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vowelparrot
2023-04-17 15:47:29 -07:00
parent cef05cf60e
commit 569d7f78fe
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docs/modules/agents/tools/custom_tools.ipynb
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@@ -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": {

13
langchain/agents/tools.py
View File

@@ -1,6 +1,8 @@
"""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 pydantic import BaseModel
from langchain.tools.base import BaseTool, create_args_schema_model_from_signature
@@ -14,6 +16,15 @@ class Tool(BaseTool):
coroutine: Optional[Callable[..., Awaitable[str]]] = None
"""The asynchronous version of the function."""
@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(*args, **kwargs)

2
langchain/callbacks/comet_ml_callback.py
View File

@@ -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()

4
langchain/schema.py
View File

@@ -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

2
tests/unit_tests/agents/test_mrkl.py
View File

@@ -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"]

19
tests/unit_tests/agents/test_tools.py
View File

@@ -120,17 +120,26 @@ def test_unnamed_tool_decorator_return_direct() -> None:
assert search_api.return_direct
def test_tool_with_kwarg() -> None:
def test_tool_with_kwargs() -> None:
"""Test functionality when only return direct is provided."""
@tool(return_direct=True)
def search_api(*args: Any, **kwargs: Any) -> str:
def search_api(
arg_1: float, *args: Any, ping: Optional[str] = None, **kwargs: Any
) -> str:
"""Search the API for the query."""
return f"foo={args}, kwargs={kwargs}"
return f"arg_1={arg_1}, foo={args}, ping={ping}, kwargs={kwargs}"
assert isinstance(search_api, Tool)
result = search_api.run(tool_input={"args": "fam", "kwargs": {"bar": "baz"}})
assert result == "foo=('fam',), kwargs={'bar': 'baz'}"
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: