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Merge remote-tracking branch 'origin/main' into telemetry

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This commit is contained in:
Ashwin Bharambe 2024-09-11 12:18:12 -07:00
commit 99af14b18c
21 changed files with 1270 additions and 267 deletions
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5
.github/CODEOWNERS vendored Normal file
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@ -0,0 +1,5 @@
# Each line is a file pattern followed by one or more owners.
# These owners will be the default owners for everything in
# the repo. Unless a later match takes precedence,
* @ashwinb @yanxi0830 @hardikjshah @dltn @raghotham

43
docs/cli_reference.md
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@ -295,13 +295,18 @@ As you can see above, each “distribution” details the “providers” it is
Let's imagine you are working with a 8B-Instruct model. The following command will build a package (in the form of a Conda environment) _and_ configure it. As part of the configuration, you will be asked for some inputs (model_id, max_seq_len, etc.) Since we are working with a 8B model, we will name our build `8b-instruct` to help us remember the config.
```
llama stack build local --name 8b-instruct
llama stack build
```
Once it runs successfully , you should see some outputs in the form:
Once it runs, you will be prompted to enter build name and optional arguments, and should see some outputs in the form:
```
$ llama stack build local --name 8b-instruct
$ llama stack build
Enter value for name (required): 8b-instruct
Enter value for distribution (default: local) (required): local
Enter value for api_providers (optional):
Enter value for image_type (default: conda) (required):
....
....
Successfully installed cfgv-3.4.0 distlib-0.3.8 identify-2.6.0 libcst-1.4.0 llama_toolchain-0.0.2 moreorless-0.4.0 nodeenv-1.9.1 pre-commit-3.8.0 stdlibs-2024.5.15 toml-0.10.2 tomlkit-0.13.0 trailrunner-1.4.0 ufmt-2.7.0 usort-1.0.8 virtualenv-20.26.3
@ -312,17 +317,41 @@ Successfully setup conda environment. Configuring build...
...
YAML configuration has been written to ~/.llama/builds/local/conda/8b-instruct.yaml
Target `8b-test` built with configuration at /home/xiyan/.llama/builds/local/conda/8b-test.yaml
Build spec configuration saved at /home/xiyan/.llama/distributions/local/conda/8b-test-build.yaml
```
You can re-build package based on build config
```
$ cat ~/.llama/distributions/local/conda/8b-instruct-build.yaml
name: 8b-instruct
distribution: local
api_providers: null
image_type: conda
$ llama stack build --config ~/.llama/distributions/local/conda/8b-instruct-build.yaml
Successfully setup conda environment. Configuring build...
...
...
YAML configuration has been written to ~/.llama/builds/local/conda/8b-instruct.yaml
Target `8b-instruct` built with configuration at ~/.llama/builds/local/conda/8b-instruct.yaml
Build spec configuration saved at ~/.llama/distributions/local/conda/8b-instruct-build.yaml
```
### Step 3.3: Configure a distribution
You can re-configure this distribution by running:
```
llama stack configure local --name 8b-instruct
llama stack configure ~/.llama/builds/local/conda/8b-instruct.yaml
```
Here is an example run of how the CLI will guide you to fill the configuration
```
$ llama stack configure local --name 8b-instruct
$ llama stack configure ~/.llama/builds/local/conda/8b-instruct.yaml
Configuring API: inference (meta-reference)
Enter value for model (required): Meta-Llama3.1-8B-Instruct
@ -363,12 +392,12 @@ Now let’s start Llama Stack Distribution Server.
You need the YAML configuration file which was written out at the end by the `llama stack build` step.
```
llama stack run local --name 8b-instruct --port 5000
llama stack run ~/.llama/builds/local/conda/8b-instruct.yaml --port 5000
```
You should see the Stack server start and print the APIs that it is supporting,
```
$ llama stack run local --name 8b-instruct --port 5000
$ llama stack run ~/.llama/builds/local/conda/8b-instruct.yaml --port 5000
> initializing model parallel with size 1
> initializing ddp with size 1

12
llama_toolchain/agentic_system/api/api.py
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@ -41,11 +41,19 @@ class ToolDefinitionCommon(BaseModel):
output_shields: Optional[List[ShieldDefinition]] = Field(default_factory=list)
class SearchEngineType(Enum):
bing = "bing"
brave = "brave"
@json_schema_type
class BraveSearchToolDefinition(ToolDefinitionCommon):
class SearchToolDefinition(ToolDefinitionCommon):
# NOTE: brave_search is just a placeholder since model always uses
# brave_search as tool call name
type: Literal[AgenticSystemTool.brave_search.value] = (
AgenticSystemTool.brave_search.value
)
engine: SearchEngineType = SearchEngineType.brave
remote_execution: Optional[RestAPIExecutionConfig] = None
@ -163,7 +171,7 @@ class MemoryToolDefinition(ToolDefinitionCommon):
AgenticSystemToolDefinition = Annotated[
Union[
BraveSearchToolDefinition,
SearchToolDefinition,
WolframAlphaToolDefinition,
PhotogenToolDefinition,
CodeInterpreterToolDefinition,

2
llama_toolchain/agentic_system/client.py
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@ -134,7 +134,7 @@ async def run_main(host: str, port: int):
api = AgenticSystemClient(f"http://{host}:{port}")
tool_definitions = [
BraveSearchToolDefinition(),
SearchToolDefinition(engine=SearchEngineType.bing),
WolframAlphaToolDefinition(),
CodeInterpreterToolDefinition(),
]

2
llama_toolchain/agentic_system/meta_reference/agent_instance.py
View file

@ -710,7 +710,7 @@ class ChatAgent(ShieldRunnerMixin):
def _get_tools(self) -> List[ToolDefinition]:
ret = []
for t in self.agent_config.tools:
if isinstance(t, BraveSearchToolDefinition):
if isinstance(t, SearchToolDefinition):
ret.append(ToolDefinition(tool_name=BuiltinTool.brave_search))
elif isinstance(t, WolframAlphaToolDefinition):
ret.append(ToolDefinition(tool_name=BuiltinTool.wolfram_alpha))

16
llama_toolchain/agentic_system/meta_reference/agentic_system.py
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@ -15,9 +15,9 @@ from llama_toolchain.memory.api import Memory
from llama_toolchain.safety.api import Safety
from llama_toolchain.agentic_system.api import * # noqa: F403
from llama_toolchain.tools.builtin import (
BraveSearchTool,
CodeInterpreterTool,
PhotogenTool,
SearchTool,
WolframAlphaTool,
)
from llama_toolchain.tools.safety import with_safety
@ -62,17 +62,19 @@ class MetaReferenceAgenticSystemImpl(AgenticSystem):
if not key:
raise ValueError("Wolfram API key not defined in config")
tool = WolframAlphaTool(key)
elif isinstance(tool_defn, BraveSearchToolDefinition):
elif isinstance(tool_defn, SearchToolDefinition):
key = None
if tool_defn.engine == SearchEngineType.brave:
key = self.config.brave_search_api_key
elif tool_defn.engine == SearchEngineType.bing:
key = self.config.bing_search_api_key
if not key:
raise ValueError("Brave API key not defined in config")
tool = BraveSearchTool(key)
raise ValueError("API key not defined in config")
tool = SearchTool(tool_defn.engine, key)
elif isinstance(tool_defn, CodeInterpreterToolDefinition):
tool = CodeInterpreterTool()
elif isinstance(tool_defn, PhotogenToolDefinition):
tool = PhotogenTool(
dump_dir=tempfile.mkdtemp(),
)
tool = PhotogenTool(dump_dir=tempfile.mkdtemp())
else:
continue

1
llama_toolchain/agentic_system/meta_reference/config.py
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@ -11,4 +11,5 @@ from pydantic import BaseModel
class MetaReferenceImplConfig(BaseModel):
brave_search_api_key: Optional[str] = None
bing_search_api_key: Optional[str] = None
wolfram_api_key: Optional[str] = None

108
llama_toolchain/cli/stack/build.py
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@ -8,6 +8,7 @@ import argparse
from llama_toolchain.cli.subcommand import Subcommand
from llama_toolchain.core.datatypes import * # noqa: F403
import yaml
def parse_api_provider_tuples(
@ -47,55 +48,45 @@ class StackBuild(Subcommand):
self.parser.set_defaults(func=self._run_stack_build_command)
def _add_arguments(self):
from llama_toolchain.core.distribution_registry import available_distribution_specs
from llama_toolchain.core.package import (
BuildType,
from llama_toolchain.core.distribution_registry import (
available_distribution_specs,
)
from llama_toolchain.core.package import ImageType
allowed_ids = [d.distribution_type for d in available_distribution_specs()]
self.parser.add_argument(
"distribution",
"--config",
type=str,
help="Distribution to build (either \"adhoc\" OR one of: {})".format(allowed_ids),
)
self.parser.add_argument(
"api_providers",
nargs='?',
help="Comma separated list of (api=provider) tuples",
help="Path to a config file to use for the build",
)
self.parser.add_argument(
"--name",
type=str,
help="Name of the build target (image, conda env)",
required=True,
)
self.parser.add_argument(
"--type",
type=str,
default="conda_env",
choices=[v.value for v in BuildType],
)
def _run_stack_build_command_from_build_config(
self, build_config: BuildConfig
) -> None:
import json
import os
def _run_stack_build_command(self, args: argparse.Namespace) -> None:
from llama_toolchain.common.config_dirs import DISTRIBS_BASE_DIR
from llama_toolchain.common.serialize import EnumEncoder
from llama_toolchain.core.distribution_registry import resolve_distribution_spec
from llama_toolchain.core.package import (
ApiInput,
BuildType,
build_package,
)
from llama_toolchain.core.package import ApiInput, build_package, ImageType
from termcolor import cprint
api_inputs = []
if args.distribution == "adhoc":
if not args.api_providers:
self.parser.error("You must specify API providers with (api=provider,...) for building an adhoc distribution")
if build_config.distribution == "adhoc":
if not build_config.api_providers:
self.parser.error(
"You must specify API providers with (api=provider,...) for building an adhoc distribution"
)
return
parsed = parse_api_provider_tuples(args.api_providers, self.parser)
parsed = parse_api_provider_tuples(build_config.api_providers, self.parser)
for api, provider_spec in parsed.items():
for dep in provider_spec.api_dependencies:
if dep not in parsed:
self.parser.error(f"API {api} needs dependency {dep} provided also")
self.parser.error(
f"API {api} needs dependency {dep} provided also"
)
return
api_inputs.append(
@ -106,13 +97,17 @@ class StackBuild(Subcommand):
)
docker_image = None
else:
if args.api_providers:
self.parser.error("You cannot specify API providers for pre-registered distributions")
if build_config.api_providers:
self.parser.error(
"You cannot specify API providers for pre-registered distributions"
)
return
dist = resolve_distribution_spec(args.distribution)
dist = resolve_distribution_spec(build_config.distribution)
if dist is None:
self.parser.error(f"Could not find distribution {args.distribution}")
self.parser.error(
f"Could not find distribution {build_config.distribution}"
)
return
for api, provider_type in dist.providers.items():
@ -126,8 +121,41 @@ class StackBuild(Subcommand):
build_package(
api_inputs,
build_type=BuildType(args.type),
name=args.name,
distribution_type=args.distribution,
image_type=ImageType(build_config.image_type),
name=build_config.name,
distribution_type=build_config.distribution,
docker_image=docker_image,
)
# save build.yaml spec for building same distribution again
build_dir = (
DISTRIBS_BASE_DIR / build_config.distribution / build_config.image_type
)
os.makedirs(build_dir, exist_ok=True)
build_file_path = build_dir / f"{build_config.name}-build.yaml"
with open(build_file_path, "w") as f:
to_write = json.loads(json.dumps(build_config.dict(), cls=EnumEncoder))
f.write(yaml.dump(to_write, sort_keys=False))
cprint(
f"Build spec configuration saved at {str(build_file_path)}",
color="green",
)
def _run_stack_build_command(self, args: argparse.Namespace) -> None:
from llama_toolchain.common.prompt_for_config import prompt_for_config
from llama_toolchain.core.dynamic import instantiate_class_type
if args.config:
with open(args.config, "r") as f:
try:
build_config = BuildConfig(**yaml.safe_load(f))
except Exception as e:
self.parser.error(f"Could not parse config file {args.config}: {e}")
return
self._run_stack_build_command_from_build_config(build_config)
return
build_config = prompt_for_config(BuildConfig, None)
self._run_stack_build_command_from_build_config(build_config)

31
llama_toolchain/cli/stack/configure.py
View file

@ -9,10 +9,10 @@ import json
from pathlib import Path
import yaml
from termcolor import cprint
from llama_toolchain.cli.subcommand import Subcommand
from llama_toolchain.common.config_dirs import BUILDS_BASE_DIR
from termcolor import cprint
from llama_toolchain.core.datatypes import * # noqa: F403
@ -34,38 +34,19 @@ class StackConfigure(Subcommand):
from llama_toolchain.core.distribution_registry import (
available_distribution_specs,
)
from llama_toolchain.core.package import BuildType
from llama_toolchain.core.package import ImageType
allowed_ids = [d.distribution_type for d in available_distribution_specs()]
self.parser.add_argument(
"distribution",
"config",
type=str,
help='Distribution ("adhoc" or one of: {})'.format(allowed_ids),
)
self.parser.add_argument(
"--name",
type=str,
help="Name of the build",
required=True,
)
self.parser.add_argument(
"--type",
type=str,
default="conda_env",
choices=[v.value for v in BuildType],
help="Path to the package config file (e.g. ~/.llama/builds/<distribution>/<image_type>/<name>.yaml)",
)
def _run_stack_configure_cmd(self, args: argparse.Namespace) -> None:
from llama_toolchain.core.package import BuildType
from llama_toolchain.core.package import ImageType
build_type = BuildType(args.type)
name = args.name
config_file = (
BUILDS_BASE_DIR
/ args.distribution
/ build_type.descriptor()
/ f"{name}.yaml"
)
config_file = Path(args.config)
if not config_file.exists():
self.parser.error(
f"Could not find {config_file}. Please run `llama stack build` first"

27
llama_toolchain/cli/stack/run.py
View file

@ -29,24 +29,12 @@ class StackRun(Subcommand):
self.parser.set_defaults(func=self._run_stack_run_cmd)
def _add_arguments(self):
from llama_toolchain.core.package import BuildType
from llama_toolchain.core.package import ImageType
self.parser.add_argument(
"distribution",
"config",
type=str,
help="Distribution whose build you want to start",
)
self.parser.add_argument(
"--name",
type=str,
help="Name of the build you want to start",
required=True,
)
self.parser.add_argument(
"--type",
type=str,
default="conda_env",
choices=[v.value for v in BuildType],
help="Path to config file to use for the run",
)
self.parser.add_argument(
"--port",
@ -63,12 +51,13 @@ class StackRun(Subcommand):
def _run_stack_run_cmd(self, args: argparse.Namespace) -> None:
from llama_toolchain.common.exec import run_with_pty
from llama_toolchain.core.package import BuildType
from llama_toolchain.core.package import ImageType
build_type = BuildType(args.type)
build_dir = BUILDS_BASE_DIR / args.distribution / build_type.descriptor()
path = build_dir / f"{args.name}.yaml"
if not args.config:
self.parser.error("Must specify a config file to run")
return
path = args.config
config_file = Path(path)
if not config_file.exists():

8
llama_toolchain/common/deployment_types.py
View file

@ -5,7 +5,7 @@
# the root directory of this source tree.
from enum import Enum
from typing import Dict, Optional
from typing import Any, Dict, Optional
from llama_models.llama3.api.datatypes import URL
@ -26,6 +26,6 @@ class RestAPIMethod(Enum):
class RestAPIExecutionConfig(BaseModel):
url: URL
method: RestAPIMethod
params: Optional[Dict[str, str]] = None
headers: Optional[Dict[str, str]] = None
body: Optional[Dict[str, str]] = None
params: Optional[Dict[str, Any]] = None
headers: Optional[Dict[str, Any]] = None
body: Optional[Dict[str, Any]] = None

7
llama_toolchain/core/build_conda_env.sh
View file

@ -19,7 +19,7 @@ fi
set -euo pipefail
if [ "$#" -ne 3 ]; then
if [ "$#" -ne 4 ]; then
echo "Usage: $0 <distribution_type> <build_name> <pip_dependencies>" >&2
echo "Example: $0 <distribution_type> mybuild 'numpy pandas scipy'" >&2
exit 1
@ -28,7 +28,8 @@ fi
distribution_type="$1"
build_name="$2"
env_name="llamastack-$build_name"
pip_dependencies="$3"
config_file="$3"
pip_dependencies="$4"
# Define color codes
RED='\033[0;31m'
@ -117,4 +118,4 @@ ensure_conda_env_python310 "$env_name" "$pip_dependencies"
printf "${GREEN}Successfully setup conda environment. Configuring build...${NC}\n"
$CONDA_PREFIX/bin/python3 -m llama_toolchain.cli.llama stack configure $distribution_type --name "$build_name" --type conda_env
$CONDA_PREFIX/bin/python3 -m llama_toolchain.cli.llama stack configure $config_file

7
llama_toolchain/core/build_container.sh
View file

@ -4,7 +4,7 @@ LLAMA_MODELS_DIR=${LLAMA_MODELS_DIR:-}
LLAMA_TOOLCHAIN_DIR=${LLAMA_TOOLCHAIN_DIR:-}
TEST_PYPI_VERSION=${TEST_PYPI_VERSION:-}
if [ "$#" -ne 4 ]; then
if [ "$#" -ne 5 ]; then
echo "Usage: $0 <distribution_type> <build_name> <docker_base> <pip_dependencies>
echo "Example: $0 distribution_type my-fastapi-app python:3.9-slim 'fastapi uvicorn'
exit 1
@ -14,7 +14,8 @@ distribution_type=$1
build_name="$2"
image_name="llamastack-$build_name"
docker_base=$3
pip_dependencies=$4
config_file=$4
pip_dependencies=$5
# Define color codes
RED='\033[0;31m'
@ -110,4 +111,4 @@ set +x
printf "${GREEN}Succesfully setup Podman image. Configuring build...${NC}"
echo "You can run it with: podman run -p 8000:8000 $image_name"
$CONDA_PREFIX/bin/python3 -m llama_toolchain.cli.llama stack configure $distribution_type --name "$build_name" --type container
$CONDA_PREFIX/bin/python3 -m llama_toolchain.cli.llama stack configure $config_file

16
llama_toolchain/core/datatypes.py
View file

@ -189,3 +189,19 @@ Provider configurations for each of the APIs provided by this package. This incl
the dependencies of these providers as well.
""",
)
@json_schema_type
class BuildConfig(BaseModel):
name: str
distribution: str = Field(
default="local", description="Type of distribution to build (adhoc | {})"
)
api_providers: Optional[str] = Field(
default_factory=list,
description="List of API provider names to build",
)
image_type: str = Field(
default="conda",
description="Type of package to build (conda | container)",
)

27
llama_toolchain/core/package.py
View file

@ -12,24 +12,21 @@ from typing import List, Optional
import pkg_resources
import yaml
from pydantic import BaseModel
from termcolor import cprint
from llama_toolchain.common.config_dirs import BUILDS_BASE_DIR
from llama_toolchain.common.exec import run_with_pty
from llama_toolchain.common.serialize import EnumEncoder
from pydantic import BaseModel
from termcolor import cprint
from llama_toolchain.core.datatypes import * # noqa: F403
from llama_toolchain.core.distribution import api_providers, SERVER_DEPENDENCIES
class BuildType(Enum):
container = "container"
conda_env = "conda_env"
def descriptor(self) -> str:
return "docker" if self == self.container else "conda"
class ImageType(Enum):
docker = "docker"
conda = "conda"
class Dependencies(BaseModel):
@ -44,7 +41,7 @@ class ApiInput(BaseModel):
def build_package(
api_inputs: List[ApiInput],
build_type: BuildType,
image_type: ImageType,
name: str,
distribution_type: Optional[str] = None,
docker_image: Optional[str] = None,
@ -52,7 +49,7 @@ def build_package(
if not distribution_type:
distribution_type = "adhoc"
build_dir = BUILDS_BASE_DIR / distribution_type / build_type.descriptor()
build_dir = BUILDS_BASE_DIR / distribution_type / image_type.value
os.makedirs(build_dir, exist_ok=True)
package_name = name.replace("::", "-")
@ -106,14 +103,14 @@ def build_package(
)
c.distribution_type = distribution_type
c.docker_image = package_name if build_type == BuildType.container else None
c.conda_env = package_name if build_type == BuildType.conda_env else None
c.docker_image = package_name if image_type == ImageType.docker else None
c.conda_env = package_name if image_type == ImageType.conda else None
with open(package_file, "w") as f:
to_write = json.loads(json.dumps(c.dict(), cls=EnumEncoder))
f.write(yaml.dump(to_write, sort_keys=False))
if build_type == BuildType.container:
if image_type == ImageType.docker:
script = pkg_resources.resource_filename(
"llama_toolchain", "core/build_container.sh"
)
@ -122,6 +119,7 @@ def build_package(
distribution_type,
package_name,
package_deps.docker_image,
str(package_file),
" ".join(package_deps.pip_packages),
]
else:
@ -132,6 +130,7 @@ def build_package(
script,
distribution_type,
package_name,
str(package_file),
" ".join(package_deps.pip_packages),
]

2
llama_toolchain/stack.py
View file

@ -15,6 +15,7 @@ from llama_toolchain.telemetry.api import * # noqa: F403
from llama_toolchain.post_training.api import * # noqa: F403
from llama_toolchain.reward_scoring.api import * # noqa: F403
from llama_toolchain.synthetic_data_generation.api import * # noqa: F403
from llama_toolchain.safety.api import * # noqa: F403
class LlamaStack(
@ -22,6 +23,7 @@ class LlamaStack(
BatchInference,
AgenticSystem,
RewardScoring,
Safety,
SyntheticDataGeneration,
Datasets,
Telemetry,

62
llama_toolchain/tools/builtin.py
View file

@ -83,14 +83,72 @@ class PhotogenTool(SingleMessageBuiltinTool):
raise NotImplementedError()
class BraveSearchTool(SingleMessageBuiltinTool):
def __init__(self, api_key: str) -> None:
class SearchTool(SingleMessageBuiltinTool):
def __init__(self, engine: SearchEngineType, api_key: str, **kwargs) -> None:
self.api_key = api_key
if engine == SearchEngineType.bing:
self.engine = BingSearch(api_key, **kwargs)
elif engine == SearchEngineType.brave:
self.engine = BraveSearch(api_key, **kwargs)
else:
raise ValueError(f"Unknown search engine: {engine}")
def get_name(self) -> str:
return BuiltinTool.brave_search.value
async def run_impl(self, query: str) -> str:
return await self.engine.search(query)
class BingSearch:
def __init__(self, api_key: str, top_k: int = 3, **kwargs) -> None:
self.api_key = api_key
self.top_k = top_k
async def search(self, query: str) -> str:
url = "https://api.bing.microsoft.com/v7.0/search"
headers = {
"Ocp-Apim-Subscription-Key": self.api_key,
}
params = {
"count": self.top_k,
"textDecorations": True,
"textFormat": "HTML",
"q": query,
}
response = requests.get(url=url, params=params, headers=headers)
response.raise_for_status()
clean = self._clean_response(response.json())
return json.dumps(clean)
def _clean_response(self, search_response):
clean_response = []
query = search_response["queryContext"]["originalQuery"]
if "webPages" in search_response:
pages = search_response["webPages"]["value"]
for p in pages:
selected_keys = {"name", "url", "snippet"}
clean_response.append(
{k: v for k, v in p.items() if k in selected_keys}
)
if "news" in search_response:
clean_news = []
news = search_response["news"]["value"]
for n in news:
selected_keys = {"name", "url", "description"}
clean_news.append({k: v for k, v in n.items() if k in selected_keys})
clean_response.append(clean_news)
return {"query": query, "top_k": clean_response}
class BraveSearch:
def __init__(self, api_key: str) -> None:
self.api_key = api_key
async def search(self, query: str) -> str:
url = "https://api.search.brave.com/res/v1/web/search"
headers = {
"X-Subscription-Token": self.api_key,

679
rfcs/RFC-0001-llama-stack-assets/llama-stack-spec.html
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461
rfcs/RFC-0001-llama-stack-assets/llama-stack-spec.yaml
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5
rfcs/openapi_generator/generate.py
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@ -35,7 +35,10 @@ from llama_toolchain.stack import LlamaStack
# TODO: this should be fixed in the generator itself so it reads appropriate annotations
STREAMING_ENDPOINTS = ["/agentic_system/turn/create"]
STREAMING_ENDPOINTS = [
"/agentic_system/turn/create",
"/inference/chat_completion",
]
def patch_sse_stream_responses(spec: Specification):

10
rfcs/openapi_generator/pyopenapi/generator.py
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@ -468,12 +468,14 @@ class Generator:
builder = ContentBuilder(self.schema_builder)
first = next(iter(op.request_params))
request_name, request_type = first
if len(op.request_params) == 1 and "Request" in first[1].__name__:
# TODO(ashwin): Undo the "Request" hack and this entire block eventually
request_name, request_type = first
else:
from dataclasses import make_dataclass
if len(op.request_params) == 1 and "Request" in first[1].__name__:
# TODO(ashwin): Undo the "Request" hack and this entire block eventually
request_name = first[1].__name__ + "Wrapper"
request_type = make_dataclass(request_name, op.request_params)
else:
op_name = "".join(word.capitalize() for word in op.name.split("_"))
request_name = f"{op_name}Request"
request_type = make_dataclass(request_name, op.request_params)