Make suggestions from soumik

wandb · Aug 12, 2024 · 8a64310 · 8a64310
1 parent 5fa9587
commit 8a64310
Show file tree

Hide file tree

Showing 2 changed files with 22 additions and 10 deletions.
diff --git a/docs/docs/guides/cookbooks/codegen/index.md b/docs/docs/guides/cookbooks/codegen/index.md
@@ -1,6 +1,8 @@
-# Code Generation using Weave and OpenAI
-
+---
+hide_table_of_contents: true
+---
 
+# Code Generation using Weave and OpenAI
 
 Generating high-quality code with proper structure, documentation, and tests is a challenging task. This guide demonstrates how to implement a code generation pipeline using Weave, a powerful framework for building, tracking, and evaluating LLM applications. By combining OpenAI's language models with Weave's robust tooling, you'll learn to create a code generation pipeline that produces high-quality Python functions, complete with main program runners and unit tests, while gaining insights into the generation process.
 
@@ -17,7 +19,7 @@ Our pipeline will:
 
 For a visual demonstration of the code generation pipeline using Weave, Groq, and E2B check out this video:
 
-[![Code Generation Pipeline Demo](https://img.youtube.com/vi/B70jJYPVAzE/0.jpg)](https://www.youtube.com/watch?v=B70jJYPVAzE&t=1561s)
+<iframe width="560" height="315" src="https://www.youtube.com/embed/B70jJYPVAzE?si=75Z4Fg_DBxAiu9_6&amp" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
 
 This video provides a step-by-step walkthrough of the process, showcasing how Weave integrates with Groq to create a powerful code generation tool and then running the code in E2B, to validate the code.
 
@@ -58,9 +60,9 @@ weave.init(WEAVE_PROJECT)
 client = OpenAI()
 ```
 
-
-> [!NOTE]
-> Weave automatically tracks OpenAI API calls, including inputs, outputs, and metadata. This means you don't need to add any additional logging code for your OpenAI interactions – Weave handles it seamlessly in the background.
+:::note
+Weave automatically tracks OpenAI API calls, including inputs, outputs, and metadata. This means you don't need to add any additional logging code for your OpenAI interactions – Weave handles it seamlessly in the background.
+:::
 
 ## Leveraging Structured Outputs and Pydantic Models
 

diff --git a/examples/cookbooks/codegen/codegen.ipynb b/examples/cookbooks/codegen/codegen.ipynb
@@ -1,5 +1,14 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -qU autopep8 autoflake weave isort openai set-env-colab-kaggle-dotenv\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -14,12 +23,14 @@
     "import isort\n",
     "import weave\n",
     "from autoflake import fix_code\n",
-    "from dotenv import load_dotenv\n",
     "from openai import OpenAI\n",
     "from pydantic import BaseModel\n",
     "from weave import Dataset, Evaluation\n",
     "\n",
-    "load_dotenv()"
+    "from set_env import set_env\n",
+    "\n",
+    "set_env(\"WANDB_API_KEY\")\n",
+    "set_env(\"OPENAI_API_KEY\")\n"
    ]
   },
   {
@@ -335,8 +346,7 @@
     "        dataset=prompt_dataset,\n",
     "        scorers=[test_result_scorer]\n",
     "    )\n",
-    "    results = await evaluation.evaluate(pipeline)\n",
-    "    print(results)"
+    "    results = await evaluation.evaluate(pipeline)"
    ]
   }
  ],