Archives/langchain
2
0
Fork 1
mirror of https://github.com/hwchase17/langchain synced 2024-11-10 01:10:59 +00:00
Code Issues Packages Projects Releases Wiki Activity

docs: more tool call -> tool message docs (#24271)

Browse Source
This commit is contained in:
Bagatur 2024-07-15 07:55:07 -07:00 committed by GitHub
parent 620b118c70
commit a0958c0607
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
3 changed files with 253 additions and 40 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

182
docs/docs/how_to/custom_tools.ipynb
View File

@ -768,13 +768,189 @@
"\n",
"get_weather_tool.invoke({\"city\": \"foobar\"})"
]
},
{
"cell_type": "markdown",
"id": "1a8d8383-11b3-445e-956f-df4e96995e00",
"metadata": {},
"source": [
"## Returning artifacts of Tool execution\n",
"\n",
"Sometimes there are artifacts of a tool's execution that we want to make accessible to downstream components in our chain or agent, but that we don't want to expose to the model itself. For example if a tool returns custom objects like Documents, we may want to pass some view or metadata about this output to the model without passing the raw output to the model. At the same time, we may want to be able to access this full output elsewhere, for example in downstream tools.\n",
"\n",
"The Tool and [ToolMessage](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.tool.ToolMessage.html) interfaces make it possible to distinguish between the parts of the tool output meant for the model (this is the ToolMessage.content) and those parts which are meant for use outside the model (ToolMessage.artifact).\n",
"\n",
":::info Requires ``langchain-core >= 0.2.19``\n",
"\n",
"This functionality was added in ``langchain-core == 0.2.19``. Please make sure your package is up to date.\n",
"\n",
":::\n",
"\n",
"If we want our tool to distinguish between message content and other artifacts, we need to specify `response_format=\"content_and_artifact\"` when defining our tool and make sure that we return a tuple of (content, artifact):"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "14905425-0334-43a0-9de9-5bcf622ede0e",
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"from typing import List, Tuple\n",
"\n",
"from langchain_core.tools import tool\n",
"\n",
"\n",
"@tool(response_format=\"content_and_artifact\")\n",
"def generate_random_ints(min: int, max: int, size: int) -> Tuple[str, List[int]]:\n",
" \"\"\"Generate size random ints in the range [min, max].\"\"\"\n",
" array = [random.randint(min, max) for _ in range(size)]\n",
" content = f\"Successfully generated array of {size} random ints in [{min}, {max}].\"\n",
" return content, array"
]
},
{
"cell_type": "markdown",
"id": "49f057a6-8938-43ea-8faf-ae41e797ceb8",
"metadata": {},
"source": [
"If we invoke our tool directly with the tool arguments, we'll get back just the content part of the output:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0f2e1528-404b-46e6-b87c-f0957c4b9217",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Successfully generated array of 10 random ints in [0, 9].'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"generate_random_ints.invoke({\"min\": 0, \"max\": 9, \"size\": 10})"
]
},
{
"cell_type": "markdown",
"id": "1e62ebba-1737-4b97-b61a-7313ade4e8c2",
"metadata": {},
"source": [
"If we invoke our tool with a ToolCall (like the ones generated by tool-calling models), we'll get back a ToolMessage that contains both the content and artifact generated by the Tool:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "cc197777-26eb-46b3-a83b-c2ce116c6311",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ToolMessage(content='Successfully generated array of 10 random ints in [0, 9].', name='generate_random_ints', tool_call_id='123', artifact=[1, 4, 2, 5, 3, 9, 0, 4, 7, 7])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"generate_random_ints.invoke(\n",
" {\n",
" \"name\": \"generate_random_ints\",\n",
" \"args\": {\"min\": 0, \"max\": 9, \"size\": 10},\n",
" \"id\": \"123\", # required\n",
" \"type\": \"tool_call\", # required\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"id": "dfdc1040-bf25-4790-b4c3-59452db84e11",
"metadata": {},
"source": [
"We can do the same when subclassing BaseTool:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "fe1a09d1-378b-4b91-bb5e-0697c3d7eb92",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.tools import BaseTool\n",
"\n",
"\n",
"class GenerateRandomFloats(BaseTool):\n",
" name: str = \"generate_random_floats\"\n",
" description: str = \"Generate size random floats in the range [min, max].\"\n",
" response_format: str = \"content_and_artifact\"\n",
"\n",
" ndigits: int = 2\n",
"\n",
" def _run(self, min: float, max: float, size: int) -> Tuple[str, List[float]]:\n",
" range_ = max - min\n",
" array = [\n",
" round(min + (range_ * random.random()), ndigits=self.ndigits)\n",
" for _ in range(size)\n",
" ]\n",
" content = f\"Generated {size} floats in [{min}, {max}], rounded to {self.ndigits} decimals.\"\n",
" return content, array\n",
"\n",
" # Optionally define an equivalent async method\n",
"\n",
" # async def _arun(self, min: float, max: float, size: int) -> Tuple[str, List[float]]:\n",
" # ..."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "8c3d16f6-1c4a-48ab-b05a-38547c592e79",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ToolMessage(content='Generated 3 floats in [0.1, 3.3333], rounded to 4 decimals.', name='generate_random_floats', tool_call_id='123', artifact=[1.4277, 0.7578, 2.4871])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rand_gen = GenerateRandomFloats(ndigits=4)\n",
"\n",
"rand_gen.invoke(\n",
" {\n",
" \"name\": \"generate_random_floats\",\n",
" \"args\": {\"min\": 0.1, \"max\": 3.3333, \"size\": 3},\n",
" \"id\": \"123\",\n",
" \"type\": \"tool_call\",\n",
" }\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "poetry-venv-311",
"language": "python",
"name": "python3"
"name": "poetry-venv-311"
},
"language_info": {
"codemirror_mode": {
@ -786,7 +962,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
"version": "3.11.9"
},
"vscode": {
"interpreter": {

49
docs/docs/how_to/tool_artifacts.ipynb
View File

@ -19,9 +19,9 @@
"\n",
"The Tool and [ToolMessage](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.tool.ToolMessage.html) interfaces make it possible to distinguish between the parts of the tool output meant for the model (this is the ToolMessage.content) and those parts which are meant for use outside the model (ToolMessage.artifact).\n",
"\n",
":::info Requires ``langchain-core >= 0.2.18``\n",
":::info Requires ``langchain-core >= 0.2.19``\n",
"\n",
"This functionality was added in ``langchain-core == 0.2.18``. Please make sure your package is up to date.\n",
"This functionality was added in ``langchain-core == 0.2.19``. Please make sure your package is up to date.\n",
"\n",
":::\n",
"\n",
@ -37,7 +37,7 @@
"metadata": {},
"outputs": [],
"source": [
"%pip install -qU \"langchain-core>=0.2.18\""
"%pip install -qU \"langchain-core>=0.2.19\""
]
},
{
@ -73,7 +73,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 3,
"id": "5e7d5e77-3102-4a59-8ade-e4e699dd1817",
"metadata": {},
"outputs": [
@ -83,9 +83,16 @@
"'Successfully generated array of 10 random ints in [0, 9].'"
]
},
"execution_count": 8,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Failed to batch ingest runs: LangSmithRateLimitError('Rate limit exceeded for https://api.smith.langchain.com/runs/batch. HTTPError(\\'429 Client Error: Too Many Requests for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Monthly unique traces usage limit exceeded\"}\\')')\n"
]
}
],
"source": [
@ -109,7 +116,7 @@
{
"data": {
"text/plain": [
"ToolMessage(content='Successfully generated array of 10 random ints in [0, 9].', tool_call_id='123', artifact=[7, 0, 5, 3, 1, 7, 9, 3, 1, 0])"
"ToolMessage(content='Successfully generated array of 10 random ints in [0, 9].', name='generate_random_ints', tool_call_id='123', artifact=[2, 8, 0, 6, 0, 0, 1, 5, 0, 0])"
]
},
"execution_count": 4,
@ -122,8 +129,8 @@
" {\n",
" \"name\": \"generate_random_ints\",\n",
" \"args\": {\"min\": 0, \"max\": 9, \"size\": 10},\n",
" \"id\": \"123\",\n",
" \"type\": \"tool_call\",\n",
" \"id\": \"123\", # required\n",
" \"type\": \"tool_call\", # required\n",
" }\n",
")"
]
@ -172,7 +179,7 @@
"text/plain": [
"[{'name': 'generate_random_ints',\n",
" 'args': {'min': 1, 'max': 24, 'size': 6},\n",
" 'id': 'toolu_014wkiiCjbnJzUiR7fJXnCCY',\n",
" 'id': 'toolu_01EtALY3Wz1DVYhv1TLvZGvE',\n",
" 'type': 'tool_call'}]"
]
},
@ -197,7 +204,7 @@
{
"data": {
"text/plain": [
"ToolMessage(content='Successfully generated array of 6 random ints in [1, 24].', tool_call_id='toolu_014wkiiCjbnJzUiR7fJXnCCY', artifact=[9, 13, 10, 16, 23, 11])"
"ToolMessage(content='Successfully generated array of 6 random ints in [1, 24].', name='generate_random_ints', tool_call_id='toolu_01EtALY3Wz1DVYhv1TLvZGvE', artifact=[2, 20, 23, 8, 1, 15])"
]
},
"execution_count": 7,
@ -219,7 +226,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 8,
"id": "f4a6c9a6-0ffc-4b0e-a59f-f3c3d69d824d",
"metadata": {},
"outputs": [
@ -229,7 +236,7 @@
"'Successfully generated array of 6 random ints in [1, 24].'"
]
},
"execution_count": 7,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@ -248,17 +255,17 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 9,
"id": "eb55ec23-95a4-464e-b886-d9679bf3aaa2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[ToolMessage(content='Successfully generated array of 1 random ints in [1, 5].', tool_call_id='toolu_01UZiQLczkDx3ELv27ureuCP', artifact=[1])]"
"[ToolMessage(content='Successfully generated array of 1 random ints in [1, 5].', name='generate_random_ints', tool_call_id='toolu_01FwYhnkwDPJPbKdGq4ng6uD', artifact=[5])]"
]
},
"execution_count": 10,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@ -283,7 +290,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 10,
"id": "9a9129e1-6aee-4a10-ad57-62ef3bf0276c",
"metadata": {},
"outputs": [],
@ -315,7 +322,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 11,
"id": "d7322619-f420-4b29-8ee5-023e693d0179",
"metadata": {},
"outputs": [
@ -325,7 +332,7 @@
"'Generated 3 floats in [0.1, 3.3333], rounded to 4 decimals.'"
]
},
"execution_count": 21,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@ -337,17 +344,17 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 12,
"id": "0892f277-23a6-4bb8-a0e9-59f533ac9750",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ToolMessage(content='Generated 3 floats in [0.1, 3.3333], rounded to 4 decimals.', tool_call_id='123', artifact=[0.7306, 1.8991, 0.1615])"
"ToolMessage(content='Generated 3 floats in [0.1, 3.3333], rounded to 4 decimals.', name='generate_random_floats', tool_call_id='123', artifact=[1.5789, 2.464, 2.2719])"
]
},
"execution_count": 22,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}

62
docs/docs/how_to/tool_results_pass_to_model.ipynb
View File

@ -6,12 +6,20 @@
"source": [
"# How to pass tool outputs to the model\n",
"\n",
"If we're using the model-generated tool invocations to actually call tools and want to pass the tool results back to the model, we can do so using `ToolMessage`s. First, let's define our tools and our model."
":::info Prerequisites\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [Tools](/docs/concepts/#tools)\n",
"- [Function/tool calling](/docs/concepts/#functiontool-calling)\n",
"\n",
":::\n",
"\n",
"If we're using the model-generated tool invocations to actually call tools and want to pass the tool results back to the model, we can do so using `ToolMessage`s and `ToolCall`s. First, let's define our tools and our model."
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@ -35,7 +43,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@ -54,25 +62,32 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can use ``ToolMessage`` to pass back the output of the tool calls to the model."
"The nice thing about Tools is that if we invoke them with a ToolCall, we'll automatically get back a ToolMessage that can be fed back to the model: \n",
"\n",
":::info Requires ``langchain-core >= 0.2.19``\n",
"\n",
"This functionality was added in ``langchain-core == 0.2.19``. Please make sure your package is up to date.\n",
"\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[HumanMessage(content='What is 3 * 12? Also, what is 11 + 49?'),\n",
" AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_svc2GLSxNFALbaCAbSjMI9J8', 'function': {'arguments': '{\"a\": 3, \"b\": 12}', 'name': 'Multiply'}, 'type': 'function'}, {'id': 'call_r8jxte3zW6h3MEGV3zH2qzFh', 'function': {'arguments': '{\"a\": 11, \"b\": 49}', 'name': 'Add'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 50, 'prompt_tokens': 105, 'total_tokens': 155}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_d9767fc5b9', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-a79ad1dd-95f1-4a46-b688-4c83f327a7b3-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_svc2GLSxNFALbaCAbSjMI9J8'}, {'name': 'Add', 'args': {'a': 11, 'b': 49}, 'id': 'call_r8jxte3zW6h3MEGV3zH2qzFh'}]),\n",
" ToolMessage(content='36', tool_call_id='call_svc2GLSxNFALbaCAbSjMI9J8'),\n",
" ToolMessage(content='60', tool_call_id='call_r8jxte3zW6h3MEGV3zH2qzFh')]"
" AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Smg3NHJNxrKfAmd4f9GkaYn3', 'function': {'arguments': '{\"a\": 3, \"b\": 12}', 'name': 'multiply'}, 'type': 'function'}, {'id': 'call_55K1C0DmH6U5qh810gW34xZ0', 'function': {'arguments': '{\"a\": 11, \"b\": 49}', 'name': 'add'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 49, 'prompt_tokens': 88, 'total_tokens': 137}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-56657feb-96dd-456c-ab8e-1857eab2ade0-0', tool_calls=[{'name': 'multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_Smg3NHJNxrKfAmd4f9GkaYn3', 'type': 'tool_call'}, {'name': 'add', 'args': {'a': 11, 'b': 49}, 'id': 'call_55K1C0DmH6U5qh810gW34xZ0', 'type': 'tool_call'}], usage_metadata={'input_tokens': 88, 'output_tokens': 49, 'total_tokens': 137}),\n",
" ToolMessage(content='36', name='multiply', tool_call_id='call_Smg3NHJNxrKfAmd4f9GkaYn3'),\n",
" ToolMessage(content='60', name='add', tool_call_id='call_55K1C0DmH6U5qh810gW34xZ0')]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "display_data"
"output_type": "execute_result"
}
],
"source": [
@ -85,24 +100,25 @@
"messages.append(ai_msg)\n",
"for tool_call in ai_msg.tool_calls:\n",
" selected_tool = {\"add\": add, \"multiply\": multiply}[tool_call[\"name\"].lower()]\n",
" tool_output = selected_tool.invoke(tool_call[\"args\"])\n",
" messages.append(ToolMessage(tool_output, tool_call_id=tool_call[\"id\"]))\n",
" tool_msg = selected_tool.invoke(tool_call)\n",
" messages.append(tool_msg)\n",
"messages"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='3 * 12 is 36 and 11 + 49 is 60.', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 171, 'total_tokens': 189}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_d9767fc5b9', 'finish_reason': 'stop', 'logprobs': None}, id='run-20b52149-e00d-48ea-97cf-f8de7a255f8c-0')"
"AIMessage(content='3 * 12 is 36 and 11 + 49 is 60.', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 153, 'total_tokens': 171}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-ba5032f0-f773-406d-a408-8314e66511d0-0', usage_metadata={'input_tokens': 153, 'output_tokens': 18, 'total_tokens': 171})"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "display_data"
"output_type": "execute_result"
}
],
"source": [
@ -118,10 +134,24 @@
}
],
"metadata": {
"kernelspec": {
"display_name": "poetry-venv-311",
"language": "python",
"name": "poetry-venv-311"
},
"language_info": {
"name": "python"
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 4
}