start with adapting example, and seperating out tooling into file

hw2/.requirements.txt

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+langchain-community
+langchain
+langchain_openai
+python-dotenv
+firecrawl-py
+chromadb
+langchainhub
+dnspython
+validators
+google-search-results
+langchain-experimental

hw2/README.md

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+## HW2 for gensec
+
+
+### Enviornment
+Install python3, then run the following:
+```
+pip install -r requirements.txt
+cp .env.example .env
+```
+After, populate .env with your OPENAI_API_KEY and FIREWALL_API_KEY
+
+
+### Running
+```
+python3 app.py
+```
+
+### Example Queries
+```
+
+
+```
+
+Enjoy!

hw2/app.py

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+from langchain import hub
+from langchain.agents import AgentExecutor, create_react_agent, load_tools
+from langchain.tools import tool
+from langchain_openai import ChatOpenAI
+from dotenv import load_dotenv
+from tools import lookup_ip, lookup_name
+from langsmith import Client
+
+"""
+This is the main runner of the custom agent. Agent tools are defined seperatly and imported from tools.py
+
+So far, wholly adapted from https://github.com/wu4f/cs410g-src/blob/main/04_Agents/07_tools_custom_agent.py
+
+"""
+
+load_dotenv()
+#os.environ["LANGCHAIN_TRACING_V2"] = "true"
+#os.environ["LANGCHAIN_PROJECT"] = f"LangSmith Introduction"
+#os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
+#client = Client()
+llm = ChatOpenAI(model_name="gpt-4-turbo", temperature=0)
+
+tools = load_tools(["serpapi", "terminal"], allow_dangerous_tools=True)
+tools.extend([lookup_name, lookup_ip])
+
+base_prompt = hub.pull("langchain-ai/react-agent-template")
+prompt = base_prompt.partial(instructions="Answer the user's request utilizing at most 8 tool calls")
+agent = create_react_agent(llm,tools,prompt)
+agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+print("Welcome to my application. I am configured with these tools:")
+for tool in agent_executor.tools:
+  print(f'  Tool: {tool.name} = {tool.description}')
+while True:
+    line = input("llm>> ")
+    try:
+        if line:
+            result = agent_executor.invoke({"input":line})
+            print(result)
+        else:
+            break
+    except Exception as e:
+        print(e)

hw2/requirements.txt

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+langchain
+langchain_openai
+langchain-experimental
+langchainhub
+python-dotenv
+dnspython
+validators
+google-search-results

hw2/tools.py

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+from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
+from langchain.tools import tool
+import dns.resolver, dns.reversename
+import validators
+import validators
+
+"""
+Tools to be run by my custom agent.
+
+So far, these are the same tooling provided by the example in https://github.com/wu4f/cs410g-src/blob/main/04_Agents/07_tools_custom_agent.py,
+But I will be updating this to build my own tooling
+"""
+
+class LookupNameInput(BaseModel):
+    hostname: str = Field(description="Should be a hostname such as www.google.com")
+    @root_validator
+    def is_dns_address(cls, values: dict[str,any]) -> str:
+        if validators.domain(values.get("hostname")):
+            return values
+        raise ValueError("Malformed hostname")
+class LookupIPInput(BaseModel):
+    address: str = Field(description="Should be an IP address such as 208.91.197.27 or 143.95.239.83")
+    @root_validator
+    def is_ip_address(cls, values: dict[str,any]) -> str:
+        if validators.ip_address.ipv4(values.get("address")):
+            return values
+        raise ValueError("Malformed IP address")
+    
+
+@tool("lookup_name",args_schema=LookupNameInput, return_direct=False)
+def lookup_name(hostname):
+    """Given a DNS hostname, it will return its IPv4 addresses"""
+    result = dns.resolver.resolve(hostname, 'A')
+    res = [ r.to_text() for r in result ]
+    return res[0]
+
+
+@tool("lookup_ip", args_schema=LookupIPInput, return_direct=False)
+def lookup_ip(address):
+    """Given an IP address, returns names associated with it"""
+    n = dns.reversename.from_address(address)
+    result = dns.resolver.resolve(n, 'PTR')
+    res = [ r.to_text() for r in result ]
+    return res[0]