forked from Archives/langchain
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:
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19116010ee
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@ -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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@ -19,13 +19,12 @@
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},
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"outputs": [],
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"source": [
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"from langchain.agents import initialize_agent, Tool\n",
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"from langchain.agents import AgentType\n",
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"from typing import Any, Dict\n",
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"\n",
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"from langchain.agents import AgentType, initialize_agent\n",
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"from langchain.llms import OpenAI\n",
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"from langchain.tools.python.tool import PythonREPLTool\n",
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"from pydantic import BaseModel\n",
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"from pydantic import Field, root_validator\n",
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"from typing import Dict, Any"
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"from langchain.tools.requests.tool import RequestsGetTool, TextRequestsWrapper\n",
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"from pydantic import BaseModel, Field, root_validator\n"
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]
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},
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{
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@ -42,22 +41,20 @@
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
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]
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}
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],
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"source": [
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"class ToolInputModel(BaseModel):\n",
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" query: str = Field(...)\n",
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" \n",
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" @root_validator\n",
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" def validate_query(cls, values: Dict[str, Any]) -> Dict:\n",
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" # Note: this is NOT a safe REPL! This is used for instructive purposes only\n",
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" if \"import os\" in values[\"query\"]:\n",
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" raise ValueError(\"'import os' not permitted in this python REPL.\")\n",
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" return values\n",
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" \n",
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"tool = PythonREPLTool(args_schema=ToolInputModel)"
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"!pip install tldextract > /dev/null"
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]
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},
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{
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@ -68,7 +65,27 @@
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},
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"outputs": [],
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"source": [
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"agent = initialize_agent([tool], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
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"import tldextract\n",
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"\n",
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"_APPROVED_DOMAINS = {\n",
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" \"langchain\",\n",
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" \"wikipedia\",\n",
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"}\n",
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"\n",
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"class ToolInputSchema(BaseModel):\n",
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"\n",
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" url: str = Field(...)\n",
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" \n",
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" @root_validator\n",
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" def validate_query(cls, values: Dict[str, Any]) -> Dict:\n",
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" url = values[\"url\"]\n",
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" domain = tldextract.extract(url).domain\n",
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" if domain not in _APPROVED_DOMAINS:\n",
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" raise ValueError(f\"Domain {domain} is not on the approved list:\"\n",
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" f\" {sorted(_APPROVED_DOMAINS)}\")\n",
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" return values\n",
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" \n",
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"tool = RequestsGetTool(args_schema=ToolInputSchema, requests_wrapper=TextRequestsWrapper())"
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]
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},
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{
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m I need to define a function that adds two numbers\n",
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"Action: Python REPL\n",
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"Action Input: def add_two_numbers(a, b):\n",
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" 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",
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"Action: Python REPL\n",
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"Action Input: print(add_two_numbers(2, 2))\u001b[0m\u001b[36;1m\u001b[1;3m4\n",
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"\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: 4\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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},
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{
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"data": {
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"text/plain": [
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"'4'"
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]
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},
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"execution_count": 5,
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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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"outputs": [],
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"source": [
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"# This will succeed, since there aren't any arguments that will be triggered during validation\n",
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"agent.run(\"Run a python function that adds 2 and 2\")"
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"agent = initialize_agent([tool], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False)"
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]
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},
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{
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@ -124,38 +110,46 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m I need to import os and then list the dir\n",
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"Action: Python REPL\n",
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"Action Input: import os; print(os.listdir())\u001b[0m"
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]
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},
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{
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"ename": "ValidationError",
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"evalue": "1 validation error for ToolInputModel\n__root__\n 'import os' not (type=value_error)",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)",
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"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",
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"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)"
|
||||
"The main title of langchain.com is \"LANG CHAIN 🦜️🔗 Official Home Page\"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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\")"
|
||||
"# This will succeed, since there aren't any arguments that will be triggered during validation\n",
|
||||
"answer = agent.run(\"What's the main title on langchain.com?\")\n",
|
||||
"print(answer)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ValidationError",
|
||||
"evalue": "1 validation error for ToolInputSchema\n__root__\n Domain google is not on the approved list: ['langchain', 'wikipedia'] (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[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m agent\u001b[39m.\u001b[39;49mrun(\u001b[39m\"\u001b[39;49m\u001b[39mWhat\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39ms the main title on google.com?\u001b[39;49m\u001b[39m\"\u001b[39;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[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m!=\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 212\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39m`run` supports only one positional argument.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 213\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m(args[\u001b[39m0\u001b[39;49m])[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutput_keys[\u001b[39m0\u001b[39m]]\n\u001b[1;32m 215\u001b[0m \u001b[39mif\u001b[39;00m kwargs \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m args:\n\u001b[1;32m 216\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m(kwargs)[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutput_keys[\u001b[39m0\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[39mexcept\u001b[39;00m (\u001b[39mKeyboardInterrupt\u001b[39;00m, \u001b[39mException\u001b[39;00m) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 115\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_error(e, verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose)\n\u001b[0;32m--> 116\u001b[0m \u001b[39mraise\u001b[39;00m e\n\u001b[1;32m 117\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_end(outputs, verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose)\n\u001b[1;32m 118\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\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[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_start(\n\u001b[1;32m 108\u001b[0m {\u001b[39m\"\u001b[39m\u001b[39mname\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m},\n\u001b[1;32m 109\u001b[0m inputs,\n\u001b[1;32m 110\u001b[0m verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose,\n\u001b[1;32m 111\u001b[0m )\n\u001b[1;32m 112\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 113\u001b[0m outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call(inputs)\n\u001b[1;32m 114\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mKeyboardInterrupt\u001b[39;00m, \u001b[39mException\u001b[39;00m) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 115\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_error(e, verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\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[39m# We now enter the agent loop (until it returns something).\u001b[39;00m\n\u001b[1;32m 791\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_should_continue(iterations, time_elapsed):\n\u001b[0;32m--> 792\u001b[0m next_step_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_take_next_step(\n\u001b[1;32m 793\u001b[0m name_to_tool_map, color_mapping, inputs, intermediate_steps\n\u001b[1;32m 794\u001b[0m )\n\u001b[1;32m 795\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(next_step_output, AgentFinish):\n\u001b[1;32m 796\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\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[39m\"\u001b[39m\u001b[39mllm_prefix\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 694\u001b[0m \u001b[39m# 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[39m=\u001b[39m tool\u001b[39m.\u001b[39;49mrun(\n\u001b[1;32m 696\u001b[0m agent_action\u001b[39m.\u001b[39;49mtool_input,\n\u001b[1;32m 697\u001b[0m verbose\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mverbose,\n\u001b[1;32m 698\u001b[0m color\u001b[39m=\u001b[39;49mcolor,\n\u001b[1;32m 699\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mtool_run_kwargs,\n\u001b[1;32m 700\u001b[0m )\n\u001b[1;32m 701\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 702\u001b[0m tool_run_kwargs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39magent\u001b[39m.\u001b[39mtool_run_logging_kwargs()\n",
|
||||
"File \u001b[0;32m~/code/lc/lckg/langchain/tools/base.py:110\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, **kwargs)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mrun\u001b[39m(\n\u001b[1;32m 102\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[1;32m 103\u001b[0m tool_input: Union[\u001b[39mstr\u001b[39m, Dict],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs: Any,\n\u001b[1;32m 108\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mstr\u001b[39m:\n\u001b[1;32m 109\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"Run the tool.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 110\u001b[0m run_input \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parse_input(tool_input)\n\u001b[1;32m 111\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose \u001b[39mand\u001b[39;00m verbose \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 112\u001b[0m verbose_ \u001b[39m=\u001b[39m verbose\n",
|
||||
"File \u001b[0;32m~/code/lc/lckg/langchain/tools/base.py:71\u001b[0m, in \u001b[0;36mBaseTool._parse_input\u001b[0;34m(self, tool_input)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39missubclass\u001b[39m(input_args, BaseModel):\n\u001b[1;32m 70\u001b[0m key_ \u001b[39m=\u001b[39m \u001b[39mnext\u001b[39m(\u001b[39miter\u001b[39m(input_args\u001b[39m.\u001b[39m__fields__\u001b[39m.\u001b[39mkeys()))\n\u001b[0;32m---> 71\u001b[0m input_args\u001b[39m.\u001b[39;49mparse_obj({key_: tool_input})\n\u001b[1;32m 72\u001b[0m \u001b[39m# Passing as a positional argument is more straightforward for\u001b[39;00m\n\u001b[1;32m 73\u001b[0m \u001b[39m# backwards compatability\u001b[39;00m\n\u001b[1;32m 74\u001b[0m \u001b[39mreturn\u001b[39;00m tool_input\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 ToolInputSchema\n__root__\n Domain google is not on the approved list: ['langchain', 'wikipedia'] (type=value_error)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"agent.run(\"What's the main title on google.com?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
@ -2,9 +2,9 @@
|
||||
from inspect import signature
|
||||
from typing import Any, Awaitable, Callable, Optional, Type, Union
|
||||
|
||||
from pydantic import BaseModel
|
||||
from pydantic import BaseModel, validate_arguments
|
||||
|
||||
from langchain.tools.base import BaseTool, create_args_schema_model_from_signature
|
||||
from langchain.tools.base import BaseTool
|
||||
|
||||
|
||||
class Tool(BaseTool):
|
||||
@ -16,15 +16,6 @@ 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)
|
||||
@ -60,9 +51,23 @@ class InvalidTool(BaseTool):
|
||||
return f"{tool_name} is not a valid tool, try another one."
|
||||
|
||||
|
||||
def tool(*args: Union[str, Callable], return_direct: bool = False) -> Callable:
|
||||
def tool(
|
||||
*args: Union[str, Callable],
|
||||
return_direct: bool = False,
|
||||
args_schema: Optional[Type[BaseModel]] = None,
|
||||
infer_schema: bool = True,
|
||||
) -> Callable:
|
||||
"""Make tools out of functions, can be used with or without arguments.
|
||||
|
||||
Args:
|
||||
*args: The arguments to the tool.
|
||||
return_direct: Whether to return directly from the tool rather
|
||||
than continuing the agent loop.
|
||||
args_schema: optional argument schema for user to specify
|
||||
infer_schema: Whether to infer the schema of the arguments from
|
||||
the function's signature. This also makes the resultant tool
|
||||
accept a dictionary input to its `run()` function.
|
||||
|
||||
Requires:
|
||||
- Function must be of type (str) -> str
|
||||
- Function must have a docstring
|
||||
@ -87,11 +92,13 @@ def tool(*args: Union[str, Callable], return_direct: bool = False) -> Callable:
|
||||
# Description example:
|
||||
# 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)
|
||||
_args_schema = args_schema
|
||||
if _args_schema is None and infer_schema:
|
||||
_args_schema = validate_arguments(func).model # type: ignore
|
||||
tool_ = Tool(
|
||||
name=tool_name,
|
||||
func=func,
|
||||
args_schema=args_schema,
|
||||
args_schema=_args_schema,
|
||||
description=description,
|
||||
return_direct=return_direct,
|
||||
)
|
||||
|
@ -1,75 +1,21 @@
|
||||
"""Base implementation for tools or skills."""
|
||||
|
||||
import inspect
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Type, Union
|
||||
from typing import Any, Dict, Optional, Sequence, Tuple, Type, Union
|
||||
|
||||
from pydantic import BaseModel, Extra, Field, create_model, validator
|
||||
from pydantic import BaseModel, Extra, Field, validator
|
||||
|
||||
from langchain.callbacks import get_callback_manager
|
||||
from langchain.callbacks.base import BaseCallbackManager
|
||||
|
||||
|
||||
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
|
||||
def _to_args_and_kwargs(run_input: Union[str, Dict]) -> Tuple[Sequence, dict]:
|
||||
# For backwards compatability, if run_input is a string,
|
||||
# pass as a positional argument.
|
||||
if isinstance(run_input, str):
|
||||
return (run_input,), {}
|
||||
else:
|
||||
return [], run_input
|
||||
|
||||
|
||||
class BaseTool(ABC, BaseModel):
|
||||
@ -90,31 +36,28 @@ class BaseTool(ABC, BaseModel):
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
@property
|
||||
def args(self) -> Type[BaseModel]:
|
||||
def args(self) -> Union[Type[BaseModel], Type[str]]:
|
||||
"""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)
|
||||
return str if self.args_schema is None else self.args_schema
|
||||
|
||||
def _parse_input(
|
||||
self,
|
||||
tool_input: Union[str, Dict],
|
||||
) -> BaseModel:
|
||||
) -> None:
|
||||
"""Convert tool input to pydantic model."""
|
||||
pydantic_input_type = self.args
|
||||
input_args = 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)
|
||||
if issubclass(input_args, BaseModel):
|
||||
key_ = next(iter(input_args.__fields__.keys()))
|
||||
input_args.validate({key_: tool_input})
|
||||
else:
|
||||
raise ValueError(
|
||||
f"args_schema required for tool {self.name} in order to"
|
||||
f" accept input of type {type(tool_input)}"
|
||||
)
|
||||
if issubclass(input_args, BaseModel):
|
||||
input_args.validate(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(
|
||||
@ -143,20 +86,20 @@ class BaseTool(ABC, BaseModel):
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Run the tool."""
|
||||
run_input = self._parse_input(tool_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},
|
||||
str(run_input),
|
||||
tool_input if isinstance(tool_input, str) else str(tool_input),
|
||||
verbose=verbose_,
|
||||
color=start_color,
|
||||
**kwargs,
|
||||
)
|
||||
try:
|
||||
args, kwargs = _to_args_and_kwargs(run_input)
|
||||
args, kwargs = _to_args_and_kwargs(tool_input)
|
||||
observation = self._run(*args, **kwargs)
|
||||
except (Exception, KeyboardInterrupt) as e:
|
||||
self.callback_manager.on_tool_error(e, verbose=verbose_)
|
||||
@ -175,7 +118,7 @@ class BaseTool(ABC, BaseModel):
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Run the tool asynchronously."""
|
||||
run_input = self._parse_input(tool_input)
|
||||
self._parse_input(tool_input)
|
||||
if not self.verbose and verbose is not None:
|
||||
verbose_ = verbose
|
||||
else:
|
||||
@ -183,7 +126,7 @@ class BaseTool(ABC, BaseModel):
|
||||
if self.callback_manager.is_async:
|
||||
await self.callback_manager.on_tool_start(
|
||||
{"name": self.name, "description": self.description},
|
||||
str(run_input.dict()),
|
||||
tool_input if isinstance(tool_input, str) else str(tool_input),
|
||||
verbose=verbose_,
|
||||
color=start_color,
|
||||
**kwargs,
|
||||
@ -191,14 +134,14 @@ class BaseTool(ABC, BaseModel):
|
||||
else:
|
||||
self.callback_manager.on_tool_start(
|
||||
{"name": self.name, "description": self.description},
|
||||
str(run_input.dict()),
|
||||
tool_input if isinstance(tool_input, str) else str(tool_input),
|
||||
verbose=verbose_,
|
||||
color=start_color,
|
||||
**kwargs,
|
||||
)
|
||||
try:
|
||||
# We then call the tool on the tool input to get an observation
|
||||
args, kwargs = _to_args_and_kwargs(run_input)
|
||||
args, kwargs = _to_args_and_kwargs(tool_input)
|
||||
observation = await self._arun(*args, **kwargs)
|
||||
except (Exception, KeyboardInterrupt) as e:
|
||||
if self.callback_manager.is_async:
|
||||
|
@ -29,6 +29,6 @@ class WriteFileTool(BaseTool):
|
||||
except Exception as e:
|
||||
return "Error: " + str(e)
|
||||
|
||||
async def _arun(self, tool_input: str) -> str:
|
||||
async def _arun(self, file_path: str, text: str) -> str:
|
||||
# TODO: Add aiofiles method
|
||||
raise NotImplementedError
|
||||
|
@ -1,6 +1,6 @@
|
||||
"""Test tool utils."""
|
||||
from datetime import datetime
|
||||
from typing import Any, Optional, Type, Union
|
||||
from typing import Optional, Type, Union
|
||||
|
||||
import pytest
|
||||
from pydantic import BaseModel
|
||||
@ -125,21 +125,27 @@ def test_tool_with_kwargs() -> None:
|
||||
|
||||
@tool(return_direct=True)
|
||||
def search_api(
|
||||
arg_1: float, *args: Any, ping: Optional[str] = None, **kwargs: Any
|
||||
arg_1: float,
|
||||
ping: str = "hi",
|
||||
) -> str:
|
||||
"""Search the API for the query."""
|
||||
return f"arg_1={arg_1}, foo={args}, ping={ping}, kwargs={kwargs}"
|
||||
return f"arg_1={arg_1}, ping={ping}"
|
||||
|
||||
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'}"
|
||||
assert result == "arg_1=3.2, ping=pong"
|
||||
|
||||
result = search_api.run(
|
||||
tool_input={
|
||||
"arg_1": 3.2,
|
||||
}
|
||||
)
|
||||
assert result == "arg_1=3.2, ping=hi"
|
||||
|
||||
|
||||
def test_missing_docstring() -> None:
|
||||
|
Loading…
Reference in New Issue
Block a user