agent with run flow components as tools

[SudeeraDilshan]

I have an agent and I have connected run flow components as tools for the agent.each run flow component represent flow .How can I implement agent with python code by using json file related to the flow.

[dosubot[bot]]

Hey there, @SudeeraDilshan! I'm here to help you out. If you have any questions or need assistance with anything, feel free to ask.

To implement an agent in Python using a JSON file to configure run flow components as tools, you can use the JsonAgentComponent class. This class constructs a JSON agent from a language model and tools. Here's a simplified example of how you can do it:

1. Define Inputs: The JsonAgentComponent requires a language model (llm) and a file path (path) to a JSON or YAML file.

2. Build the Agent: The build_agent method reads the configuration from the specified file. If the file is YAML, it loads the content into a dictionary. For JSON files, it uses the JsonSpec.from_file method to create a JsonSpec object.

3. Create the Toolkit: A JsonToolkit is created using the JsonSpec.

4. Create the Agent: The create_json_agent function is used to create the agent with the language model and toolkit.

Here is a code snippet illustrating this process:

from langflow.base.agents.agent import LCAgentComponent
from langflow.inputs import FileInput, HandleInput
from pathlib import Path
import yaml
from langchain.agents import AgentExecutor
from langchain_community.agent_toolkits import create_json_agent
from langchain_community.agent_toolkits.json.toolkit import JsonToolkit
from langchain_community.tools.json.tool import JsonSpec

class JsonAgentComponent(LCAgentComponent):
    display_name = "JsonAgent"
    description = "Construct a json agent from an LLM and tools."
    name = "JsonAgent"

    inputs = [
        HandleInput(name="llm", display_name="Language Model", input_types=["LanguageModel"], required=True),
        FileInput(name="path", display_name="File Path", file_types=["json", "yaml", "yml"], required=True),
    ]

    def build_agent(self) -> AgentExecutor:
        path = Path(self.path)
        if path.suffix in ("yaml", "yml"):
            with path.open(encoding="utf-8") as file:
                yaml_dict = yaml.load(file, Loader=yaml.FullLoader)
            spec = JsonSpec(dict_=yaml_dict)
        else:
            spec = JsonSpec.from_file(path)
        toolkit = JsonToolkit(spec=spec)

        return create_json_agent(llm=self.llm, toolkit=toolkit, **self.get_agent_kwargs())

This code sets up an agent that can be configured using a JSON or YAML file, where each component in the file represents a flow ^[1].

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[SudeeraDilshan]

No .I am asking how to implement the agent using below code.this is the code for related to the flow that I uploaded above

from langflow.load import run_flow_from_json
TWEAKS = {
"Agent-K5Dkf": {},
"RunFlow-CHXCW": {},
"RunFlow-jQUqT": {},
"ChatOutput-txkIl": {},
"ChatInput-VMfzw": {},
"RunFlow-9bCUd": {}
}

result = run_flow_from_json(flow="Agent.json",
input_value="message",
session_id="", # provide a session id if you want to use session state
fallback_to_env_vars=True, # False by default
tweaks=TWEAKS)

[dosubot[bot]]

To implement an agent using the provided Python code with the run_flow_from_json function, follow these steps:

1. Prepare the JSON File: Ensure that your Agent.json file contains the flow configuration that you want to execute. This file should define the nodes and edges of your flow.

2. Define Tweaks: The TWEAKS dictionary allows you to specify any modifications or configurations for the nodes in your flow. In your case, it seems you have placeholders for different components like Agent-K5Dkf, RunFlow-CHXCW, etc. You can add specific configurations to these components if needed.

3. Run the Flow: Use the run_flow_from_json function to execute the flow. You can specify the input_value as the message you want to process. The session_id can be used if you want to maintain session state across executions. The fallback_to_env_vars parameter is set to True to allow the function to use environment variables as a fallback for any global variables not explicitly provided.

Here's the code snippet you provided, which is already set up to run the flow:

from langflow.load import run_flow_from_json

TWEAKS = {
  "Agent-K5Dkf": {},
  "RunFlow-CHXCW": {},
  "RunFlow-jQUqT": {},
  "ChatOutput-txkIl": {},
  "ChatInput-VMfzw": {},
  "RunFlow-9bCUd": {}
}

result = run_flow_from_json(
    flow="Agent.json",
    input_value="message",
    session_id="",  # Provide a session id if you want to use session state
    fallback_to_env_vars=True  # Set to True to use environment variables as a fallback
)

# Use the result as needed
print(result)

Make sure your Agent.json file is correctly formatted and contains the necessary flow configuration. Adjust the TWEAKS dictionary as needed to customize the behavior of your flow components ^[1][2].

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