mirror of
https://github.com/hwchase17/langchain.git
synced 2025-07-03 11:47:49 +00:00
tools refactor (#2961)
Co-authored-by: vowelparrot <130414180+vowelparrot@users.noreply.github.com>
This commit is contained in:
Harrison Chase
GitHub
parent
7a8c935b90
commit
db968284f8
@ -9,28 +9,29 @@
|
||||
"\n",
|
||||
"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",
|
||||
"\n",
|
||||
"- name (str), is required\n",
|
||||
"- description (str), is optional\n",
|
||||
"- name (str), is required and must be unique within a set of tools provided to an agent\n",
|
||||
"- description (str), is optional but recommended, as it is used by an agent to determine tool use\n",
|
||||
"- return_direct (bool), defaults to False\n",
|
||||
"\n",
|
||||
"The function that should be called when the tool is selected should take as input a single string and return a single string.\n",
|
||||
"The function that should be called when the tool is selected should return a single string.\n",
|
||||
"\n",
|
||||
"There are two ways to define a tool, we will cover both in the example below."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": 1,
|
||||
"id": "1aaba18c",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Import things that are needed generically\n",
|
||||
"from langchain.agents import initialize_agent, Tool\n",
|
||||
"from langchain.agents import AgentType\n",
|
||||
"from langchain.tools import BaseTool\n",
|
||||
"from langchain.llms import OpenAI\n",
|
||||
"from langchain import LLMMathChain, SerpAPIWrapper"
|
||||
"from langchain import LLMMathChain, SerpAPIWrapper\n",
|
||||
"from langchain.agents import AgentType, Tool, initialize_agent, tool\n",
|
||||
"from langchain.chat_models import ChatOpenAI\n",
|
||||
"from langchain.tools import BaseTool"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -43,12 +44,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 2,
|
||||
"id": "36ed392e",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"llm = OpenAI(temperature=0)"
|
||||
"llm = ChatOpenAI(temperature=0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -74,7 +77,9 @@
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "56ff7670",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Load the tool configs that are needed.\n",
|
||||
@ -98,7 +103,9 @@
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "5b93047d",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Construct the agent. We will use the default agent type here.\n",
|
||||
@ -110,7 +117,9 @@
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "6f96a891",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
@ -119,29 +128,24 @@
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
|
||||
"\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",
|
||||
"\u001b[32;1m\u001b[1;3mI need to find out Leo DiCaprio's girlfriend's name and her age\n",
|
||||
"Action: Search\n",
|
||||
"Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now need to calculate her age 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;3mI need to find out Gigi Hadid's age\n",
|
||||
"Action: Search\n",
|
||||
"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",
|
||||
"Action: Calculator\n",
|
||||
"Action Input: 22^0.43\u001b[0m\n",
|
||||
"Action Input: 27^(0.43)\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
|
||||
"22^0.43\u001b[32;1m\u001b[1;3m\n",
|
||||
"```python\n",
|
||||
"import math\n",
|
||||
"print(math.pow(22, 0.43))\n",
|
||||
"27^(0.43)\u001b[32;1m\u001b[1;3m```text\n",
|
||||
"27**(0.43)\n",
|
||||
"```\n",
|
||||
"...numexpr.evaluate(\"27**(0.43)\")...\n",
|
||||
"\u001b[0m\n",
|
||||
"Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
|
||||
"\u001b[0m\n",
|
||||
"Answer: \u001b[33;1m\u001b[1;3m4.125593352125936\u001b[0m\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n",
|
||||
"\n",
|
||||
"Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.777824273683966\n",
|
||||
"\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
|
||||
"Final Answer: Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\u001b[0m\n",
|
||||
"\u001b[33;1m\u001b[1;3mAnswer: 4.125593352125936\u001b[0m\u001b[32;1m\u001b[1;3mI now know the final answer\n",
|
||||
"Final Answer: 4.125593352125936\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
@ -149,7 +153,7 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"\"Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\""
|
||||
"'4.125593352125936'"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
@ -171,9 +175,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 6,
|
||||
"id": "c58a7c40",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class CustomSearchTool(BaseTool):\n",
|
||||
@ -203,9 +209,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 7,
|
||||
"id": "3318a46f",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tools = [CustomSearchTool(), CustomCalculatorTool()]"
|
||||
@ -213,9 +221,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 8,
|
||||
"id": "ee2d0f3a",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
|
||||
@ -223,9 +233,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 9,
|
||||
"id": "6a2cebbf",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
@ -234,29 +246,24 @@
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
|
||||
"\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",
|
||||
"\u001b[32;1m\u001b[1;3mI need to find out Leo DiCaprio's girlfriend's name and her age\n",
|
||||
"Action: Search\n",
|
||||
"Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now need to calculate her age 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;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",
|
||||
"Action: Search\n",
|
||||
"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",
|
||||
"Action: Calculator\n",
|
||||
"Action Input: 22^0.43\u001b[0m\n",
|
||||
"Action Input: 25^(0.43)\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
|
||||
"22^0.43\u001b[32;1m\u001b[1;3m\n",
|
||||
"```python\n",
|
||||
"import math\n",
|
||||
"print(math.pow(22, 0.43))\n",
|
||||
"25^(0.43)\u001b[32;1m\u001b[1;3m```text\n",
|
||||
"25**(0.43)\n",
|
||||
"```\n",
|
||||
"...numexpr.evaluate(\"25**(0.43)\")...\n",
|
||||
"\u001b[0m\n",
|
||||
"Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
|
||||
"\u001b[0m\n",
|
||||
"Answer: \u001b[33;1m\u001b[1;3m3.991298452658078\u001b[0m\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n",
|
||||
"\n",
|
||||
"Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.777824273683966\n",
|
||||
"\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
|
||||
"Final Answer: Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\u001b[0m\n",
|
||||
"\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",
|
||||
"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"
|
||||
]
|
||||
@ -264,10 +271,10 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"\"Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\""
|
||||
"\"Camila Morrone's current age raised to the 0.43 power is approximately 3.99.\""
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -288,9 +295,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 10,
|
||||
"id": "8f15307d",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.agents import tool\n",
|
||||
@ -298,22 +307,24 @@
|
||||
"@tool\n",
|
||||
"def search_api(query: str) -> str:\n",
|
||||
" \"\"\"Searches the API for the query.\"\"\"\n",
|
||||
" return \"Results\""
|
||||
" return f\"Results for query {query}\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 11,
|
||||
"id": "0a23b91b",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"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)"
|
||||
"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)"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -332,9 +343,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 12,
|
||||
"id": "28cdf04d",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"@tool(\"search\", return_direct=True)\n",
|
||||
@ -345,17 +358,17 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 13,
|
||||
"id": "1085a4bd",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"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)"
|
||||
"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)"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -376,7 +389,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 14,
|
||||
"id": "79213f40",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -386,7 +399,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 15,
|
||||
"id": "e1067dcb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -396,7 +409,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 16,
|
||||
"id": "6c66ffe8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -406,7 +419,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 17,
|
||||
"id": "f45b5bc3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -416,7 +429,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 18,
|
||||
"id": "565e2b9b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -427,21 +440,12 @@
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
|
||||
"\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",
|
||||
"\u001b[32;1m\u001b[1;3mI need to find out Leo DiCaprio's girlfriend's name and her age.\n",
|
||||
"Action: Google Search\n",
|
||||
"Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I need to find out Camila Morrone's age\n",
|
||||
"Action: Google Search\n",
|
||||
"Action Input: \"Camila Morrone age\"\u001b[0m\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3m25 years\u001b[0m\n",
|
||||
"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
|
||||
}
|
||||
@ -1,25 +1,38 @@
|
||||
"""Interface for tools."""
|
||||
from inspect import signature
|
||||
from typing import Any, Awaitable, Callable, Optional, Union
|
||||
from typing import Any, Awaitable, Callable, Optional, Type, Union
|
||||
|
||||
from langchain.tools.base import BaseTool
|
||||
from pydantic import BaseModel
|
||||
|
||||
from langchain.tools.base import BaseTool, create_args_schema_model_from_signature
|
||||
|
||||
|
||||
class Tool(BaseTool):
|
||||
"""Tool that takes in function or coroutine directly."""
|
||||
|
||||
description: str = ""
|
||||
func: Callable[[str], str]
|
||||
coroutine: Optional[Callable[[str], Awaitable[str]]] = None
|
||||
func: Callable[..., str]
|
||||
"""The function to run when the tool is called."""
|
||||
coroutine: Optional[Callable[..., Awaitable[str]]] = None
|
||||
"""The asynchronous version of the function."""
|
||||
|
||||
def _run(self, tool_input: str) -> str:
|
||||
@property
|
||||
def args(self) -> Type[BaseModel]:
|
||||
"""Generate an input pydantic model."""
|
||||
if self.args_schema is not None:
|
||||
return self.args_schema
|
||||
# Infer the schema directly from the function to add more structured
|
||||
# arguments.
|
||||
return create_args_schema_model_from_signature(self.func)
|
||||
|
||||
def _run(self, *args: Any, **kwargs: Any) -> str:
|
||||
"""Use the tool."""
|
||||
return self.func(tool_input)
|
||||
return self.func(*args, **kwargs)
|
||||
|
||||
async def _arun(self, tool_input: str) -> str:
|
||||
async def _arun(self, *args: Any, **kwargs: Any) -> str:
|
||||
"""Use the tool asynchronously."""
|
||||
if self.coroutine:
|
||||
return await self.coroutine(tool_input)
|
||||
return await self.coroutine(*args, **kwargs)
|
||||
raise NotImplementedError("Tool does not support async")
|
||||
|
||||
# TODO: this is for backwards compatibility, remove in future
|
||||
@ -69,14 +82,16 @@ def tool(*args: Union[str, Callable], return_direct: bool = False) -> Callable:
|
||||
"""
|
||||
|
||||
def _make_with_name(tool_name: str) -> Callable:
|
||||
def _make_tool(func: Callable[[str], str]) -> Tool:
|
||||
def _make_tool(func: Callable) -> Tool:
|
||||
assert func.__doc__, "Function must have a docstring"
|
||||
# Description example:
|
||||
# search_api(query: str) - Searches the API for the query.
|
||||
# search_api(query: str) - Searches the API for the query.
|
||||
description = f"{tool_name}{signature(func)} - {func.__doc__.strip()}"
|
||||
args_schema = create_args_schema_model_from_signature(func)
|
||||
tool_ = Tool(
|
||||
name=tool_name,
|
||||
func=func,
|
||||
args_schema=args_schema,
|
||||
description=description,
|
||||
return_direct=return_direct,
|
||||
)
|
||||
|
||||
@ -371,7 +371,7 @@ class CometCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
|
||||
self.starts += 1
|
||||
|
||||
tool = action.tool
|
||||
tool_input = action.tool_input
|
||||
tool_input = str(action.tool_input)
|
||||
log = action.log
|
||||
|
||||
resp = self._init_resp()
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any, Dict, Generic, List, NamedTuple, Optional, TypeVar
|
||||
from typing import Any, Dict, Generic, List, NamedTuple, Optional, TypeVar, Union
|
||||
|
||||
from pydantic import BaseModel, Extra, Field, root_validator
|
||||
|
||||
@ -31,7 +31,7 @@ class AgentAction(NamedTuple):
|
||||
"""Agent's action to take."""
|
||||
|
||||
tool: str
|
||||
tool_input: str
|
||||
tool_input: Union[str, dict]
|
||||
log: str
|
||||
|
||||
|
||||
|
||||
@ -1,19 +1,84 @@
|
||||
"""Base implementation for tools or skills."""
|
||||
|
||||
from abc import abstractmethod
|
||||
from typing import Any, Optional
|
||||
import inspect
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Type, Union
|
||||
|
||||
from pydantic import BaseModel, Extra, Field, validator
|
||||
from pydantic import BaseModel, Extra, Field, create_model, validator
|
||||
|
||||
from langchain.callbacks import get_callback_manager
|
||||
from langchain.callbacks.base import BaseCallbackManager
|
||||
|
||||
|
||||
class BaseTool(BaseModel):
|
||||
"""Class responsible for defining a tool or skill for an LLM."""
|
||||
def create_args_schema_model_from_signature(run_func: Callable) -> Type[BaseModel]:
|
||||
"""Create a pydantic model type from a function's signature."""
|
||||
signature_ = inspect.signature(run_func)
|
||||
field_definitions: Dict[str, Any] = {}
|
||||
|
||||
for name, param in signature_.parameters.items():
|
||||
if name == "self":
|
||||
continue
|
||||
default_value = (
|
||||
param.default if param.default != inspect.Parameter.empty else None
|
||||
)
|
||||
annotation = (
|
||||
param.annotation if param.annotation != inspect.Parameter.empty else Any
|
||||
)
|
||||
# Handle functions with *args in the signature
|
||||
if param.kind == inspect.Parameter.VAR_POSITIONAL:
|
||||
field_definitions[name] = (
|
||||
Any,
|
||||
Field(default=None, extra={"is_var_positional": True}),
|
||||
)
|
||||
# handle functions with **kwargs in the signature
|
||||
elif param.kind == inspect.Parameter.VAR_KEYWORD:
|
||||
field_definitions[name] = (
|
||||
Any,
|
||||
Field(default=None, extra={"is_var_keyword": True}),
|
||||
)
|
||||
# Handle all other named parameters
|
||||
else:
|
||||
is_keyword_only = param.kind == inspect.Parameter.KEYWORD_ONLY
|
||||
field_definitions[name] = (
|
||||
annotation,
|
||||
Field(
|
||||
default=default_value, extra={"is_keyword_only": is_keyword_only}
|
||||
),
|
||||
)
|
||||
return create_model("ArgsModel", **field_definitions) # type: ignore
|
||||
|
||||
|
||||
def _to_args_and_kwargs(model: BaseModel) -> Tuple[Sequence, dict]:
|
||||
args = []
|
||||
kwargs = {}
|
||||
for name, field in model.__fields__.items():
|
||||
value = getattr(model, name)
|
||||
# Handle *args in the function signature
|
||||
if field.field_info.extra.get("extra", {}).get("is_var_positional"):
|
||||
if isinstance(value, str):
|
||||
# Base case for backwards compatability
|
||||
args.append(value)
|
||||
elif value is not None:
|
||||
args.extend(value)
|
||||
# Handle **kwargs in the function signature
|
||||
elif field.field_info.extra.get("extra", {}).get("is_var_keyword"):
|
||||
if value is not None:
|
||||
kwargs.update(value)
|
||||
elif field.field_info.extra.get("extra", {}).get("is_keyword_only"):
|
||||
kwargs[name] = value
|
||||
else:
|
||||
args.append(value)
|
||||
|
||||
return tuple(args), kwargs
|
||||
|
||||
|
||||
class BaseTool(ABC, BaseModel):
|
||||
"""Interface LangChain tools must implement."""
|
||||
|
||||
name: str
|
||||
description: str
|
||||
args_schema: Optional[Type[BaseModel]] = None
|
||||
"""Pydantic model class to validate and parse the tool's input arguments."""
|
||||
return_direct: bool = False
|
||||
verbose: bool = False
|
||||
callback_manager: BaseCallbackManager = Field(default_factory=get_callback_manager)
|
||||
@ -24,6 +89,33 @@ class BaseTool(BaseModel):
|
||||
extra = Extra.forbid
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
@property
|
||||
def args(self) -> Type[BaseModel]:
|
||||
"""Generate an input pydantic model."""
|
||||
if self.args_schema is not None:
|
||||
return self.args_schema
|
||||
return create_args_schema_model_from_signature(self._run)
|
||||
|
||||
def _parse_input(
|
||||
self,
|
||||
tool_input: Union[str, Dict],
|
||||
) -> BaseModel:
|
||||
"""Convert tool input to pydantic model."""
|
||||
pydantic_input_type = self.args
|
||||
if isinstance(tool_input, str):
|
||||
# For backwards compatibility, a tool that only takes
|
||||
# a single string input will be converted to a dict.
|
||||
# to be validated.
|
||||
field_name = next(iter(pydantic_input_type.__fields__))
|
||||
tool_input = {field_name: tool_input}
|
||||
if pydantic_input_type is not None:
|
||||
return pydantic_input_type.parse_obj(tool_input)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"args_schema required for tool {self.name} in order to"
|
||||
f" accept input of type {type(tool_input)}"
|
||||
)
|
||||
|
||||
@validator("callback_manager", pre=True, always=True)
|
||||
def set_callback_manager(
|
||||
cls, callback_manager: Optional[BaseCallbackManager]
|
||||
@ -35,39 +127,37 @@ class BaseTool(BaseModel):
|
||||
return callback_manager or get_callback_manager()
|
||||
|
||||
@abstractmethod
|
||||
def _run(self, tool_input: str) -> str:
|
||||
def _run(self, *args: Any, **kwargs: Any) -> str:
|
||||
"""Use the tool."""
|
||||
|
||||
@abstractmethod
|
||||
async def _arun(self, tool_input: str) -> str:
|
||||
async def _arun(self, *args: Any, **kwargs: Any) -> str:
|
||||
"""Use the tool asynchronously."""
|
||||
|
||||
def __call__(self, tool_input: str) -> str:
|
||||
"""Make tools callable with str input."""
|
||||
return self.run(tool_input)
|
||||
|
||||
def run(
|
||||
self,
|
||||
tool_input: str,
|
||||
tool_input: Union[str, Dict],
|
||||
verbose: Optional[bool] = None,
|
||||
start_color: Optional[str] = "green",
|
||||
color: Optional[str] = "green",
|
||||
**kwargs: Any
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Run the tool."""
|
||||
run_input = self._parse_input(tool_input)
|
||||
if not self.verbose and verbose is not None:
|
||||
verbose_ = verbose
|
||||
else:
|
||||
verbose_ = self.verbose
|
||||
self.callback_manager.on_tool_start(
|
||||
{"name": self.name, "description": self.description},
|
||||
tool_input,
|
||||
str(run_input),
|
||||
verbose=verbose_,
|
||||
color=start_color,
|
||||
**kwargs,
|
||||
)
|
||||
try:
|
||||
observation = self._run(tool_input)
|
||||
args, kwargs = _to_args_and_kwargs(run_input)
|
||||
observation = self._run(*args, **kwargs)
|
||||
except (Exception, KeyboardInterrupt) as e:
|
||||
self.callback_manager.on_tool_error(e, verbose=verbose_)
|
||||
raise e
|
||||
@ -78,13 +168,14 @@ class BaseTool(BaseModel):
|
||||
|
||||
async def arun(
|
||||
self,
|
||||
tool_input: str,
|
||||
tool_input: Union[str, Dict],
|
||||
verbose: Optional[bool] = None,
|
||||
start_color: Optional[str] = "green",
|
||||
color: Optional[str] = "green",
|
||||
**kwargs: Any
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Run the tool asynchronously."""
|
||||
run_input = self._parse_input(tool_input)
|
||||
if not self.verbose and verbose is not None:
|
||||
verbose_ = verbose
|
||||
else:
|
||||
@ -92,7 +183,7 @@ class BaseTool(BaseModel):
|
||||
if self.callback_manager.is_async:
|
||||
await self.callback_manager.on_tool_start(
|
||||
{"name": self.name, "description": self.description},
|
||||
tool_input,
|
||||
str(run_input.dict()),
|
||||
verbose=verbose_,
|
||||
color=start_color,
|
||||
**kwargs,
|
||||
@ -100,14 +191,15 @@ class BaseTool(BaseModel):
|
||||
else:
|
||||
self.callback_manager.on_tool_start(
|
||||
{"name": self.name, "description": self.description},
|
||||
tool_input,
|
||||
str(run_input.dict()),
|
||||
verbose=verbose_,
|
||||
color=start_color,
|
||||
**kwargs,
|
||||
)
|
||||
try:
|
||||
# We then call the tool on the tool input to get an observation
|
||||
observation = await self._arun(tool_input)
|
||||
args, kwargs = _to_args_and_kwargs(run_input)
|
||||
observation = await self._arun(*args, **kwargs)
|
||||
except (Exception, KeyboardInterrupt) as e:
|
||||
if self.callback_manager.is_async:
|
||||
await self.callback_manager.on_tool_error(e, verbose=verbose_)
|
||||
@ -123,3 +215,7 @@ class BaseTool(BaseModel):
|
||||
observation, verbose=verbose_, color=color, name=self.name, **kwargs
|
||||
)
|
||||
return observation
|
||||
|
||||
def __call__(self, tool_input: str) -> str:
|
||||
"""Make tool callable."""
|
||||
return self.run(tool_input)
|
||||
|
||||
0
langchain/tools/file_management/__init__.py
Normal file
0
langchain/tools/file_management/__init__.py
Normal file
29
langchain/tools/file_management/read.py
Normal file
29
langchain/tools/file_management/read.py
Normal file
@ -0,0 +1,29 @@
|
||||
from typing import Type
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from langchain.tools.base import BaseTool
|
||||
|
||||
|
||||
class ReadFileInput(BaseModel):
|
||||
"""Input for ReadFileTool."""
|
||||
|
||||
file_path: str = Field(..., description="name of file")
|
||||
|
||||
|
||||
class ReadFileTool(BaseTool):
|
||||
name: str = "read_file"
|
||||
tool_args: Type[BaseModel] = ReadFileInput
|
||||
description: str = "Read file from disk"
|
||||
|
||||
def _run(self, file_path: str) -> str:
|
||||
try:
|
||||
with open(file_path, "r", encoding="utf-8") as f:
|
||||
content = f.read()
|
||||
return content
|
||||
except Exception as e:
|
||||
return "Error: " + str(e)
|
||||
|
||||
async def _arun(self, tool_input: str) -> str:
|
||||
# TODO: Add aiofiles method
|
||||
raise NotImplementedError
|
||||
34
langchain/tools/file_management/write.py
Normal file
34
langchain/tools/file_management/write.py
Normal file
@ -0,0 +1,34 @@
|
||||
import os
|
||||
from typing import Type
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from langchain.tools.base import BaseTool
|
||||
|
||||
|
||||
class WriteFileInput(BaseModel):
|
||||
"""Input for WriteFileTool."""
|
||||
|
||||
file_path: str = Field(..., description="name of file")
|
||||
text: str = Field(..., description="text to write to file")
|
||||
|
||||
|
||||
class WriteFileTool(BaseTool):
|
||||
name: str = "write_file"
|
||||
tool_args: Type[BaseModel] = WriteFileInput
|
||||
description: str = "Write file to disk"
|
||||
|
||||
def _run(self, file_path: str, text: str) -> str:
|
||||
try:
|
||||
directory = os.path.dirname(file_path)
|
||||
if not os.path.exists(directory) and directory:
|
||||
os.makedirs(directory)
|
||||
with open(file_path, "w", encoding="utf-8") as f:
|
||||
f.write(text)
|
||||
return "File written to successfully."
|
||||
except Exception as e:
|
||||
return "Error: " + str(e)
|
||||
|
||||
async def _arun(self, tool_input: str) -> str:
|
||||
# TODO: Add aiofiles method
|
||||
raise NotImplementedError
|
||||
@ -16,7 +16,7 @@ from tests.unit_tests.llms.fake_llm import FakeLLM
|
||||
def get_action_and_input(text: str) -> Tuple[str, str]:
|
||||
output = MRKLOutputParser().parse(text)
|
||||
if isinstance(output, AgentAction):
|
||||
return output.tool, output.tool_input
|
||||
return output.tool, str(output.tool_input)
|
||||
else:
|
||||
return "Final Answer", output.return_values["output"]
|
||||
|
||||
|
||||
@ -1,7 +1,12 @@
|
||||
"""Test tool utils."""
|
||||
from datetime import datetime
|
||||
from typing import Any, Optional, Type, Union
|
||||
|
||||
import pytest
|
||||
from pydantic import BaseModel
|
||||
|
||||
from langchain.agents.tools import Tool, tool
|
||||
from langchain.tools.base import BaseTool
|
||||
|
||||
|
||||
def test_unnamed_decorator() -> None:
|
||||
@ -18,6 +23,64 @@ def test_unnamed_decorator() -> None:
|
||||
assert search_api("test") == "API result"
|
||||
|
||||
|
||||
class _MockSchema(BaseModel):
|
||||
arg1: int
|
||||
arg2: bool
|
||||
arg3: Optional[dict] = None
|
||||
|
||||
|
||||
class _MockStructuredTool(BaseTool):
|
||||
name = "structured_api"
|
||||
args_schema: Type[BaseModel] = _MockSchema
|
||||
description = "A Structured Tool"
|
||||
|
||||
def _run(self, arg1: int, arg2: bool, arg3: Optional[dict] = None) -> str:
|
||||
return f"{arg1} {arg2} {arg3}"
|
||||
|
||||
async def _arun(self, arg1: int, arg2: bool, arg3: Optional[dict] = None) -> str:
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
def test_structured_args() -> None:
|
||||
"""Test functionality with structured arguments."""
|
||||
structured_api = _MockStructuredTool()
|
||||
assert isinstance(structured_api, BaseTool)
|
||||
assert structured_api.name == "structured_api"
|
||||
expected_result = "1 True {'foo': 'bar'}"
|
||||
args = {"arg1": 1, "arg2": True, "arg3": {"foo": "bar"}}
|
||||
assert structured_api.run(args) == expected_result
|
||||
|
||||
|
||||
def test_structured_args_decorator() -> None:
|
||||
"""Test functionality with structured arguments parsed as a decorator."""
|
||||
|
||||
@tool
|
||||
def structured_tool_input(
|
||||
arg1: int, arg2: Union[float, datetime], opt_arg: Optional[dict] = None
|
||||
) -> str:
|
||||
"""Return the arguments directly."""
|
||||
return f"{arg1}, {arg2}, {opt_arg}"
|
||||
|
||||
assert isinstance(structured_tool_input, Tool)
|
||||
assert structured_tool_input.name == "structured_tool_input"
|
||||
args = {"arg1": 1, "arg2": 0.001, "opt_arg": {"foo": "bar"}}
|
||||
expected_result = "1, 0.001, {'foo': 'bar'}"
|
||||
assert structured_tool_input.run(args) == expected_result
|
||||
|
||||
|
||||
def test_empty_args_decorator() -> None:
|
||||
"""Test functionality with no args parsed as a decorator."""
|
||||
|
||||
@tool
|
||||
def empty_tool_input() -> str:
|
||||
"""Return a constant."""
|
||||
return "the empty result"
|
||||
|
||||
assert isinstance(empty_tool_input, Tool)
|
||||
assert empty_tool_input.name == "empty_tool_input"
|
||||
assert empty_tool_input.run({}) == "the empty result"
|
||||
|
||||
|
||||
def test_named_tool_decorator() -> None:
|
||||
"""Test functionality when arguments are provided as input to decorator."""
|
||||
|
||||
@ -57,6 +120,28 @@ def test_unnamed_tool_decorator_return_direct() -> None:
|
||||
assert search_api.return_direct
|
||||
|
||||
|
||||
def test_tool_with_kwargs() -> None:
|
||||
"""Test functionality when only return direct is provided."""
|
||||
|
||||
@tool(return_direct=True)
|
||||
def search_api(
|
||||
arg_1: float, *args: Any, ping: Optional[str] = None, **kwargs: Any
|
||||
) -> str:
|
||||
"""Search the API for the query."""
|
||||
return f"arg_1={arg_1}, foo={args}, ping={ping}, kwargs={kwargs}"
|
||||
|
||||
assert isinstance(search_api, Tool)
|
||||
result = search_api.run(
|
||||
tool_input={
|
||||
"arg_1": 3.2,
|
||||
"args": "fam",
|
||||
"kwargs": {"bar": "baz"},
|
||||
"ping": "pong",
|
||||
}
|
||||
)
|
||||
assert result == "arg_1=3.2, foo=('fam',), ping=pong, kwargs={'bar': 'baz'}"
|
||||
|
||||
|
||||
def test_missing_docstring() -> None:
|
||||
"""Test error is raised when docstring is missing."""
|
||||
# expect to throw a value error if theres no docstring
|
||||
@ -67,7 +152,7 @@ def test_missing_docstring() -> None:
|
||||
return "API result"
|
||||
|
||||
|
||||
def test_create_tool_posistional_args() -> None:
|
||||
def test_create_tool_positional_args() -> None:
|
||||
"""Test that positional arguments are allowed."""
|
||||
test_tool = Tool("test_name", lambda x: x, "test_description")
|
||||
assert test_tool("foo") == "foo"
|
||||
|
||||
0
tests/unit_tests/tools/requests/__init__.py
Normal file
0
tests/unit_tests/tools/requests/__init__.py
Normal file
100
tests/unit_tests/tools/requests/test_tool.py
Normal file
100
tests/unit_tests/tools/requests/test_tool.py
Normal file
@ -0,0 +1,100 @@
|
||||
import asyncio
|
||||
from typing import Any, Dict
|
||||
|
||||
import pytest
|
||||
|
||||
from langchain.requests import TextRequestsWrapper
|
||||
from langchain.tools.requests.tool import (
|
||||
RequestsDeleteTool,
|
||||
RequestsGetTool,
|
||||
RequestsPatchTool,
|
||||
RequestsPostTool,
|
||||
RequestsPutTool,
|
||||
_parse_input,
|
||||
)
|
||||
|
||||
|
||||
class _MockTextRequestsWrapper(TextRequestsWrapper):
|
||||
@staticmethod
|
||||
def get(url: str, **kwargs: Any) -> str:
|
||||
return "get_response"
|
||||
|
||||
@staticmethod
|
||||
async def aget(url: str, **kwargs: Any) -> str:
|
||||
return "aget_response"
|
||||
|
||||
@staticmethod
|
||||
def post(url: str, data: Dict[str, Any], **kwargs: Any) -> str:
|
||||
return f"post {str(data)}"
|
||||
|
||||
@staticmethod
|
||||
async def apost(url: str, data: Dict[str, Any], **kwargs: Any) -> str:
|
||||
return f"apost {str(data)}"
|
||||
|
||||
@staticmethod
|
||||
def patch(url: str, data: Dict[str, Any], **kwargs: Any) -> str:
|
||||
return f"patch {str(data)}"
|
||||
|
||||
@staticmethod
|
||||
async def apatch(url: str, data: Dict[str, Any], **kwargs: Any) -> str:
|
||||
return f"apatch {str(data)}"
|
||||
|
||||
@staticmethod
|
||||
def put(url: str, data: Dict[str, Any], **kwargs: Any) -> str:
|
||||
return f"put {str(data)}"
|
||||
|
||||
@staticmethod
|
||||
async def aput(url: str, data: Dict[str, Any], **kwargs: Any) -> str:
|
||||
return f"aput {str(data)}"
|
||||
|
||||
@staticmethod
|
||||
def delete(url: str, **kwargs: Any) -> str:
|
||||
return "delete_response"
|
||||
|
||||
@staticmethod
|
||||
async def adelete(url: str, **kwargs: Any) -> str:
|
||||
return "adelete_response"
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_requests_wrapper() -> TextRequestsWrapper:
|
||||
return _MockTextRequestsWrapper()
|
||||
|
||||
|
||||
def test_parse_input() -> None:
|
||||
input_text = '{"url": "https://example.com", "data": {"key": "value"}}'
|
||||
expected_output = {"url": "https://example.com", "data": {"key": "value"}}
|
||||
assert _parse_input(input_text) == expected_output
|
||||
|
||||
|
||||
def test_requests_get_tool(mock_requests_wrapper: TextRequestsWrapper) -> None:
|
||||
tool = RequestsGetTool(requests_wrapper=mock_requests_wrapper)
|
||||
assert tool.run("https://example.com") == "get_response"
|
||||
assert asyncio.run(tool.arun("https://example.com")) == "aget_response"
|
||||
|
||||
|
||||
def test_requests_post_tool(mock_requests_wrapper: TextRequestsWrapper) -> None:
|
||||
tool = RequestsPostTool(requests_wrapper=mock_requests_wrapper)
|
||||
input_text = '{"url": "https://example.com", "data": {"key": "value"}}'
|
||||
assert tool.run(input_text) == "post {'key': 'value'}"
|
||||
assert asyncio.run(tool.arun(input_text)) == "apost {'key': 'value'}"
|
||||
|
||||
|
||||
def test_requests_patch_tool(mock_requests_wrapper: TextRequestsWrapper) -> None:
|
||||
tool = RequestsPatchTool(requests_wrapper=mock_requests_wrapper)
|
||||
input_text = '{"url": "https://example.com", "data": {"key": "value"}}'
|
||||
assert tool.run(input_text) == "patch {'key': 'value'}"
|
||||
assert asyncio.run(tool.arun(input_text)) == "apatch {'key': 'value'}"
|
||||
|
||||
|
||||
def test_requests_put_tool(mock_requests_wrapper: TextRequestsWrapper) -> None:
|
||||
tool = RequestsPutTool(requests_wrapper=mock_requests_wrapper)
|
||||
input_text = '{"url": "https://example.com", "data": {"key": "value"}}'
|
||||
assert tool.run(input_text) == "put {'key': 'value'}"
|
||||
assert asyncio.run(tool.arun(input_text)) == "aput {'key': 'value'}"
|
||||
|
||||
|
||||
def test_requests_delete_tool(mock_requests_wrapper: TextRequestsWrapper) -> None:
|
||||
tool = RequestsDeleteTool(requests_wrapper=mock_requests_wrapper)
|
||||
assert tool.run("https://example.com") == "delete_response"
|
||||
assert asyncio.run(tool.arun("https://example.com")) == "adelete_response"
|
||||
Loading…
Reference in New Issue
Block a user