mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
229 lines
6.0 KiB
Plaintext
229 lines
6.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "82a4c2cc-20ea-4b20-a565-63e905dee8ff",
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"metadata": {},
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"source": [
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"# Python Agent\n",
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"\n",
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"This notebook showcases an agent designed to write and execute python code to answer a question."
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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": "f98e9c90-5c37-4fb9-af3e-d09693af8543",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.agents.agent_toolkits import create_python_agent\n",
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"from langchain.tools.python.tool import PythonREPLTool\n",
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"from langchain.python import PythonREPL\n",
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"from langchain.llms.openai import OpenAI"
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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": "cc422f53-c51c-4694-a834-72ecd1e68363",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"agent_executor = create_python_agent(\n",
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" llm=OpenAI(temperature=0, max_tokens=1000),\n",
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" tool=PythonREPLTool(),\n",
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" verbose=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": "c16161de",
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"metadata": {},
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"source": [
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"## Fibonacci Example\n",
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"This example was created by [John Wiseman](https://twitter.com/lemonodor/status/1628270074074398720?s=20)."
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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": 3,
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"id": "25cd4f92-ea9b-4fe6-9838-a4f85f81eebe",
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"metadata": {
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"tags": []
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},
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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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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m I need to calculate the 10th fibonacci number\n",
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"Action: Python REPL\n",
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"Action Input: def fibonacci(n):\n",
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" if n == 0:\n",
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" return 0\n",
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" elif n == 1:\n",
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" return 1\n",
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" else:\n",
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" return fibonacci(n-1) + fibonacci(n-2)\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I need to call the function with 10 as the argument\n",
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"Action: Python REPL\n",
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"Action Input: fibonacci(10)\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: 55\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'55'"
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]
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},
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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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],
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"source": [
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"agent_executor.run(\"What is the 10th fibonacci number?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7caa30de",
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"metadata": {},
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"source": [
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"## Training neural net\n",
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"This example was created by [Samee Ur Rehman](https://twitter.com/sameeurehman/status/1630130518133207046?s=20)."
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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": "4b9f60e7-eb6a-4f14-8604-498d863d4482",
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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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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m I need to write a neural network in PyTorch and train it on the given data.\n",
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"Action: Python REPL\n",
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"Action Input: \n",
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"import torch\n",
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"\n",
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"# Define the model\n",
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"model = torch.nn.Sequential(\n",
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" torch.nn.Linear(1, 1)\n",
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")\n",
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"\n",
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"# Define the loss\n",
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"loss_fn = torch.nn.MSELoss()\n",
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"\n",
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"# Define the optimizer\n",
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"optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n",
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"\n",
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"# Define the data\n",
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"x_data = torch.tensor([[1.0], [2.0], [3.0], [4.0]])\n",
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"y_data = torch.tensor([[2.0], [4.0], [6.0], [8.0]])\n",
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"\n",
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"# Train the model\n",
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"for epoch in range(1000):\n",
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" # Forward pass\n",
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" y_pred = model(x_data)\n",
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"\n",
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" # Compute and print loss\n",
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" loss = loss_fn(y_pred, y_data)\n",
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" if (epoch+1) % 100 == 0:\n",
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" print(f'Epoch {epoch+1}: loss = {loss.item():.4f}')\n",
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"\n",
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" # Zero the gradients\n",
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" optimizer.zero_grad()\n",
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"\n",
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" # Backward pass\n",
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" loss.backward()\n",
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"\n",
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" # Update the weights\n",
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" optimizer.step()\n",
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"\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3mEpoch 100: loss = 0.0013\n",
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"Epoch 200: loss = 0.0007\n",
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"Epoch 300: loss = 0.0004\n",
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"Epoch 400: loss = 0.0002\n",
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"Epoch 500: loss = 0.0001\n",
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"Epoch 600: loss = 0.0001\n",
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"Epoch 700: loss = 0.0000\n",
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"Epoch 800: loss = 0.0000\n",
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"Epoch 900: loss = 0.0000\n",
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"Epoch 1000: loss = 0.0000\n",
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"\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: The prediction for x = 5 is 10.0.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'The prediction for x = 5 is 10.0.'"
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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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"source": [
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"agent_executor.run(\"\"\"Understand, write a single neuron neural network in PyTorch.\n",
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"Take synthetic data for y=2x. Train for 1000 epochs and print every 100 epochs.\n",
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"Return prediction for x = 5\"\"\")"
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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": "eb654671",
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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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"kernelspec": {
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"display_name": "LangChain",
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"language": "python",
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"name": "langchain"
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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": "3.9.16"
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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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