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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 @@
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"source": [
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"# OllamaFunctions\n",
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"\n",
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"This notebook shows how to use an experimental wrapper around Ollama that gives it the same API as OpenAI Functions.\n",
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"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",
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"\n",
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"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",
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"For a complete list of supported models and model variants, see the [Ollama model library](https://ollama.ai/library).\n",
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@ -25,81 +25,75 @@
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"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",
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"\n",
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":::\n",
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"## Overview\n",
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"\n",
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"### Integration details\n",
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"\n",
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"| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
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"|:-----------------------------------------------------------------------------------------------------------------------------------:|:-------:|:-----:|:------------:|:----------:|:-----------------:|:--------------:|\n",
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"| [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",
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"\n",
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"### Model features\n",
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"\n",
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"| [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",
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"| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n",
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"| ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |\n",
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"\n",
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"## Setup\n",
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"\n",
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"Follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance.\n",
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"To access `OllamaFunctions` you will need to install `langchain-experimental` integration package.\n",
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"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",
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"\n",
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"## Usage\n",
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"### Credentials\n",
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"\n",
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"You can initialize OllamaFunctions in a similar way to how you'd initialize a standard ChatOllama instance:"
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"Credentials support is not present at this time.\n",
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"\n",
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"### Installation\n",
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"\n",
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"The `OllamaFunctions` class lives in the `langchain-experimental` package:\n"
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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": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU langchain-experimental"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Instantiation\n",
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"\n",
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"`OllamaFunctions` takes the same init parameters as `ChatOllama`. \n",
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"\n",
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"In order to use tool calling, you must also specify `format=\"json\"`."
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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": 6,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-28T00:53:25.276543Z",
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"start_time": "2024-04-28T00:53:24.881202Z"
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},
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"scrolled": true
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"end_time": "2024-06-23T15:20:21.818089Z",
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"start_time": "2024-06-23T15:20:21.815759Z"
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}
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},
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"outputs": [],
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"source": [
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"from langchain_experimental.llms.ollama_functions import OllamaFunctions\n",
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"\n",
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"model = OllamaFunctions(model=\"llama3\", format=\"json\")"
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"llm = OllamaFunctions(model=\"phi3\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can then bind functions defined with JSON Schema parameters and a `function_call` parameter to force the model to call the given function:"
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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": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-26T04:59:17.270931Z",
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"start_time": "2024-04-26T04:59:17.263347Z"
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}
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},
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"outputs": [],
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"source": [
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"model = model.bind_tools(\n",
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" tools=[\n",
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" {\n",
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" \"name\": \"get_current_weather\",\n",
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" \"description\": \"Get the current weather in a given location\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"location\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"The city and state, \" \"e.g. San Francisco, CA\",\n",
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" },\n",
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" \"unit\": {\n",
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" \"type\": \"string\",\n",
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" \"enum\": [\"celsius\", \"fahrenheit\"],\n",
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" },\n",
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" },\n",
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" \"required\": [\"location\"],\n",
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" },\n",
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" }\n",
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" ],\n",
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" function_call={\"name\": \"get_current_weather\"},\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Calling a function with this model then results in JSON output matching the provided schema:"
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"## Invocation"
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]
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},
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{
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@ -107,15 +101,15 @@
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"execution_count": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-26T04:59:26.092428Z",
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"start_time": "2024-04-26T04:59:17.272627Z"
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"end_time": "2024-06-23T15:20:46.794689Z",
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"start_time": "2024-06-23T15:20:44.982632Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_current_weather', 'arguments': '{\"location\": \"Boston, MA\"}'}}, id='run-1791f9fe-95ad-4ca4-bdf7-9f73eab31e6f-0')"
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"AIMessage(content=\"J'adore programmer.\", id='run-94815fcf-ae11-438a-ba3f-00819328b5cd-0')"
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]
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},
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"execution_count": 3,
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@ -124,79 +118,55 @@
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}
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],
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"source": [
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"from langchain_core.messages import HumanMessage\n",
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"\n",
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"model.invoke(\"what is the weather in Boston?\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Structured Output\n",
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"\n",
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"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:"
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"messages = [\n",
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" (\n",
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" \"system\",\n",
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" \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
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" ),\n",
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" (\"human\", \"I love programming.\"),\n",
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"]\n",
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"ai_msg = llm.invoke(messages)\n",
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"ai_msg"
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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": 4,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-26T04:59:26.098828Z",
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"start_time": "2024-04-26T04:59:26.094021Z"
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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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"\"J'adore programmer.\""
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]
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},
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"execution_count": 4,
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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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],
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"source": [
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"from langchain_core.prompts import PromptTemplate\n",
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"from langchain_core.pydantic_v1 import BaseModel, Field\n",
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"\n",
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"\n",
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"# Schema for structured response\n",
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"class Person(BaseModel):\n",
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" name: str = Field(description=\"The person's name\", required=True)\n",
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" height: float = Field(description=\"The person's height\", required=True)\n",
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" hair_color: str = Field(description=\"The person's hair color\")\n",
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"\n",
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"\n",
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"# Prompt template\n",
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"prompt = PromptTemplate.from_template(\n",
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" \"\"\"Alex is 5 feet tall. \n",
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"Claudia is 1 feet taller than Alex and jumps higher than him. \n",
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"Claudia is a brunette and Alex is blonde.\n",
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"\n",
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"Human: {question}\n",
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"AI: \"\"\"\n",
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")\n",
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"\n",
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"# Chain\n",
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"llm = OllamaFunctions(model=\"phi3\", format=\"json\", temperature=0)\n",
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"structured_llm = llm.with_structured_output(Person)\n",
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"chain = prompt | structured_llm"
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"ai_msg.content"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Extracting data about Alex"
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"## Chaining\n",
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"\n",
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"We can [chain](https://python.langchain.com/v0.2/docs/how_to/sequence/) our model with a prompt template like so:"
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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": 5,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-26T04:59:30.164955Z",
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"start_time": "2024-04-26T04:59:26.099790Z"
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}
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},
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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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"Person(name='Alex', height=5.0, hair_color='blonde')"
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"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')"
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]
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},
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"execution_count": 5,
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@ -205,41 +175,123 @@
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}
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],
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"source": [
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"alex = chain.invoke(\"Describe Alex\")\n",
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"alex"
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\n",
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" \"system\",\n",
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" \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
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" ),\n",
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" (\"human\", \"{input}\"),\n",
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" ]\n",
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")\n",
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"\n",
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"chain = prompt | llm\n",
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"chain.invoke(\n",
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" {\n",
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" \"input_language\": \"English\",\n",
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" \"output_language\": \"German\",\n",
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" \"input\": \"I love programming.\",\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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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Extracting data about Claudia"
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"## Tool Calling\n",
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"\n",
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"### OllamaFunctions.bind_tools()\n",
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"\n",
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"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:"
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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": 6,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-26T04:59:31.509846Z",
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"start_time": "2024-04-26T04:59:30.165662Z"
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}
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},
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.pydantic_v1 import BaseModel, Field\n",
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"\n",
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"\n",
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"class GetWeather(BaseModel):\n",
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" \"\"\"Get the current weather in a given location\"\"\"\n",
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"\n",
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" location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
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"\n",
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"\n",
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"llm_with_tools = llm.bind_tools([GetWeather])"
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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": 8,
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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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"Person(name='Claudia', height=6.0, hair_color='brunette')"
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"AIMessage(content='', id='run-b9769435-ec6a-4cb8-8545-5a5035fc19bd-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': 'call_064c4e1cb27e4adb9e4e7ed60362ecc9'}])"
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]
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},
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"execution_count": 6,
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"execution_count": 8,
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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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"claudia = chain.invoke(\"Describe Claudia\")\n",
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"claudia"
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"ai_msg = llm_with_tools.invoke(\n",
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" \"what is the weather like in San Francisco\",\n",
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")\n",
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"ai_msg"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### AIMessage.tool_calls\n",
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"\n",
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"Notice that the AIMessage has a `tool_calls` attribute. This contains in a standardized `ToolCall` format that is model-provider agnostic."
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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": 10,
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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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"[{'name': 'GetWeather',\n",
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" 'args': {'location': 'San Francisco, CA'},\n",
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" 'id': 'call_064c4e1cb27e4adb9e4e7ed60362ecc9'}]"
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]
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},
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"execution_count": 10,
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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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"ai_msg.tool_calls"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": "For more on binding tools and tool call outputs, head to the [tool calling](docs/how_to/function_calling) docs."
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## API reference\n",
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"\n",
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"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"
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]
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}
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],
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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": "3.9.1"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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