Initial implementation

.env.example

@@ -5,4 +5,6 @@ TAVILY_API_KEY=placeholder
 AZURE_OPENAI_DEPLOYMENT_NAME=placeholder
 AZURE_OPENAI_API_KEY=placeholder
 AZURE_OPENAI_API_BASE=placeholder
-AZURE_OPENAI_API_VERSION=placeholder
+AZURE_OPENAI_API_VERSION=placeholder
+ROBOCORP_ACTION_SERVER_URL=https://dummy-action-server.robocorp.link
+ROBOCORP_ACTION_SERVER_KEY=dummy-api-key

README.md

@@ -230,6 +230,16 @@ See [this guide](https://python.langchain.com/docs/modules/agents/tools/custom_t
 
 If you want to use some preconfigured tools, these include:
 
+**_Robocorp Action Server_**
+
+Run AI Python based actions with [Robocorp Action Server](https://github.com/robocorp/robocorp).
+Does not require a service API key, but it requires the credentials for a running Action Server instance to be defined:
+
+```shell
+ROBOCORP_ACTION_SERVER_URL=https://dummy-action-server.robocorp.link
+ROBOCORP_ACTION_SERVER_KEY=dummy-api-key
+```
+
 **DuckDuckGo Search**
 
 Search the web with [DuckDuckGo](https://pypi.org/project/duckduckgo-search/). Does not require any API keys.

backend/app/agent.py

@@ -92,7 +92,11 @@ def __init__(
                     )
                 _tools.append(get_retrieval_tool(assistant_id, retrieval_description))
             else:
-                _tools.append(TOOLS[_tool]())
+                _returned_tools = TOOLS[_tool]()
+                if isinstance(_returned_tools, list):
+                    _tools.extend(_returned_tools)
+                else:
+                    _tools.append(_returned_tools)
         _agent = get_agent_executor(_tools, agent, system_message)
         agent_executor = _agent.with_config({"recursion_limit": 50})
         super().__init__(

backend/app/tools.py

@@ -1,3 +1,4 @@
+import os
 from enum import Enum
 
 from langchain.pydantic_v1 import BaseModel, Field
@@ -13,6 +14,7 @@
 from langchain_community.utilities.arxiv import ArxivAPIWrapper
 from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper
 from langchain_community.vectorstores.redis import RedisFilter
+from langchain_robocorp import ActionServerToolkit
 
 from app.upload import vstore
 
@@ -100,8 +102,17 @@ def _get_tavily_answer():
     tavily_search = TavilySearchAPIWrapper()
     return TavilyAnswer(api_wrapper=tavily_search)
 
+def _get_action_server():
+    toolkit = ActionServerToolkit(
+        url=os.environ.get("ROBOCORP_ACTION_SERVER_URL"),
+        api_key=os.environ.get("ROBOCORP_ACTION_SERVER_KEY")
+    )
+    tools = toolkit.get_tools()
+    return tools
+
 
 class AvailableTools(str, Enum):
+    ACTION_SERVER = "Action Server by Robocorp"
     DDG_SEARCH = "DDG Search"
     TAVILY = "Search (Tavily)"
     TAVILY_ANSWER = "Search (short answer, Tavily)"
@@ -115,6 +126,7 @@ class AvailableTools(str, Enum):
 
 
 TOOLS = {
+    AvailableTools.ACTION_SERVER: _get_action_server,
     AvailableTools.DDG_SEARCH: _get_duck_duck_go,
     AvailableTools.ARXIV: _get_arxiv,
     AvailableTools.YOU_SEARCH: _get_you_search,

backend/requirements.txt

@@ -5,3 +5,5 @@ langchain>=0.1.0
 langserve>=0.0.23
 langgraph
 python-multipart
+langchain-robocorp>=0.0.1
+

frontend/src/components/Config.tsx

@@ -86,6 +86,8 @@ export default function SingleOptionField(props: {
 
 const TOOL_DESCRIPTIONS = {
   Retrieval: "Look up information in uploaded files.",
+  "Action Server by Robocorp":
+    "Run AI actions with [Robocorp Action Server](https://github.com/robocorp/robocorp).",
   "DDG Search":
     "Search the web with [DuckDuckGo](https://pypi.org/project/duckduckgo-search/).",
   "Search (Tavily)":