Understanding the Target Audience
The target audience for this article includes AI developers, data scientists, and business managers who are keen on integrating advanced AI capabilities into their operations. These professionals are typically familiar with programming and AI concepts, but they seek practical applications that can enhance productivity and improve decision-making processes.
Pain Points
- Many struggle with implementing complex AI systems due to a lack of clear guidance.
- Ensuring the reliability and accuracy of AI outputs poses a significant challenge.
- There is a pressing need for modularity and flexibility in AI solutions to adapt to varying tasks.
Goals
The primary goals of the target audience include:
- Designing AI agents capable of effective planning, information retrieval, computation, and output critique.
- Streamlining workflows and improving task management through AI integration.
- Leveraging advanced AI models, such as Gemini, for enhanced performance in various applications.
Interests
This audience is particularly interested in:
- Exploring innovative AI frameworks and models.
- Learning about practical implementations of AI in business contexts.
- Understanding how to integrate AI with existing tools and systems.
Implementing a Graph-Structured AI Agent with Gemini
In this section, we will implement an advanced graph-based AI agent utilizing the GraphAgent framework and the Gemini 1.5 Flash model. The architecture consists of a directed graph of nodes, each assigned a specific function:
- A planner to break down the task.
- A router to control the flow of information.
- Research and math nodes to provide external evidence and computation.
- A writer to synthesize the answer.
- A critic to validate and refine the output.
We will integrate Gemini through a wrapper that manages structured JSON prompts, while local Python functions will serve as tools for safe math evaluation and document search. By executing this pipeline end-to-end, we will demonstrate how reasoning, retrieval, and validation can be modularized within a single cohesive system.
Code Implementation
We begin by importing the necessary Python libraries for data handling, timing, and safe evaluation. Additionally, we utilize dataclasses and typing helpers to structure our state. The following code snippet outlines the initial setup:
import os, json, time, ast, math, getpass
from dataclasses import dataclass, field
from typing import Dict, List, Callable, Any
import google.generativeai as genai
try:
import networkx as nx
except ImportError:
nx = None
Model Configuration
Next, we configure the model using the following function:
def make_model(api_key: str, model_name: str = "gemini-1.5-flash"):
genai.configure(api_key=api_key)
return genai.GenerativeModel(model_name, system_instruction=(
"You are GraphAgent, a principled planner-executor. "
"Prefer structured, concise outputs; use provided tools when asked."
))
Calling the LLM
We then create a function to call the large language model (LLM):
def call_llm(model, prompt: str, temperature=0.2) -> str:
r = model.generate_content(prompt, generation_config={"temperature": temperature})
return (r.text or "").strip()
Safe Math Evaluation
To ensure safe mathematical evaluations, we implement the following function:
def safe_eval_math(expr: str) -> str:
node = ast.parse(expr, mode="eval")
allowed = (ast.Expression, ast.BinOp, ast.UnaryOp, ast.Num, ast.Constant,
ast.Add, ast.Sub, ast.Mult, ast.Div, ast.Pow, ast.Mod,
ast.USub, ast.UAdd, ast.FloorDiv, ast.AST)
def check(n):
if not isinstance(n, allowed): raise ValueError("Unsafe expression")
for c in ast.iter_child_nodes(n): check(c)
check(node)
return str(eval(compile(node, "", "eval"), {"__builtins__": {}}, {}))
Document Search
For document retrieval, we create a simple search function:
DOCS = [
"Solar panels convert sunlight to electricity; capacity factor ~20%.",
"Wind turbines harvest kinetic energy; onshore capacity factor ~35%.",
"RAG = retrieval-augmented generation joins search with prompting.",
"LangGraph enables cyclic graphs of agents; good for tool orchestration.",
]
def search_docs(q: str, k: int = 3) -> List[str]:
ql = q.lower()
scored = sorted(DOCS, key=lambda d: -sum(w in d.lower() for w in ql.split()))
return scored[:k]
Node Functions
We implement key node functions to manage the state as the graph executes:
@dataclass
class State:
task: str
plan: str = ""
scratch: List[str] = field(default_factory=list)
evidence: List[str] = field(default_factory=list)
result: str = ""
step: int = 0
done: bool = False
def node_plan(state: State, model) -> str:
prompt = f"""Plan step-by-step to solve the user task.
Task: {state.task}
Return JSON: subtasks, "success_criteria": ["..."]}}"""
js = call_llm(model, prompt)
try:
plan = json.loads(js[js.find("{"): js.rfind("}")+1])
except Exception:
plan = {"subtasks": ["Research", "Synthesize"], "tools": {"search": True, "math": False}, "success_criteria": ["clear answer"]}
state.plan = json.dumps(plan, indent=2)
state.scratch.append("PLAN:\n"+state.plan)
return "route"
Execution of the Graph
To run the graph, we define the following function:
def run_graph(task: str, api_key: str) -> State:
model = make_model(api_key)
state = State(task=task)
cur = "plan"
max_steps = 12
while not state.done and state.step < max_steps:
state.step += 1
nxt = NODES[cur](state, model)
if nxt == "end": break
cur = nxt
return state
Program Entry Point
Finally, we set up the program entry point:
if __name__ == "__main__":
key = os.getenv("GEMINI_API_KEY") or getpass.getpass(" Enter GEMINI_API_KEY: ")
task = input(" Enter your task: ").strip() or "Compare solar vs wind for reliability; compute 5*7."
t0 = time.time()
state = run_graph(task, key)
dt = time.time() - t0
print("\n=== GRAPH ===", ascii_graph())
print(f"\n Result in {dt:.2f}s:\n{state.result}\n")
print("---- Evidence ----")
print("\n".join(state.evidence))
print("\n---- Scratch (last 5) ----")
print("\n".join(state.scratch[-5:]))
Conclusion
This article demonstrates how a graph-structured agent can facilitate the design of deterministic workflows around a probabilistic large language model. The planner node enforces task decomposition, the router dynamically selects between research and math, and the critic provides iterative improvements for factuality and clarity. Gemini serves as the central reasoning engine, while the graph nodes ensure structure, safety checks, and transparent state management.
FAQ
- What is a graph-structured AI agent? A graph-structured AI agent organizes tasks into a directed graph, where each node performs a specific function, allowing for modular and flexible execution.
- How does Gemini enhance AI performance? Gemini utilizes advanced modeling techniques to improve reasoning, information retrieval, and output validation, making it suitable for complex tasks.
- What programming languages are used in this implementation? The implementation primarily uses Python, leveraging libraries for data handling and AI integration.
- Can this framework be applied to different business contexts? Yes, the modular nature of the framework allows it to adapt to various business needs, from data analysis to customer service automation.
- What are some common mistakes to avoid when implementing AI solutions? Common mistakes include neglecting data quality, failing to define clear objectives, and overlooking the importance of testing and validation.
