shaytan/ai-agents-masterclass
1
0
Fork 0
mirror of https://github.com/coleam00/ai-agents-masterclass.git synced 2025-11-29 00:23:14 +00:00
Code Issues Packages Projects Releases Wiki Activity

Groq Llama 3 AI Agent

Browse Source
This commit is contained in:
Cole Medin
2024-07-20 10:24:08 -05:00
parent 0a215eb7cd
commit f290a960cc
3 changed files with 321 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

20
llama3-function-calling-agent/.env.example Normal file
View File

@@ -0,0 +1,20 @@
# Rename this file to .env once you have filled in the below environment variables!
# Get your GROQ API Key here -
# https://console.groq.com/keys
GROQ_API_KEY=
# Use a GROQ model ID for a model that is good at function calling
LLM_MODEL=llama3-groq-70b-8192-tool-use-preview
# Get your personal Asana access token through the developer console in Asana.
# Feel free to follow these instructions -
# https://developers.asana.com/docs/personal-access-token
ASANA_ACCESS_TOKEN=
# The Asana workspace ID is in the URL when you visit your Asana Admin Console (when logged in).
# Go to the URL "https://app.asana.com/admin" and then your workspace ID
# will appear in the URL as a slew of digits once the site loads.
# If your URL is https://app.asana.com/admin/987654321/insights, then your
# Asana workspace ID is 987654321
ASANA_WORKPLACE_ID=

294
llama3-function-calling-agent/llama3-task-agent.py Normal file
View File

@@ -0,0 +1,294 @@
import asana
from asana.rest import ApiException
from dotenv import load_dotenv
from datetime import datetime
import streamlit as st
import json
import os
from langchain_groq import ChatGroq
from langchain_core.tools import tool
from langchain_core.messages import SystemMessage, AIMessage, HumanMessage, ToolMessage
load_dotenv()
model = os.getenv('LLM_MODEL', 'llama3-groq-70b-8192-tool-use-preview')
configuration = asana.Configuration()
configuration.access_token = os.getenv('ASANA_ACCESS_TOKEN', '')
api_client = asana.ApiClient(configuration)
# create an instance of the different Asana API classes
projects_api_instance = asana.ProjectsApi(api_client)
tasks_api_instance = asana.TasksApi(api_client)
workspace_gid = os.getenv("ASANA_WORKPLACE_ID", "")
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~ AI Agent Tool Functions ~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@tool
def create_asana_task(task_name, project_gid, due_on="today"):
"""
Creates a task in Asana given the name of the task and when it is due
Example call:
create_asana_task("Test Task", "2024-06-24")
Args:
task_name (str): The name of the task in Asana
project_gid (str): The ID of the project to add the task to (not the name but the project ID)
due_on (str): The date the task is due in the format YYYY-MM-DD. If not given, the current day is used
Returns:
str: The API response of adding the task to Asana or an error message if the API call threw an error
"""
if due_on == "today":
due_on = str(datetime.now().date())
task_body = {
"data": {
"name": task_name,
"due_on": due_on,
"projects": [project_gid]
}
}
try:
api_response = tasks_api_instance.create_task(task_body, {})
# return json.dumps(api_response, indent=2)
return "Task created successfully!"
except ApiException as e:
return f"Exception when calling TasksApi->create_task: {e}"
@tool
def get_asana_projects():
"""
Gets all of the projects in the user's Asana workspace
Returns:
str: The API response from getting the projects or an error message if the projects couldn't be fetched.
The API response is an array of project objects, where each project object looks like:
{'gid': '1207789085525921', 'name': 'Project Name', 'resource_type': 'project'}
"""
opts = {
'limit': 50, # int | Results per page. The number of objects to return per page. The value must be between 1 and 100.
'workspace': workspace_gid, # str | The workspace or organization to filter projects on.
'archived': False # bool | Only return projects whose `archived` field takes on the value of this parameter.
}
try:
api_response = projects_api_instance.get_projects(opts)
return json.dumps(list(api_response), indent=2)
except ApiException as e:
return "Exception when calling ProjectsApi->create_project: %s\n" % e
@tool
def create_asana_project(project_name, due_on=None):
"""
Creates a project in Asana given the name of the project and optionally when it is due
Example call:
create_asana_project("Test Project", "2024-06-24")
Args:
project_name (str): The name of the project in Asana
due_on (str): The date the project is due in the format YYYY-MM-DD. If not supplied, the project is not given a due date
Returns:
str: The API response of adding the project to Asana or an error message if the API call threw an error
"""
body = {
"data": {
"name": project_name, "due_on": due_on, "workspace": workspace_gid
}
} # dict | The project to create.
try:
# Create a project
api_response = projects_api_instance.create_project(body, {})
return json.dumps(api_response, indent=2)
except ApiException as e:
return "Exception when calling ProjectsApi->create_project: %s\n" % e
@tool
def get_asana_tasks(project_gid):
"""
Gets all the Asana tasks in a project
Example call:
get_asana_tasks("1207789085525921")
Args:
project_gid (str): The ID of the project in Asana to fetch the tasks for
Returns:
str: The API response from fetching the tasks for the project in Asana or an error message if the API call threw an error
The API response is an array of tasks objects where each task object is in the format:
{'gid': '1207780961742158', 'created_at': '2024-07-11T16:25:46.380Z', 'due_on': None or date in format "YYYY-MM-DD", 'name': 'Test Task'}
"""
opts = {
'limit': 50, # int | Results per page. The number of objects to return per page. The value must be between 1 and 100.
'project': project_gid, # str | The project to filter tasks on.
'opt_fields': "created_at,name,due_on", # list[str] | This endpoint returns a compact resource, which excludes some properties by default. To include those optional properties, set this query parameter to a comma-separated list of the properties you wish to include.
}
try:
# Get multiple tasks
api_response = tasks_api_instance.get_tasks(opts)
return json.dumps(list(api_response), indent=2)
except ApiException as e:
return "Exception when calling TasksApi->get_tasks: %s\n" % e
@tool
def update_asana_task(task_gid, data):
"""
Updates a task in Asana by updating one or both of completed and/or the due date
Example call:
update_asana_task("1207780961742158", {"completed": True, "due_on": "2024-07-13"})
Args:
task_gid (str): The ID of the task to update
data (dict): A dictionary with either one or both of the keys 'completed' and/or 'due_on'
If given, completed needs to be either True or False.
If given, the due date needs to be in the format 'YYYY-MM-DD'.
Returns:
str: The API response of updating the task or an error message if the API call threw an error
"""
# Data: {"completed": True or False, "due_on": "YYYY-MM-DD"}
body = {"data": data} # dict | The task to update.
try:
# Update a task
api_response = tasks_api_instance.update_task(body, task_gid, {})
return json.dumps(api_response, indent=2)
except ApiException as e:
return "Exception when calling TasksApi->update_task: %s\n" % e
@tool
def delete_task(task_gid):
"""
Deletes a task in Asana
Example call:
delete_task("1207780961742158")
Args:
task_gid (str): The ID of the task to delete
Returns:
str: The API response of deleting the task or an error message if the API call threw an error
"""
try:
# Delete a task
api_response = tasks_api_instance.delete_task(task_gid)
return json.dumps(api_response, indent=2)
except ApiException as e:
return "Exception when calling TasksApi->delete_task: %s\n" % e
# Maps the function names to the actual function object in the script
# This mapping will also be used to create the list of tools to bind to the agent
available_functions = {
"create_asana_task": create_asana_task,
"get_asana_projects": get_asana_projects,
"create_asana_project": create_asana_project,
"get_asana_tasks": get_asana_tasks,
"update_asana_task": update_asana_task,
"delete_task": delete_task
}
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~ AI Prompting Function ~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
def prompt_ai(messages, nested_calls=0):
if nested_calls > 5:
raise "AI is tool calling too much!"
# First, prompt the AI with the latest user message
tools = [tool for _, tool in available_functions.items()]
asana_chatbot = ChatGroq(model=model)
asana_chatbot_with_tools = asana_chatbot.bind_tools(tools)
stream = asana_chatbot_with_tools.stream(messages)
first = True
for chunk in stream:
if first:
gathered = chunk
first = False
else:
gathered = gathered + chunk
yield chunk
has_tool_calls = len(gathered.tool_calls) > 0
# Second, see if the AI decided it needs to invoke a tool
if has_tool_calls:
# Add the tool request to the list of messages so the AI knows later it invoked the tool
messages.append(gathered)
# If the AI decided to invoke a tool, invoke it
# For each tool the AI wanted to call, call it and add the tool result to the list of messages
for tool_call in gathered.tool_calls:
tool_name = tool_call["name"].lower()
selected_tool = available_functions[tool_name]
tool_output = selected_tool.invoke(tool_call["args"])
messages.append(ToolMessage(tool_output, tool_call_id=tool_call["id"]))
# Call the AI again so it can produce a response with the result of calling the tool(s)
additional_stream = prompt_ai(messages, nested_calls + 1)
for additional_chunk in additional_stream:
yield additional_chunk
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~ Main Function with UI Creation ~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
system_message = f"""
You are a personal assistant who helps manage tasks in Asana.
You never give IDs to the user since those are just for you to keep track of.
When a user asks to create a task and you don't know the project to add it to for sure, clarify with the user.
The current date is: {datetime.now().date()}
"""
def main():
st.title("Asana Chatbot")
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = [
SystemMessage(content=system_message)
]
# Display chat messages from history on app rerun
for message in st.session_state.messages:
message_json = json.loads(message.json())
message_type = message_json["type"]
if message_type in ["human", "ai", "system"]:
with st.chat_message(message_type):
st.markdown(message_json["content"])
# React to user input
if prompt := st.chat_input("What would you like to do today?"):
# Display user message in chat message container
st.chat_message("user").markdown(prompt)
# Add user message to chat history
st.session_state.messages.append(HumanMessage(content=prompt))
# Display assistant response in chat message container
with st.chat_message("assistant"):
stream = prompt_ai(st.session_state.messages)
response = st.write_stream(stream)
st.session_state.messages.append(AIMessage(content=response))
if __name__ == "__main__":
main()

7
llama3-function-calling-agent/requirements.txt Normal file
View File

@@ -0,0 +1,7 @@
asana==5.0.7
python-dotenv==0.13.0
langchain==0.2.6
langchain-groq==0.1.5
langchain-community==0.2.6
langchain-core==0.2.10
streamlit==1.36.0