mirror of https://github.com/hwchase17/langchain
templates: robocorp action server template (#15776)
--------- Co-authored-by: Rihards Gravis <rihards@gravis.lv> Co-authored-by: Mikko Korpela <mikko@robocorp.com>pull/15777/head
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__pycache__
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MIT License
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Copyright (c) 2023 LangChain, Inc.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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# Langchain - Robocorp Action Server
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This template enables using [Robocorp Action Server](https://github.com/robocorp/robocorp) served actions as tools for an Agent.
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package robocorp-action-server
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add robocorp-action-server
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```
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And add the following code to your `server.py` file:
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```python
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from robocorp_action_server import agent_executor as action_server_chain
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add_routes(app, action_server_chain, path="/robocorp-action-server")
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```
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### Running the Action Server
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To run the Action Server, you need to have the Robocorp Action Server installed
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```bash
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pip install -U robocorp-action-server
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```
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Then you can run the Action Server with:
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```bash
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action-server new
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cd ./your-project-name
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action-server start
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```
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### Configure LangSmith (Optional)
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LangSmith will help us trace, monitor and debug LangChain applications.
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LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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### Start LangServe instance
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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We can access the playground at [http://127.0.0.1:8000/robocorp-action-server/playground](http://127.0.0.1:8000/robocorp-action-server/playground)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/robocorp-action-server")
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```
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[tool.poetry]
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name = "robocorp-action-server"
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version = "0.0.1"
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description = ""
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authors = ["Robocorp Technologies <info@robocorp.com>"]
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readme = "README.md"
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[tool.poetry.dependencies]
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python = ">=3.8.1,<4.0"
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langchain = "^0.1"
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langchain-openai = ">=0.0.2,<0.1"
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langchain-robocorp = ">=0.0.1,<0.1"
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[tool.poetry.group.dev.dependencies]
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langchain-cli = ">=0.0.20"
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fastapi = "^0.104.0"
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sse-starlette = "^1.6.5"
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[tool.langserve]
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export_module = "robocorp_action_server"
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export_attr = "agent_executor"
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[build-system]
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requires = ["poetry-core"]
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build-backend = "poetry.core.masonry.api"
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from robocorp_action_server.agent import agent_executor
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__all__ = ["agent_executor"]
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from langchain.agents import AgentExecutor, OpenAIFunctionsAgent
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from langchain_core.messages import SystemMessage
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_openai import ChatOpenAI
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from langchain_robocorp import ActionServerToolkit
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# Initialize LLM chat model
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llm = ChatOpenAI(model="gpt-4", temperature=0)
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# Initialize Action Server Toolkit
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toolkit = ActionServerToolkit(url="http://localhost:8080")
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tools = toolkit.get_tools()
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# Initialize Agent
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system_message = SystemMessage(content="You are a helpful assistant")
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prompt = OpenAIFunctionsAgent.create_prompt(system_message)
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agent = OpenAIFunctionsAgent(
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llm=llm,
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prompt=prompt,
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tools=tools,
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)
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# Initialize Agent executor
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agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
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# Typings for Langserve playground
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class Input(BaseModel):
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input: str
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class Output(BaseModel):
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output: str
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agent_executor = agent_executor.with_types(input_type=Input, output_type=Output) # type: ignore[arg-type, assignment]
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