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toolsprompting #20

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toolsprompting #20

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d3d6c50
tools
mehakhafeez Jan 16, 2025
4bc2ceb
toolss
mehakhafeez Jan 16, 2025
1b015b2
tools
mehakhafeez Jan 16, 2025
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263 changes: 239 additions & 24 deletions lab-tools-prompting.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -42,10 +42,18 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"id": "8c556c5e-b785-428b-8e7d-efd34a2a1adb",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install --upgrade --quiet langchain langchain-community"
]
Expand All @@ -60,7 +68,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 2,
"id": "5efb4170-b95b-4d29-8f57-09509f3ba6df",
"metadata": {},
"outputs": [],
Expand Down Expand Up @@ -89,10 +97,19 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 3,
"id": "424be968-2806-4d1a-a6aa-5499ae20fac5",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\mehak\\AppData\\Local\\Temp\\ipykernel_11292\\1427064109.py:3: LangChainDeprecationWarning: The class `Ollama` was deprecated in LangChain 0.3.1 and will be removed in 1.0.0. An updated version of the class exists in the :class:`~langchain-ollama package and should be used instead. To use it run `pip install -U :class:`~langchain-ollama` and import as `from :class:`~langchain_ollama import OllamaLLM``.\n",
" model = Ollama(model=\"phi3\")\n"
]
}
],
"source": [
"from langchain_community.llms import Ollama\n",
"\n",
Expand Down Expand Up @@ -213,7 +230,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 7,
"id": "f02f1dce-76e7-4ca9-9bac-5af496131fe1",
"metadata": {},
"outputs": [],
Expand All @@ -238,7 +255,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 8,
"id": "f8623e03-60eb-4439-b57b-ecbcebc61b58",
"metadata": {},
"outputs": [
Expand All @@ -247,11 +264,11 @@
"output_type": "stream",
"text": [
"{\n",
" \"name\": \"add\",\n",
" \"arguments\": {\n",
" \"x\": 3,\n",
" \"y\": 1132\n",
" }\n",
" \"name\": \"add\",\n",
" \"arguments\": {\n",
" \"x\": 3,\n",
" \"y\": 1132\n",
" }\n",
"}\n"
]
}
Expand Down Expand Up @@ -280,17 +297,17 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 9,
"id": "f129f5bd-127c-4c95-8f34-8f437da7ca8f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'name': 'multiply', 'arguments': {'x': 13.0, 'y': 4.0}}"
"{'name': 'multiply', 'arguments': {'x': 13, 'y': 4}}"
]
},
"execution_count": 19,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
Expand Down Expand Up @@ -330,7 +347,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 10,
"id": "faee95e0-4095-4310-991f-9e9465c6738e",
"metadata": {},
"outputs": [],
Expand Down Expand Up @@ -378,7 +395,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 11,
"id": "d0ea3b2a-8fb2-4016-83c8-a5d3e78fedbc",
"metadata": {},
"outputs": [
Expand All @@ -388,7 +405,7 @@
"15.0"
]
},
"execution_count": 21,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -409,7 +426,7 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 12,
"id": "0555b384-fde6-4404-86e0-7ea199003d58",
"metadata": {},
"outputs": [
Expand All @@ -419,7 +436,7 @@
"53.83784653"
]
},
"execution_count": 22,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -441,7 +458,7 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 13,
"id": "45404406-859d-4caa-8b9d-5838162c80a0",
"metadata": {},
"outputs": [
Expand All @@ -453,7 +470,7 @@
" 'output': 53.83784653}"
]
},
"execution_count": 23,
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
Expand Down Expand Up @@ -483,11 +500,209 @@
"1. Provide few shot examples.\n",
"2. Add error handling (e.g., catch the exception and feed it back to the LLM to ask it to correct its previous output)."
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "461c49f9",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.tools import tool\n",
"\n",
"# Define the subtract tool\n",
"@tool\n",
"def subtract(x: int, y: int) -> int:\n",
" \"\"\"Subtract two numbers.\"\"\"\n",
" return x - y\n",
"\n",
"@tool\n",
"def divide(x: float, y: float) -> float:\n",
" \"\"\"Divide two numbers.\"\"\"\n",
" if y == 0:\n",
" raise ValueError(\"Cannot divide by zero!\")\n",
" return x / y\n",
"\n",
"@tool\n",
"def power(x: float, y: int) -> float:\n",
" \"\"\"Raise x to the power of y.\"\"\"\n",
" return x ** y\n",
"\n",
"# List the new tools\n",
"tools = [subtract, divide, power]\n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "5d8f0008",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"subtract(x: int, y: int) -> int - Subtract two numbers.\n",
"divide(x: float, y: float) -> float - Divide two numbers.\n",
"power(x: float, y: int) -> float - Raise x to the power of y.\n"
]
}
],
"source": [
"# Render the description of the tools\n",
"from langchain_core.tools import render_text_description\n",
"rendered_tools = render_text_description(tools)\n",
"print(rendered_tools)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "dd914d4f",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<>:12: SyntaxWarning: invalid escape sequence '\\ '\n",
"<>:12: SyntaxWarning: invalid escape sequence '\\ '\n",
"C:\\Users\\mehak\\AppData\\Local\\Temp\\ipykernel_11292\\2665789327.py:12: SyntaxWarning: invalid escape sequence '\\ '\n",
" \"\"\"\n"
]
}
],
"source": [
"system_prompt = f\"\"\"\\ \n",
"You are an assistant that has access to the following set of tools. \n",
"Here are the names and descriptions for each tool:\n",
"\n",
"{rendered_tools}\n",
"\n",
"Given the user input, return the name and input of the tool to use. \n",
"Return your response as a JSON blob with 'name' and 'arguments' keys.\n",
"\n",
"The `arguments` should be a dictionary, with keys corresponding \n",
"to the argument names and the values corresponding to the requested values.\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "2f2eb88d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'{\\n \"name\": \"subtcur\",\\n \"arguments\": {\\n \"x\": 5,\\n \"y\": 3\\n }\\n}\\n\\nIn this case, the user wants to subtract two numbers. The correct tool is `subtract`, and its arguments are clearly defined as x=5 and y=3 for subtraction of (5-3).'"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prompt = ChatPromptTemplate.from_messages(\n",
" [(\"system\", system_prompt), (\"user\", \"{input}\")]\n",
")\n",
"\n",
"chain = prompt | model\n",
"\n",
"chain.invoke({\"input\": \"what's 5 minus 3\"})"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "2f514573",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'name': 'divide', 'arguments': {'x': 10, 'y': 2}}"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Use the JsonOutputParser\n",
"chain = prompt | model | JsonOutputParser()\n",
"\n",
"# Example: Testing the 'divide' operation\n",
"chain.invoke({\"input\": \"what's 10 divided by 2\"})"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "89adf3aa",
"metadata": {},
"outputs": [],
"source": [
"def invoke_tool(\n",
" tool_call_request: ToolCallRequest, config: Optional[RunnableConfig] = None\n",
"):\n",
" \"\"\"A function that we can use the perform a tool invocation.\"\"\"\n",
" tool_name_to_tool = {tool.name: tool for tool in tools}\n",
" name = tool_call_request[\"name\"]\n",
" requested_tool = tool_name_to_tool[name]\n",
" return requested_tool.invoke(tool_call_request[\"arguments\"], config=config)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "52d1a87f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'name': 'power', 'arguments': {'x': 2, 'y': 3}}\n"
]
}
],
"source": [
"try:\n",
" result = chain.invoke({\"input\": \"what's 2 to the power of 3\"})\n",
" print(result)\n",
"except OutputParserException as e:\n",
" print(f\"Output parsing error: {e}\")\n",
" print(f\"Raw LLM output: {e.llm_output}\")"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "9ba251f4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"8.0\n"
]
}
],
"source": [
"chain = prompt | model | JsonOutputParser() | invoke_tool\n",
"result = chain.invoke({\"input\": \"what's 2 to the power of 3\"})\n",
"print(result)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "base",
"language": "python",
"name": "python3"
},
Expand All @@ -501,7 +716,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
"version": "3.12.7"
}
},
"nbformat": 4,
Expand Down