Tool Calling Code

Below are the steps executed for creation of Virtual environment python -m venv .venv python3 -m venv .venv apt install python3.12-venv python3.12 -m venv .venv source .venv/bin/activate python3 pip install -r requirements.txt If virtual environment to be removed, rm -rf .venv Things which are required - Using groq url --> https://console.groq.com/keys - Key you need to generate from the groq url. - Groq is a infra provider for cloud LLM. - ChatGroq --> Functionality from Groq if you are integrating with Chatgroq Functionality - get_weather --> i need specific result. - description is available. - In langchain --> we have tool method --> its like a rapper functionality --> its like decorator. - In list , I am putting two functions. [ get_weather & add number ] - Now put them in the dictionary. - User Message - Result - 3 queries are given as input. user , first reply and then agent. - Message is which data-structure ? --> Tuple or dictionary or List ? --> Its LIST , then only we can upend. - Message upend , this is the list example Code - tool_calling.py from dotenv import load_dotenv from langchain_core.messages import HumanMessage from langchain_core.tools import tool from langchain_groq import ChatGroq load_dotenv() @tool # Decorator def get_weather(city: str) -> str: """ Get the current weather for a given city. city: name of the city. """ # Docstrings # Replace with a real weather API call return f"The weather in {city} is sunny and 25°C." @tool def add_numbers(a: int, b: int) -> int: """Add two numbers together.""" return a + b tools = [get_weather, add_numbers] tool_map = {t.name: t for t in tools} print(tool_map) llm = ChatGroq(model="openai/gpt-oss-120b", temperature=0) llm_with_tools = llm.bind_tools(tools) def run_query(question: str) -> str: messages = [HumanMessage(question)] print("Initial Messages ", messages) # input("Wait ....") # First call: model decides whether to answer directly or call a tool ai_msg = llm_with_tools.invoke(messages) messages.append(ai_msg) print("Messages after first call ", messages) # input("Wait ....") if ai_msg.tool_calls: print("tool calls ", ai_msg.tool_calls) # input("wait ...") # Execute each requested tool call for call in ai_msg.tool_calls: selected_tool = tool_map[call["name"]] tool_result = selected_tool.invoke(call["args"]) messages.append( { "role": "tool", "content": str(tool_result), "tool_call_id": call["id"], } ) print("Messages after tool call ", messages) # input("Wait ....") # Second call: let the model turn tool results into a final answer print("Total Messages ", messages) input("Wait Final....") final_response = llm_with_tools.invoke(messages) return final_response.content else: # No tool needed - first response is already the final answer return ai_msg.content if name == "main": # print(run_query("Give me climate in paris and london?")) # print(run_query("What is 15 plus 27?")) print(run_query("convert 100rs to usd")) Code - requirements.txt aiohappyeyeballs==2.7.1 aiohttp==3.14.3 aiosignal==1.4.0 annotated-doc==0.0.5 annotated-types==0.8.0 anyio==4.14.2 asgiref==3.12.1 async-timeout==4.0.3 attrs==26.1.0 bcrypt==5.0.0 build==1.6.1 certifi==2026.7.22 charset-normalizer==3.5.1 chromadb==1.5.9 click==8.5.0 coloredlogs==15.0.1 distro==1.9.0 durationpy==0.11 exceptiongroup==1.3.1 fastapi==0.141.1 filelock==3.32.6 flatbuffers==25.12.19 frozenlist==1.8.0 fsspec==2026.7.0 googleapis-common-protos==1.75.3 greenlet==3.5.5 groq==0.37.1 grpcio==1.83.1 h11==0.16.0 hf-xet==1.6.0 httpcore==1.0.9 httpcore2==2.12.0 httptools==0.8.0 httpx==0.28.1 httpx-sse==0.4.3 httpx2==2.12.0 huggingface_hub==1.31.0 humanfriendly==10.0 idna==3.19 importlib_resources==7.1.0 jsonpatch==1.33 jsonpointer==3.1.1 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 kubernetes==36.0.3 langchain==1.3.18 langchain-chroma==1.1.0 langchain-classic==1.0.8 langchain-community==0.4.2 langchain-core==1.6.1 langchain-groq==1.1.3 langchain-ollama==1.1.0 langchain-protocol==0.0.19 langchain-text-splitters==1.1.2 langgraph==1.2.11 langgraph-checkpoint==4.2.0 langgraph-prebuilt==1.1.0 langgraph-sdk==0.4.4 langsmith==0.12.1 markdown-it-py==4.2.0 mdurl==0.1.2 mmh3==5.3.0 mpmath==1.3.0 multidict==6.8.0 numpy==2.2.6 oauthlib==3.3.1 ollama==0.6.2 onnxruntime==1.23.2 opentelemetry-api==1.44.0 opentelemetry-exporter-otlp-proto-common==1.44.0 opentelemetry-exporter-otlp-proto-grpc==1.44.0 opentelemetry-proto==1.44.0 opentelemetry-sdk==1.44.0 opentelemetry-semantic-conventions==0.65b0 orjson==3.12.0 ormsgpack==1.12.2 overrides==7.7.0 packaging==26.3 propcache==0.5.2 protobuf==7.36.1 pybase64==1.5.0 pydantic==2.13.5 pydantic-settings==2.15.0 pydantic_core==2.46.5 Pygments==2.21.0 pypdf==6.18.1 PyPika==0.51.1 pyproject_hooks==1.2.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 PyYAML==6.0.3 referencing==0.37.0 requests==2.34.2 requests-oauthlib==2.0.0 requests-toolbelt==1.0.0 rich==15.0.0 rpds-py==0.30.0 ruff==0.16.5 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 SQLAlchemy==2.0.52 sqlparse==0.6.0 starlette==1.6.0 sympy==1.14.0 tenacity==9.1.4 tokenizers==0.23.2 tomli==2.4.1 tqdm==4.70.1 truststore==0.10.4 typer==0.27.2 typing-inspection==0.4.4 typing_extensions==4.16.0 urllib3==2.7.0 uuid_utils==0.17.0 uvicorn==0.52.4 uvloop==0.22.1 watchfiles==1.2.0 websocket-client==1.9.2 websockets==16.1.1 xxhash==4.0.1 yarl==1.24.5 zstandard==0.25.0 Code - .env GROQ_API_KEY="" Commands python -m pipe freeze > requirements.txt Output {'get_weather': StructuredTool(name='get_weather', description='Get the current weather for a given city.\ncity: name of the city.', args_schema=, func=), 'add_numbers': StructuredTool(name='add_numbers', description='Add two numbers together.', args_schema=, func=)} Initial Messages [HumanMessage(content='Give me climate in Chennai and Mumbai?', additional_kwargs={}, response_metadata={})] Messages after first call [HumanMessage(content='Give me climate in Chennai and Mumbai?', additional_kwargs={}, response_metadata={}), AIMessage(content='', additional_kwargs={'reasoning_content': 'The user asks: "Give me climate in Chennai and Mumbai?" Likely they want current weather/climate. We have a function get_weather that can get current weather for a city. We can call it for Chennai and Mumbai. Probably need to call twice. Use function calls.', 'tool_calls': [{'id': 'fc_8ee481c4-db27-4a4f-a4e8-839700ea29bb', 'function': {'arguments': '{"city":"Chennai"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 85, 'prompt_tokens': 161, 'total_tokens': 246, 'completion_time': 0.176815886, 'completion_tokens_details': {'reasoning_tokens': 57}, 'prompt_time': 0.007317037, 'prompt_tokens_details': None, 'queue_time': 0.34890584, 'total_time': 0.184132923}, 'model_name': 'openai/gpt-oss-120b', 'system_fingerprint': 'fp_068241849b', 'service_tier': 'on_demand', 'finish_reason': 'tool_calls', 'logprobs': None, 'model_provider': 'groq'}, id='lc_run--01a0de65-11eb-75a2-b3b4-2770359461ad-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'Chennai'}, 'id': 'fc_8ee481c4-db27-4a4f-a4e8-839700ea29bb', 'type': 'tool_call'}], invalid_tool_calls=[], usage_metadata={'input_tokens': 161, 'output_tokens': 85, 'total_tokens': 246, 'output_token_details': {'reasoning': 57}})] tool calls [{'name': 'get_weather', 'args': {'city': 'Chennai'}, 'id': 'fc_8ee481c4-db27-4a4f-a4e8-839700ea29bb', 'type': 'tool_call'}] Messages after tool call [HumanMessage(content='Give me climate in Chennai and Mumbai?', additional_kwargs={}, response_metadata={}), AIMessage(content='', additional_kwargs={'reasoning_content': 'The user asks: "Give me climate in Chennai and Mumbai?" Likely they want current weather/climate. We have a function get_weather that can get current weather for a city. We can call it for Chennai and Mumbai. Probably need to call twice. Use function calls.', 'tool_calls': [{'id': 'fc_8ee481c4-db27-4a4f-a4e8-839700ea29bb', 'function': {'arguments': '{"city":"Chennai"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 85, 'prompt_tokens': 161, 'total_tokens': 246, 'completion_time': 0.176815886, 'completion_tokens_details': {'reasoning_tokens': 57}, 'prompt_time': 0.007317037, 'prompt_tokens_details': None, 'queue_time': 0.34890584, 'total_time': 0.184132923}, 'model_name': 'openai/gpt-oss-120b', 'system_fingerprint': 'fp_068241849b', 'service_tier': 'on_demand', 'finish_reason': 'tool_calls', 'logprobs': None, 'model_provider': 'groq'}, id='lc_run--01a0de65-11eb-75a2-b3b4-2770359461ad-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'Chennai'}, 'id': 'fc_8ee481c4-db27-4a4f-a4e8-839700ea29bb', 'type': 'tool_call'}], invalid_tool_calls=[], usage_metadata={'input_tokens': 161, 'output_tokens': 85, 'total_tokens': 246, 'output_token_details': {'reasoning': 57}}), {'role': 'tool', 'content': 'The weather in Chennai is sunny and 25°C.', 'tool_call_id': 'fc_8ee481c4-db27-4a4f-a4e8-839700ea29bb'}] Total Messages [HumanMessage(content='Give me climate in Chennai and Mumbai?', additional_kwargs={}, response_metadata={}), AIMessage(content='', additional_kwargs={'reasoning_content': 'The user asks: "Give me climate in Chennai and Mumbai?" Likely they want current weather/climate. We have a function get_weather that can get current weather for a city. We can call it for Chennai and Mumbai. Probably need to call twice. Use function calls.', 'tool_calls': [{'id': 'fc_8ee481c4-db27-4a4f-a4e8-839700ea29bb', 'function': {'arguments': '{"city":"Chennai"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 85, 'prompt_tokens': 161, 'total_tokens': 246, 'completion_time': 0.176815886, 'completion_tokens_details': {'reasoning_tokens': 57}, 'prompt_time': 0.007317037, 'prompt_tokens_details': None, 'queue_time': 0.34890584, 'total_time': 0.184132923}, 'model_name': 'openai/gpt-oss-120b', 'system_fingerprint': 'fp_068241849b', 'service_tier': 'on_demand', 'finish_r