mirror of
https://github.com/hwchase17/langchain.git
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tools refactor (#2961)
Co-authored-by: vowelparrot <130414180+vowelparrot@users.noreply.github.com>
This commit is contained in:
Harrison Chase
GitHub
@@ -9,28 +9,29 @@
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"\n",
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"When constructing your own agent, you will need to provide it with a list of Tools that it can use. Besides the actual function that is called, the Tool consists of several components:\n",
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"\n",
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"- name (str), is required\n",
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"- description (str), is optional\n",
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"- name (str), is required and must be unique within a set of tools provided to an agent\n",
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"- description (str), is optional but recommended, as it is used by an agent to determine tool use\n",
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"- return_direct (bool), defaults to False\n",
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"\n",
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"The function that should be called when the tool is selected should take as input a single string and return a single string.\n",
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"The function that should be called when the tool is selected should return a single string.\n",
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"\n",
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"There are two ways to define a tool, we will cover both in the example below."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 1,
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"id": "1aaba18c",
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Import things that are needed generically\n",
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"from langchain.agents import initialize_agent, Tool\n",
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"from langchain.agents import AgentType\n",
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"from langchain.tools import BaseTool\n",
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"from langchain.llms import OpenAI\n",
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"from langchain import LLMMathChain, SerpAPIWrapper"
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"from langchain import LLMMathChain, SerpAPIWrapper\n",
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"from langchain.agents import AgentType, Tool, initialize_agent, tool\n",
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.tools import BaseTool"
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]
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},
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{
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@@ -43,12 +44,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 2,
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"id": "36ed392e",
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"llm = OpenAI(temperature=0)"
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"llm = ChatOpenAI(temperature=0)"
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]
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},
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{
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@@ -74,7 +77,9 @@
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"cell_type": "code",
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"execution_count": 3,
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"id": "56ff7670",
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Load the tool configs that are needed.\n",
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@@ -98,7 +103,9 @@
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"cell_type": "code",
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"execution_count": 4,
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"id": "5b93047d",
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Construct the agent. We will use the default agent type here.\n",
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@@ -110,7 +117,9 @@
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"cell_type": "code",
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"execution_count": 5,
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"id": "6f96a891",
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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@@ -119,29 +128,24 @@
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
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"\u001b[32;1m\u001b[1;3mI need to find out Leo DiCaprio's girlfriend's name and her age\n",
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"Action: Search\n",
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"Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now need to calculate her age raised to the 0.43 power\n",
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"Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\u001b[36;1m\u001b[1;3mI draw the lime at going to get a Mohawk, though.\" DiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years. He's since been linked to another famous supermodel – Gigi Hadid.\u001b[0m\u001b[32;1m\u001b[1;3mI need to find out Gigi Hadid's age\n",
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"Action: Search\n",
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"Action Input: \"Gigi Hadid age\"\u001b[0m\u001b[36;1m\u001b[1;3m27 years\u001b[0m\u001b[32;1m\u001b[1;3mI need to calculate her age raised to the 0.43 power\n",
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"Action: Calculator\n",
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"Action Input: 22^0.43\u001b[0m\n",
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"Action Input: 27^(0.43)\u001b[0m\n",
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"\n",
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"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
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"22^0.43\u001b[32;1m\u001b[1;3m\n",
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"```python\n",
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"import math\n",
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"print(math.pow(22, 0.43))\n",
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"27^(0.43)\u001b[32;1m\u001b[1;3m```text\n",
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"27**(0.43)\n",
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"```\n",
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"...numexpr.evaluate(\"27**(0.43)\")...\n",
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"\u001b[0m\n",
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"Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
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"\u001b[0m\n",
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"Answer: \u001b[33;1m\u001b[1;3m4.125593352125936\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"\n",
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"Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.777824273683966\n",
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"\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\u001b[0m\n",
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"\u001b[33;1m\u001b[1;3mAnswer: 4.125593352125936\u001b[0m\u001b[32;1m\u001b[1;3mI now know the final answer\n",
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"Final Answer: 4.125593352125936\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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@@ -149,7 +153,7 @@
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{
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"data": {
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"text/plain": [
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"\"Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\""
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"'4.125593352125936'"
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]
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},
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"execution_count": 5,
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@@ -171,9 +175,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 6,
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"id": "c58a7c40",
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"class CustomSearchTool(BaseTool):\n",
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@@ -203,9 +209,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 7,
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"id": "3318a46f",
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"tools = [CustomSearchTool(), CustomCalculatorTool()]"
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@@ -213,9 +221,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 8,
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"id": "ee2d0f3a",
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
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@@ -223,9 +233,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 9,
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"id": "6a2cebbf",
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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@@ -234,29 +246,24 @@
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
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"\u001b[32;1m\u001b[1;3mI need to find out Leo DiCaprio's girlfriend's name and her age\n",
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"Action: Search\n",
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"Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now need to calculate her age raised to the 0.43 power\n",
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"Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\u001b[36;1m\u001b[1;3mI draw the lime at going to get a Mohawk, though.\" DiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years. He's since been linked to another famous supermodel – Gigi Hadid.\u001b[0m\u001b[32;1m\u001b[1;3mI now know Leo DiCaprio's girlfriend's name and that he's currently linked to Gigi Hadid. I need to find out Camila Morrone's age.\n",
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"Action: Search\n",
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"Action Input: \"Camila Morrone age\"\u001b[0m\u001b[36;1m\u001b[1;3m25 years\u001b[0m\u001b[32;1m\u001b[1;3mI have Camila Morrone's age. I need to calculate her age raised to the 0.43 power.\n",
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"Action: Calculator\n",
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"Action Input: 22^0.43\u001b[0m\n",
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"Action Input: 25^(0.43)\u001b[0m\n",
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"\n",
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"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
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"22^0.43\u001b[32;1m\u001b[1;3m\n",
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"```python\n",
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"import math\n",
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"print(math.pow(22, 0.43))\n",
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"25^(0.43)\u001b[32;1m\u001b[1;3m```text\n",
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"25**(0.43)\n",
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"```\n",
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"...numexpr.evaluate(\"25**(0.43)\")...\n",
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"\u001b[0m\n",
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"Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
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"\u001b[0m\n",
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"Answer: \u001b[33;1m\u001b[1;3m3.991298452658078\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"\n",
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"Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.777824273683966\n",
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"\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\u001b[0m\n",
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"\u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\u001b[0m\u001b[32;1m\u001b[1;3mI now know the answer to the original question.\n",
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"Final Answer: Camila Morrone's current age raised to the 0.43 power is approximately 3.99.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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@@ -264,10 +271,10 @@
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{
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"data": {
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"text/plain": [
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"\"Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\""
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"\"Camila Morrone's current age raised to the 0.43 power is approximately 3.99.\""
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]
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},
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"execution_count": 11,
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -288,9 +295,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 10,
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"id": "8f15307d",
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.agents import tool\n",
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@@ -298,22 +307,24 @@
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"@tool\n",
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"def search_api(query: str) -> str:\n",
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" \"\"\"Searches the API for the query.\"\"\"\n",
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" return \"Results\""
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" return f\"Results for query {query}\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 11,
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"id": "0a23b91b",
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Tool(name='search_api', description='search_api(query: str) -> str - Searches the API for the query.', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1184e0cd0>, func=<function search_api at 0x1635f8700>, coroutine=None)"
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"Tool(name='search_api', description='search_api(query: str) -> str - Searches the API for the query.', args_schema=<class 'pydantic.main.ArgsModel'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x124346f10>, func=<function search_api at 0x16ad6e020>, coroutine=None)"
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]
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},
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"execution_count": 5,
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -332,9 +343,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 12,
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"id": "28cdf04d",
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"@tool(\"search\", return_direct=True)\n",
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@@ -345,17 +358,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 13,
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"id": "1085a4bd",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1184e0cd0>, func=<function search_api at 0x1635f8670>, coroutine=None)"
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"Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', args_schema=<class 'pydantic.main.ArgsModel'>, return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x124346f10>, func=<function search_api at 0x16ad6d3a0>, coroutine=None)"
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]
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},
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"execution_count": 7,
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -376,7 +389,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 14,
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"id": "79213f40",
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"metadata": {},
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"outputs": [],
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@@ -386,7 +399,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 15,
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"id": "e1067dcb",
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"metadata": {},
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"outputs": [],
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@@ -396,7 +409,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 16,
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"id": "6c66ffe8",
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"metadata": {},
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"outputs": [],
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@@ -406,7 +419,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 17,
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"id": "f45b5bc3",
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"metadata": {},
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"outputs": [],
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@@ -416,7 +429,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 18,
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"id": "565e2b9b",
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"metadata": {},
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"outputs": [
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@@ -427,21 +440,12 @@
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
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"\u001b[32;1m\u001b[1;3mI need to find out Leo DiCaprio's girlfriend's name and her age.\n",
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"Action: Google Search\n",
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"Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I need to find out Camila Morrone's age\n",
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"Action: Google Search\n",
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"Action Input: \"Camila Morrone age\"\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m25 years\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.43 power\n",
|
||||
"Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\u001b[36;1m\u001b[1;3mI draw the lime at going to get a Mohawk, though.\" DiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years. He's since been linked to another famous supermodel – Gigi Hadid.\u001b[0m\u001b[32;1m\u001b[1;3mNow I need to find out Camila Morrone's current age.\n",
|
||||
"Action: Calculator\n",
|
||||
"Action Input: 25^0.43\u001b[0m\n",
|
||||
"Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\n",
|
||||
"\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
|
||||
"Final Answer: Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\u001b[0m\n",
|
||||
"Action Input: 25^0.43\u001b[0m\u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\u001b[0m\u001b[32;1m\u001b[1;3mI now know the final answer.\n",
|
||||
"Final Answer: Camila Morrone's current age raised to the 0.43 power is approximately 3.99.\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
@@ -449,10 +453,10 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"\"Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\""
|
||||
"\"Camila Morrone's current age raised to the 0.43 power is approximately 3.99.\""
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -478,7 +482,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 19,
|
||||
"id": "3450512e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -507,7 +511,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 20,
|
||||
"id": "4b9a7849",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -520,9 +524,7 @@
|
||||
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
|
||||
"\u001b[32;1m\u001b[1;3m I should use a music search engine to find the answer\n",
|
||||
"Action: Music Search\n",
|
||||
"Action Input: most famous song of christmas\u001b[0m\n",
|
||||
"Observation: \u001b[33;1m\u001b[1;3m'All I Want For Christmas Is You' by Mariah Carey.\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
|
||||
"Action Input: most famous song of christmas\u001b[0m\u001b[33;1m\u001b[1;3m'All I Want For Christmas Is You' by Mariah Carey.\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer\n",
|
||||
"Final Answer: 'All I Want For Christmas Is You' by Mariah Carey.\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
@@ -534,7 +536,7 @@
|
||||
"\"'All I Want For Christmas Is You' by Mariah Carey.\""
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -554,7 +556,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 21,
|
||||
"id": "3bb6185f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -572,7 +574,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 22,
|
||||
"id": "113ddb84",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -583,9 +585,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 23,
|
||||
"id": "582439a6",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
@@ -596,9 +600,7 @@
|
||||
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
|
||||
"\u001b[32;1m\u001b[1;3m I need to calculate this\n",
|
||||
"Action: Calculator\n",
|
||||
"Action Input: 2**.12\u001b[0m\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.2599210498948732\u001b[0m\n",
|
||||
"\u001b[32;1m\u001b[1;3m\u001b[0m\n",
|
||||
"Action Input: 2**.12\u001b[0m\u001b[36;1m\u001b[1;3mAnswer: 1.086734862526058\u001b[0m\u001b[32;1m\u001b[1;3m\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
@@ -606,10 +608,10 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'Answer: 1.2599210498948732'"
|
||||
"'Answer: 1.086734862526058'"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -618,10 +620,149 @@
|
||||
"agent.run(\"whats 2**.12\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "8aa3c353-bd89-467c-9c27-b83a90cd4daa",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Multi-argument tools\n",
|
||||
"\n",
|
||||
"Many functions expect structured inputs. These can also be supported using the Tool decorator or by directly subclassing `BaseTool`! We have to modify the LLM's OutputParser to map its string output to a dictionary to pass to the action, however."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"id": "537bc628",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from typing import Optional, Union\n",
|
||||
"\n",
|
||||
"@tool\n",
|
||||
"def custom_search(k: int, query: str, other_arg: Optional[str] = None):\n",
|
||||
" \"\"\"The custom search function.\"\"\"\n",
|
||||
" return f\"Here are the results for the custom search: k={k}, query={query}, other_arg={other_arg}\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"id": "d5c992cf-776a-40cd-a6c4-e7cf65ea709e",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import re\n",
|
||||
"from langchain.schema import (\n",
|
||||
" AgentAction,\n",
|
||||
" AgentFinish,\n",
|
||||
")\n",
|
||||
"from langchain.agents import AgentOutputParser\n",
|
||||
"\n",
|
||||
"# We will add a custom parser to map the arguments to a dictionary\n",
|
||||
"class CustomOutputParser(AgentOutputParser):\n",
|
||||
" \n",
|
||||
" def parse_tool_input(self, action_input: str) -> dict:\n",
|
||||
" # Regex pattern to match arguments and their values\n",
|
||||
" pattern = r\"(\\w+)\\s*=\\s*(None|\\\"[^\\\"]*\\\"|\\d+)\"\n",
|
||||
" matches = re.findall(pattern, action_input)\n",
|
||||
" \n",
|
||||
" if not matches:\n",
|
||||
" raise ValueError(f\"Could not parse action input: `{action_input}`\")\n",
|
||||
"\n",
|
||||
" # Create a dictionary with the parsed arguments and their values\n",
|
||||
" parsed_input = {}\n",
|
||||
" for arg, value in matches:\n",
|
||||
" if value == \"None\":\n",
|
||||
" parsed_value = None\n",
|
||||
" elif value.isdigit():\n",
|
||||
" parsed_value = int(value)\n",
|
||||
" else:\n",
|
||||
" parsed_value = value.strip('\"')\n",
|
||||
" parsed_input[arg] = parsed_value\n",
|
||||
"\n",
|
||||
" return parsed_input\n",
|
||||
" \n",
|
||||
" def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:\n",
|
||||
" # Check if agent should finish\n",
|
||||
" if \"Final Answer:\" in llm_output:\n",
|
||||
" return AgentFinish(\n",
|
||||
" # Return values is generally always a dictionary with a single `output` key\n",
|
||||
" # It is not recommended to try anything else at the moment :)\n",
|
||||
" return_values={\"output\": llm_output.split(\"Final Answer:\")[-1].strip()},\n",
|
||||
" log=llm_output,\n",
|
||||
" )\n",
|
||||
" # Parse out the action and action input\n",
|
||||
" regex = r\"Action\\s*\\d*\\s*:(.*?)\\nAction\\s*\\d*\\s*Input\\s*\\d*\\s*:[\\s]*(.*)\"\n",
|
||||
" match = re.search(regex, llm_output, re.DOTALL)\n",
|
||||
" if not match:\n",
|
||||
" raise ValueError(f\"Could not parse LLM output: `{llm_output}`\")\n",
|
||||
" action = match.group(1).strip()\n",
|
||||
" action_input = match.group(2)\n",
|
||||
" tool_input = self.parse_tool_input(action_input)\n",
|
||||
" # Return the action and action \n",
|
||||
" return AgentAction(tool=action, tool_input=tool_input, log=llm_output)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"id": "68269547-1482-4138-a6ea-58f00b4a9548",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"llm = OpenAI(temperature=0)\n",
|
||||
"agent = initialize_agent([custom_search], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, agent_kwargs={\"output_parser\": CustomOutputParser()})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"id": "0947835a-691c-4f51-b8f4-6744e0e48ab1",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
|
||||
"\u001b[32;1m\u001b[1;3m I need to use a search function to find the answer\n",
|
||||
"Action: custom_search\n",
|
||||
"Action Input: k=1, query=\"me\"\u001b[0m\u001b[36;1m\u001b[1;3mHere are the results for the custom search: k=1, query=me, other_arg=None\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer\n",
|
||||
"Final Answer: The results of the custom search for k=1, query=me, other_arg=None.\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'The results of the custom search for k=1, query=me, other_arg=None.'"
|
||||
]
|
||||
},
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"agent.run(\"Search for me and tell me whatever it says\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "537bc628",
|
||||
"id": "caf39c66-102b-42c1-baf2-777a49886ce4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
@@ -643,7 +784,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.1"
|
||||
"version": "3.11.2"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
|
||||
190
docs/modules/agents/tools/tool_input_validation.ipynb
Normal file
190
docs/modules/agents/tools/tool_input_validation.ipynb
Normal file
@@ -0,0 +1,190 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"source": [
|
||||
"# Tool Input Schema\n",
|
||||
"\n",
|
||||
"By default, tools infer the argument schema by inspecting the function signature. For more strict requirements, custom input schema can be specified, along with custom validation logic."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.agents import initialize_agent, Tool\n",
|
||||
"from langchain.agents import AgentType\n",
|
||||
"from langchain.llms import OpenAI\n",
|
||||
"from langchain.tools.python.tool import PythonREPLTool\n",
|
||||
"from pydantic import BaseModel\n",
|
||||
"from pydantic import Field, root_validator\n",
|
||||
"from typing import Dict, Any"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"llm = OpenAI(temperature=0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class ToolInputModel(BaseModel):\n",
|
||||
" query: str = Field(...)\n",
|
||||
" \n",
|
||||
" @root_validator\n",
|
||||
" def validate_query(cls, values: Dict[str, Any]) -> Dict:\n",
|
||||
" # Note: this is NOT a safe REPL! This is used for instructive purposes only\n",
|
||||
" if \"import os\" in values[\"query\"]:\n",
|
||||
" raise ValueError(\"'import os' not permitted in this python REPL.\")\n",
|
||||
" return values\n",
|
||||
" \n",
|
||||
"tool = PythonREPLTool(args_schema=ToolInputModel)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"agent = initialize_agent([tool], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
|
||||
"\u001b[32;1m\u001b[1;3m I need to define a function that adds two numbers\n",
|
||||
"Action: Python REPL\n",
|
||||
"Action Input: def add_two_numbers(a, b):\n",
|
||||
" return a + b\u001b[0m\u001b[36;1m\u001b[1;3m\u001b[0m\u001b[32;1m\u001b[1;3m I need to call the function\n",
|
||||
"Action: Python REPL\n",
|
||||
"Action Input: print(add_two_numbers(2, 2))\u001b[0m\u001b[36;1m\u001b[1;3m4\n",
|
||||
"\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer\n",
|
||||
"Final Answer: 4\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'4'"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# This will succeed, since there aren't any arguments that will be triggered during validation\n",
|
||||
"agent.run(\"Run a python function that adds 2 and 2\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
|
||||
"\u001b[32;1m\u001b[1;3m I need to import os and then list the dir\n",
|
||||
"Action: Python REPL\n",
|
||||
"Action Input: import os; print(os.listdir())\u001b[0m"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "ValidationError",
|
||||
"evalue": "1 validation error for ToolInputModel\n__root__\n 'import os' not (type=value_error)",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43magent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mRun a python function that imports os and lists the dir\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/code/lc/lckg/langchain/chains/base.py:213\u001b[0m, in \u001b[0;36mChain.run\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`run` supports only one positional argument.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_keys[\u001b[38;5;241m0\u001b[39m]]\n\u001b[1;32m 215\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m(kwargs)[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_keys[\u001b[38;5;241m0\u001b[39m]]\n",
|
||||
"File \u001b[0;32m~/code/lc/lckg/langchain/chains/base.py:116\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m, \u001b[38;5;167;01mException\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose)\n\u001b[0;32m--> 116\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 117\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_manager\u001b[38;5;241m.\u001b[39mon_chain_end(outputs, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose)\n\u001b[1;32m 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprep_outputs(inputs, outputs, return_only_outputs)\n",
|
||||
"File \u001b[0;32m~/code/lc/lckg/langchain/chains/base.py:113\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs)\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_manager\u001b[38;5;241m.\u001b[39mon_chain_start(\n\u001b[1;32m 108\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m},\n\u001b[1;32m 109\u001b[0m inputs,\n\u001b[1;32m 110\u001b[0m verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose,\n\u001b[1;32m 111\u001b[0m )\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 113\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m, \u001b[38;5;167;01mException\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose)\n",
|
||||
"File \u001b[0;32m~/code/lc/lckg/langchain/agents/agent.py:792\u001b[0m, in \u001b[0;36mAgentExecutor._call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 790\u001b[0m \u001b[38;5;66;03m# We now enter the agent loop (until it returns something).\u001b[39;00m\n\u001b[1;32m 791\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_continue(iterations, time_elapsed):\n\u001b[0;32m--> 792\u001b[0m next_step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_take_next_step\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 793\u001b[0m \u001b[43m \u001b[49m\u001b[43mname_to_tool_map\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor_mapping\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mintermediate_steps\u001b[49m\n\u001b[1;32m 794\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 795\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(next_step_output, AgentFinish):\n\u001b[1;32m 796\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_return(next_step_output, intermediate_steps)\n",
|
||||
"File \u001b[0;32m~/code/lc/lckg/langchain/agents/agent.py:695\u001b[0m, in \u001b[0;36mAgentExecutor._take_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps)\u001b[0m\n\u001b[1;32m 693\u001b[0m tool_run_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mllm_prefix\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 694\u001b[0m \u001b[38;5;66;03m# We then call the tool on the tool input to get an observation\u001b[39;00m\n\u001b[0;32m--> 695\u001b[0m observation \u001b[38;5;241m=\u001b[39m \u001b[43mtool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 696\u001b[0m \u001b[43m \u001b[49m\u001b[43magent_action\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtool_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 697\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 698\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 699\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mtool_run_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 700\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 701\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 702\u001b[0m tool_run_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39magent\u001b[38;5;241m.\u001b[39mtool_run_logging_kwargs()\n",
|
||||
"File \u001b[0;32m~/code/lc/lckg/langchain/tools/base.py:146\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, **kwargs)\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 139\u001b[0m tool_input: Union[\u001b[38;5;28mstr\u001b[39m, Dict],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 144\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mstr\u001b[39m:\n\u001b[1;32m 145\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Run the tool.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 146\u001b[0m run_input \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_input\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose \u001b[38;5;129;01mand\u001b[39;00m verbose \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 148\u001b[0m verbose_ \u001b[38;5;241m=\u001b[39m verbose\n",
|
||||
"File \u001b[0;32m~/code/lc/lckg/langchain/tools/base.py:112\u001b[0m, in \u001b[0;36mBaseTool._parse_input\u001b[0;34m(self, tool_input)\u001b[0m\n\u001b[1;32m 110\u001b[0m tool_input \u001b[38;5;241m=\u001b[39m {field_name: tool_input}\n\u001b[1;32m 111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pydantic_input_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpydantic_input_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_obj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 115\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124margs_schema required for tool \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m in order to\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 116\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m accept input of type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(tool_input)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 117\u001b[0m )\n",
|
||||
"File \u001b[0;32m~/code/lc/lckg/.venv/lib/python3.11/site-packages/pydantic/main.py:526\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.parse_obj\u001b[0;34m()\u001b[0m\n",
|
||||
"File \u001b[0;32m~/code/lc/lckg/.venv/lib/python3.11/site-packages/pydantic/main.py:341\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n",
|
||||
"\u001b[0;31mValidationError\u001b[0m: 1 validation error for ToolInputModel\n__root__\n 'import os' not (type=value_error)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# This will fail, because the attempt to import os will trigger a validation error\n",
|
||||
"agent.run(\"Run a python function that imports os and lists the dir\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Reference in New Issue
Block a user