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feat: add bedrock client (#2913)

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This commit is contained in:
cthomas
2025-06-19 12:07:00 -07:00
committed by GitHub
parent eab5a60311
commit 56493de971
13 changed files with 257 additions and 39 deletions
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6
letta/agents/letta_agent.py
View File

@@ -27,7 +27,7 @@ from letta.otel.context import get_ctx_attributes
from letta.otel.metric_registry import MetricRegistry
from letta.otel.tracing import log_event, trace_method, tracer
from letta.schemas.agent import AgentState, UpdateAgent
from letta.schemas.enums import MessageRole
from letta.schemas.enums import MessageRole, ProviderType
from letta.schemas.letta_message import MessageType
from letta.schemas.letta_message_content import OmittedReasoningContent, ReasoningContent, RedactedReasoningContent, TextContent
from letta.schemas.letta_response import LettaResponse
@@ -512,12 +512,12 @@ class LettaAgent(BaseAgent):
# TODO: THIS IS INCREDIBLY UGLY
# TODO: THERE ARE MULTIPLE COPIES OF THE LLM_CONFIG EVERYWHERE THAT ARE GETTING MANIPULATED
if agent_state.llm_config.model_endpoint_type == "anthropic":
if agent_state.llm_config.model_endpoint_type in [ProviderType.anthropic, ProviderType.bedrock]:
interface = AnthropicStreamingInterface(
use_assistant_message=use_assistant_message,
put_inner_thoughts_in_kwarg=agent_state.llm_config.put_inner_thoughts_in_kwargs,
)
elif agent_state.llm_config.model_endpoint_type == "openai":
elif agent_state.llm_config.model_endpoint_type == ProviderType.openai:
interface = OpenAIStreamingInterface(
use_assistant_message=use_assistant_message,
put_inner_thoughts_in_kwarg=agent_state.llm_config.put_inner_thoughts_in_kwargs,

6
letta/llm_api/anthropic_client.py
View File

@@ -49,20 +49,20 @@ class AnthropicClient(LLMClientBase):
@trace_method
def request(self, request_data: dict, llm_config: LLMConfig) -> dict:
client = self._get_anthropic_client(llm_config, async_client=False)
response = client.beta.messages.create(**request_data, betas=["tools-2024-04-04"])
response = client.beta.messages.create(**request_data)
return response.model_dump()
@trace_method
async def request_async(self, request_data: dict, llm_config: LLMConfig) -> dict:
client = await self._get_anthropic_client_async(llm_config, async_client=True)
response = await client.beta.messages.create(**request_data, betas=["tools-2024-04-04"])
response = await client.beta.messages.create(**request_data)
return response.model_dump()
@trace_method
async def stream_async(self, request_data: dict, llm_config: LLMConfig) -> AsyncStream[BetaRawMessageStreamEvent]:
client = await self._get_anthropic_client_async(llm_config, async_client=True)
request_data["stream"] = True
return await client.beta.messages.create(**request_data, betas=["tools-2024-04-04"])
return await client.beta.messages.create(**request_data)
@trace_method
async def send_llm_batch_request_async(

22
letta/llm_api/aws_bedrock.py
View File

@@ -64,6 +64,28 @@ def bedrock_get_model_list(region_name: str) -> List[dict]:
raise e
async def bedrock_get_model_list_async(
access_key_id: Optional[str] = None,
secret_access_key: Optional[str] = None,
default_region: Optional[str] = None,
) -> List[dict]:
from aioboto3.session import Session
try:
session = Session()
async with session.client(
"bedrock",
aws_access_key_id=access_key_id,
aws_secret_access_key=secret_access_key,
region_name=default_region,
) as bedrock:
response = await bedrock.list_inference_profiles()
return response["inferenceProfileSummaries"]
except Exception as e:
print(f"Error getting model list: {str(e)}")
raise e
def bedrock_get_model_details(region_name: str, model_id: str) -> Dict[str, Any]:
"""
Get details for a specific model from Bedrock.

74
letta/llm_api/bedrock_client.py Normal file
View File

@@ -0,0 +1,74 @@
from typing import List, Optional, Union
import anthropic
from aioboto3.session import Session
from letta.llm_api.anthropic_client import AnthropicClient
from letta.log import get_logger
from letta.otel.tracing import trace_method
from letta.schemas.enums import ProviderCategory
from letta.schemas.llm_config import LLMConfig
from letta.schemas.message import Message as PydanticMessage
from letta.services.provider_manager import ProviderManager
from letta.settings import model_settings
logger = get_logger(__name__)
class BedrockClient(AnthropicClient):
@trace_method
async def _get_anthropic_client_async(
self, llm_config: LLMConfig, async_client: bool = False
) -> Union[anthropic.AsyncAnthropic, anthropic.Anthropic, anthropic.AsyncAnthropicBedrock, anthropic.AnthropicBedrock]:
override_access_key_id, override_secret_access_key, override_default_region = None, None, None
if llm_config.provider_category == ProviderCategory.byok:
(
override_access_key_id,
override_secret_access_key,
override_default_region,
) = await ProviderManager().get_bedrock_credentials_async(
llm_config.provider_name,
actor=self.actor,
)
session = Session()
async with session.client(
"sts",
aws_access_key_id=override_access_key_id or model_settings.aws_access_key_id,
aws_secret_access_key=override_secret_access_key or model_settings.aws_secret_access_key,
region_name=override_default_region or model_settings.aws_default_region,
) as sts_client:
session_token = await sts_client.get_session_token()
credentials = session_token["Credentials"]
if async_client:
return anthropic.AsyncAnthropicBedrock(
aws_access_key=credentials["AccessKeyId"],
aws_secret_key=credentials["SecretAccessKey"],
aws_session_token=credentials["SessionToken"],
aws_region=override_default_region or model_settings.aws_default_region,
max_retries=model_settings.anthropic_max_retries,
)
else:
return anthropic.AnthropicBedrock(
aws_access_key=credentials["AccessKeyId"],
aws_secret_key=credentials["SecretAccessKey"],
aws_session_token=credentials["SessionToken"],
aws_region=override_default_region or model_settings.aws_default_region,
max_retries=model_settings.anthropic_max_retries,
)
@trace_method
def build_request_data(
self,
messages: List[PydanticMessage],
llm_config: LLMConfig,
tools: Optional[List[dict]] = None,
force_tool_call: Optional[str] = None,
) -> dict:
data = super().build_request_data(messages, llm_config, tools, force_tool_call)
# remove disallowed fields
if "tool_choice" in data:
del data["tool_choice"]["disable_parallel_tool_use"]
return data

7
letta/llm_api/llm_client.py
View File

@@ -51,6 +51,13 @@ class LLMClient:
put_inner_thoughts_first=put_inner_thoughts_first,
actor=actor,
)
case ProviderType.bedrock:
from letta.llm_api.bedrock_client import BedrockClient
return BedrockClient(
put_inner_thoughts_first=put_inner_thoughts_first,
actor=actor,
)
case ProviderType.openai | ProviderType.together:
from letta.llm_api.openai_client import OpenAIClient

34
letta/schemas/providers.py
View File

@@ -98,7 +98,7 @@ class Provider(ProviderBase):
case ProviderType.anthropic:
return AnthropicProvider(**self.model_dump(exclude_none=True))
case ProviderType.bedrock:
return AnthropicBedrockProvider(**self.model_dump(exclude_none=True))
return BedrockProvider(**self.model_dump(exclude_none=True))
case ProviderType.ollama:
return OllamaProvider(**self.model_dump(exclude_none=True))
case ProviderType.google_ai:
@@ -1513,7 +1513,7 @@ class CohereProvider(OpenAIProvider):
pass
class AnthropicBedrockProvider(Provider):
class BedrockProvider(Provider):
provider_type: Literal[ProviderType.bedrock] = Field(ProviderType.bedrock, description="The type of the provider.")
provider_category: ProviderCategory = Field(ProviderCategory.base, description="The category of the provider (base or byok)")
region: str = Field(..., description="AWS region for Bedrock")
@@ -1539,6 +1539,32 @@ class AnthropicBedrockProvider(Provider):
)
return configs
async def list_llm_models_async(self) -> List[LLMConfig]:
from letta.llm_api.aws_bedrock import bedrock_get_model_list_async
models = await bedrock_get_model_list_async(
self.access_key,
self.api_key,
self.region,
)
configs = []
for model_summary in models:
model_arn = model_summary["inferenceProfileArn"]
configs.append(
LLMConfig(
model=model_arn,
model_endpoint_type=self.provider_type.value,
model_endpoint=None,
context_window=self.get_model_context_window(model_arn),
handle=self.get_handle(model_arn),
provider_name=self.name,
provider_category=self.provider_category,
)
)
return configs
def list_embedding_models(self):
return []
@@ -1548,7 +1574,7 @@ class AnthropicBedrockProvider(Provider):
return bedrock_get_model_context_window(model_name)
def get_handle(self, model_name: str) -> str:
def get_handle(self, model_name: str, is_embedding: bool = False, base_name: Optional[str] = None) -> str:
print(model_name)
model = model_name.split(".")[-1]
return f"bedrock/{model}"
return f"{self.name}/{model}"

21
letta/server/rest_api/routers/v1/agents.py
View File

@@ -13,7 +13,7 @@ from sqlalchemy.exc import IntegrityError, OperationalError
from starlette.responses import Response, StreamingResponse
from letta.agents.letta_agent import LettaAgent
from letta.constants import DEFAULT_MAX_STEPS, DEFAULT_MESSAGE_TOOL, DEFAULT_MESSAGE_TOOL_KWARG
from letta.constants import DEFAULT_MAX_STEPS, DEFAULT_MESSAGE_TOOL, DEFAULT_MESSAGE_TOOL_KWARG, LETTA_MODEL_ENDPOINT
from letta.groups.sleeptime_multi_agent_v2 import SleeptimeMultiAgentV2
from letta.helpers.datetime_helpers import get_utc_timestamp_ns
from letta.log import get_logger
@@ -686,7 +686,7 @@ async def send_message(
# TODO: This is redundant, remove soon
agent = await server.agent_manager.get_agent_by_id_async(agent_id, actor, include_relationships=["multi_agent_group"])
agent_eligible = agent.multi_agent_group is None or agent.multi_agent_group.manager_type in ["sleeptime", "voice_sleeptime"]
model_compatible = agent.llm_config.model_endpoint_type in ["anthropic", "openai", "together", "google_ai", "google_vertex"]
model_compatible = agent.llm_config.model_endpoint_type in ["anthropic", "openai", "together", "google_ai", "google_vertex", "bedrock"]
if agent_eligible and model_compatible:
if agent.enable_sleeptime and agent.agent_type != AgentType.voice_convo_agent:
@@ -768,9 +768,9 @@ async def send_message_streaming(
# TODO: This is redundant, remove soon
agent = await server.agent_manager.get_agent_by_id_async(agent_id, actor, include_relationships=["multi_agent_group"])
agent_eligible = agent.multi_agent_group is None or agent.multi_agent_group.manager_type in ["sleeptime", "voice_sleeptime"]
model_compatible = agent.llm_config.model_endpoint_type in ["anthropic", "openai", "together", "google_ai", "google_vertex"]
model_compatible_token_streaming = agent.llm_config.model_endpoint_type in ["anthropic", "openai"]
not_letta_endpoint = not ("inference.letta.com" in agent.llm_config.model_endpoint)
model_compatible = agent.llm_config.model_endpoint_type in ["anthropic", "openai", "together", "google_ai", "google_vertex", "bedrock"]
model_compatible_token_streaming = agent.llm_config.model_endpoint_type in ["anthropic", "openai", "bedrock"]
not_letta_endpoint = LETTA_MODEL_ENDPOINT != agent.llm_config.model_endpoint
if agent_eligible and model_compatible:
if agent.enable_sleeptime and agent.agent_type != AgentType.voice_convo_agent:
@@ -857,7 +857,14 @@ async def process_message_background(
try:
agent = await server.agent_manager.get_agent_by_id_async(agent_id, actor, include_relationships=["multi_agent_group"])
agent_eligible = agent.multi_agent_group is None or agent.multi_agent_group.manager_type in ["sleeptime", "voice_sleeptime"]
model_compatible = agent.llm_config.model_endpoint_type in ["anthropic", "openai", "together", "google_ai", "google_vertex"]
model_compatible = agent.llm_config.model_endpoint_type in [
"anthropic",
"openai",
"together",
"google_ai",
"google_vertex",
"bedrock",
]
if agent_eligible and model_compatible:
if agent.enable_sleeptime and agent.agent_type != AgentType.voice_convo_agent:
agent_loop = SleeptimeMultiAgentV2(
@@ -1021,7 +1028,7 @@ async def summarize_agent_conversation(
actor = await server.user_manager.get_actor_or_default_async(actor_id=actor_id)
agent = await server.agent_manager.get_agent_by_id_async(agent_id, actor, include_relationships=["multi_agent_group"])
agent_eligible = agent.multi_agent_group is None or agent.multi_agent_group.manager_type in ["sleeptime", "voice_sleeptime"]
model_compatible = agent.llm_config.model_endpoint_type in ["anthropic", "openai", "together", "google_ai", "google_vertex"]
model_compatible = agent.llm_config.model_endpoint_type in ["anthropic", "openai", "together", "google_ai", "google_vertex", "bedrock"]
if agent_eligible and model_compatible:
agent = LettaAgent(

4
letta/server/server.py
View File

@@ -54,9 +54,9 @@ from letta.schemas.memory import ArchivalMemorySummary, Memory, RecallMemorySumm
from letta.schemas.message import Message, MessageCreate, MessageUpdate
from letta.schemas.passage import Passage, PassageUpdate
from letta.schemas.providers import (
AnthropicBedrockProvider,
AnthropicProvider,
AzureProvider,
BedrockProvider,
DeepSeekProvider,
GoogleAIProvider,
GoogleVertexProvider,
@@ -367,7 +367,7 @@ class SyncServer(Server):
)
if model_settings.aws_access_key_id and model_settings.aws_secret_access_key and model_settings.aws_default_region:
self._enabled_providers.append(
AnthropicBedrockProvider(
BedrockProvider(
name="bedrock",
region=model_settings.aws_default_region,
)

10
letta/services/provider_manager.py
View File

@@ -1,4 +1,4 @@
from typing import List, Optional, Union
from typing import List, Optional, Tuple, Union
from letta.orm.provider import Provider as ProviderModel
from letta.otel.tracing import trace_method
@@ -196,10 +196,12 @@ class ProviderManager:
@enforce_types
@trace_method
async def get_bedrock_credentials_async(self, provider_name: Union[str, None], actor: PydanticUser) -> Optional[str]:
async def get_bedrock_credentials_async(
self, provider_name: Union[str, None], actor: PydanticUser
) -> Tuple[Optional[str], Optional[str], Optional[str]]:
providers = await self.list_providers_async(name=provider_name, actor=actor)
access_key = providers[0].api_key if providers else None
secret_key = providers[0].api_secret if providers else None
access_key = providers[0].access_key if providers else None
secret_key = providers[0].api_key if providers else None
region = providers[0].region if providers else None
return access_key, secret_key, region

106
poetry.lock generated
View File

@@ -1,5 +1,66 @@
# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand.
[[package]]
name = "aioboto3"
version = "14.3.0"
description = "Async boto3 wrapper"
optional = true
python-versions = "<4.0,>=3.8"
groups = ["main"]
markers = "extra == \"bedrock\""
files = [
{file = "aioboto3-14.3.0-py3-none-any.whl", hash = "sha256:aec5de94e9edc1ffbdd58eead38a37f00ddac59a519db749a910c20b7b81bca7"},
{file = "aioboto3-14.3.0.tar.gz", hash = "sha256:1d18f88bb56835c607b62bb6cb907754d717bedde3ddfff6935727cb48a80135"},
]
[package.dependencies]
aiobotocore = {version = "2.22.0", extras = ["boto3"]}
aiofiles = ">=23.2.1"
[package.extras]
chalice = ["chalice (>=1.24.0)"]
s3cse = ["cryptography (>=44.0.1)"]
[[package]]
name = "aiobotocore"
version = "2.22.0"
description = "Async client for aws services using botocore and aiohttp"
optional = true
python-versions = ">=3.8"
groups = ["main"]
markers = "extra == \"bedrock\""
files = [
{file = "aiobotocore-2.22.0-py3-none-any.whl", hash = "sha256:b4e6306f79df9d81daff1f9d63189a2dbee4b77ce3ab937304834e35eaaeeccf"},
{file = "aiobotocore-2.22.0.tar.gz", hash = "sha256:11091477266b75c2b5d28421c1f2bc9a87d175d0b8619cb830805e7a113a170b"},
]
[package.dependencies]
aiohttp = ">=3.9.2,<4.0.0"
aioitertools = ">=0.5.1,<1.0.0"
boto3 = {version = ">=1.37.2,<1.37.4", optional = true, markers = "extra == \"boto3\""}
botocore = ">=1.37.2,<1.37.4"
jmespath = ">=0.7.1,<2.0.0"
multidict = ">=6.0.0,<7.0.0"
python-dateutil = ">=2.1,<3.0.0"
wrapt = ">=1.10.10,<2.0.0"
[package.extras]
awscli = ["awscli (>=1.38.2,<1.38.4)"]
boto3 = ["boto3 (>=1.37.2,<1.37.4)"]
[[package]]
name = "aiofiles"
version = "24.1.0"
description = "File support for asyncio."
optional = true
python-versions = ">=3.8"
groups = ["main"]
markers = "extra == \"bedrock\""
files = [
{file = "aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5"},
{file = "aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c"},
]
[[package]]
name = "aiohappyeyeballs"
version = "2.6.1"
@@ -116,6 +177,23 @@ yarl = ">=1.17.0,<2.0"
[package.extras]
speedups = ["Brotli ; platform_python_implementation == \"CPython\"", "aiodns (>=3.2.0) ; sys_platform == \"linux\" or sys_platform == \"darwin\"", "brotlicffi ; platform_python_implementation != \"CPython\""]
[[package]]
name = "aioitertools"
version = "0.12.0"
description = "itertools and builtins for AsyncIO and mixed iterables"
optional = true
python-versions = ">=3.8"
groups = ["main"]
markers = "extra == \"bedrock\""
files = [
{file = "aioitertools-0.12.0-py3-none-any.whl", hash = "sha256:fc1f5fac3d737354de8831cbba3eb04f79dd649d8f3afb4c5b114925e662a796"},
{file = "aioitertools-0.12.0.tar.gz", hash = "sha256:c2a9055b4fbb7705f561b9d86053e8af5d10cc845d22c32008c43490b2d8dd6b"},
]
[package.extras]
dev = ["attribution (==1.8.0)", "black (==24.8.0)", "build (>=1.2)", "coverage (==7.6.1)", "flake8 (==7.1.1)", "flit (==3.9.0)", "mypy (==1.11.2)", "ufmt (==2.7.1)", "usort (==1.0.8.post1)"]
docs = ["sphinx (==8.0.2)", "sphinx-mdinclude (==0.6.2)"]
[[package]]
name = "aiomultiprocess"
version = "0.9.1"
@@ -620,19 +698,19 @@ files = [
[[package]]
name = "boto3"
version = "1.37.31"
version = "1.37.3"
description = "The AWS SDK for Python"
optional = true
python-versions = ">=3.8"
groups = ["main"]
markers = "extra == \"bedrock\""
files = [
{file = "boto3-1.37.31-py3-none-any.whl", hash = "sha256:cf8997be0742a5cab9d33a138ef56e423a8ebd8881f6f73e95076b26656b36dc"},
{file = "boto3-1.37.31.tar.gz", hash = "sha256:dfee02b2f8f632a239a2f4ba6a2d568e2edd7f7464e9afd8a487fdb3fa9a0ad3"},
{file = "boto3-1.37.3-py3-none-any.whl", hash = "sha256:2063b40af99fd02f6228ff52397b552ff3353831edaf8d25cc04801827ab9794"},
{file = "boto3-1.37.3.tar.gz", hash = "sha256:21f3ce0ef111297e63a6eb998a25197b8c10982970c320d4c6e8db08be2157be"},
]
[package.dependencies]
botocore = ">=1.37.31,<1.38.0"
botocore = ">=1.37.3,<1.38.0"
jmespath = ">=0.7.1,<2.0.0"
s3transfer = ">=0.11.0,<0.12.0"
@@ -641,15 +719,15 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
[[package]]
name = "botocore"
version = "1.37.31"
version = "1.37.3"
description = "Low-level, data-driven core of boto 3."
optional = true
python-versions = ">=3.8"
groups = ["main"]
markers = "extra == \"bedrock\""
files = [
{file = "botocore-1.37.31-py3-none-any.whl", hash = "sha256:598a33a7a0e5a014bd1416c999a0b9c634fbbba3d1363e2368e6a92da4544df4"},
{file = "botocore-1.37.31.tar.gz", hash = "sha256:eb3dfa44a87187bd82c3b493d568d8436270d4d000f237b49b669a01fcd8a21c"},
{file = "botocore-1.37.3-py3-none-any.whl", hash = "sha256:d01bd3bf4c80e61fa88d636ad9f5c9f60a551d71549b481386c6b4efe0bb2b2e"},
{file = "botocore-1.37.3.tar.gz", hash = "sha256:fe8403eb55a88faf9b0f9da6615e5bee7be056d75e17af66c3c8f0a3b0648da4"},
]
[package.dependencies]
@@ -6326,22 +6404,22 @@ pyasn1 = ">=0.1.3"
[[package]]
name = "s3transfer"
version = "0.11.4"
version = "0.11.3"
description = "An Amazon S3 Transfer Manager"
optional = true
python-versions = ">=3.8"
groups = ["main"]
markers = "extra == \"bedrock\""
files = [
{file = "s3transfer-0.11.4-py3-none-any.whl", hash = "sha256:ac265fa68318763a03bf2dc4f39d5cbd6a9e178d81cc9483ad27da33637e320d"},
{file = "s3transfer-0.11.4.tar.gz", hash = "sha256:559f161658e1cf0a911f45940552c696735f5c74e64362e515f333ebed87d679"},
{file = "s3transfer-0.11.3-py3-none-any.whl", hash = "sha256:ca855bdeb885174b5ffa95b9913622459d4ad8e331fc98eb01e6d5eb6a30655d"},
{file = "s3transfer-0.11.3.tar.gz", hash = "sha256:edae4977e3a122445660c7c114bba949f9d191bae3b34a096f18a1c8c354527a"},
]
[package.dependencies]
botocore = ">=1.37.4,<2.0a.0"
botocore = ">=1.36.0,<2.0a.0"
[package.extras]
crt = ["botocore[crt] (>=1.37.4,<2.0a.0)"]
crt = ["botocore[crt] (>=1.36.0,<2.0a.0)"]
[[package]]
name = "scramp"
@@ -7783,7 +7861,7 @@ cffi = ["cffi (>=1.11)"]
[extras]
all = ["autoflake", "black", "docker", "fastapi", "granian", "isort", "langchain", "langchain-community", "locust", "pexpect", "pg8000", "pgvector", "pre-commit", "psycopg2", "psycopg2-binary", "pyright", "pytest-asyncio", "pytest-order", "redis", "uvicorn", "uvloop", "wikipedia"]
bedrock = ["boto3"]
bedrock = ["aioboto3", "boto3"]
cloud-tool-sandbox = ["e2b-code-interpreter"]
desktop = ["docker", "fastapi", "langchain", "langchain-community", "locust", "pg8000", "pgvector", "psycopg2", "psycopg2-binary", "pyright", "uvicorn", "wikipedia"]
dev = ["autoflake", "black", "isort", "locust", "pexpect", "pre-commit", "pyright", "pytest-asyncio", "pytest-order"]
@@ -7798,4 +7876,4 @@ tests = ["wikipedia"]
[metadata]
lock-version = "2.1"
python-versions = "<3.14,>=3.10"
content-hash = "87b1d77da4ccba13d41d7b6ed9fe24302982e181f84ad93f0cb409f216e33255"
content-hash = "9372e0eacfc54bd204ddafcbfc11ac7bf0688a270e1906a10f57fda0191b4d73"

3
pyproject.toml
View File

@@ -98,6 +98,7 @@ granian = {version = "^2.3.2", extras = ["uvloop", "reload"], optional = true}
redis = {version = "^6.2.0", optional = true}
structlog = "^25.4.0"
certifi = "^2025.6.15"
aioboto3 = {version = "^14.3.0", optional = true}
[tool.poetry.extras]
@@ -109,7 +110,7 @@ server = ["websockets", "fastapi", "uvicorn"]
cloud-tool-sandbox = ["e2b-code-interpreter"]
external-tools = ["docker", "langchain", "wikipedia", "langchain-community", "firecrawl-py"]
tests = ["wikipedia"]
bedrock = ["boto3"]
bedrock = ["boto3", "aioboto3"]
google = ["google-genai"]
desktop = ["pgvector", "pg8000", "psycopg2-binary", "psycopg2", "pyright", "websockets", "fastapi", "uvicorn", "docker", "langchain", "wikipedia", "langchain-community", "locust"]
all = ["pgvector", "pg8000", "psycopg2-binary", "psycopg2", "pytest", "pytest-asyncio", "pexpect", "black", "pre-commit", "pyright", "pytest-order", "autoflake", "isort", "websockets", "fastapi", "uvicorn", "docker", "langchain", "wikipedia", "langchain-community", "locust", "uvloop", "granian", "redis"]

2
tests/configs/llm_model_configs/bedrock-claude-3-5-sonnet.json → tests/configs/llm_model_configs/bedrock-claude-4-sonnet.json
View File

@@ -1,6 +1,6 @@
{
"context_window": 200000,
"model": "arn:aws:bedrock:us-west-2:850995572407:inference-profile/us.anthropic.claude-3-5-sonnet-20241022-v2:0",
"model": "arn:aws:bedrock:us-east-1:474668403324:inference-profile/us.anthropic.claude-sonnet-4-20250514-v1:0",
"model_endpoint_type": "bedrock",
"model_endpoint": null,
"model_wrapper": null,

1
tests/integration_test_send_message.py
View File

@@ -110,6 +110,7 @@ all_configs = [
"claude-3-5-sonnet.json",
"claude-3-7-sonnet.json",
"claude-3-7-sonnet-extended.json",
"bedrock-claude-4-sonnet.json",
"gemini-1.5-pro.json",
"gemini-2.5-flash-vertex.json",
"gemini-2.5-pro-vertex.json",