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Update "natural conversation" examples to use universal LLMContext

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This commit is contained in:
Paul Kompfner
2025-09-23 15:37:58 -04:00
parent 677f69971c
commit 6ccbfd9b57
2 changed files with 58 additions and 72 deletions
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65
examples/foundational/22b-natural-conversation-proposal.py
View File

@@ -9,8 +9,9 @@ import os
from dotenv import load_dotenv
from loguru import logger
from openai.types.chat import ChatCompletionToolParam
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
CancelFrame,
@@ -19,6 +20,7 @@ from pipecat.frames.frames import (
FunctionCallInProgressFrame,
FunctionCallResultFrame,
InterruptionFrame,
LLMContextFrame,
LLMRunFrame,
StartFrame,
SystemFrame,
@@ -32,10 +34,8 @@ from pipecat.pipeline.parallel_pipeline import ParallelPipeline
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import (
OpenAILLMContext,
OpenAILLMContextFrame,
)
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
from pipecat.processors.filters.function_filter import FunctionFilter
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.processors.user_idle_processor import UserIdleProcessor
@@ -66,13 +66,13 @@ class StatementJudgeContextFilter(FrameProcessor):
await self.push_frame(frame, direction)
return
# We only want to handle OpenAILLMContextFrames, and only want to push through a simplified
# We only want to handle LLMContextFrames, and only want to push through a simplified
# context frame that contains a system prompt and the most recent user messages,
# concatenated.
if isinstance(frame, OpenAILLMContextFrame):
if isinstance(frame, LLMContextFrame):
logger.debug(f"Context Frame: {frame}")
# Take text content from the most recent user messages.
messages = frame.context.messages
messages = frame.context.get_messages()
user_text_messages = []
last_assistant_message = None
for message in reversed(messages):
@@ -100,7 +100,7 @@ class StatementJudgeContextFilter(FrameProcessor):
if last_assistant_message:
messages.append(last_assistant_message)
messages.append({"role": "user", "content": user_message})
await self.push_frame(OpenAILLMContextFrame(OpenAILLMContext(messages)))
await self.push_frame(LLMContextFrame(LLMContext(messages)))
class CompletenessCheck(FrameProcessor):
@@ -231,7 +231,7 @@ class TurnDetectionLLM(Pipeline):
async def pass_only_llm_trigger_frames(frame):
return (
isinstance(frame, OpenAILLMContextFrame)
isinstance(frame, LLMContextFrame)
or isinstance(frame, InterruptionFrame)
or isinstance(frame, FunctionCallInProgressFrame)
or isinstance(frame, FunctionCallResultFrame)
@@ -244,7 +244,7 @@ class TurnDetectionLLM(Pipeline):
[
ParallelPipeline(
[
# Ignore everything except an OpenAILLMContextFrame. Pass a specially constructed
# Ignore everything except an LLMContextFrame. Pass a specially constructed
# simplified context frame to the statement classifier LLM. The only frame this
# sub-pipeline will output is a UserStoppedSpeakingFrame.
statement_judge_context_filter,
@@ -306,30 +306,23 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_function_calls_started(service, function_calls):
await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
tools = [
ChatCompletionToolParam(
type="function",
function={
"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"],
},
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"],
)
tools = ToolsSchema(standard_tools=[weather_function])
messages = [
{
@@ -338,8 +331,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
},
]
context = OpenAILLMContext(messages, tools)
context_aggregator = llm_main.create_context_aggregator(context)
context = LLMContext(messages, tools)
context_aggregator = LLMContextAggregatorPair(context)
# LLM + turn detection (with an extra LLM as a judge)
llm = TurnDetectionLLM(llm_main)

65
examples/foundational/22c-natural-conversation-mixed-llms.py
View File

@@ -9,8 +9,9 @@ import os
from dotenv import load_dotenv
from loguru import logger
from openai.types.chat import ChatCompletionToolParam
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
CancelFrame,
@@ -19,6 +20,7 @@ from pipecat.frames.frames import (
FunctionCallInProgressFrame,
FunctionCallResultFrame,
InterruptionFrame,
LLMContextFrame,
LLMRunFrame,
StartFrame,
SystemFrame,
@@ -32,10 +34,8 @@ from pipecat.pipeline.parallel_pipeline import ParallelPipeline
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import (
OpenAILLMContext,
OpenAILLMContextFrame,
)
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
from pipecat.processors.filters.function_filter import FunctionFilter
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.processors.user_idle_processor import UserIdleProcessor
@@ -272,11 +272,11 @@ class StatementJudgeContextFilter(FrameProcessor):
await self.push_frame(frame, direction)
return
# We only want to handle OpenAILLMContextFrames, and only want to push through a simplified
# We only want to handle LLMContextFrames, and only want to push through a simplified
# context frame that contains a system prompt and the most recent user messages,
if isinstance(frame, OpenAILLMContextFrame):
if isinstance(frame, LLMContextFrame):
# Take text content from the most recent user messages.
messages = frame.context.messages
messages = frame.context.get_messages()
user_text_messages = []
last_assistant_message = None
for message in reversed(messages):
@@ -303,7 +303,7 @@ class StatementJudgeContextFilter(FrameProcessor):
if last_assistant_message:
messages.append(last_assistant_message)
messages.append({"role": "user", "content": user_message})
await self.push_frame(OpenAILLMContextFrame(OpenAILLMContext(messages)))
await self.push_frame(LLMContextFrame(LLMContext(messages)))
class CompletenessCheck(FrameProcessor):
@@ -425,7 +425,7 @@ class TurnDetectionLLM(Pipeline):
async def pass_only_llm_trigger_frames(frame):
return (
isinstance(frame, OpenAILLMContextFrame)
isinstance(frame, LLMContextFrame)
or isinstance(frame, InterruptionFrame)
or isinstance(frame, FunctionCallInProgressFrame)
or isinstance(frame, FunctionCallResultFrame)
@@ -443,7 +443,7 @@ class TurnDetectionLLM(Pipeline):
FunctionFilter(filter=block_user_stopped_speaking),
],
[
# Ignore everything except an OpenAILLMContextFrame. Pass a specially constructed
# Ignore everything except an LLMContextFrame. Pass a specially constructed
# simplified context frame to the statement classifier LLM. The only frame this
# sub-pipeline will output is a UserStoppedSpeakingFrame.
statement_judge_context_filter,
@@ -509,30 +509,23 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_function_calls_started(service, function_calls):
await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
tools = [
ChatCompletionToolParam(
type="function",
function={
"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"],
},
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"],
)
tools = ToolsSchema(standard_tools=[weather_function])
messages = [
{
@@ -541,8 +534,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
},
]
context = OpenAILLMContext(messages, tools)
context_aggregator = llm_main.create_context_aggregator(context)
context = LLMContext(messages, tools)
context_aggregator = LLMContextAggregatorPair(context)
# LLM + turn detection (with an extra LLM as a judge)
llm = TurnDetectionLLM(llm_main)