"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: 5.449689683556195\u001b[0m\n",
"\u001b[0m\u001b[36;1m\u001b[1;3m5.585696017507576\u001b[0m\u001b[32;1m\u001b[1;3mThe square root of the average age is approximately 5.59.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
@ -157,10 +211,10 @@
{
"data": {
"text/plain": [
"'5.449689683556195'"
"'The square root of the average age is approximately 5.59.'"
]
},
"execution_count": 7,
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@ -181,23 +235,26 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 8,
"id": "15f11fbd",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Error in on_chain_start callback: 'name'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32;1m\u001b[1;3m\n",
"Invoking: `python_repl_ast` with `df1['Age'].nunique() - df2['Age'].nunique()`\n",
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I need to compare the age columns in both dataframes\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: 177 rows in the age column are different.\u001b[0m\n",
"\u001b[0m\u001b[36;1m\u001b[1;3m-1\u001b[0m\u001b[32;1m\u001b[1;3mThere is 1 row in the age column that is different between the two dataframes.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
@ -205,17 +262,17 @@
{
"data": {
"text/plain": [
"'177 rows in the age column are different.'"
"'There is 1 row in the age column that is different between the two dataframes.'"
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: 55\u001b[0m\n",
" return fibonacci(n-1) + fibonacci(n-2)\n",
"\n",
"fibonacci(10)`\n",
"\n",
"\n",
"\u001b[0m\u001b[36;1m\u001b[1;3m\u001b[0m\u001b[32;1m\u001b[1;3mThe 10th Fibonacci number is 55.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
@ -88,10 +124,10 @@
{
"data": {
"text/plain": [
"'55'"
"'The 10th Fibonacci number is 55.'"
]
},
"execution_count": 3,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@ -111,9 +147,11 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"id": "4b9f60e7-eb6a-4f14-8604-498d863d4482",
"metadata": {},
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
@ -121,59 +159,70 @@
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to write a neural network in PyTorch and train it on the given data.\n",
"Action: Python REPL\n",
"Action Input: \n",
"import torch\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mCould not parse tool input: {'name': 'python', 'arguments': 'import torch\\nimport torch.nn as nn\\nimport torch.optim as optim\\n\\n# Define the neural network\\nclass SingleNeuron(nn.Module):\\n def __init__(self):\\n super(SingleNeuron, self).__init__()\\n self.linear = nn.Linear(1, 1)\\n \\n def forward(self, x):\\n return self.linear(x)\\n\\n# Create the synthetic data\\nx_train = torch.tensor([[1.0], [2.0], [3.0], [4.0]], dtype=torch.float32)\\ny_train = torch.tensor([[2.0], [4.0], [6.0], [8.0]], dtype=torch.float32)\\n\\n# Create the neural network\\nmodel = SingleNeuron()\\n\\n# Define the loss function and optimizer\\ncriterion = nn.MSELoss()\\noptimizer = optim.SGD(model.parameters(), lr=0.01)\\n\\n# Train the neural network\\nfor epoch in range(1, 1001):\\n # Forward pass\\n y_pred = model(x_train)\\n \\n # Compute loss\\n loss = criterion(y_pred, y_train)\\n \\n # Backward pass and optimization\\n optimizer.zero_grad()\\n loss.backward()\\n optimizer.step()\\n \\n # Print the loss every 100 epochs\\n if epoch % 100 == 0:\\n print(f\"Epoch {epoch}: Loss = {loss.item()}\")\\n\\n# Make a prediction for x = 5\\nx_test = torch.tensor([[5.0]], dtype=torch.float32)\\ny_pred = model(x_test)\\ny_pred.item()'} because the `arguments` is not valid JSON.\u001b[0mInvalid or incomplete response\u001b[32;1m\u001b[1;3m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: The PlaylistTrack table has two columns, PlaylistId and TrackId, and is linked to the Playlist and Track tables.\u001b[0m\n",
"/*\n",
"3 rows from PlaylistTrack table:\n",
"PlaylistId\tTrackId\n",
"1\t3402\n",
"1\t3389\n",
"1\t3390\n",
"*/\u001b[0m\u001b[32;1m\u001b[1;3mThe `PlaylistTrack` table has two columns: `PlaylistId` and `TrackId`. It is a junction table that represents the relationship between playlists and tracks. \n",
"\n",
"Here is the schema of the `PlaylistTrack` table:\n",
"Here are three sample rows from the `PlaylistTrack` table:\n",
"\n",
"```\n",
"PlaylistId TrackId\n",
"1 3402\n",
"1 3389\n",
"1 3390\n",
"```\n",
"\n",
"Please let me know if there is anything else I can help you with.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
@ -111,10 +182,10 @@
{
"data": {
"text/plain": [
"'The PlaylistTrack table has two columns, PlaylistId and TrackId, and is linked to the Playlist and Track tables.'"
"'The `PlaylistTrack` table has two columns: `PlaylistId` and `TrackId`. It is a junction table that represents the relationship between playlists and tracks. \\n\\nHere is the schema of the `PlaylistTrack` table:\\n\\n```\\nCREATE TABLE \"PlaylistTrack\" (\\n\\t\"PlaylistId\" INTEGER NOT NULL, \\n\\t\"TrackId\" INTEGER NOT NULL, \\n\\tPRIMARY KEY (\"PlaylistId\", \"TrackId\"), \\n\\tFOREIGN KEY(\"TrackId\") REFERENCES \"Track\" (\"TrackId\"), \\n\\tFOREIGN KEY(\"PlaylistId\") REFERENCES \"Playlist\" (\"PlaylistId\")\\n)\\n```\\n\\nHere are three sample rows from the `PlaylistTrack` table:\\n\\n```\\nPlaylistId TrackId\\n1 3402\\n1 3389\\n1 3390\\n```\\n\\nPlease let me know if there is anything else I can help you with.'"
SQL_FUNCTIONS_SUFFIX="""I should look at the tables in the database to see what I can query. Then I should query the schema of the most relevant tables."""
Harrison Chase
11 months ago
committed by
GitHub