mirror of
https://github.com/hwchase17/langchain.git
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Bagatur/oai assistant (#13010)
This commit is contained in:
Bagatur
GitHub
@@ -17,12 +17,12 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install \"openai>=1\""
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"!pip install -U openai \"langchain>=0.0.331rc1\" langchain-experimental"
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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": 2,
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"id": "c3e067ce-7a43-47a7-bc89-41f1de4cf136",
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"metadata": {},
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"outputs": [],
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@@ -43,17 +43,17 @@
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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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"execution_count": 3,
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"id": "1c8c3965-d3c9-4186-b5f3-5e67855ef916",
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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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"AIMessage(content='The image appears to be a diagram representing the architecture or components of a software platform named \"LangChain.\" This diagram outlines various layers and elements of the platform, which seems to be related to language processing or computational linguistics, as suggested by the context clues in the names of the components.\\n\\nHere\\'s a breakdown of the components shown:\\n\\n- **LangSmith**: This seems to be a tool or suite related to testing, evaluation, monitoring, feedback, and annotation within the platform.\\n\\n- **LangServe**: This could represent a service layer that exposes the platform\\'s capabilities as REST API endpoints.\\n\\n- **Templates**: These are likely reference applications provided as starting points or examples for users of the platform.\\n\\n- **Chains, agents, agent executors**: This section describes the common application logic, perhaps indicating that the platform uses a chain of agents or processes to execute tasks.\\n\\n- **Model I/O**: This includes the components related to input/output processing for a model, like prompt, example selector, model, and output parser.\\n\\n- **Retrieval**: These components are involved in retrieving documents, splitting text, and managing embeddings and vector stores, which are important for tasks like search and information retrieval.\\n\\n- **Agent tooling**: This might refer to the tools used for creating,')"
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"AIMessage(content='The image appears to be a diagram of a software architecture or framework related to language processing, named \"LangChain.\" This architecture seems to be designed to handle various aspects of natural language understanding or processing tasks. Here are the components as labeled in the diagram:\\n\\n1. **LangSmith**: This component is related to testing, evaluation, monitoring, feedback, and annotation. It also has a debugging aspect to it.\\n2. **LangServe**: This is a service that can chain as REST API, indicating it might serve language processing capabilities over a network.\\n3. **Templates**: Reference applications are mentioned here, which might be used as starting points or examples for building new applications.\\n4. **Chains, agents, agent executors**: This part of the system handles common application logic, potentially acting as the middleware or the operational logic of the framework.\\n5. **Model I/O**: Input/output management for the model, including prompts, example selectors, the models themselves, and output parsers.\\n6. **Retrieval**: Components related to retrieving documents, including a document loader, text splitter, embedding model, vector store, and retriever. These are likely used for finding and processing relevant information from a large corpus of text.\\n7. **Agent tooling**: This might refer')"
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]
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},
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"execution_count": 2,
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -78,6 +78,227 @@
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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": "210f8248-fcf3-4052-a4a3-0684e08f8785",
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"metadata": {},
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"source": [
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"## [OpenAI assistants](https://platform.openai.com/docs/assistants/overview)\n",
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"\n",
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"> The Assistants API 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": "318da28d-4cec-42ab-ae3e-76d95bb34fa5",
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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": 4,
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"id": "a9064bbe-d9f7-4a29-a7b3-73933b3197e7",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_experimental.openai_assistant import OpenAIAssistantRunnable\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": 5,
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"id": "7a20a008-49ac-46d2-aa26-b270118af5ea",
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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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"[ThreadMessage(id='msg_RGOsJ2RBYp79rILrZ0NsAX68', assistant_id='asst_9Xb1ZgefoAbp2V5ddRlDGisy', content=[MessageContentText(text=Text(annotations=[], value='\\\\( 10 - 4^{2.7} \\\\) is approximately -32.2243.'), type='text')], created_at=1699385426, file_ids=[], metadata={}, object='thread.message', role='assistant', run_id='run_E2PRoP04ryly4p5ds5ek2qxs', thread_id='thread_pu9CpsYIWWZtxemZxpbYfhm4')]"
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]
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},
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"execution_count": 5,
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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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"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": "a8ddd181-ac63-4ab6-a40d-a236120379c1",
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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": 7,
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"id": "48681ac7-b267-48d4-972c-8a7df8393a21",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.tools import E2BDataAnalysisTool\n",
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"\n",
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"tools = [E2BDataAnalysisTool(api_key=\"...\")]"
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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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"id": "1c01dd79-dd3e-4509-a2e2-009a7f99f16a",
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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": "markdown",
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"id": "1ac71d8b-4b4b-4f98-b826-6b3c57a34166",
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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": 9,
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"id": "1f137f94-801f-4766-9ff5-2de9df5e8079",
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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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"{'content': \"What's 10 - 4 raised to the 2.7\",\n",
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" 'output': 'The result of \\\\(10 - 4\\\\) raised to the power of \\\\(2.7\\\\) is approximately \\\\(126.19\\\\).'}"
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]
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},
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"execution_count": 9,
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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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"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 10 - 4 raised to the 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": "2d0a0b1d-c1b3-4b50-9dce-1189b51a6206",
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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": 19,
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"id": "c0475fa7-b6c1-4331-b8e2-55407466c724",
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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": 22,
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"id": "b76cb669-6aba-4827-868f-00aa960026f2",
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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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"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({\"output\": tool_output, \"tool_call_id\": action.tool_call_id})\n",
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" response = agent.invoke({\"tool_outputs\": tool_outputs, \"run_id\": action.run_id, \"thread_id\": action.thread_id})\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": 23,
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"id": "7946116a-b82f-492e-835e-ca958a8949a5",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"e2b_data_analysis {'python_code': 'print((10 - 4) ** 2.7)'} {\"stdout\": \"126.18518711065899\", \"stderr\": \"\", \"artifacts\": []}\n",
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"\n",
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"\\( 10 - 4 \\) raised to the power of 2.7 is approximately 126.185.\n"
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]
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}
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],
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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": 24,
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"id": "f2744a56-9f4f-4899-827a-fa55821c318c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"e2b_data_analysis {'python_code': 'result = (10 - 4) ** 2.7 + 17.241\\nprint(result)'} {\"stdout\": \"143.426187110659\", \"stderr\": \"\", \"artifacts\": []}\n",
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"\n",
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"\\( (10 - 4)^{2.7} + 17.241 \\) is approximately 143.426.\n"
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]
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}
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],
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"source": [
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"next_response = execute_agent(agent, tools, {\"content\": \"now add 17.241\", \"thread_id\": response.thread_id})\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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"cell_type": "markdown",
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"id": "71c34763-d1e7-4b9a-a9d7-3e4cc0dfc2c4",
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@@ -92,7 +313,7 @@
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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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"execution_count": 16,
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"id": "db6072c4-f3f3-415d-872b-71ea9f3c02bb",
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"metadata": {},
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"outputs": [
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@@ -135,7 +356,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 17,
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"id": "08e00ccf-b991-4249-846b-9500a0ccbfa0",
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"metadata": {},
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"outputs": [
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@@ -146,7 +367,7 @@
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" {'name': 'Deepmind', 'origin': 'UK'}]}"
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]
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},
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"execution_count": 9,
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -169,7 +390,7 @@
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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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"execution_count": 18,
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"id": "1281883c-bf8f-4665-89cd-4f33ccde69ab",
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"metadata": {},
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"outputs": [
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@@ -197,14 +418,6 @@
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")\n",
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"print(output.llm_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": "5c637ba1-322d-4fc9-b97e-3afa83dc4d72",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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