From 5e932c84ae8df387da9e4c434bd68bbf541c9fb9 Mon Sep 17 00:00:00 2001
From: Stupdi Go <mukund@ayyalasomayajula.net>
Date: Fri, 27 Jun 2025 16:08:45 -0500
Subject: [PATCH] test

---
 .gitignore |   1 +
 main.py    | 119 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 120 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 main.py

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..f7275bb
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+venv/
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..1af2b12
--- /dev/null
+++ b/main.py
@@ -0,0 +1,119 @@
+from llama_cpp import Llama
+from langchain_core.language_models import LLM
+from langchain.prompts import PromptTemplate
+from langchain.agents import AgentExecutor, create_react_agent
+from langchain.tools import Tool
+from typing import Optional, List
+from googlesearch import search
+from bs4 import BeautifulSoup
+import requests
+# --- LLaMA-CPP model setup ---
+llm_model = Llama(
+    model_path="ReAction-1.5B.Q5_K_M.gguf",
+    n_ctx=2**21,
+    n_threads=8,
+    use_mlock=True,
+    verbose=False
+)
+
+# --- LangChain wrapper ---
+class LlamaCppLLM(LLM):
+    @property
+    def _llm_type(self) -> str:
+        return "llama-cpp"
+
+    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
+        result = llm_model(
+            prompt,
+            stop=stop,
+            max_tokens=1024,
+            echo=False,
+            
+        )
+        output = result["choices"][0]["text"].strip()
+        return output
+
+custom_llm = LlamaCppLLM()
+
+# --- Tool definition ---
+def extract_text(html):
+    soup = BeautifulSoup(html, "html.parser")
+    # Remove scripts and styles
+    for tag in soup(["script", "style"]):
+        tag.decompose()
+    text = soup.get_text(separator=" ", strip=True)
+    return text
+def weather(city: str) -> str:
+    return f"The weather in {city} is Sunny."
+def search_google(query:str):
+    print(f"serach tool called {query}")
+    return [url for url in search(query,stop=5)]
+def get_webhtml(url):
+    print(url)
+    url = url.replace("'","")
+    if url:
+        r = requests.get(url)
+    else:
+        return None
+    return extract_text(r.text) if r.status_code == 200 else None
+weather_tool = Tool(
+    name="weather",
+    func=weather,
+    description="Use this tool to get the weather of a given city. Input should be the city name."
+)
+search_tool = Tool(
+        name = "search_tool",
+        func=search_google,
+        description="Returns the first 5 results from google with a specific query"
+        )
+html_tool = Tool(
+        name="get_webhtml",
+        func=get_webhtml,
+        description="Given a url, it provides the HTML for it. If it returns None, the website isn't available."
+        )
+
+# --- Custom ReAct prompt ---
+prompt_template = PromptTemplate.from_template("""
+You are a helpful assistant that can use tools to answer questions.
+
+TOOLS:
+{tools}
+
+FORMAT:
+Question: the input question
+Thought: think about what to do
+Action: pick one of [{tool_names}]
+Action Input: "<the input to the action>"
+
+Observation: result of the action
+... (you can repeat Thought/Action/Observation until you reach a Final Answer) ...
+... (Also, only use Action Input AFTER you use Action, so before you use Action input again, you have to call a tool again. Reach a final Answer before 3 iterations) ...
+Thought: I now know the final answer
+Final Answer: <the final answer>
+        
+
+NEW QUESTION:
+
+Question: {input}
+{agent_scratchpad}
+""")
+
+# --- Create ReAct agent ---
+react_agent = create_react_agent(
+    llm=custom_llm,
+    tools=[search_tool,html_tool],
+    prompt=prompt_template
+)
+
+agent = AgentExecutor(
+    agent=react_agent,
+    tools=[search_tool,html_tool],
+    verbose=True,
+    handle_parsing_errors=True,
+    max_iterations=5
+)
+
+# --- Run it ---
+response = agent.invoke({"input": "What is the weather in San Francisco"})
+print("\nFinal Output:\n", response)
+