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https://github.com/hwchase17/langchain.git
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Update Tool Input (#3103)
- Remove dynamic model creation in the `args()` property. _Only infer for the decorator (and add an argument to NOT infer if someone wishes to only pass as a string)_ - Update the validation example to make it less likely to be misinterpreted as a "safe" way to run a repl There is one example of "Multi-argument tools" in the custom_tools.ipynb from yesterday, but we could add more. The output parsing for the base MRKL agent hasn't been adapted to handle structured args at this point in time --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This commit is contained in:
Zander Chase
GitHub
@@ -12,6 +12,7 @@
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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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"- args_schema (Pydantic BaseModel), is optional but recommended, can be used to provide more information or validation for expected parameters.\n",
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"\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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@@ -91,12 +92,22 @@
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" func=search.run,\n",
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" description=\"useful for when you need to answer questions about current events\"\n",
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" ),\n",
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"]\n",
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"# You can also define an args_schema to provide more information about inputs\n",
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"from pydantic import BaseModel, Field\n",
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"\n",
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"class CalculatorInput(BaseModel):\n",
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" query: str = Field(description=\"should be a math expression\")\n",
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" \n",
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"\n",
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"tools.append(\n",
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" Tool(\n",
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" name=\"Calculator\",\n",
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" func=llm_math_chain.run,\n",
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" description=\"useful for when you need to answer questions about math\"\n",
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" description=\"useful for when you need to answer questions about math\",\n",
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" args_schema=CalculatorInput\n",
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" )\n",
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"]"
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")"
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]
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},
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{
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@@ -130,22 +141,20 @@
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\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\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 Input: \"Leo DiCaprio girlfriend\"\u001b[0m\u001b[36;1m\u001b[1;3mDiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years.\u001b[0m\u001b[32;1m\u001b[1;3mI need to find out Camila Morrone's current age\n",
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"Action: Calculator\n",
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"Action Input: 27^(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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"27^(0.43)\u001b[32;1m\u001b[1;3m```text\n",
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"27**(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(\"27**(0.43)\")...\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;3m4.125593352125936\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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"\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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"\u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\u001b[0m\u001b[32;1m\u001b[1;3mI now know the final answer\n",
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"Final Answer: 3.991298452658078\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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@@ -153,7 +162,7 @@
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{
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"data": {
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"text/plain": [
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"'4.125593352125936'"
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"'3.991298452658078'"
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]
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},
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"execution_count": 5,
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@@ -197,6 +206,7 @@
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"class CustomCalculatorTool(BaseTool):\n",
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" name = \"Calculator\"\n",
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" description = \"useful for when you need to answer questions about math\"\n",
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" args_schema=CalculatorInput\n",
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"\n",
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" def _run(self, query: str) -> str:\n",
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" \"\"\"Use the tool.\"\"\"\n",
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@@ -248,9 +258,7 @@
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\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\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 Input: \"Leo DiCaprio girlfriend\"\u001b[0m\u001b[36;1m\u001b[1;3mDiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years.\u001b[0m\u001b[32;1m\u001b[1;3mI need to find out Camila Morrone's current age\n",
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"Action: Calculator\n",
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"Action Input: 25^(0.43)\u001b[0m\n",
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"\n",
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@@ -262,8 +270,8 @@
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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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"\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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"\u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\u001b[0m\u001b[32;1m\u001b[1;3mI now know the final answer\n",
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"Final Answer: 3.991298452658078\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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@@ -271,7 +279,7 @@
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{
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"data": {
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"text/plain": [
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"\"Camila Morrone's current age raised to the 0.43 power is approximately 3.99.\""
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"'3.991298452658078'"
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]
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},
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"execution_count": 9,
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@@ -321,7 +329,7 @@
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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.', 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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"Tool(name='search_api', description='search_api(query: str) -> str - Searches the API for the query.', args_schema=<class 'pydantic.main.SearchApi'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bd664c0>, coroutine=None)"
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]
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},
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"execution_count": 11,
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@@ -365,7 +373,7 @@
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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.', 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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"Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', args_schema=<class 'pydantic.main.SearchApi'>, return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bd66310>, coroutine=None)"
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]
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},
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"execution_count": 13,
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@@ -377,6 +385,51 @@
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"search_api"
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]
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},
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{
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"cell_type": "markdown",
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"id": "de34a6a3",
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"metadata": {},
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"source": [
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"You can also provide `args_schema` to provide more information about the argument"
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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": 14,
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"id": "f3a5c106",
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"metadata": {},
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"outputs": [],
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"source": [
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"class SearchInput(BaseModel):\n",
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" query: str = Field(description=\"should be a search query\")\n",
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" \n",
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"@tool(\"search\", return_direct=True, args_schema=SearchInput)\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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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "7914ba6b",
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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.', args_schema=<class '__main__.SearchInput'>, return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bcf0ee0>, coroutine=None)"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"search_api"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1d0430d6",
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@@ -784,7 +837,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
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"version": "3.9.1"
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},
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"vscode": {
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"interpreter": {
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