326 lines
12 KiB
Python
326 lines
12 KiB
Python
#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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"""AWS Bedrock LLM adapter for Pipecat."""
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import base64
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import copy
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import json
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from dataclasses import dataclass
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from typing import Any, Dict, List, Literal, Optional, TypedDict
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from loguru import logger
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from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
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from pipecat.adapters.schemas.function_schema import FunctionSchema
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.processors.aggregators.llm_context import (
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LLMContext,
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LLMContextMessage,
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LLMContextToolChoice,
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LLMSpecificMessage,
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LLMStandardMessage,
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)
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class AWSBedrockLLMInvocationParams(TypedDict):
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"""Context-based parameters for invoking AWS Bedrock's LLM API."""
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system: Optional[List[dict[str, Any]]] # [{"text": "system message"}]
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messages: List[dict[str, Any]]
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tools: List[dict[str, Any]]
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tool_choice: LLMContextToolChoice
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class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
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"""Adapter for AWS Bedrock LLM integration with Pipecat.
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Provides conversion utilities for transforming Pipecat function schemas
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into AWS Bedrock's expected tool format for function calling capabilities.
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"""
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@property
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def id_for_llm_specific_messages(self) -> str:
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"""Get the identifier used in LLMSpecificMessage instances for AWS Bedrock."""
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return "aws"
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def get_llm_invocation_params(self, context: LLMContext) -> AWSBedrockLLMInvocationParams:
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"""Get AWS Bedrock-specific LLM invocation parameters from a universal LLM context.
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Args:
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context: The LLM context containing messages, tools, etc.
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Returns:
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Dictionary of parameters for invoking AWS Bedrock's LLM API.
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"""
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messages = self._from_universal_context_messages(self.get_messages(context))
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return {
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"system": messages.system,
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"messages": messages.messages,
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# NOTE: LLMContext's tools are guaranteed to be a ToolsSchema (or NOT_GIVEN)
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"tools": self.from_standard_tools(context.tools) or [],
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# To avoid refactoring in AWSBedrockLLMService, we just pass through tool_choice.
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# Eventually (when we don't have to maintain the non-LLMContext code path) we should do
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# the conversion to Bedrock's expected format here rather than in AWSBedrockLLMService.
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"tool_choice": context.tool_choice,
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}
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def get_messages_for_logging(self, context) -> List[Dict[str, Any]]:
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"""Get messages from a universal LLM context in a format ready for logging about AWS Bedrock.
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Removes or truncates sensitive data like image content for safe logging.
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Args:
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context: The LLM context containing messages.
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Returns:
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List of messages in a format ready for logging about AWS Bedrock.
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"""
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# Get messages in Anthropic's format
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messages = self._from_universal_context_messages(self.get_messages(context)).messages
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# Sanitize messages for logging
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messages_for_logging = []
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for message in messages:
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msg = copy.deepcopy(message)
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if "content" in msg:
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if isinstance(msg["content"], list):
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for item in msg["content"]:
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if item.get("image"):
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item["image"]["source"]["bytes"] = "..."
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messages_for_logging.append(msg)
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return messages_for_logging
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@dataclass
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class ConvertedMessages:
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"""Container for Anthropic-formatted messages converted from universal context."""
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messages: List[dict[str, Any]]
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system: Optional[str]
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def _from_universal_context_messages(
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self, universal_context_messages: List[LLMContextMessage]
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) -> ConvertedMessages:
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system = None
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messages = []
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# First, map messages using self._from_universal_context_message(m)
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try:
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messages = [self._from_universal_context_message(m) for m in universal_context_messages]
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except Exception as e:
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logger.error(f"Error mapping messages: {e}")
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# See if we should pull the system message out of our messages list
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if messages and messages[0]["role"] == "system":
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system = messages[0]["content"]
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messages.pop(0)
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# Convert any subsequent "system"-role messages to "user"-role
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# messages, as AWS Bedrock doesn't support system input messages.
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for message in messages:
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if message["role"] == "system":
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message["role"] = "user"
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# Merge consecutive messages with the same role.
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i = 0
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while i < len(messages) - 1:
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current_message = messages[i]
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next_message = messages[i + 1]
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if current_message["role"] == next_message["role"]:
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# Convert content to list of dictionaries if it's a string
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if isinstance(current_message["content"], str):
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current_message["content"] = [
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{"type": "text", "text": current_message["content"]}
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]
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if isinstance(next_message["content"], str):
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next_message["content"] = [{"type": "text", "text": next_message["content"]}]
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# Concatenate the content
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current_message["content"].extend(next_message["content"])
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# Remove the next message from the list
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messages.pop(i + 1)
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else:
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i += 1
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# Avoid empty content in messages
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for message in messages:
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if isinstance(message["content"], str) and message["content"] == "":
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message["content"] = "(empty)"
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elif isinstance(message["content"], list) and len(message["content"]) == 0:
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message["content"] = [{"type": "text", "text": "(empty)"}]
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return self.ConvertedMessages(messages=messages, system=system)
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def _from_universal_context_message(self, message: LLMContextMessage) -> dict[str, Any]:
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if isinstance(message, LLMSpecificMessage):
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return copy.deepcopy(message.message)
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return self._from_standard_message(message)
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def _from_standard_message(self, message: LLMStandardMessage) -> dict[str, Any]:
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"""Convert standard format message to AWS Bedrock format.
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Handles conversion of text content, tool calls, and tool results.
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Empty text content is converted to "(empty)".
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Args:
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message: Message in standard format.
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Returns:
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Message in AWS Bedrock format.
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Examples:
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Standard format input::
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{
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"role": "assistant",
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"tool_calls": [
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{
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"id": "123",
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"function": {"name": "search", "arguments": '{"q": "test"}'}
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}
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]
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}
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AWS Bedrock format output::
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{
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"role": "assistant",
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"content": [
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{
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"toolUse": {
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"toolUseId": "123",
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"name": "search",
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"input": {"q": "test"}
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}
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}
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]
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}
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"""
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message = copy.deepcopy(message)
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if message["role"] == "tool":
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# Try to parse the content as JSON if it looks like JSON
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try:
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if message["content"].strip().startswith("{") and message[
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"content"
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].strip().endswith("}"):
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content_json = json.loads(message["content"])
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tool_result_content = [{"json": content_json}]
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else:
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tool_result_content = [{"text": message["content"]}]
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except:
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tool_result_content = [{"text": message["content"]}]
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return {
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"role": "user",
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"content": [
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{
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"toolResult": {
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"toolUseId": message["tool_call_id"],
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"content": tool_result_content,
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},
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},
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],
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}
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if message.get("tool_calls"):
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tc = message["tool_calls"]
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ret = {"role": "assistant", "content": []}
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for tool_call in tc:
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function = tool_call["function"]
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arguments = json.loads(function["arguments"])
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new_tool_use = {
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"toolUse": {
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"toolUseId": tool_call["id"],
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"name": function["name"],
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"input": arguments,
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}
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}
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ret["content"].append(new_tool_use)
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return ret
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# Handle text content
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content = message.get("content")
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if isinstance(content, str):
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if content == "":
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return {"role": message["role"], "content": [{"text": "(empty)"}]}
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else:
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return {"role": message["role"], "content": [{"text": content}]}
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elif isinstance(content, list):
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new_content = []
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for item in content:
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# fix empty text
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if item.get("type", "") == "text":
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text_content = item["text"] if item["text"] != "" else "(empty)"
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new_content.append({"text": text_content})
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# handle image_url -> image conversion
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if item["type"] == "image_url":
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if item["image_url"]["url"].startswith("data:"):
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new_item = {
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"image": {
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"format": "jpeg",
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"source": {
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"bytes": base64.b64decode(
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item["image_url"]["url"].split(",")[1]
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)
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},
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}
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}
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new_content.append(new_item)
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else:
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url = item["image_url"]["url"]
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logger.warning(f"Unsupported 'image_url': {url}")
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# In the case where there's a single image in the list (like what
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# would result from a UserImageRawFrame), ensure that the image
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# comes before text
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image_indices = [i for i, item in enumerate(new_content) if "image" in item]
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text_indices = [i for i, item in enumerate(new_content) if "text" in item]
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if len(image_indices) == 1 and text_indices:
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img_idx = image_indices[0]
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first_txt_idx = text_indices[0]
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if img_idx > first_txt_idx:
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# Move image before the first text
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image_item = new_content.pop(img_idx)
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new_content.insert(first_txt_idx, image_item)
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return {"role": message["role"], "content": new_content}
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return message
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@staticmethod
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def _to_bedrock_function_format(function: FunctionSchema) -> Dict[str, Any]:
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"""Convert a function schema to Bedrock's tool format.
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Args:
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function: The function schema to convert.
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Returns:
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Dictionary formatted for Bedrock's tool specification.
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"""
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return {
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"toolSpec": {
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"name": function.name,
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"description": function.description,
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"inputSchema": {
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"json": {
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"type": "object",
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"properties": function.properties,
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"required": function.required,
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},
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},
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}
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}
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def to_provider_tools_format(self, tools_schema: ToolsSchema) -> List[Dict[str, Any]]:
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"""Convert function schemas to Bedrock's function-calling format.
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Args:
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tools_schema: The tools schema containing functions to convert.
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Returns:
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List of Bedrock formatted function call definitions.
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"""
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functions_schema = tools_schema.standard_tools
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return [self._to_bedrock_function_format(func) for func in functions_schema]
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