robocorp[minor]: Add robocorp action server toolkit (#15766)

Co-authored-by: Rihards Gravis <rihards@gravis.lv>
Co-authored-by: Mikko Korpela <mikko@robocorp.com>
pull/15551/head
Erick Friis 6 months ago committed by GitHub
parent 7bc100fd43
commit 7562f70c95
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# Robocorp
>[Robocorp](https://robocorp.com/) helps build and operate Python workers that run seamlessly anywhere at any scale
## Installation and Setup
You need to install `langchain-robocorp` python package, as well as the `robocorp-action-server` package to run the action server locally.
```bash
pip install langchain-robocorp robocorp-action-server
```
You will need a running instance of Action Server to communicate with from your agent application. You can bootstrap a new project using Action Server `new` command.
```bash
action-server new
cd ./your-project-name
action-server start
```
## Toolkit
See a [usage example](/docs/integrations/toolkits/robocorp).
```python
from langchain_robocorp import ActionServerToolkit
```

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{
"cells": [
{
"cell_type": "markdown",
"id": "e49f1e0d",
"metadata": {},
"source": [
"# Robocorp\n",
"\n",
"This notebook covers how to get started with [Robocorp Action Server](https://github.com/robocorp/robo/tree/master/action_server/docs) action toolkit and LangChain.\n",
"\n",
"## Installation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c3bef91",
"metadata": {},
"outputs": [],
"source": [
"# Install package and Action Server\n",
"%pip install --upgrade --quiet langchain-robocorp robocorp-action-server"
]
},
{
"cell_type": "markdown",
"id": "8e2ca5c5",
"metadata": {},
"source": [
"## Action Server setup\n",
"\n",
"You will need a running instance of Action Server to communicate with from your agent application. You can bootstrap a new project using Action Server `new` command.\n",
"\n",
"```bash\n",
"!action-server new\n",
"cd ./your-project-name\n",
"action-server start\n",
"```\n"
]
},
{
"cell_type": "markdown",
"id": "2b4f3e15",
"metadata": {},
"source": [
"## Environment Setup\n",
"\n",
"Optionally you can set the following environment variables:\n",
"\n",
"- `LANGCHAIN_TRACING_V2=true`: To enable LangSmith log run tracing that can also be bind to respective Action Server action run logs. See [LangSmith documentation](https://docs.smith.langchain.com/tracing#log-runs) for more.\n",
"\n",
"## Usage"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62e0dbc3",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.agents import AgentExecutor, OpenAIFunctionsAgent\n",
"from langchain.chat_models import ChatOpenAI\n",
"from langchain_core.messages import SystemMessage\n",
"from langchain_robocorp import ActionServerToolkit\n",
"\n",
"# Initialize LLM chat model\n",
"llm = ChatOpenAI(model=\"gpt-4\", temperature=0)\n",
"\n",
"# Initialize Action Server Toolkit\n",
"toolkit = ActionServerToolkit(url=\"http://localhost:8080\", report_trace=True)\n",
"tools = toolkit.get_tools()\n",
"\n",
"# Initialize Agent\n",
"system_message = SystemMessage(content=\"You are a helpful assistant\")\n",
"prompt = OpenAIFunctionsAgent.create_prompt(system_message)\n",
"agent = OpenAIFunctionsAgent(llm=llm, prompt=prompt, tools=tools)\n",
"\n",
"executor = AgentExecutor(agent=agent, tools=tools, verbose=True)\n",
"\n",
"\n",
"executor.invoke(\"What is the current date?\")"
]
},
{
"cell_type": "markdown",
"id": "aa9fbbf5",
"metadata": {},
"source": [
"### Single input tools\n",
"\n",
"By default `toolkit.get_tools()` will return the actions as Structured Tools. To return single input tools, pass a Chat model to be used for processing the inputs."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1dc7db86",
"metadata": {},
"outputs": [],
"source": [
"# Initialize single input Action Server Toolkit\n",
"toolkit = ActionServerToolkit(url=\"http://localhost:8080\")\n",
"tools = toolkit.get_tools(llm=llm)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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__pycache__

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MIT License
Copyright (c) 2023 LangChain, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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.PHONY: all format lint test tests integration_tests docker_tests help extended_tests
# Default target executed when no arguments are given to make.
all: help
# Define a variable for the test file path.
TEST_FILE ?= tests/unit_tests/
test:
poetry run pytest $(TEST_FILE)
tests:
poetry run pytest $(TEST_FILE)
######################
# LINTING AND FORMATTING
######################
# Define a variable for Python and notebook files.
PYTHON_FILES=.
MYPY_CACHE=.mypy_cache
lint format: PYTHON_FILES=.
lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/action-server --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
lint_package: PYTHON_FILES=langchain_robocorp
lint_tests: PYTHON_FILES=tests
lint_tests: MYPY_CACHE=.mypy_cache_test
lint lint_diff lint_package lint_tests:
poetry run ruff .
poetry run ruff format $(PYTHON_FILES) --diff
poetry run ruff --select I $(PYTHON_FILES)
mkdir $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
format format_diff:
poetry run ruff format $(PYTHON_FILES)
poetry run ruff --select I --fix $(PYTHON_FILES)
spell_check:
poetry run codespell --toml pyproject.toml
spell_fix:
poetry run codespell --toml pyproject.toml -w
check_imports: $(shell find langchain_robocorp -name '*.py')
poetry run python ./scripts/check_imports.py $^
######################
# HELP
######################
help:
@echo '----'
@echo 'check_imports - check imports'
@echo 'format - run code formatters'
@echo 'lint - run linters'
@echo 'test - run unit tests'
@echo 'tests - run unit tests'
@echo 'test TEST_FILE=<test_file> - run all tests in file'

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# langchain-robocorp
This package contains the LangChain integrations for [Robocorp](https://github.com/robocorp/robocorp).
## Installation
```bash
pip install -U langchain-robocorp
```
## Action Server Toolkit
See [ActionServerToolkit](./docs/toolkit.ipynb) for detailed documentation.

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from langchain_robocorp.toolkits import ActionServerToolkit
__all__ = [
"ActionServerToolkit",
]

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from dataclasses import dataclass
from typing import List, Tuple
from langchain_core.utils.json_schema import dereference_refs
@dataclass(frozen=True)
class ReducedOpenAPISpec:
"""A reduced OpenAPI spec.
This is reduced representation for OpenAPI specs.
Attributes:
servers: The servers in the spec.
description: The description of the spec.
endpoints: The endpoints in the spec.
"""
servers: List[dict]
description: str
endpoints: List[Tuple[str, dict]]
def reduce_openapi_spec(url: str, spec: dict) -> ReducedOpenAPISpec:
"""Simplify OpenAPI spec to only required information for the agent"""
# 1. Consider only GET and POST
endpoints = [
(route, docs)
for route, operation in spec["paths"].items()
for operation_name, docs in operation.items()
if operation_name in ["get", "post"]
]
# 2. Replace any refs so that complete docs are retrieved.
# Note: probably want to do this post-retrieval, it blows up the size of the spec.
# 3. Strip docs down to required request args + happy path response.
def reduce_endpoint_docs(docs: dict) -> dict:
out = {}
if docs.get("summary"):
out["summary"] = docs.get("summary")
if docs.get("operationId"):
out["operationId"] = docs.get("operationId")
if docs.get("description"):
out["description"] = docs.get("description")
if docs.get("parameters"):
out["parameters"] = [
parameter
for parameter in docs.get("parameters", [])
if parameter.get("required")
]
if "200" in docs["responses"]:
out["responses"] = docs["responses"]["200"]
if docs.get("requestBody"):
out["requestBody"] = docs.get("requestBody")
return out
endpoints = [
(name, reduce_endpoint_docs(dereference_refs(docs, full_schema=spec)))
for name, docs in endpoints
]
return ReducedOpenAPISpec(
servers=[
{
"url": url,
}
],
description=spec["info"].get("description", ""),
endpoints=endpoints,
)
def get_required_param_descriptions(endpoint_spec: dict) -> str:
"""Get an OpenAPI endpoint required parameter descriptions"""
descriptions = []
schema = (
endpoint_spec.get("requestBody", {})
.get("content", {})
.get("application/json", {})
.get("schema", {})
)
properties = schema.get("properties", {})
required_fields = schema.get("required", [])
for key, value in properties.items():
if "description" in value:
if value.get("required") or key in required_fields:
descriptions.append(value.get("description"))
return ", ".join(descriptions)
type_mapping = {
"string": str,
"integer": int,
"number": float,
"object": dict,
"array": list,
"boolean": bool,
"null": type(None),
}
def get_param_fields(endpoint_spec: dict) -> dict:
"""Get an OpenAPI endpoint parameter details"""
fields = {}
schema = (
endpoint_spec.get("requestBody", {})
.get("content", {})
.get("application/json", {})
.get("schema", {})
)
properties = schema.get("properties", {})
required_fields = schema.get("required", [])
for key, value in properties.items():
details = {
"description": value.get("description", ""),
"required": key in required_fields,
}
field_type = type_mapping[value.get("type", "string")]
fields[key] = (field_type, details)
return fields

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# flake8: noqa
TOOLKIT_TOOL_DESCRIPTION = """{description}. The tool must be invoked with a complete sentence starting with "{name}" and additional information on {required_params}."""
API_CONTROLLER_PROMPT = """You are turning user input into a json query for an API request tool.
The final output to the tool should be a json string with a single key "data".
The value of "data" should be a dictionary of key-value pairs you want to POST to the url.
Always use double quotes for strings in the json string.
Always respond only with the json object and nothing else.
Here is documentation on the API:
Base url: {api_url}
Endpoint documentation:
{api_docs}
User Input: {input}
"""

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"""Robocorp Action Server toolkit."""
from __future__ import annotations
import json
from typing import Any, Callable, Dict, List, Optional, TypedDict
from urllib.parse import urljoin
import requests
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.callbacks.base import BaseCallbackHandler
from langchain_core.callbacks.manager import CallbackManager
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field, PrivateAttr, create_model
from langchain_core.runnables import Runnable, RunnablePassthrough
from langchain_core.tools import BaseTool, StructuredTool, Tool
from langchain_core.tracers.context import _tracing_v2_is_enabled
from langsmith import Client
from langchain_robocorp._common import (
get_param_fields,
get_required_param_descriptions,
reduce_openapi_spec,
)
from langchain_robocorp._prompts import (
API_CONTROLLER_PROMPT,
TOOLKIT_TOOL_DESCRIPTION,
)
MAX_RESPONSE_LENGTH = 5000
LLM_TRACE_HEADER = "X-action-trace"
class RunDetailsCallbackHandler(BaseCallbackHandler):
def __init__(self, run_details: dict) -> None:
self.run_details = run_details
def on_tool_start(
self,
serialized: Dict[str, Any],
input_str: str,
**kwargs: Any,
) -> None:
if "parent_run_id" in kwargs:
self.run_details["run_id"] = kwargs["parent_run_id"]
else:
if "run_id" in self.run_details:
self.run_details.pop("run_id")
class ToolInputSchema(BaseModel):
question: str = Field(...)
class ToolArgs(TypedDict):
name: str
description: str
callback_manager: CallbackManager
class ActionServerRequestTool(BaseTool):
"""Requests POST tool with LLM-instructed extraction of truncated responses."""
name: str = "action_server_request"
"""Tool name."""
description: str = "Useful to make requests to Action Server API"
"""Tool description."""
endpoint: str
""""Action API endpoint"""
action_request: Callable[[str], str]
"""Action request execution"""
def _run(
self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None
) -> str:
try:
json_text = query[query.find("{") : query.rfind("}") + 1]
payload = json.loads(json_text)
except json.JSONDecodeError as e:
raise e
return self.action_request(self.endpoint, **payload["data"])
async def _arun(self, text: str) -> str:
raise NotImplementedError()
class ActionServerToolkit(BaseModel):
"""Toolkit exposing Robocorp Action Server provided actions as individual tools."""
url: str = Field(exclude=True)
"""Action Server URL"""
api_key: str = Field(exclude=True, default="")
"""Action Server request API key"""
report_trace: bool = Field(exclude=True, default=False)
"""Enable reporting Langsmith trace to Action Server runs"""
_run_details: dict = PrivateAttr({})
class Config:
arbitrary_types_allowed = True
def get_tools(
self,
llm: Optional[BaseChatModel] = None,
callback_manager: Optional[CallbackManager] = None,
) -> List[BaseTool]:
"""
Get Action Server actions as a toolkit
:param llm: Optionally pass a model to return single input tools
:param callback_manager: Callback manager to be passed to tools
"""
# Fetch and format the API spec
try:
spec_url = urljoin(self.url, "openapi.json")
response = requests.get(spec_url)
json_spec = response.json()
api_spec = reduce_openapi_spec(self.url, json_spec)
except Exception:
raise ValueError(
f"Failed to fetch OpenAPI schema from Action Server - {self.url}"
)
# Prepare request tools
self._run_details: dict = {}
# Prepare callback manager
if callback_manager is None:
callback_manager = CallbackManager([])
callbacks: List[BaseCallbackHandler] = []
if _tracing_v2_is_enabled():
callbacks.append(RunDetailsCallbackHandler(self._run_details))
for callback in callbacks:
callback_manager.add_handler(callback)
toolkit: List[BaseTool] = []
# Prepare tools
for endpoint, docs in api_spec.endpoints:
if not endpoint.startswith("/api/actions"):
continue
summary = docs["summary"]
tool_description = TOOLKIT_TOOL_DESCRIPTION.format(
name=summary,
description=docs.get("description", summary),
required_params=get_required_param_descriptions(docs),
)
tool_args: ToolArgs = {
"name": f"robocorp_action_server_{docs['operationId']}",
"description": tool_description,
"callback_manager": callback_manager,
}
if llm:
tool = self._get_unstructured_tool(endpoint, docs, tool_args, llm)
else:
tool = self._get_structured_tool(endpoint, docs, tool_args)
toolkit.append(tool)
return toolkit
def _get_unstructured_tool(
self,
endpoint: str,
docs: dict,
tool_args: ToolArgs,
llm: BaseChatModel,
) -> BaseTool:
request_tool = ActionServerRequestTool(
action_request=self._action_request, endpoint=endpoint
)
prompt_variables = {
"api_url": self.url,
}
tool_name = tool_args["name"]
tool_docs = json.dumps(docs, indent=4)
prompt_variables["api_docs"] = f"{tool_name}: \n{tool_docs}"
prompt = PromptTemplate(
template=API_CONTROLLER_PROMPT,
input_variables=["input"],
partial_variables=prompt_variables,
)
chain: Runnable = (
{"input": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
| request_tool
)
return Tool(func=chain.invoke, args_schema=ToolInputSchema, **tool_args)
def _get_structured_tool(
self, endpoint: str, docs: dict, tools_args: ToolArgs
) -> BaseTool:
fields = get_param_fields(docs)
def create_function(endpoint: str) -> Callable:
def func(**data: dict[str, Any]) -> str:
return self._action_request(endpoint, **data)
return func
return StructuredTool(
func=create_function(endpoint),
args_schema=create_model("DynamicToolInputSchema", **fields),
**tools_args,
)
def _action_request(self, endpoint: str, **data: dict[str, Any]) -> str:
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
}
try:
if self.report_trace and "run_id" in self._run_details:
client = Client()
run = client.read_run(self._run_details["run_id"])
if run.url:
headers[LLM_TRACE_HEADER] = run.url
except Exception:
pass
url = urljoin(self.url, endpoint)
response = requests.post(url, headers=headers, data=json.dumps(data))
output = response.text[:MAX_RESPONSE_LENGTH]
return output

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[package.dependencies]
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{file = "watchdog-3.0.0.tar.gz", hash = "sha256:4d98a320595da7a7c5a18fc48cb633c2e73cda78f93cac2ef42d42bf609a33f9"},
]
[package.extras]
watchmedo = ["PyYAML (>=3.10)"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
content-hash = "09efecb6b12a87a6f0662b33a341e069c09413fb35d99cc9b7da447bb2298bd2"

@ -0,0 +1,91 @@
[tool.poetry]
name = "langchain-robocorp"
version = "0.0.1"
description = "An integration package connecting Robocorp and LangChain"
authors = []
readme = "README.md"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
langchain-core = ">=0.0.12"
requests = "^2.31.0"
types-requests = "^2.31.0.20231231"
langsmith = "^0.0.77"
[tool.poetry.group.test]
optional = true
[tool.poetry.group.test.dependencies]
pytest = "^7.3.0"
freezegun = "^1.2.2"
pytest-mock = "^3.10.0"
syrupy = "^4.0.2"
pytest-watcher = "^0.3.4"
pytest-asyncio = "^0.21.1"
langchain-core = {path = "../../core", develop = true}
[tool.poetry.group.codespell]
optional = true
[tool.poetry.group.codespell.dependencies]
codespell = "^2.2.0"
[tool.poetry.group.test_integration]
optional = true
[tool.poetry.group.test_integration.dependencies]
[tool.poetry.group.lint]
optional = true
[tool.poetry.group.lint.dependencies]
ruff = "^0.1.5"
[tool.poetry.group.typing.dependencies]
mypy = "^0.991"
langchain-core = {path = "../../core", develop = true}
[tool.poetry.group.dev]
optional = true
[tool.poetry.group.dev.dependencies]
langchain-core = {path = "../../core", develop = true}
[tool.ruff]
select = [
"E", # pycodestyle
"F", # pyflakes
"I", # isort
]
[tool.mypy]
disallow_untyped_defs = "True"
[tool.coverage.run]
omit = [
"tests/*",
]
[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
[tool.pytest.ini_options]
# --strict-markers will raise errors on unknown marks.
# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks
#
# https://docs.pytest.org/en/7.1.x/reference/reference.html
# --strict-config any warnings encountered while parsing the `pytest`
# section of the configuration file raise errors.
#
# https://github.com/tophat/syrupy
# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite.
addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5"
# Registering custom markers.
# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers
markers = [
"requires: mark tests as requiring a specific library",
"asyncio: mark tests as requiring asyncio",
"compile: mark placeholder test used to compile integration tests without running them",
]
asyncio_mode = "auto"

@ -0,0 +1,17 @@
import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception:
has_failure = True
print(file)
traceback.print_exc()
print()
sys.exit(1 if has_failure else 0)

@ -0,0 +1,27 @@
#!/bin/bash
#
# This script searches for lines starting with "import pydantic" or "from pydantic"
# in tracked files within a Git repository.
#
# Usage: ./scripts/check_pydantic.sh /path/to/repository
# Check if a path argument is provided
if [ $# -ne 1 ]; then
echo "Usage: $0 /path/to/repository"
exit 1
fi
repository_path="$1"
# Search for lines matching the pattern within the specified repository
result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic')
# Check if any matching lines were found
if [ -n "$result" ]; then
echo "ERROR: The following lines need to be updated:"
echo "$result"
echo "Please replace the code with an import from langchain_core.pydantic_v1."
echo "For example, replace 'from pydantic import BaseModel'"
echo "with 'from langchain_core.pydantic_v1 import BaseModel'"
exit 1
fi

@ -0,0 +1,17 @@
#!/bin/bash
set -eu
# Initialize a variable to keep track of errors
errors=0
# make sure not importing from langchain or langchain_experimental
git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
# Decide on an exit status based on the errors
if [ "$errors" -gt 0 ]; then
exit 1
else
exit 0
fi

@ -0,0 +1,7 @@
import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass

@ -0,0 +1,73 @@
from typing import Any, List, Optional
from langchain_core.callbacks.manager import CallbackManagerForLLMRun
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import AIMessage, BaseMessage
from langchain_core.outputs import ChatGeneration, ChatResult
class FakeChatLLMT(BaseChatModel):
@property
def _llm_type(self) -> str:
return "fake-openai-chat-model"
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
return ChatResult(
generations=[
ChatGeneration(
message=AIMessage(
content="",
additional_kwargs={
"function_call": {
"name": "accept",
"arguments": '{\n "draft": "turtles"\n}',
}
},
)
)
]
)
openapi_endpoint_doc_mock = {
"summary": "Test Action",
"operationId": "test_action",
"description": "Greets an employee",
"responses": {
"description": "Successful Response",
"content": {
"application/json": {
"schema": {
"type": "string",
"title": "Response Test Action",
"description": "The greeting",
}
}
},
},
"requestBody": {
"content": {
"application/json": {
"schema": {
"properties": {
"name": {
"type": "string",
"title": "Name",
"description": "Name of the employee",
}
},
"type": "object",
"required": ["name"],
"title": "TestActionInput",
}
}
},
"required": True,
},
}

@ -0,0 +1,411 @@
{
"openapi": "3.1.0",
"info": {
"title": "Robocorp Actions Server",
"description": "Robocorp Actions Server",
"version": "0.1.0"
},
"paths": {
"/api/actions/test/test-action/run": {
"post": {
"summary": "Test Action",
"description": "Greets an employee",
"operationId": "test_action",
"requestBody": {
"content": {
"application/json": {
"schema": { "$ref": "#/components/schemas/TestActionInput" }
}
},
"required": true
},
"responses": {
"200": {
"description": "Successful Response",
"content": {
"application/json": {
"schema": {
"type": "string",
"title": "Response Test Action",
"description": "The greeting"
}
}
}
},
"422": {
"description": "Validation Error",
"content": {
"application/json": {
"schema": { "$ref": "#/components/schemas/HTTPValidationError" }
}
}
}
}
}
},
"/api/runs": {
"get": {
"summary": "List Runs",
"operationId": "list_runs_api_runs_get",
"responses": {
"200": {
"description": "Successful Response",
"content": {
"application/json": {
"schema": {
"items": { "$ref": "#/components/schemas/Run" },
"type": "array",
"title": "Response List Runs Api Runs Get"
}
}
}
}
}
}
},
"/api/runs/{run_id}": {
"get": {
"summary": "Show Run",
"operationId": "show_run_api_runs__run_id__get",
"parameters": [
{
"name": "run_id",
"in": "path",
"required": true,
"schema": { "type": "string", "title": "ID for run" }
}
],
"responses": {
"200": {
"description": "Successful Response",
"content": {
"application/json": {
"schema": { "$ref": "#/components/schemas/Run" }
}
}
},
"422": {
"description": "Validation Error",
"content": {
"application/json": {
"schema": { "$ref": "#/components/schemas/HTTPValidationError" }
}
}
}
}
}
},
"/api/runs/{run_id}/artifacts": {
"get": {
"summary": "Get Run Artifacts",
"operationId": "get_run_artifacts_api_runs__run_id__artifacts_get",
"parameters": [
{
"name": "run_id",
"in": "path",
"required": true,
"schema": { "type": "string", "title": "ID for run" }
}
],
"responses": {
"200": {
"description": "Successful Response",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": { "$ref": "#/components/schemas/ArtifactInfo" },
"description": "Provides a list with the artifacts available\nfor a given run (i.e.: [{'name': '__action_server_output.txt', 'size_in_bytes': 22}])\n",
"title": "Response Get Run Artifacts Api Runs Run Id Artifacts Get"
}
}
}
},
"422": {
"description": "Validation Error",
"content": {
"application/json": {
"schema": { "$ref": "#/components/schemas/HTTPValidationError" }
}
}
}
}
}
},
"/api/runs/{run_id}/log.html": {
"get": {
"summary": "Get Run Log Html",
"operationId": "get_run_log_html_api_runs__run_id__log_html_get",
"parameters": [
{
"name": "run_id",
"in": "path",
"required": true,
"schema": { "type": "string", "title": "ID for run" }
}
],
"responses": {
"200": {
"description": "Successful Response",
"content": { "application/json": { "schema": {} } }
},
"422": {
"description": "Validation Error",
"content": {
"application/json": {
"schema": { "$ref": "#/components/schemas/HTTPValidationError" }
}
}
}
}
}
},
"/api/runs/{run_id}/artifacts/text-content": {
"get": {
"summary": "Get Run Artifact Text",
"operationId": "get_run_artifact_text_api_runs__run_id__artifacts_text_content_get",
"parameters": [
{
"name": "run_id",
"in": "path",
"required": true,
"schema": { "type": "string", "title": "ID for run" }
},
{
"name": "artifact_names",
"in": "query",
"required": false,
"schema": {
"anyOf": [
{ "type": "array", "items": { "type": "string" } },
{ "type": "null" }
],
"title": "Artifact names for which the content should be gotten."
}
},
{
"name": "artifact_name_regexp",
"in": "query",
"required": false,
"schema": {
"anyOf": [{ "type": "string" }, { "type": "null" }],
"title": "A regexp to match artifact names."
}
}
],
"responses": {
"200": {
"description": "Successful Response",
"content": {
"application/json": {
"schema": {
"type": "object",
"additionalProperties": { "type": "string" },
"title": "Response Get Run Artifact Text Api Runs Run Id Artifacts Text Content Get"
}
}
}
},
"422": {
"description": "Validation Error",
"content": {
"application/json": {
"schema": { "$ref": "#/components/schemas/HTTPValidationError" }
}
}
}
}
}
},
"/api/runs/{run_id}/artifacts/binary-content": {
"get": {
"summary": "Get Run Artifact Binary",
"operationId": "get_run_artifact_binary_api_runs__run_id__artifacts_binary_content_get",
"parameters": [
{
"name": "run_id",
"in": "path",
"required": true,
"schema": { "type": "string", "title": "ID for run" }
},
{
"name": "artifact_name",
"in": "query",
"required": true,
"schema": {
"type": "string",
"title": "Artifact name for which the content should be gotten."
}
}
],
"responses": {
"200": { "description": "Successful Response" },
"422": {
"description": "Validation Error",
"content": {
"application/json": {
"schema": { "$ref": "#/components/schemas/HTTPValidationError" }
}
}
}
}
}
},
"/api/actionPackages": {
"get": {
"summary": "List Action Packages",
"operationId": "list_action_packages_api_actionPackages_get",
"responses": {
"200": {
"description": "Successful Response",
"content": {
"application/json": {
"schema": {
"items": { "$ref": "#/components/schemas/ActionPackageApi" },
"type": "array",
"title": "Response List Action Packages Api Actionpackages Get"
}
}
}
}
}
}
}
},
"components": {
"schemas": {
"Action": {
"properties": {
"id": { "type": "string", "title": "Id" },
"action_package_id": {
"type": "string",
"title": "Action Package Id"
},
"name": { "type": "string", "title": "Name" },
"docs": { "type": "string", "title": "Docs" },
"file": { "type": "string", "title": "File" },
"lineno": { "type": "integer", "title": "Lineno" },
"input_schema": { "type": "string", "title": "Input Schema" },
"output_schema": { "type": "string", "title": "Output Schema" },
"enabled": { "type": "boolean", "title": "Enabled", "default": true },
"is_consequential": {
"anyOf": [{ "type": "boolean" }, { "type": "null" }],
"title": "Is Consequential"
}
},
"type": "object",
"required": [
"id",
"action_package_id",
"name",
"docs",
"file",
"lineno",
"input_schema",
"output_schema"
],
"title": "Action"
},
"ActionPackageApi": {
"properties": {
"id": { "type": "string", "title": "Id" },
"name": { "type": "string", "title": "Name" },
"actions": {
"items": { "$ref": "#/components/schemas/Action" },
"type": "array",
"title": "Actions"
}
},
"type": "object",
"required": ["id", "name", "actions"],
"title": "ActionPackageApi"
},
"ArtifactInfo": {
"properties": {
"name": { "type": "string", "title": "Name" },
"size_in_bytes": { "type": "integer", "title": "Size In Bytes" }
},
"type": "object",
"required": ["name", "size_in_bytes"],
"title": "ArtifactInfo"
},
"HTTPValidationError": {
"properties": {
"errors": {
"items": { "$ref": "#/components/schemas/ValidationError" },
"type": "array",
"title": "Errors"
}
},
"type": "object",
"title": "HTTPValidationError"
},
"Run": {
"properties": {
"id": { "type": "string", "title": "Id" },
"status": { "type": "integer", "title": "Status" },
"action_id": { "type": "string", "title": "Action Id" },
"start_time": { "type": "string", "title": "Start Time" },
"run_time": {
"anyOf": [{ "type": "number" }, { "type": "null" }],
"title": "Run Time"
},
"inputs": { "type": "string", "title": "Inputs" },
"result": {
"anyOf": [{ "type": "string" }, { "type": "null" }],
"title": "Result"
},
"error_message": {
"anyOf": [{ "type": "string" }, { "type": "null" }],
"title": "Error Message"
},
"relative_artifacts_dir": {
"type": "string",
"title": "Relative Artifacts Dir"
},
"numbered_id": { "type": "integer", "title": "Numbered Id" }
},
"type": "object",
"required": [
"id",
"status",
"action_id",
"start_time",
"run_time",
"inputs",
"result",
"error_message",
"relative_artifacts_dir",
"numbered_id"
],
"title": "Run"
},
"TestActionInput": {
"properties": {
"name": {
"type": "string",
"title": "Name",
"description": "Name of the employee"
}
},
"type": "object",
"required": ["name"],
"title": "TestActionInput"
},
"ValidationError": {
"properties": {
"loc": {
"items": { "anyOf": [{ "type": "string" }, { "type": "integer" }] },
"type": "array",
"title": "Location"
},
"msg": { "type": "string", "title": "Message" },
"type": { "type": "string", "title": "Error Type" }
},
"type": "object",
"required": ["loc", "msg", "type"],
"title": "ValidationError"
}
}
}
}

@ -0,0 +1,24 @@
"""Test common functions."""
import json
import unittest
from pathlib import Path
from langchain_robocorp._common import reduce_openapi_spec
from ._fixtures import openapi_endpoint_doc_mock
class TestReduceOpenAPISpec(unittest.TestCase):
maxDiff = None
def test_reduce_openapi_spec(self) -> None:
with Path(__file__).with_name("_openapi.fixture.json").open("r") as file:
original = json.load(file)
output = reduce_openapi_spec("https://foo.bar", original)
self.assertEqual(output.servers, [{"url": "https://foo.bar"}])
self.assertEqual(output.description, "Robocorp Actions Server")
self.assertEqual(len(output.endpoints), 8)
self.assertEqual(output.endpoints[0][1], openapi_endpoint_doc_mock)

@ -0,0 +1,9 @@
from langchain_robocorp import __all__
EXPECTED_ALL = [
"ActionServerToolkit",
]
def test_all_imports() -> None:
assert sorted(EXPECTED_ALL) == sorted(__all__)

@ -0,0 +1,9 @@
"""Test toolkit integration."""
from langchain_robocorp.toolkits import ActionServerToolkit
from ._fixtures import FakeChatLLMT
def test_initialization() -> None:
"""Test toolkit initialization."""
ActionServerToolkit(url="http://localhost", llm=FakeChatLLMT())
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