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Update more examples to use universal LLMContext. Specifically, update examples we didn't update before because they weren't using ToolsSchema for their tool definitions, which is a requirement for using LLMContext.

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NOTE: oops! Turns out some of these files had *already* been updated to use universal `LLMContext` even though they weren't yet using `ToolsSchema`. This commit should fix those examples.
This commit is contained in:
Paul Kompfner
2025-09-23 12:14:10 -04:00
parent 88337fc21f
commit d4b1e1ab41
4 changed files with 67 additions and 89 deletions
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33
examples/foundational/15-switch-voices.py
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@@ -9,8 +9,9 @@ import os
from dotenv import load_dotenv from dotenv import load_dotenv
from loguru import logger 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.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
@@ -121,25 +122,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY")) llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
llm.register_function("switch_voice", tts.switch_voice) llm.register_function("switch_voice", tts.switch_voice)
tools = [ switch_voice_function = FunctionSchema(
ChatCompletionToolParam( name="switch_voice",
type="function", description="Switch your voice only when the user asks you to",
function={ properties={
"name": "switch_voice", "voice": {
"description": "Switch your voice only when the user asks you to", "type": "string",
"parameters": { "description": "The voice the user wants you to use",
"type": "object",
"properties": {
"voice": {
"type": "string",
"description": "The voice the user wants you to use",
},
},
"required": ["voice"],
},
}, },
) },
] required=["voice"],
)
tools = ToolsSchema(standard_tools=[switch_voice_function])
messages = [ messages = [
{ {
"role": "system", "role": "system",

32
examples/foundational/15a-switch-languages.py
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@@ -10,8 +10,9 @@ import os
from deepgram import LiveOptions from deepgram import LiveOptions
from dotenv import load_dotenv from dotenv import load_dotenv
from loguru import logger 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.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
@@ -112,25 +113,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY")) llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
llm.register_function("switch_language", tts.switch_language) llm.register_function("switch_language", tts.switch_language)
tools = [ switch_language_function = FunctionSchema(
ChatCompletionToolParam( name="switch_language",
type="function", description="Switch to another language when the user asks you to",
function={ properties={
"name": "switch_language", "language": {
"description": "Switch to another language when the user asks you to", "type": "string",
"parameters": { "description": "The language the user wants you to speak",
"type": "object",
"properties": {
"language": {
"type": "string",
"description": "The language the user wants you to speak",
},
},
"required": ["language"],
},
}, },
) },
] required=["language"],
)
tools = ToolsSchema(standard_tools=[switch_language_function])
messages = [ messages = [
{ {
"role": "system", "role": "system",

48
examples/foundational/33-gemini-rag.py
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@@ -55,6 +55,8 @@ from dotenv import load_dotenv
from google import genai from google import genai
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
@@ -63,7 +65,8 @@ 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 OpenAILLMContext from pipecat.processors.aggregators.llm_context import LLMContext
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
@@ -121,11 +124,7 @@ async def query_knowledge_base(params: FunctionCallParams):
# for our case, the first two messages are the instructions and the user message # for our case, the first two messages are the instructions and the user message
# so we remove them. # so we remove them.
conversation_turns = params.context.messages[2:] conversation_turns = params.context.get_messages()[2:]
# convert to standard messages
messages = []
for turn in conversation_turns:
messages.extend(params.context.to_standard_messages(turn))
def _is_tool_call(turn): def _is_tool_call(turn):
if turn.get("role", None) == "tool": if turn.get("role", None) == "tool":
@@ -135,7 +134,7 @@ async def query_knowledge_base(params: FunctionCallParams):
return False return False
# filter out tool calls # filter out tool calls
messages = [turn for turn in messages if not _is_tool_call(turn)] messages = [turn for turn in conversation_turns if not _is_tool_call(turn)]
# use the last 3 turns as the conversation history/context # use the last 3 turns as the conversation history/context
messages = messages[-3:] messages = messages[-3:]
messages_json = json.dumps(messages, ensure_ascii=False, indent=2) messages_json = json.dumps(messages, ensure_ascii=False, indent=2)
@@ -199,25 +198,20 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
api_key=os.getenv("GOOGLE_API_KEY"), api_key=os.getenv("GOOGLE_API_KEY"),
) )
llm.register_function("query_knowledge_base", query_knowledge_base) llm.register_function("query_knowledge_base", query_knowledge_base)
tools = [
{ query_function = FunctionSchema(
"function_declarations": [ name="query_knowledge_base",
{ description="Query the knowledge base for the answer to the question.",
"name": "query_knowledge_base", properties={
"description": "Query the knowledge base for the answer to the question.", "question": {
"parameters": { "type": "string",
"type": "object", "description": "The question to query the knowledge base with.",
"properties": { },
"question": {
"type": "string",
"description": "The question to query the knowledge base with.",
},
},
},
},
],
}, },
] required=["question"],
)
tools = ToolsSchema(standard_tools=[query_function])
system_prompt = """\ system_prompt = """\
You are a helpful assistant who converses with a user and answers questions. You are a helpful assistant who converses with a user and answers questions.
@@ -230,8 +224,8 @@ Your response will be turned into speech so use only simple words and punctuatio
{"role": "user", "content": "Greet the user."}, {"role": "user", "content": "Greet the user."},
] ]
context = OpenAILLMContext(messages, tools) context = LLMContext(messages, tools)
context_aggregator = llm.create_context_aggregator(context) context_aggregator = LLMContextAggregatorPair(context)
pipeline = Pipeline( pipeline = Pipeline(
[ [

43
examples/foundational/36-user-email-gathering.py
View File

@@ -9,8 +9,9 @@ import os
from dotenv import load_dotenv from dotenv import load_dotenv
from loguru import logger 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.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
@@ -19,14 +20,14 @@ 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 OpenAILLMContext from pipecat.processors.aggregators.llm_context import LLMContext
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
from pipecat.services.deepgram.stt import DeepgramSTTService from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.llm_service import FunctionCallParams from pipecat.services.llm_service import FunctionCallParams
from pipecat.services.openai.llm import OpenAILLMService from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.rime.tts import RimeHttpTTSService
from pipecat.transports.base_transport import BaseTransport, TransportParams from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -90,26 +91,20 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
# sent to the same callback with an additional function_name parameter. # sent to the same callback with an additional function_name parameter.
llm.register_function("store_user_emails", store_user_emails) llm.register_function("store_user_emails", store_user_emails)
tools = [ store_emails_function = FunctionSchema(
ChatCompletionToolParam( name="store_user_emails",
type="function", description="Store user emails when confirmed",
function={ properties={
"name": "store_user_emails", "emails": {
"description": "Store user emails when confirmed", "type": "array",
"parameters": { "description": "The list of user emails",
"type": "object", "items": {"type": "string"},
"properties": {
"emails": {
"type": "array",
"description": "The list of user emails",
"items": {"type": "string"},
},
},
"required": ["emails"],
},
}, },
) },
] required=["emails"],
)
tools = ToolsSchema(standard_tools=[store_emails_function])
messages = [ messages = [
{ {
"role": "system", "role": "system",
@@ -120,8 +115,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
}, },
] ]
context = OpenAILLMContext(messages, tools) context = LLMContext(messages, tools)
context_aggregator = llm.create_context_aggregator(context) context_aggregator = LLMContextAggregatorPair(context)
pipeline = Pipeline( pipeline = Pipeline(
[ [