
},
"required": ["user_request_text"],
},
},
{
"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.",
"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"],
},
},
]
}, },
] 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 # 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("load_conversation", load_conversation)
llm.register_function("get_image", get_image) llm.register_function("get_image", get_image)
context = OpenAILLMContext(messages, tools) context = LLMContext(messages, tools)
context_aggregator = llm.create_context_aggregator(context) context_aggregator = LLMContextAggregatorPair(context)
pipeline = Pipeline( pipeline = Pipeline(
[ [