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https://github.com/hwchase17/langchain.git
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Move OAI assistants to langchain and add callbacks (#13236)
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Bagatur
GitHub
224
docs/docs/modules/agents/agent_types/openai_assistants.ipynb
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docs/docs/modules/agents/agent_types/openai_assistants.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ab4ffc65-4ec2-41f5-b225-e8a7a4c3799f",
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"metadata": {},
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"source": [
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"# OpenAI assistants\n",
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"\n",
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"> The [Assistants API](https://platform.openai.com/docs/assistants/overview) allows you to build AI assistants within your own applications. An Assistant has instructions and can leverage models, tools, and knowledge to respond to user queries. The Assistants API currently supports three types of tools: Code Interpreter, Retrieval, and Function calling\n",
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"\n",
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"\n",
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"You can interact with OpenAI Assistants using OpenAI tools or custom tools. When using exclusively OpenAI tools, you can just invoke the assistant directly and get final answers. When using custom tools, you can run the assistant and tool execution loop using the built-in AgentExecutor or easily write your own executor.\n",
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"\n",
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"Below we show the different ways to interact with Assistants. As a simple example, let's build a math tutor that can write and run code."
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]
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},
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{
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"cell_type": "markdown",
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"id": "f2128b7b-01ff-477e-a41c-98026e0180ac",
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"metadata": {},
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"source": [
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"### Using only OpenAI tools"
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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": null,
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"id": "aa761a93-caa1-4e56-b901-5ff50a89bc82",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents.openai_assistant import OpenAIAssistantRunnable"
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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": null,
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"id": "5944a18a-95eb-44ce-a66f-5f50db1d3e1f",
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"metadata": {},
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"outputs": [],
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"source": [
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"interpreter_assistant = OpenAIAssistantRunnable.create_assistant(\n",
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" name=\"langchain assistant\",\n",
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" instructions=\"You are a personal math tutor. Write and run code to answer math questions.\",\n",
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" tools=[{\"type\": \"code_interpreter\"}],\n",
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" model=\"gpt-4-1106-preview\",\n",
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")\n",
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"output = interpreter_assistant.invoke({\"content\": \"What's 10 - 4 raised to the 2.7\"})\n",
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"output"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2dbaba4e-3f2f-4b5d-bfc9-fc5ad4a05232",
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"metadata": {},
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"source": [
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"### As a LangChain agent with arbitrary tools\n",
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"\n",
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"Now let's recreate this functionality using our own tools. For this example we'll use the [E2B sandbox runtime tool](https://e2b.dev/docs?ref=landing-page-get-started)."
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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": null,
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"id": "b4fb3305-525b-4739-a485-7a686beff5b6",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install e2b duckduckgo-search"
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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": null,
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"id": "cc0cba70-8507-498d-92ac-fe47133db200",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.tools import E2BDataAnalysisTool, DuckDuckGoSearchRun\n",
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"\n",
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"tools = [E2BDataAnalysisTool(api_key=\"...\"), DuckDuckGoSearchRun()]"
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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": null,
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"id": "91e6973d-3d9a-477f-99e2-4aaad16004ec",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent = OpenAIAssistantRunnable.create_assistant(\n",
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" name=\"langchain assistant e2b tool\",\n",
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" instructions=\"You are a personal math tutor. Write and run code to answer math questions. You can also search the internet.\",\n",
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" tools=tools,\n",
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" model=\"gpt-4-1106-preview\",\n",
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" as_agent=True,\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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"id": "78fa9320-06fc-4cbc-a3cf-39aaf2427080",
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"metadata": {},
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"source": [
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"#### Using AgentExecutor"
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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": null,
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"id": "e38007a4-fcc1-419b-9ae4-70d36c3fc1cd",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import AgentExecutor\n",
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"\n",
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"agent_executor = AgentExecutor(agent=agent, tools=tools)\n",
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"agent_executor.invoke({\"content\": \"What's the weather in SF today divided by 2.7\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6bf4199a-eed1-485a-8da3-aed948c0e1e2",
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"metadata": {},
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"source": [
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"#### Custom execution"
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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": null,
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"id": "357361ff-f54d-4fd0-b69b-77689f56f40e",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent = OpenAIAssistantRunnable.create_assistant(\n",
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" name=\"langchain assistant e2b tool\",\n",
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" instructions=\"You are a personal math tutor. Write and run code to answer math questions.\",\n",
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" tools=tools,\n",
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" model=\"gpt-4-1106-preview\",\n",
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" as_agent=True,\n",
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")"
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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": null,
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"id": "864e7f9b-0501-4bb7-8aad-a7aa19b601af",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.schema.agent import AgentFinish\n",
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"\n",
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"\n",
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"def execute_agent(agent, tools, input):\n",
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" tool_map = {tool.name: tool for tool in tools}\n",
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" response = agent.invoke(input)\n",
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" while not isinstance(response, AgentFinish):\n",
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" tool_outputs = []\n",
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" for action in response:\n",
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" tool_output = tool_map[action.tool].invoke(action.tool_input)\n",
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" print(action.tool, action.tool_input, tool_output, end=\"\\n\\n\")\n",
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" tool_outputs.append(\n",
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" {\"output\": tool_output, \"tool_call_id\": action.tool_call_id}\n",
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" )\n",
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" response = agent.invoke(\n",
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" {\n",
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" \"tool_outputs\": tool_outputs,\n",
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" \"run_id\": action.run_id,\n",
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" \"thread_id\": action.thread_id,\n",
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" }\n",
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" )\n",
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"\n",
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" return response"
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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": null,
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"id": "5ad6bb07-aac4-4b71-9e67-cc177fcbc537",
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"metadata": {},
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"outputs": [],
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"source": [
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"response = execute_agent(agent, tools, {\"content\": \"What's 10 - 4 raised to the 2.7\"})\n",
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"print(response.return_values[\"output\"])"
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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": null,
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"id": "f55a3a3a-8169-491e-aa15-cf30a2b230df",
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"metadata": {},
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"outputs": [],
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"source": [
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"next_response = execute_agent(\n",
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" agent, tools, {\"content\": \"now add 17.241\", \"thread_id\": response.thread_id}\n",
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")\n",
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"print(next_response.return_values[\"output\"])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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