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Merge pull request #1904 from pipecat-ai/mb/aws-bedrock-no-op-tool

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Add no_op tool to AWSBedrockLLMService
This commit is contained in:
Mark Backman
2025-05-29 10:29:19 -04:00
committed by GitHub
parent ae3a7d9bed bee242b781
commit a33d0b4b53
2 changed files with 56 additions and 11 deletions
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7
CHANGELOG.md
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@@ -13,6 +13,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
`DailyTransport.stop_transcription()` to be able to start and stop Daily
transcription dynamically (maybe with different settings).
### Fixed
- In `AWSBedrockLLMService`, worked around a possible bug in AWS Bedrock where
a `toolConfig` is required if there has been previous tool use in the
messages array. This workaround includes a no_op factory function call is
used to satisfy the requirement.
## [0.0.68] - 2025-05-28
### Added

60
src/pipecat/services/aws/llm.py
View File

@@ -606,6 +606,21 @@ class AWSBedrockLLMService(LLMService):
assistant = AWSBedrockAssistantContextAggregator(context, params=assistant_params)
return AWSBedrockContextAggregatorPair(_user=user, _assistant=assistant)
def _create_no_op_tool(self):
"""Create a no-operation tool for AWS Bedrock when tool content exists but no tools are defined.
This is required because AWS Bedrock doesn't allow empty tool configurations after tools were
previously set. Other LLM vendors allow NOT_GIVEN or empty tool configurations,
but AWS Bedrock requires at least one tool to be defined.
"""
return {
"toolSpec": {
"name": "no_operation",
"description": "Internal placeholder function. Do not call this function.",
"inputSchema": {"json": {"type": "object", "properties": {}, "required": []}},
}
}
@traced_llm
async def _process_context(self, context: AWSBedrockLLMContext):
# Usage tracking
@@ -616,6 +631,8 @@ class AWSBedrockLLMService(LLMService):
cache_creation_input_tokens = 0
use_completion_tokens_estimate = False
using_noop_tool = False
try:
await self.push_frame(LLMFullResponseStartFrame())
await self.start_processing_metrics()
@@ -640,12 +657,28 @@ class AWSBedrockLLMService(LLMService):
# Add system message
request_params["system"] = context.system
# Add tools if present
if context.tools:
tool_config = {"tools": context.tools}
# Check if messages contain tool use or tool result content blocks
has_tool_content = False
for message in context.messages:
if isinstance(message.get("content"), list):
for content_item in message["content"]:
if "toolUse" in content_item or "toolResult" in content_item:
has_tool_content = True
break
if has_tool_content:
break
# Add tool_choice if specified
if context.tool_choice:
# Handle tools: use current tools, or no-op if tool content exists but no current tools
tools = context.tools or []
if has_tool_content and not tools:
tools = [self._create_no_op_tool()]
using_noop_tool = True
if tools:
tool_config = {"tools": tools}
# Only add tool_choice if we have real tools (not just no-op)
if not using_noop_tool and context.tool_choice:
if context.tool_choice == "auto":
tool_config["toolChoice"] = {"auto": {}}
elif context.tool_choice == "none":
@@ -704,12 +737,17 @@ class AWSBedrockLLMService(LLMService):
if event["messageStop"]["stopReason"] == "tool_use" and tool_use_block:
try:
arguments = json.loads(json_accumulator) if json_accumulator else {}
await self.call_function(
context=context,
tool_call_id=tool_use_block["id"],
function_name=tool_use_block["name"],
arguments=arguments,
)
# Only call function if it's not the no_operation tool
if not using_noop_tool:
await self.call_function(
context=context,
tool_call_id=tool_use_block["id"],
function_name=tool_use_block["name"],
arguments=arguments,
)
else:
logger.debug("Ignoring no_operation tool call")
except json.JSONDecodeError:
logger.error(f"Failed to parse tool arguments: {json_accumulator}")