Feature/external libload

backend/app/app/schemas/tool_schema.py

@@ -31,6 +31,7 @@ class ToolConfig(BaseModel):
  system_context_refinement: Optional[str]
  prompt_inputs: list[PromptInput]
  additional: Optional[Box] = None
+ class_name: Optional[str] = None
 
 
 class SqlToolConfig(ToolConfig):

backend/app/app/services/chat_agent/tools/tools.py

@@ -1,29 +1,32 @@
 # -*- coding: utf-8 -*-
 # pylint: disable=cyclic-import
-from typing import List, Tuple, Type
+import copy
+import importlib
+import logging
+from typing import List
 
 from langchain.tools import BaseTool
 
 from app.services.chat_agent.tools.ExtendedBaseTool import ExtendedBaseTool
 from app.services.chat_agent.tools.library.basellm_tool.basellm_tool import BaseLLM
-from app.services.chat_agent.tools.library.image_generation_tool.image_generation_tool import ImageGenerationTool
-from app.services.chat_agent.tools.library.pdf_tool.pdf_tool import PDFTool
-from app.services.chat_agent.tools.library.sql_tool.sql_tool import SQLTool
-from app.services.chat_agent.tools.library.summarizer_tool.summarizer_tool import SummarizerTool
-from app.services.chat_agent.tools.library.visualizer_tool.visualizer_tool import JsxVisualizerTool
 from app.utils.config_loader import get_agent_config
 
+logger = logging.getLogger(__name__)
 
-def get_nested_classes() -> List[Tuple[str, Type[ExtendedBaseTool]]]:
- """separated to avoid circular imports."""
- from app.services.chat_agent.tools.library.chain_tool.nested_meta_agent_tool import ( # pylint: disable=import-outside-toplevel # noqa:E501
- ChainTool,
- )
 
- nested_classes = [
- ("chain_tool", ChainTool),
- ]
- return nested_classes # type: ignore
+STATIC_SET_OF_TOOLS = {
+ "sql_tool": ["app.services.chat_agent.tools.library.sql_tool.sql_tool", "SQLTool"],
+ "visualizer_tool": ["app.services.chat_agent.tools.library.visualizer_tool.visualizer_tool", "JsxVisualizerTool"],
+ "summarizer_tool": ["app.services.chat_agent.tools.library.summarizer_tool.summarizer_tool", "SummarizerTool"],
+ "pdf_tool": ["app.services.chat_agent.tools.library.pdf_tool.pdf_tool", "PDFTool"],
+ "image_generation_tool": [
+ "app.services.chat_agent.tools.library.image_generation_tool.image_generation_tool",
+ "ImageGenerationTool",
+ ],
+ "clarify_tool": ["app.services.chat_agent.tools.library.basellm_tool.basellm_tool", "BaseLLM"],
+ "expert_tool": ["app.services.chat_agent.tools.library.basellm_tool.basellm_tool", "BaseLLM"],
+ "entertainer_tool": ["app.services.chat_agent.tools.library.basellm_tool.basellm_tool", "BaseLLM"],
+}
 
 
 def get_tools(tools: List[str], load_nested: bool = True) -> List[BaseTool]:
@@ -46,48 +49,47 @@ def get_tools(tools: List[str], load_nested: bool = True) -> List[BaseTool]:
  ValueError: If any tool name in the input list is not in the list of all tools.
  """
  agent_config = get_agent_config()
- all_tool_classes = [
- (
- "sql_tool",
- SQLTool,
- ),
- (
- "visualizer_tool",
- JsxVisualizerTool,
- ),
- (
- "summarizer_tool",
- SummarizerTool,
- ),
- (
- "pdf_tool",
- PDFTool,
- ),
- (
- "image_generation_tool",
- ImageGenerationTool,
- ),
- ("clarify_tool", BaseLLM),
- ("expert_tool", BaseLLM),
- ("entertainer_tool", BaseLLM),
- ]
+ static_set_of_tools = copy.deepcopy(STATIC_SET_OF_TOOLS)
+
  if load_nested:
- all_tool_classes.extend(get_nested_classes())
- all_tools: list[ExtendedBaseTool] = [
- c.from_config( # type: ignore
- config=agent_config.tools_library.library[name],
- common_config=agent_config.common,
- **({"name": name} if issubclass(c, BaseLLM) else {}),
- )
- for (
- name,
- c,
- ) in all_tool_classes
- if name in agent_config.tools
- ]
- tools_map = {tool.name: tool for tool in all_tools}
-
- if any(tool_name not in tools_map for tool_name in tools):
- raise ValueError(f"Invalid tool name(s): {[tool_name for tool_name in tools if tool_name not in tools_map]}")
-
- return [tools_map[tool_name] for tool_name in tools]
+ static_set_of_tools["chain_tool"] = [
+ "app.services.chat_agent.tools.library.chain_tool.nested_meta_agent_tool",
+ "ChainTool",
+ ]
+
+ # first consolidate all tools classes.
+ all_tool_classes = {}
+ for tool_name in tools:
+ # if the tool has a class_name definition I pick it, client comes first :)
+ if configured_classname := agent_config.tools_library.library[tool_name].class_name:
+ module_name, class_name = configured_classname.split(":")
+ logger.debug("Loading CustomTool %s from module %s with class %s", tool_name, module_name, class_name)
+
+ else:
+ # otherwise I pick it from the static set of tools.
+ module_name, class_name = static_set_of_tools[tool_name]
+ logger.debug(
+ "Loading AgentKit tool class %s from module %s with class %s", tool_name, module_name, class_name
+ )
+
+ # import the module and hook the class
+
+ module = importlib.import_module(f"{module_name}")
+ tool_class = getattr(module, class_name)
+
+ # keep the class in a corner
+ all_tool_classes[tool_name] = tool_class
+
+ # then create the tools object by instanciating the classes
+ all_tools: list[ExtendedBaseTool] = []
+ for name, tool_class in all_tool_classes.items():
+ if name in agent_config.tools:
+ tool_instance = tool_class.from_config(
+ config=agent_config.tools_library.library[name],
+ common_config=agent_config.common,
+ **({"name": name} if issubclass(tool_class, BaseLLM) else {}),
+ )
+ logger.debug("Tool %s loaded, type: %s", name, type(tool_instance))
+ all_tools.append(tool_instance)
+
+ return all_tools # type: ignore

docs/docusaurus/docs/tool_library/template_tool.md

@@ -1,7 +1,151 @@
 # Template tool
-Below is an example of a template tool to configure for your use case.
 
+Here is how you can extend Agentkit Tools with your own custom tool. 
+
+## Option 1: Use an existing tools
+
+AgentKit already provide a set of existing Tool implementations. 
+If your need it to simply adjust the prompts and options of an existing tools without changing the logic, you can do so by adjusting the `tools.yaml` configuration file.
+
+For example, for a poem generator tool, you might want to adjust the prompts and options of the `BaseLLM` tool.
+
+```yaml
+library:
+ poem_generator:
+ class_name: "app.services.chat_agent.tools.library.basellm_tool.basellm_tool:BaseLLM" # reference the LLMTool, is nice starting point
+ description: "Generate a poem based on a given input."
+ prompt_message: "Generate a poem based on the following input: {question}"
+ system_context: "You are a poem generator. You will generate a poem based on the input."
+```
+
+Then just use the tool in your action plan as you would with any other tool.
+
+
+```yaml
+tools: # 
+ - poem_generator
+action_plans: 
+ '0':
+ name: ''
+ description: Answer the user's request
+ actions: # each sublist is 1 action step, i.e. add tools as subitems if you want to execute in parallel
+ - - poem_generator
+```
+
+You can pick your implementation from the following list:
+
+* **BaseLLM**
+
+ class_name: `app.services.chat_agent.tools.library.basellm_tool.basellm_tool:BaseLLM`
+
+ Implements the ability to use a LLM model to generate text based on configured prompts
+
+* **SQLTool**
+
+ class_name: `app.services.chat_agent.tools.library.sql_tool.sql_tool:SQLTool`
+
+ Implements SQL database interactions and queries.
+
+* **JsxVisualizerTool**
+
+ class_name: `app.services.chat_agent.tools.library.visualizer_tool.visualizer_tool:JsxVisualizerTool`
+
+ Enables the visualization of JSX components.
+
+* **SummarizerTool**
+
+ class_name: `app.services.chat_agent.tools.library.summarizer_tool.summarizer_tool:SummarizerTool`
+
+ Provides functionality for summarizing text content.
+
+* **PDFTool**
+
+ class_name: `app.services.chat_agent.tools.library.pdf_tool.pdf_tool:PDFTool`
+
+ Perform a RAG operation on textual documents.
+
+* **ImageGenerationTool**
+
+ class_name: `app.services.chat_agent.tools.library.image_generation_tool.image_generation_tool:ImageGenerationTool`
+
+ Supports the generation of images based on specified prompt.
+
+
+## Option2: Create a new tool
+
+If you need to create a new tool, you can do so by creating a new class that extends the `ExtendedBaseTool` class. 
+You don't *have to* nest your tool inside the `app.services.chat_agent.tools` package, the only requirement is that the class is importable by the backend of AgentKit (i.e. it is in the python path).
+
+Assuming you have you code in a folder called `my_tools` and the file is called `echo.py` and the `__init__.py` file along with it.
+
+The minimal requirements for a tool are a class that extends `ExtendedBaseTool` and implements the `_arun` methods.
+
+```python 
+# my_tools/echo.py
+from app.services.chat_agent.tools.ExtendedBaseTool import ExtendedBaseTool
+
+class EchoTool(ExtendedBaseTool):
+ # define the name of your tool, matching the name in the config [TODO: remove this constraint.] 
+ name = "echo_tool"
+
+ async def _arun(self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:
+ # very simple tool that just Echoes the input back. See bellow for more complex example
+ return query 
+```
+
+Then reference your class in the `tools.yaml` configuration file :
+
+```yaml
+library:
+ echo_tool: 
+ class_name: "my_tools.echo:EchoTool" # package_name:class_name
+ description: "A dummy tool for testing"
+ prompt_message: "This is a dummy tool. You will see this message: {question}"
+ system_context: "You are using the dummy tool."
+```
+
+The final step is to make sure agent kit can load your package. 
+
+
+### For docker users
+
+The docker that runs the backend agent is actually "Guaranted" to have the `/code` folder in the python path. So you can just volume mount your folder in the `/code` folder and it will be importable. 
+
+```yaml
+
+# somewhere in the docker-compose-xxxx.yml you are using
+services:
+ fastapi_server:
+ ... # some options about the server
+ volumes:
+ ... # probeably existing mounts
+ - ./mytools:/code/my_tools # Here, add this at the bottom of the list
 ```
+
+Finally you need to down & up (restart might not be enough for a such a change) the docker container with `docker compose -f <your compose file> down` and `docker compose -f <your compose file> up -d`.
+
+
+
+### For "on my machine" developpers
+
+You might already run the back end with hand like with `uvicorn app.main:app` and the current folder is usually considered as part of the python path. 
+
+If not, then trouble shoot by listing the python path with `python -c "import sys; print(sys.path)"` and make sure your package is in one of the listed folders.
+
+Adjust the PYTHONPATH environment variable with
+* Linux `export PYTHONPATH=$PYTHONPATH:/path/to/your/folder`
+* Windows `set PYTHONPATH=%PYTHONPATH%;C:\path\to\your\folder`
+
+If still not, just consider using docker. 
+
+
+
+### Example of a more complex tool
+
+The following tool is a more complex example of a tool that uses a LLM model to generate extra messages and return them as an appendix DataType. 
+
+
+```python
 from typing import Optional, List, Tuple
 from app.db.session import sql_tool_db
 from langchain.schema import HumanMessage, SystemMessage
@@ -16,6 +160,8 @@ from app.services.chat_agent.helpers.llm import get_llm
 from app.services.chat_agent.tools.ExtendedBaseTool import ExtendedBaseTool
 
 
+
+
 class ReadMeTool(ExtendedBaseTool):
  # define the name of your tool, matching the name in the config
  name = "readme_tool"