Spaces:
Runtime error
Runtime error
File size: 19,515 Bytes
acc4ffe |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 |
{
"cells": [
{
"cell_type": "markdown",
"id": "5436020b",
"metadata": {},
"source": [
"# Defining Custom Tools\n",
"\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",
"- 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",
"\n",
"There are two ways to define a tool, we will cover both in the example below."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1aaba18c",
"metadata": {},
"outputs": [],
"source": [
"# Import things that are needed generically\n",
"from langchain.agents import initialize_agent, Tool\n",
"from langchain.tools import BaseTool\n",
"from langchain.llms import OpenAI\n",
"from langchain import LLMMathChain, SerpAPIWrapper"
]
},
{
"cell_type": "markdown",
"id": "8e2c3874",
"metadata": {},
"source": [
"Initialize the LLM to use for the agent."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "36ed392e",
"metadata": {},
"outputs": [],
"source": [
"llm = OpenAI(temperature=0)"
]
},
{
"cell_type": "markdown",
"id": "f8bc72c2",
"metadata": {},
"source": [
"## Completely New Tools \n",
"First, we show how to create completely new tools from scratch.\n",
"\n",
"There are two ways to do this: either by using the Tool dataclass, or by subclassing the BaseTool class."
]
},
{
"cell_type": "markdown",
"id": "b63fcc3b",
"metadata": {},
"source": [
"### Tool dataclass"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "56ff7670",
"metadata": {},
"outputs": [],
"source": [
"# Load the tool configs that are needed.\n",
"search = SerpAPIWrapper()\n",
"llm_math_chain = LLMMathChain(llm=llm, verbose=True)\n",
"tools = [\n",
" Tool(\n",
" name = \"Search\",\n",
" func=search.run,\n",
" description=\"useful for when you need to answer questions about current events\"\n",
" ),\n",
" Tool(\n",
" name=\"Calculator\",\n",
" func=llm_math_chain.run,\n",
" description=\"useful for when you need to answer questions about math\"\n",
" )\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5b93047d",
"metadata": {},
"outputs": [],
"source": [
"# Construct the agent. We will use the default agent type here.\n",
"# See documentation for a full list of options.\n",
"agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6f96a891",
"metadata": {},
"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 find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\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: Calculator\n",
"Action Input: 22^0.43\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",
"```\n",
"\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
"\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",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
]
},
{
"cell_type": "markdown",
"id": "6f12eaf0",
"metadata": {},
"source": [
"### Subclassing the BaseTool class"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c58a7c40",
"metadata": {},
"outputs": [],
"source": [
"class CustomSearchTool(BaseTool):\n",
" name = \"Search\"\n",
" description = \"useful for when you need to answer questions about current events\"\n",
"\n",
" def _run(self, query: str) -> str:\n",
" \"\"\"Use the tool.\"\"\"\n",
" return search.run(query)\n",
" \n",
" async def _arun(self, query: str) -> str:\n",
" \"\"\"Use the tool asynchronously.\"\"\"\n",
" raise NotImplementedError(\"BingSearchRun does not support async\")\n",
" \n",
"class CustomCalculatorTool(BaseTool):\n",
" name = \"Calculator\"\n",
" description = \"useful for when you need to answer questions about math\"\n",
"\n",
" def _run(self, query: str) -> str:\n",
" \"\"\"Use the tool.\"\"\"\n",
" return llm_math_chain.run(query)\n",
" \n",
" async def _arun(self, query: str) -> str:\n",
" \"\"\"Use the tool asynchronously.\"\"\"\n",
" raise NotImplementedError(\"BingSearchRun does not support async\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "3318a46f",
"metadata": {},
"outputs": [],
"source": [
"tools = [CustomSearchTool(), CustomCalculatorTool()]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ee2d0f3a",
"metadata": {},
"outputs": [],
"source": [
"agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6a2cebbf",
"metadata": {},
"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 find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\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: Calculator\n",
"Action Input: 22^0.43\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",
"```\n",
"\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
"\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",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
]
},
{
"cell_type": "markdown",
"id": "824eaf74",
"metadata": {},
"source": [
"## Using the `tool` decorator\n",
"\n",
"To make it easier to define custom tools, a `@tool` decorator is provided. This decorator can be used to quickly create a `Tool` from a simple function. The decorator uses the function name as the tool name by default, but this can be overridden by passing a string as the first argument. Additionally, the decorator will use the function's docstring as the tool's description."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8f15307d",
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import tool\n",
"\n",
"@tool\n",
"def search_api(query: str) -> str:\n",
" \"\"\"Searches the API for the query.\"\"\"\n",
" return \"Results\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "0a23b91b",
"metadata": {},
"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)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search_api"
]
},
{
"cell_type": "markdown",
"id": "cc6ee8c1",
"metadata": {},
"source": [
"You can also provide arguments like the tool name and whether to return directly."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "28cdf04d",
"metadata": {},
"outputs": [],
"source": [
"@tool(\"search\", return_direct=True)\n",
"def search_api(query: str) -> str:\n",
" \"\"\"Searches the API for the query.\"\"\"\n",
" return \"Results\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"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)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search_api"
]
},
{
"cell_type": "markdown",
"id": "1d0430d6",
"metadata": {},
"source": [
"## Modify existing tools\n",
"\n",
"Now, we show how to load existing tools and just modify them. In the example below, we do something really simple and change the Search tool to have the name `Google Search`."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "79213f40",
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import load_tools"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e1067dcb",
"metadata": {},
"outputs": [],
"source": [
"tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "6c66ffe8",
"metadata": {},
"outputs": [],
"source": [
"tools[0].name = \"Google Search\""
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f45b5bc3",
"metadata": {},
"outputs": [],
"source": [
"agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "565e2b9b",
"metadata": {},
"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 find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\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: 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",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
]
},
{
"cell_type": "markdown",
"id": "376813ed",
"metadata": {},
"source": [
"## Defining the priorities among Tools\n",
"When you made a Custom tool, you may want the Agent to use the custom tool more than normal tools.\n",
"\n",
"For example, you made a custom tool, which gets information on music from your database. When a user wants information on songs, You want the Agent to use `the custom tool` more than the normal `Search tool`. But the Agent might prioritize a normal Search tool.\n",
"\n",
"This can be accomplished by adding a statement such as `Use this more than the normal search if the question is about Music, like 'who is the singer of yesterday?' or 'what is the most popular song in 2022?'` to the description.\n",
"\n",
"An example is below."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "3450512e",
"metadata": {},
"outputs": [],
"source": [
"# Import things that are needed generically\n",
"from langchain.agents import initialize_agent, Tool\n",
"from langchain.llms import OpenAI\n",
"from langchain import LLMMathChain, SerpAPIWrapper\n",
"search = SerpAPIWrapper()\n",
"tools = [\n",
" Tool(\n",
" name = \"Search\",\n",
" func=search.run,\n",
" description=\"useful for when you need to answer questions about current events\"\n",
" ),\n",
" Tool(\n",
" name=\"Music Search\",\n",
" func=lambda x: \"'All I Want For Christmas Is You' by Mariah Carey.\", #Mock Function\n",
" description=\"A Music search engine. Use this more than the normal search if the question is about Music, like 'who is the singer of yesterday?' or 'what is the most popular song in 2022?'\",\n",
" )\n",
"]\n",
"\n",
"agent = initialize_agent(tools, OpenAI(temperature=0), agent=\"zero-shot-react-description\", verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "4b9a7849",
"metadata": {},
"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 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",
"Final Answer: 'All I Want For Christmas Is You' by Mariah Carey.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"'All I Want For Christmas Is You' by Mariah Carey.\""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"what is the most famous song of christmas\")"
]
},
{
"cell_type": "markdown",
"id": "bc477d43",
"metadata": {},
"source": [
"## Using tools to return directly\n",
"Often, it can be desirable to have a tool output returned directly to the user, if it’s called. You can do this easily with LangChain by setting the return_direct flag for a tool to be True."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "3bb6185f",
"metadata": {},
"outputs": [],
"source": [
"llm_math_chain = LLMMathChain(llm=llm)\n",
"tools = [\n",
" Tool(\n",
" name=\"Calculator\",\n",
" func=llm_math_chain.run,\n",
" description=\"useful for when you need to answer questions about math\",\n",
" return_direct=True\n",
" )\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "113ddb84",
"metadata": {},
"outputs": [],
"source": [
"llm = OpenAI(temperature=0)\n",
"agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "582439a6",
"metadata": {},
"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 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",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Answer: 1.2599210498948732'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"whats 2**.12\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "537bc628",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
},
"vscode": {
"interpreter": {
"hash": "e90c8aa204a57276aa905271aff2d11799d0acb3547adabc5892e639a5e45e34"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|