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Merge pull request #1321 from pipecat-ai/aleix/allow-setting-context-aggregator-parameters

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LLMService: add user/assistant args to create_context_aggregator()
This commit is contained in:
Aleix Conchillo Flaqué
2025-03-03 09:48:31 -08:00
committed by GitHub
parent b9100beee3 17a1f30572
commit d6867bd12f
7 changed files with 168 additions and 34 deletions
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4
CHANGELOG.md
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@@ -9,6 +9,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
### Added
- Allow passing user (`user_kwargs`) and assistant (`assistant_kwargs`) context
aggregator parameters when using `create_context_aggregator()`. The values are
passed as a mapping that will then be converted to arguments.
- Added `speed` as an `InputParam` for both `ElevenLabsTTSService` and
`ElevenLabsHttpTTSService`.

32
src/pipecat/services/anthropic.py
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@@ -11,7 +11,7 @@ import io
import json
import re
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Union
from typing import Any, Dict, List, Mapping, Optional, Union
import httpx
from loguru import logger
@@ -125,14 +125,34 @@ class AnthropicLLMService(LLMService):
@staticmethod
def create_context_aggregator(
context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = True
context: OpenAILLMContext,
*,
user_kwargs: Mapping[str, Any] = {},
assistant_kwargs: Mapping[str, Any] = {},
) -> AnthropicContextAggregatorPair:
"""Create an instance of AnthropicContextAggregatorPair from an
OpenAILLMContext. Constructor keyword arguments for both the user and
assistant aggregators can be provided.
Args:
context (OpenAILLMContext): The LLM context.
user_kwargs (Mapping[str, Any], optional): Additional keyword
arguments for the user context aggregator constructor. Defaults
to an empty mapping.
assistant_kwargs (Mapping[str, Any], optional): Additional keyword
arguments for the assistant context aggregator
constructor. Defaults to an empty mapping.
Returns:
AnthropicContextAggregatorPair: A pair of context aggregators, one
for the user and one for the assistant, encapsulated in an
AnthropicContextAggregatorPair.
"""
if isinstance(context, OpenAILLMContext):
context = AnthropicLLMContext.from_openai_context(context)
user = AnthropicUserContextAggregator(context)
assistant = AnthropicAssistantContextAggregator(
context, expect_stripped_words=assistant_expect_stripped_words
)
user = AnthropicUserContextAggregator(context, **user_kwargs)
assistant = AnthropicAssistantContextAggregator(context, **assistant_kwargs)
return AnthropicContextAggregatorPair(_user=user, _assistant=assistant)
async def _process_context(self, context: OpenAILLMContext):

34
src/pipecat/services/gemini_multimodal_live/gemini.py
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@@ -9,7 +9,7 @@ import base64
import json
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, List, Optional
from typing import Any, Dict, List, Mapping, Optional
import websockets
from loguru import logger
@@ -701,11 +701,37 @@ class GeminiMultimodalLiveLLMService(LLMService):
await self.push_frame(TTSStoppedFrame())
def create_context_aggregator(
self, context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = False
self,
context: OpenAILLMContext,
*,
user_kwargs: Mapping[str, Any] = {},
assistant_kwargs: Mapping[str, Any] = {},
) -> GeminiMultimodalLiveContextAggregatorPair:
"""Create an instance of GeminiMultimodalLiveContextAggregatorPair from
an OpenAILLMContext. Constructor keyword arguments for both the user and
assistant aggregators can be provided.
Args:
context (OpenAILLMContext): The LLM context.
user_kwargs (Mapping[str, Any], optional): Additional keyword
arguments for the user context aggregator constructor. Defaults
to an empty mapping.
assistant_kwargs (Mapping[str, Any], optional): Additional keyword
arguments for the assistant context aggregator
constructor. Defaults to an empty mapping.
Returns:
GeminiMultimodalLiveContextAggregatorPair: A pair of context
aggregators, one for the user and one for the assistant,
encapsulated in an GeminiMultimodalLiveContextAggregatorPair.
"""
GeminiMultimodalLiveContext.upgrade(context)
user = GeminiMultimodalLiveUserContextAggregator(context)
user = GeminiMultimodalLiveUserContextAggregator(context, **user_kwargs)
default_assistant_kwargs = {"expect_stripped_words": False}
default_assistant_kwargs.update(assistant_kwargs)
assistant = GeminiMultimodalLiveAssistantContextAggregator(
context, expect_stripped_words=assistant_expect_stripped_words
context, **default_assistant_kwargs
)
return GeminiMultimodalLiveContextAggregatorPair(_user=user, _assistant=assistant)

32
src/pipecat/services/google/google.py
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@@ -19,7 +19,7 @@ from openai.types.chat import ChatCompletionChunk
os.environ["GRPC_ENABLE_FORK_SUPPORT"] = "false"
from dataclasses import dataclass
from typing import Any, AsyncGenerator, Dict, List, Literal, Optional, Union
from typing import Any, AsyncGenerator, Dict, List, Literal, Mapping, Optional, Union
from loguru import logger
from PIL import Image
@@ -1182,14 +1182,34 @@ class GoogleLLMService(LLMService):
@staticmethod
def create_context_aggregator(
context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = True
context: OpenAILLMContext,
*,
user_kwargs: Mapping[str, Any] = {},
assistant_kwargs: Mapping[str, Any] = {},
) -> GoogleContextAggregatorPair:
"""Create an instance of GoogleContextAggregatorPair from an
OpenAILLMContext. Constructor keyword arguments for both the user and
assistant aggregators can be provided.
Args:
context (OpenAILLMContext): The LLM context.
user_kwargs (Mapping[str, Any], optional): Additional keyword
arguments for the user context aggregator constructor. Defaults
to an empty mapping.
assistant_kwargs (Mapping[str, Any], optional): Additional keyword
arguments for the assistant context aggregator
constructor. Defaults to an empty mapping.
Returns:
GoogleContextAggregatorPair: A pair of context aggregators, one for
the user and one for the assistant, encapsulated in an
GoogleContextAggregatorPair.
"""
if isinstance(context, OpenAILLMContext):
context = GoogleLLMContext.upgrade_to_google(context)
user = GoogleUserContextAggregator(context)
assistant = GoogleAssistantContextAggregator(
context, expect_stripped_words=assistant_expect_stripped_words
)
user = GoogleUserContextAggregator(context, **user_kwargs)
assistant = GoogleAssistantContextAggregator(context, **assistant_kwargs)
return GoogleContextAggregatorPair(_user=user, _assistant=assistant)

32
src/pipecat/services/grok.py
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@@ -7,7 +7,7 @@
import json
from dataclasses import dataclass
from typing import Optional
from typing import Any, Mapping, Optional
from loguru import logger
@@ -208,10 +208,30 @@ class GrokLLMService(OpenAILLMService):
@staticmethod
def create_context_aggregator(
context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = True
context: OpenAILLMContext,
*,
user_kwargs: Mapping[str, Any] = {},
assistant_kwargs: Mapping[str, Any] = {},
) -> GrokContextAggregatorPair:
user = OpenAIUserContextAggregator(context)
assistant = GrokAssistantContextAggregator(
context, expect_stripped_words=assistant_expect_stripped_words
)
"""Create an instance of GrokContextAggregatorPair from an
OpenAILLMContext. Constructor keyword arguments for both the user and
assistant aggregators can be provided.
Args:
context (OpenAILLMContext): The LLM context.
user_kwargs (Mapping[str, Any], optional): Additional keyword
arguments for the user context aggregator constructor. Defaults
to an empty mapping.
assistant_kwargs (Mapping[str, Any], optional): Additional keyword
arguments for the assistant context aggregator
constructor. Defaults to an empty mapping.
Returns:
GrokContextAggregatorPair: A pair of context aggregators, one for
the user and one for the assistant, encapsulated in an
GrokContextAggregatorPair.
"""
user = OpenAIUserContextAggregator(context, **user_kwargs)
assistant = GrokAssistantContextAggregator(context, **assistant_kwargs)
return GrokContextAggregatorPair(_user=user, _assistant=assistant)

32
src/pipecat/services/openai.py
View File

@@ -8,7 +8,7 @@ import base64
import io
import json
from dataclasses import dataclass
from typing import Any, AsyncGenerator, Dict, List, Literal, Optional
from typing import Any, AsyncGenerator, Dict, List, Literal, Mapping, Optional
import aiohttp
import httpx
@@ -345,12 +345,32 @@ class OpenAILLMService(BaseOpenAILLMService):
@staticmethod
def create_context_aggregator(
context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = True
context: OpenAILLMContext,
*,
user_kwargs: Mapping[str, Any] = {},
assistant_kwargs: Mapping[str, Any] = {},
) -> OpenAIContextAggregatorPair:
user = OpenAIUserContextAggregator(context)
assistant = OpenAIAssistantContextAggregator(
context, expect_stripped_words=assistant_expect_stripped_words
)
"""Create an instance of OpenAIContextAggregatorPair from an
OpenAILLMContext. Constructor keyword arguments for both the user and
assistant aggregators can be provided.
Args:
context (OpenAILLMContext): The LLM context.
user_kwargs (Mapping[str, Any], optional): Additional keyword
arguments for the user context aggregator constructor. Defaults
to an empty mapping.
assistant_kwargs (Mapping[str, Any], optional): Additional keyword
arguments for the assistant context aggregator
constructor. Defaults to an empty mapping.
Returns:
OpenAIContextAggregatorPair: A pair of context aggregators, one for
the user and one for the assistant, encapsulated in an
OpenAIContextAggregatorPair.
"""
user = OpenAIUserContextAggregator(context, **user_kwargs)
assistant = OpenAIAssistantContextAggregator(context, **assistant_kwargs)
return OpenAIContextAggregatorPair(_user=user, _assistant=assistant)

36
src/pipecat/services/openai_realtime_beta/openai.py
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@@ -4,11 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import base64
import json
import time
from dataclasses import dataclass
from typing import Any, Mapping
from loguru import logger
@@ -571,11 +571,35 @@ class OpenAIRealtimeBetaLLMService(LLMService):
await self.send_client_event(events.InputAudioBufferAppendEvent(audio=payload))
def create_context_aggregator(
self, context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = False
self,
context: OpenAILLMContext,
*,
user_kwargs: Mapping[str, Any] = {},
assistant_kwargs: Mapping[str, Any] = {},
) -> OpenAIContextAggregatorPair:
"""Create an instance of OpenAIContextAggregatorPair from an
OpenAILLMContext. Constructor keyword arguments for both the user and
assistant aggregators can be provided.
Args:
context (OpenAILLMContext): The LLM context.
user_kwargs (Mapping[str, Any], optional): Additional keyword
arguments for the user context aggregator constructor. Defaults
to an empty mapping.
assistant_kwargs (Mapping[str, Any], optional): Additional keyword
arguments for the assistant context aggregator
constructor. Defaults to an empty mapping.
Returns:
OpenAIContextAggregatorPair: A pair of context aggregators, one for
the user and one for the assistant, encapsulated in an
OpenAIContextAggregatorPair.
"""
OpenAIRealtimeLLMContext.upgrade_to_realtime(context)
user = OpenAIRealtimeUserContextAggregator(context)
assistant = OpenAIRealtimeAssistantContextAggregator(
context, expect_stripped_words=assistant_expect_stripped_words
)
user = OpenAIRealtimeUserContextAggregator(context, **user_kwargs)
default_assistant_kwargs = {"expect_stripped_words": False}
default_assistant_kwargs.update(assistant_kwargs)
assistant = OpenAIRealtimeAssistantContextAggregator(context, **default_assistant_kwargs)
return OpenAIContextAggregatorPair(_user=user, _assistant=assistant)