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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 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.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
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.register_function("switch_voice", tts.switch_voice)
tools = [
ChatCompletionToolParam(
type="function",
function={
"name": "switch_voice",
"description": "Switch your voice only when the user asks you to",
"parameters": {
"type": "object",
"properties": {
"voice": {
"type": "string",
"description": "The voice the user wants you to use",
},
},
"required": ["voice"],
},
switch_voice_function = FunctionSchema(
name="switch_voice",
description="Switch your voice only when the user asks you to",
properties={
"voice": {
"type": "string",
"description": "The voice the user wants you to use",
},
)
]
},
required=["voice"],
)
tools = ToolsSchema(standard_tools=[switch_voice_function])
messages = [
{
"role": "system",

32
examples/foundational/15a-switch-languages.py
View File

@@ -10,8 +10,9 @@ import os
from deepgram import LiveOptions
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.turn.smart_turn.base_smart_turn import SmartTurnParams
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
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.register_function("switch_language", tts.switch_language)
tools = [
ChatCompletionToolParam(
type="function",
function={
"name": "switch_language",
"description": "Switch to another language when the user asks you to",
"parameters": {
"type": "object",
"properties": {
"language": {
"type": "string",
"description": "The language the user wants you to speak",
},
},
"required": ["language"],
},
switch_language_function = FunctionSchema(
name="switch_language",
description="Switch to another language when the user asks you to",
properties={
"language": {
"type": "string",
"description": "The language the user wants you to speak",
},
)
]
},
required=["language"],
)
tools = ToolsSchema(standard_tools=[switch_language_function])
messages = [
{
"role": "system",

48
examples/foundational/33-gemini-rag.py
View File

@@ -55,6 +55,8 @@ from dotenv import load_dotenv
from google import genai
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.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
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.runner import PipelineRunner
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.utils import create_transport
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
# so we remove them.
conversation_turns = params.context.messages[2:]
# convert to standard messages
messages = []
for turn in conversation_turns:
messages.extend(params.context.to_standard_messages(turn))
conversation_turns = params.context.get_messages()[2:]
def _is_tool_call(turn):
if turn.get("role", None) == "tool":
@@ -135,7 +134,7 @@ async def query_knowledge_base(params: FunctionCallParams):
return False
# 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
messages = messages[-3:]
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"),
)
llm.register_function("query_knowledge_base", query_knowledge_base)
tools = [
{
"function_declarations": [
{
"name": "query_knowledge_base",
"description": "Query the knowledge base for the answer to the question.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "The question to query the knowledge base with.",
},
},
},
},
],
query_function = FunctionSchema(
name="query_knowledge_base",
description="Query the knowledge base for the answer to the question.",
properties={
"question": {
"type": "string",
"description": "The question to query the knowledge base with.",
},
},
]
required=["question"],
)
tools = ToolsSchema(standard_tools=[query_function])
system_prompt = """\
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."},
]
context = OpenAILLMContext(messages, tools)
context_aggregator = llm.create_context_aggregator(context)
context = LLMContext(messages, tools)
context_aggregator = LLMContextAggregatorPair(context)
pipeline = Pipeline(
[

43
examples/foundational/36-user-email-gathering.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.turn.smart_turn.base_smart_turn import SmartTurnParams
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
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.runner import PipelineRunner
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.utils import create_transport
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.llm_service import FunctionCallParams
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.daily.transport import DailyParams
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.
llm.register_function("store_user_emails", store_user_emails)
tools = [
ChatCompletionToolParam(
type="function",
function={
"name": "store_user_emails",
"description": "Store user emails when confirmed",
"parameters": {
"type": "object",
"properties": {
"emails": {
"type": "array",
"description": "The list of user emails",
"items": {"type": "string"},
},
},
"required": ["emails"],
},
store_emails_function = FunctionSchema(
name="store_user_emails",
description="Store user emails when confirmed",
properties={
"emails": {
"type": "array",
"description": "The list of user emails",
"items": {"type": "string"},
},
)
]
},
required=["emails"],
)
tools = ToolsSchema(standard_tools=[store_emails_function])
messages = [
{
"role": "system",
@@ -120,8 +115,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
},
]
context = OpenAILLMContext(messages, tools)
context_aggregator = llm.create_context_aggregator(context)
context = LLMContext(messages, tools)
context_aggregator = LLMContextAggregatorPair(context)
pipeline = Pipeline(
[