Improved Harvestment Codes – Using AI AI AI Performance Ai For Semantic Kernel and Gemini

!pip -q install semantic-kernel google-generativeai duckduckgo-search rich


import os, re, json, time, math, textwrap, getpass, pathlib, typing as T
from rich import print
import google.generativeai as genai
from duckduckgo_search import DDGS


GEMINI_API_KEY = os.getenv("GEMINI_API_KEY") or getpass.getpass("🔑 Enter GEMINI_API_KEY: ")
genai.configure(api_key=GEMINI_API_KEY)
GEMINI_MODEL = "gemini-1.5-flash"
model = genai.GenerativeModel(GEMINI_MODEL)


import semantic_kernel as sk
try:
   from semantic_kernel.functions import kernel_function
except Exception:
   from semantic_kernel.utils.function_decorator import kernel_function

class AgentTools:
   """Semantic Kernel-native toolset the agent can call."""


   def __init__(self):
       self._notes: list[str] = []


   @kernel_function(name="web_search", description="Search the web for fresh info; returns JSON list of {title,href,body}.")
   def web_search(self, query: str, k: int = 5) -> str:
       k = max(1, min(int(k), 10))
       hits = list(DDGS().text(query, max_results=k))
       return json.dumps(hits[:k], ensure_ascii=False)


   @kernel_function(name="calc", description="Evaluate a safe math expression, e.g., '41*73+5' or 'sin(pi/4)**2'.")
   def calc(self, expression: str) -> str:
       allowed = {"__builtins__": {}}
       for n in ("pi","e","tau"): allowed[n] = getattr(math, n)
       for fn in ("sin","cos","tan","asin", "sqrt","log","log10","exp","floor","ceil"):
           allowed[fn] = getattr(math, fn)
       return str(eval(expression, allowed, {}))


   @kernel_function(name="now", description="Get the current local time string.")
   def now(self) -> str:
       return time.strftime("%Y-%m-%d %H:%M:%S")


   @kernel_function(name="write_file", description="Write text to a file path; returns saved path.")
   def write_file(self, path: str, content: str) -> str:
       p = pathlib.Path(path).expanduser().resolve()
       p.parent.mkdir(parents=True, exist_ok=True)
       p.write_text(content, encoding="utf-8")
       return str(p)


   @kernel_function(name="read_file", description="Read text from a file path; returns first 4000 chars.")
   def read_file(self, path: str) -> str:
       p = pathlib.Path(path).expanduser().resolve()
       return p.read_text(encoding="utf-8")[:4000]


   @kernel_function(name="add_note", description="Persist a short note into memory.")
   def add_note(self, note: str) -> str:
       self._notes.append(note.strip())
       return f"Notes stored: {len(self._notes)}"


   @kernel_function(name="search_notes", description="Search notes by keyword; returns top matches.")
   def search_notes(self, query: str) -> str:
       q = query.lower()
       hits = [n for n in self._notes if q in n.lower()]
       return json.dumps(hits[:10], ensure_ascii=False)


kernel = sk.Kernel()
tools = AgentTools()
kernel.add_plugin(tools, "agent_tools")

def list_tools() -> dict[str, dict]:
   registry = {}
   for name in ("web_search","calc","now","write_file","read_file","add_note","search_notes"):
       fn = getattr(tools, name)
       desc = getattr(fn, "description", "") or fn.__doc__ or ""
       sig = "()" if name in ("now",) else "(**kwargs)"
       registry[name] = {"callable": fn, "description": desc.strip(), "signature": sig}
   return registry


TOOLS = list_tools()


CATALOG = "n".join(
   [f"- {n}{v['signature']}: {v['description']}" for n,v in TOOLS.items()]
)


SYSTEM = f"""You are a meticulous tool-using AI agent.
You can call TOOLS by returning ONLY a JSON object:
{{"tool":"","args":{{...}}}}
After finishing all steps, respond with:
{{"final_answer":""}}


TOOLS available:
{CATALOG}


Rules:
- Prefer factuality; cite web_search results as A Coding Implementation of an Advanced Tool-Using AI Agent with Semantic Kernel and Gemini(url).
- Keep steps minimal; at most 8 tool calls.
- For file outputs, use write_file and mention the saved path.
- If a tool error occurs, adjust arguments and try again.
"""


def extract_json(s: str) -> dict|None:
   for m in re.finditer(r"{.*}", s, flags=re.S):
       try: return json.loads(m.group(0))
       except Exception: continue
   return None

def run_agent(task: str, max_steps: int = 8, verbose: bool = True) -> str:
   transcript: list[dict] = [{"role":"system","parts":[SYSTEM]},
                             {"role":"user","parts":[task]}]
   observations = ""
   for step in range(1, max_steps+1):
       content = []
       for m in transcript:
           role = m["role"]
           for part in m["parts"]:
               content.append({"text": f"[{role.upper()}]n{part}n"})
       if observations:
           content.append({"text": f"[OBSERVATIONS]n{observations[-4000:]}n"})
       resp = model.generate_content(content, request_options={"timeout":60})
       text = resp.text or ""
       if verbose:
           print(f"n[bold cyan]Step {step} - Model[/bold cyan]n{textwrap.shorten(text, 1000)}")
       cmd = extract_json(text)
       if not cmd:
           transcript.append({"role":"user","parts":[
               "Please output strictly one JSON object per your rules."
           ]})
           continue
       if "final_answer" in cmd:
           return cmd["final_answer"]
       if "tool" in cmd:
           tname = cmd["tool"]; args = cmd.get("args", {})
           if tname not in TOOLS:
               observations += f"nToolError: unknown tool '{tname}'."
               continue
           try:
               out = TOOLS[tname]["callable"](**args)
               out_str = out if isinstance(out,str) else json.dumps(out, ensure_ascii=False)
               if len(out_str) > 4000: out_str = out_str[:4000] + "...[truncated]"
               observations += f"n[{tname}] {out_str}"
               transcript.append({"role":"user","parts":[f"Observation from {tname}:n{out_str}"]})
           except Exception as e:
               observations += f"nToolError {tname}: {e}"
               transcript.append({"role":"user","parts":[f"ToolError {tname}: {e}"]})
       else:
           transcript.append({"role":"user","parts":[
               "Your output must be a single JSON with either a tool call or final_answer."
           ]})
   return "Reached step limit. Summarize findings:n" + observations[-1500:]

DEMO = (
   "Find the top 3 concise facts about Chandrayaan-3 with sources, "
   "compute 41*73+5, store a 3-line summary into '/content/notes.txt', "
   "add the summary to notes, then show current time and return a clean final answer."
)


if __name__ == "__main__":
   print("[bold]🔧 Tools loaded:[/bold]", ", ".join(TOOLS.keys()))
   ans = run_agent(DEMO, max_steps=8, verbose=True)
   print("n" + "="*80 + "n[bold green]FINAL ANSWER[/bold green]n" + ans + "n")