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shadowdao 2025-06-03 09:54:05 -07:00 committed by GitHub
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examples/pipelines/providers/aws_bedrock_claude_pipeline.py
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@ -1,12 +1,12 @@
"""
title: AWS Bedrock Claude Pipeline
author: G-mario
date: 2024-08-18
version: 1.0
author: G-mario, shadowdao
date: 2025-06-02
version: 1.4
license: MIT
description: A pipeline for generating text and processing images using the AWS Bedrock API(By Anthropic claude).
requirements: requests, boto3
environment_variables: AWS_ACCESS_KEY, AWS_SECRET_KEY, AWS_REGION_NAME
environment_variables: AWS_ACCESS_KEY (optional with instance roles), AWS_SECRET_KEY (optional with instance roles), AWS_REGION_NAME
"""
import base64
import json
@ -31,8 +31,9 @@ REASONING_EFFORT_BUDGET_TOKEN_MAP = {
"max": 32768,
}
# Maximum combined token limit for Claude 3.7
# Maximum combined token limit for Claude Sonnet 3.7 and 4.0
MAX_COMBINED_TOKENS = 64000
OPUS_MAX_COMBINED_TOKENS = 32000
class Pipeline:
@ -91,21 +92,36 @@ class Pipeline:
def update_pipelines(self) -> None:
try:
self.bedrock = boto3.client(service_name="bedrock",
# Check if we have explicit credentials or should try instance role
if self.valves.AWS_ACCESS_KEY and self.valves.AWS_SECRET_KEY:
# Use explicit credentials
self.bedrock = boto3.client(
service_name="bedrock",
aws_access_key_id=self.valves.AWS_ACCESS_KEY,
aws_secret_access_key=self.valves.AWS_SECRET_KEY,
region_name=self.valves.AWS_REGION_NAME)
self.bedrock_runtime = boto3.client(service_name="bedrock-runtime",
region_name=self.valves.AWS_REGION_NAME
)
self.bedrock_runtime = boto3.client(
service_name="bedrock-runtime",
aws_access_key_id=self.valves.AWS_ACCESS_KEY,
aws_secret_access_key=self.valves.AWS_SECRET_KEY,
region_name=self.valves.AWS_REGION_NAME)
region_name=self.valves.AWS_REGION_NAME
)
print("Using provided AWS credentials")
else:
# Try to use instance role
region = self.valves.AWS_REGION_NAME if self.valves.AWS_REGION_NAME else None
self.bedrock = boto3.client(service_name="bedrock", region_name=region)
self.bedrock_runtime = boto3.client(service_name="bedrock-runtime", region_name=region)
print("No AWS credentials provided, using instance role or AWS credential chain")
self.pipelines = self.get_models()
except Exception as e:
print(f"Error: {e}")
self.pipelines = [
{
"id": "error",
"name": "Could not fetch models from Bedrock, please set up AWS Key/Secret or Instance/Task Role.",
"name": "Could not fetch models from Bedrock. Please check AWS credentials or instance role permissions.",
},
]
@ -124,11 +140,11 @@ class Pipeline:
return res
except Exception as e:
print(f"Error: {e}")
print(f"Error accessing Bedrock: {e}")
return [
{
"id": "error",
"name": "Could not fetch models from Bedrock, please check permissoin.",
"name": "Could not fetch models from Bedrock. Please check AWS credentials or instance role permissions.",
},
]
@ -140,6 +156,33 @@ class Pipeline:
return profile['inferenceProfileId']
return None
def check_supports_thinking(self, model_id: str) -> bool:
"""Helper function to determine if a model supports thinking feature"""
if "claude" not in model_id.lower():
return False
import re
# Initialize version variables
major_version = None
minor_version = None
# First try the standard pattern (claude-3-7)
version_match = re.search(r'claude-(\d+)(?:[.-](\d+))?', model_id.lower())
if version_match:
major_version = int(version_match.group(1))
minor_version = int(version_match.group(2)) if version_match.group(2) else 0
else:
# Try Claude 4 pattern with potential minor version (claude-sonnet-4-2, claude-sonnet-4.2)
version_match = re.search(r'claude-\w+-(\d+)(?:[.-](\d+))?', model_id.lower())
if version_match:
major_version = int(version_match.group(1))
minor_version = int(version_match.group(2)) if version_match.group(2) else 0
# Set supports_thinking if version was found
if major_version is not None:
return (major_version > 3) or (major_version == 3 and minor_version >= 7)
return False
def pipe(
self, user_message: str, model_id: str, messages: List[dict], body: dict
) -> Union[str, Generator, Iterator]:
@ -149,7 +192,6 @@ class Pipeline:
system_message, messages = pop_system_message(messages)
logging.info(f"pop_system_message: {json.dumps(messages)}")
try:
processed_messages = []
image_count = 0
@ -183,7 +225,12 @@ class Pipeline:
}
if body.get("stream", False):
supports_thinking = "claude-3-7" in model_id
supports_thinking = self.check_supports_thinking(model_id)
# Debug logging to help troubleshoot version detection
print(f"Model ID: {model_id}")
print(f"Supports thinking: {supports_thinking}")
reasoning_effort = body.get("reasoning_effort", "none")
budget_tokens = REASONING_EFFORT_BUDGET_TOKEN_MAP.get(reasoning_effort)
@ -202,11 +249,17 @@ class Pipeline:
# Check if the combined tokens (budget_tokens + max_tokens) exceeds the limit
max_tokens = payload.get("max_tokens", 4096)
combined_tokens = budget_tokens + max_tokens
if combined_tokens > MAX_COMBINED_TOKENS:
error_message = f"Error: Combined tokens (budget_tokens {budget_tokens} + max_tokens {max_tokens} = {combined_tokens}) exceeds the maximum limit of {MAX_COMBINED_TOKENS}"
print(error_message)
return error_message
# Opus version of the model has a lower max token theshold
if "opus" in model_id.lower():
if combined_tokens > OPUS_MAX_COMBINED_TOKENS:
error_message = f"Error: Combined tokens (budget_tokens {budget_tokens} + max_tokens {max_tokens} = {combined_tokens}) exceeds the maximum limit of {MAX_COMBINED_TOKENS}"
print(error_message)
return error_message
else:
if combined_tokens > MAX_COMBINED_TOKENS:
error_message = f"Error: Combined tokens (budget_tokens {budget_tokens} + max_tokens {max_tokens} = {combined_tokens}) exceeds the maximum limit of {MAX_COMBINED_TOKENS}"
print(error_message)
return error_message
payload["inferenceConfig"]["maxTokens"] = combined_tokens
payload["additionalModelRequestFields"]["thinking"] = {