mirror of
https://github.com/hwchase17/langchain.git
synced 2025-08-28 14:05:02 +00:00
docs[patch]: Update OllamaFunctions docs to match chat model integration template (#23179)
Added Tool Calling Agent Example with langgraph to OllamaFunctions documentation
This commit is contained in:
Karim Lalani
GitHub
parent
79c07a8ade
commit
acc8fb3ead
@ -15,7 +15,7 @@
|
||||
"source": [
|
||||
"# OllamaFunctions\n",
|
||||
"\n",
|
||||
"This notebook shows how to use an experimental wrapper around Ollama that gives it the same API as OpenAI Functions.\n",
|
||||
"This notebook shows how to use an experimental wrapper around Ollama that gives it [tool calling capabilities](https://python.langchain.com/v0.2/docs/concepts/#functiontool-calling).\n",
|
||||
"\n",
|
||||
"Note that more powerful and capable models will perform better with complex schema and/or multiple functions. The examples below use llama3 and phi3 models.\n",
|
||||
"For a complete list of supported models and model variants, see the [Ollama model library](https://ollama.ai/library).\n",
|
||||
@ -25,81 +25,75 @@
|
||||
"This is an experimental wrapper that attempts to bolt-on tool calling support to models that do not natively support it. Use with caution.\n",
|
||||
"\n",
|
||||
":::\n",
|
||||
"## Overview\n",
|
||||
"\n",
|
||||
"### Integration details\n",
|
||||
"\n",
|
||||
"| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
|
||||
"|:-----------------------------------------------------------------------------------------------------------------------------------:|:-------:|:-----:|:------------:|:----------:|:-----------------:|:--------------:|\n",
|
||||
"| [OllamaFunctions](https://api.python.langchain.com/en/latest/llms/langchain_experimental.llms.ollama_function.OllamaFunctions.html) | [langchain-experimental](https://api.python.langchain.com/en/latest/openai_api_reference.html) | ✅ | ❌ | ❌ |  |  |\n",
|
||||
"\n",
|
||||
"### Model features\n",
|
||||
"\n",
|
||||
"| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/structured_output/) | JSON mode | Image input | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
|
||||
"| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n",
|
||||
"| ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |\n",
|
||||
"\n",
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
"Follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance.\n",
|
||||
"To access `OllamaFunctions` you will need to install `langchain-experimental` integration package.\n",
|
||||
"Follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance as well as download and serve [supported models](https://ollama.com/library).\n",
|
||||
"\n",
|
||||
"## Usage\n",
|
||||
"### Credentials\n",
|
||||
"\n",
|
||||
"You can initialize OllamaFunctions in a similar way to how you'd initialize a standard ChatOllama instance:"
|
||||
"Credentials support is not present at this time.\n",
|
||||
"\n",
|
||||
"### Installation\n",
|
||||
"\n",
|
||||
"The `OllamaFunctions` class lives in the `langchain-experimental` package:\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%pip install -qU langchain-experimental"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Instantiation\n",
|
||||
"\n",
|
||||
"`OllamaFunctions` takes the same init parameters as `ChatOllama`. \n",
|
||||
"\n",
|
||||
"In order to use tool calling, you must also specify `format=\"json\"`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-04-28T00:53:25.276543Z",
|
||||
"start_time": "2024-04-28T00:53:24.881202Z"
|
||||
},
|
||||
"scrolled": true
|
||||
"end_time": "2024-06-23T15:20:21.818089Z",
|
||||
"start_time": "2024-06-23T15:20:21.815759Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_experimental.llms.ollama_functions import OllamaFunctions\n",
|
||||
"\n",
|
||||
"model = OllamaFunctions(model=\"llama3\", format=\"json\")"
|
||||
"llm = OllamaFunctions(model=\"phi3\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can then bind functions defined with JSON Schema parameters and a `function_call` parameter to force the model to call the given function:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-04-26T04:59:17.270931Z",
|
||||
"start_time": "2024-04-26T04:59:17.263347Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = model.bind_tools(\n",
|
||||
" tools=[\n",
|
||||
" {\n",
|
||||
" \"name\": \"get_current_weather\",\n",
|
||||
" \"description\": \"Get the current weather in a given location\",\n",
|
||||
" \"parameters\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"location\": {\n",
|
||||
" \"type\": \"string\",\n",
|
||||
" \"description\": \"The city and state, \" \"e.g. San Francisco, CA\",\n",
|
||||
" },\n",
|
||||
" \"unit\": {\n",
|
||||
" \"type\": \"string\",\n",
|
||||
" \"enum\": [\"celsius\", \"fahrenheit\"],\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" \"required\": [\"location\"],\n",
|
||||
" },\n",
|
||||
" }\n",
|
||||
" ],\n",
|
||||
" function_call={\"name\": \"get_current_weather\"},\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Calling a function with this model then results in JSON output matching the provided schema:"
|
||||
"## Invocation"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -107,15 +101,15 @@
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-04-26T04:59:26.092428Z",
|
||||
"start_time": "2024-04-26T04:59:17.272627Z"
|
||||
"end_time": "2024-06-23T15:20:46.794689Z",
|
||||
"start_time": "2024-06-23T15:20:44.982632Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_current_weather', 'arguments': '{\"location\": \"Boston, MA\"}'}}, id='run-1791f9fe-95ad-4ca4-bdf7-9f73eab31e6f-0')"
|
||||
"AIMessage(content=\"J'adore programmer.\", id='run-94815fcf-ae11-438a-ba3f-00819328b5cd-0')"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
@ -124,79 +118,55 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain_core.messages import HumanMessage\n",
|
||||
"\n",
|
||||
"model.invoke(\"what is the weather in Boston?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Structured Output\n",
|
||||
"\n",
|
||||
"One useful thing you can do with function calling using `with_structured_output()` function is extracting properties from a given input in a structured format:"
|
||||
"messages = [\n",
|
||||
" (\n",
|
||||
" \"system\",\n",
|
||||
" \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
|
||||
" ),\n",
|
||||
" (\"human\", \"I love programming.\"),\n",
|
||||
"]\n",
|
||||
"ai_msg = llm.invoke(messages)\n",
|
||||
"ai_msg"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-04-26T04:59:26.098828Z",
|
||||
"start_time": "2024-04-26T04:59:26.094021Z"
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"\"J'adore programmer.\""
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
],
|
||||
"source": [
|
||||
"from langchain_core.prompts import PromptTemplate\n",
|
||||
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Schema for structured response\n",
|
||||
"class Person(BaseModel):\n",
|
||||
" name: str = Field(description=\"The person's name\", required=True)\n",
|
||||
" height: float = Field(description=\"The person's height\", required=True)\n",
|
||||
" hair_color: str = Field(description=\"The person's hair color\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Prompt template\n",
|
||||
"prompt = PromptTemplate.from_template(\n",
|
||||
" \"\"\"Alex is 5 feet tall. \n",
|
||||
"Claudia is 1 feet taller than Alex and jumps higher than him. \n",
|
||||
"Claudia is a brunette and Alex is blonde.\n",
|
||||
"\n",
|
||||
"Human: {question}\n",
|
||||
"AI: \"\"\"\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Chain\n",
|
||||
"llm = OllamaFunctions(model=\"phi3\", format=\"json\", temperature=0)\n",
|
||||
"structured_llm = llm.with_structured_output(Person)\n",
|
||||
"chain = prompt | structured_llm"
|
||||
"ai_msg.content"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Extracting data about Alex"
|
||||
"## Chaining\n",
|
||||
"\n",
|
||||
"We can [chain](https://python.langchain.com/v0.2/docs/how_to/sequence/) our model with a prompt template like so:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-04-26T04:59:30.164955Z",
|
||||
"start_time": "2024-04-26T04:59:26.099790Z"
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Person(name='Alex', height=5.0, hair_color='blonde')"
|
||||
"AIMessage(content='Programmieren ist sehr verrückt! Es freut mich, dass Sie auf Programmierung so positiv eingestellt sind.', id='run-ee99be5e-4d48-4ab6-b602-35415f0bdbde-0')"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
@ -205,41 +175,123 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"alex = chain.invoke(\"Describe Alex\")\n",
|
||||
"alex"
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"\n",
|
||||
"prompt = ChatPromptTemplate.from_messages(\n",
|
||||
" [\n",
|
||||
" (\n",
|
||||
" \"system\",\n",
|
||||
" \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
|
||||
" ),\n",
|
||||
" (\"human\", \"{input}\"),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"chain = prompt | llm\n",
|
||||
"chain.invoke(\n",
|
||||
" {\n",
|
||||
" \"input_language\": \"English\",\n",
|
||||
" \"output_language\": \"German\",\n",
|
||||
" \"input\": \"I love programming.\",\n",
|
||||
" }\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Extracting data about Claudia"
|
||||
"## Tool Calling\n",
|
||||
"\n",
|
||||
"### OllamaFunctions.bind_tools()\n",
|
||||
"\n",
|
||||
"With `OllamaFunctions.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to a tool definition schemas, which looks like:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-04-26T04:59:31.509846Z",
|
||||
"start_time": "2024-04-26T04:59:30.165662Z"
|
||||
}
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class GetWeather(BaseModel):\n",
|
||||
" \"\"\"Get the current weather in a given location\"\"\"\n",
|
||||
"\n",
|
||||
" location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"llm_with_tools = llm.bind_tools([GetWeather])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Person(name='Claudia', height=6.0, hair_color='brunette')"
|
||||
"AIMessage(content='', id='run-b9769435-ec6a-4cb8-8545-5a5035fc19bd-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': 'call_064c4e1cb27e4adb9e4e7ed60362ecc9'}])"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"claudia = chain.invoke(\"Describe Claudia\")\n",
|
||||
"claudia"
|
||||
"ai_msg = llm_with_tools.invoke(\n",
|
||||
" \"what is the weather like in San Francisco\",\n",
|
||||
")\n",
|
||||
"ai_msg"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### AIMessage.tool_calls\n",
|
||||
"\n",
|
||||
"Notice that the AIMessage has a `tool_calls` attribute. This contains in a standardized `ToolCall` format that is model-provider agnostic."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[{'name': 'GetWeather',\n",
|
||||
" 'args': {'location': 'San Francisco, CA'},\n",
|
||||
" 'id': 'call_064c4e1cb27e4adb9e4e7ed60362ecc9'}]"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ai_msg.tool_calls"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": "For more on binding tools and tool call outputs, head to the [tool calling](docs/how_to/function_calling) docs."
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## API reference\n",
|
||||
"\n",
|
||||
"For detailed documentation of all ToolCallingLLM features and configurations head to the API reference: https://api.python.langchain.com/en/latest/llms/langchain_experimental.llms.ollama_functions.OllamaFunctions.html\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -259,7 +311,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.1"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user