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Gianni CrivelloGianni Crivello
Gianni Crivello
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Gianni Crivello
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added examples to index
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‎lazy_lm/core.py

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# %% ../nbs/00_core.ipynb 3
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from dotenv import load_dotenv
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import os
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from anthropic import AnthropicVertex
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from anthropic.types import (
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MessageParam,
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List,
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Optional
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)
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import os
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from nbdev.showdoc import show_doc
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# %% ../nbs/00_core.ipynb 7
@@ -160,5 +160,5 @@ def lazy(self: AnthropicVertex, problem: str) -> LazyEvaluationClient:
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Entry point of the LazyLM Framework for the `AnthropicVertex` client API
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"""
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state = LazyState(problem=problem)
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llm = LLM(client=self, model=model)
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llm = LLM(client=self, model="claude-3-5-sonnet@20240620")
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return LazyEvaluationClient(llm=llm, state=state)

‎nbs/00_core.ipynb

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"#| hide\n",
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"#| export\n",
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"from dotenv import load_dotenv\n",
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"import os\n",
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"from anthropic import AnthropicVertex\n",
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"from anthropic.types import (\n",
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" MessageParam,\n",
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" List,\n",
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" Optional\n",
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")\n",
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"import os\n",
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"from nbdev.showdoc import show_doc"
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]
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},
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],
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"source": [
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"#| hide\n",
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"project_id = os.getenv(\"CLAUDE_PROJECT_ID\")\n",
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"project_id = os.getenv(\"PROJECT_ID\")\n",
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"location = os.getenv(\"PROJECT_LOCATION\")\n",
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"model = os.getenv(\"CLAUDE_MODEL\")\n",
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"\n",
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"execution_count": 32,
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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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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 34,
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"metadata": {},
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"outputs": [],
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"source": [
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" Entry point of the LazyLM Framework for the `AnthropicVertex` client API\n",
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" \"\"\"\n",
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" state = LazyState(problem=problem)\n",
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" llm = LLM(client=self, model=model)\n",
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" llm = LLM(client=self, model=\"claude-3-5-sonnet@20240620\")\n",
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" return LazyEvaluationClient(llm=llm, state=state)"
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]
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},

‎nbs/index.ipynb

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"\n",
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"- **Engagement**: By revealing information gradually, we can maintain student engagement and encourage active participation in the problem-solving process.\n",
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"\n",
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"- **Real-world Problem Solving**: In many real-world scenarios, solutions are not immediately apparent and must be approached incrementally. Training students to think in this way prepares them for challenges beyond the classroom.\n",
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"- **Real-world Problem Solving**: In many real-world scenarios, solutions are not immediately apparent and must be approached incrementally. Training students to think in this way prepares them for challenges beyond the classroom.\n"
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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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"Installing LazyLM\n",
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"\n",
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"`pip install lazy_lm`"
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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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"Using LazyLM"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from dotenv import load_dotenv\n",
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"import os\n",
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"from anthropic import AnthropicVertex\n",
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"from lazy_lm.core import lazy\n",
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"\n",
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"load_dotenv()\n",
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"project_id = os.getenv(\"PROJECT_ID\")\n",
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"location = os.getenv(\"PROJECT_LOCATION\")\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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Initalize the Anthropic client\n",
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"client = AnthropicVertex(project_id=project_id, region=location)\n",
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"lazy_lm = client.lazy(\"What is the derivative of `2x^3 + x^2 + 2x + 1`? Give me the solution step-by-step\")"
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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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"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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"What is the derivative of `2x^3 + x^2 + 2x + 1`? Give me the solution step-by-step\n"
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]
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}
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],
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"source": [
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"# Get the current step\n",
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"print(lazy_lm.get_current_step())"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"To find the derivative of the given function, we'll use the power rule and the constant rule of differentiation. Let's start with the first term:\n",
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"\n",
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"Step 1: Find the derivative of 2x^3\n",
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"\n",
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"The power rule states that for a term ax^n, the derivative is nax^(n-1).\n",
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"For 2x^3, we have:\n",
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"a = 2, n = 3\n",
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"So, the derivative of 2x^\n"
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]
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}
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],
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"source": [
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"# Get the next step\n",
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"print(lazy_lm.get_next_step())"
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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": 11,
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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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"I apologize for any confusion. I'd be happy to explain this step in more detail without advancing to the next step.\n",
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"\n",
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"In this step, we're focusing on finding the derivative of the first term in the given expression, which is 2x^3.\n",
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"\n",
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"To do this, we're using the power rule of differentiation. The power rule states that for a term in the form ax^n (where 'a' is a constant and 'n' is the power\n"
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]
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}
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],
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"source": [
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"# Query the current step\n",
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"print(lazy_lm.ask_question(\"I don't understand this step\"))"
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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": 12,
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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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"Step 2: Complete the derivative of 2x^3\n",
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"\n",
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"Continuing from the previous step, we apply the power rule to 2x^3:\n",
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"\n",
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"The derivative of 2x^3 is:\n",
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"3 · 2x^(3-1) = 3 · 2x^2 = 6x^2\n"
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]
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}
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],
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"source": [
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"# Get the next step\n",
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"print(lazy_lm.get_next_step())"
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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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"## Understanding Lazy Evaluation\n",
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"\n",
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"## A Note on Lazy Evaluation in Programming\n",
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"### A Note on Lazy Evaluation in Programming\n",
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"\n",
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"The concept of lazy evaluation is well-established in functional programming languages, where the evaluation of an expression is only done when the value of the expression is needed. This is also known as call-by-need. The contrast to this evaluation strategy is what's called \"eager\" or strict evaluation.\n",
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"\n",
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"outputs": [],
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"source": []
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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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"metadata": {},
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"outputs": [],
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"source": []
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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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