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Merge pull request #256 from parea-ai/new-cookbook-example
Fix OpenAI Error on API when API key is not needed. Update cookbooks for OpenAI v >1 Add new cookbook example
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279
parea/cookbook/tracing_with_function_calling_and_chains.ipynb
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,279 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "formattedRanges": [], | ||
| "cell_id": "d9114afabb54438ea6c7f68a68bf6fb5", | ||
| "deepnote_cell_type": "text-cell-h1" | ||
| }, | ||
| "source": [ | ||
| "# Tracing with Function Calls and Chains" | ||
| ], | ||
| "block_group": "53e88a688fb843c381543ca9b45c9d76" | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import json\n", | ||
| "import os\n", | ||
| "from typing import List, Dict, Any, Union\n", | ||
| "\n", | ||
| "from parea import Parea\n", | ||
| "from parea.schemas.log import LLMInputs, Message, ModelParams\n", | ||
| "from parea.schemas.models import Completion\n", | ||
| "from parea.utils.trace_utils import trace, trace_insert\n", | ||
| "\n", | ||
| "p = Parea(api_key=os.environ[\"PAREA_API_KEY\"])" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "outputs": [], | ||
| "source": [ | ||
| "LIMIT = 2 # limit any loops to 2 iterations for demo purposes\n", | ||
| "\n", | ||
| "COURSE_FUNCTIONS = [\n", | ||
| " {\n", | ||
| " \"name\": \"generate_course_outline\",\n", | ||
| " \"description\": \"Generates a course outline\",\n", | ||
| " \"parameters\": {\n", | ||
| " \"type\": \"object\",\n", | ||
| " \"properties\": {\n", | ||
| " \"Description\": {\"type\": \"string\", \"description\": \"The description of the course\"},\n", | ||
| " \"chapters\": {\n", | ||
| " \"type\": \"array\",\n", | ||
| " \"items\": {\n", | ||
| " \"type\": \"object\",\n", | ||
| " \"properties\": {\n", | ||
| " \"name\": {\"type\": \"string\", \"description\": \"The title of the chapter\"},\n", | ||
| " \"description\": {\"type\": \"string\", \"description\": \"The summary of the chapter\"},\n", | ||
| " },\n", | ||
| " \"required\": [\"name\", \"description\"],\n", | ||
| " },\n", | ||
| " \"description\": \"The chapters included in the course\",\n", | ||
| " \"minItems\": 1,\n", | ||
| " \"maxItems\": LIMIT,\n", | ||
| " },\n", | ||
| " },\n", | ||
| " \"required\": [\"Description\", \"chapters\"],\n", | ||
| " },\n", | ||
| " }\n", | ||
| "]\n", | ||
| "\n", | ||
| "CHAPTER_FUNCTIONS = [\n", | ||
| " {\n", | ||
| " \"name\": \"generate_chapter_outline\",\n", | ||
| " \"description\": \"Generates a chapter outline\",\n", | ||
| " \"parameters\": {\n", | ||
| " \"type\": \"object\",\n", | ||
| " \"properties\": {\n", | ||
| " \"name\": {\"type\": \"string\", \"description\": \"The title of the chapter\"},\n", | ||
| " \"description\": {\"type\": \"string\", \"description\": \"The summary of the chapter\"},\n", | ||
| " \"sections\": {\n", | ||
| " \"type\": \"array\",\n", | ||
| " \"items\": {\n", | ||
| " \"type\": \"object\",\n", | ||
| " \"properties\": {\n", | ||
| " \"name\": {\"type\": \"string\", \"description\": \"The title of the section\"},\n", | ||
| " \"description\": {\"type\": \"string\", \"description\": \"The summary of the section\"},\n", | ||
| " },\n", | ||
| " \"required\": [\"name\", \"description\"],\n", | ||
| " },\n", | ||
| " \"description\": \"The sections included in the chapter\",\n", | ||
| " \"minItems\": 1,\n", | ||
| " \"maxItems\": LIMIT,\n", | ||
| " },\n", | ||
| " },\n", | ||
| " \"required\": [\"name\", \"description\", \"sections\"],\n", | ||
| " },\n", | ||
| " }\n", | ||
| "]" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false, | ||
| "ExecuteTime": { | ||
| "end_time": "2023-12-09T19:18:33.559895Z", | ||
| "start_time": "2023-12-09T19:18:33.291059Z" | ||
| } | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "outputs": [], | ||
| "source": [ | ||
| "USER = \"[email protected]\"\n", | ||
| "\n", | ||
| "\n", | ||
| "# Create a reusable call lmm helper function\n", | ||
| "# Parea SDK is automatically traced\n", | ||
| "def call_llm(messages: List[Dict[str, str]], name: str = \"LLM Call\") -> str:\n", | ||
| " return p.completion(\n", | ||
| " Completion(\n", | ||
| " llm_configuration=LLMInputs(\n", | ||
| " model=\"gpt-3.5-turbo-1106\",\n", | ||
| " model_params=ModelParams(temp=0.0, max_length=512),\n", | ||
| " messages=[Message(**m) for m in messages],\n", | ||
| " functions=COURSE_FUNCTIONS + CHAPTER_FUNCTIONS,\n", | ||
| " ),\n", | ||
| " end_user_identifier=USER,\n", | ||
| " trace_name=name,\n", | ||
| " )\n", | ||
| " ).content\n", | ||
| "\n", | ||
| "\n", | ||
| "# Helper function to get function call arguments if they exists\n", | ||
| "def get_function_call_or_content(response: str) -> Union[str, Dict[str, Any]]:\n", | ||
| " # Function calls are returned in triple backticks\n", | ||
| " parsed_response = response.replace(\"```\", \"\")\n", | ||
| " # return function call or content string\n", | ||
| " try:\n", | ||
| " return json.loads(parsed_response).get(\"arguments\")\n", | ||
| " except Exception as e:\n", | ||
| " return parsed_response" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# PROMPTS\n", | ||
| "\n", | ||
| "\n", | ||
| "def get_course(topic: str):\n", | ||
| " _course_outline = call_llm(\n", | ||
| " messages=[{\"role\": \"user\", \"content\": f\"Generate a course outline on {topic}\"}],\n", | ||
| " name=f\"Create {topic} Course Outline\",\n", | ||
| " )\n", | ||
| " course_outline = get_function_call_or_content(_course_outline)\n", | ||
| " print(json.dumps(course_outline, indent=4))\n", | ||
| " return course_outline\n", | ||
| "\n", | ||
| "\n", | ||
| "# I want to group all llm calls into one trace,\n", | ||
| "# so I add the trace decorator to create a parent trace,\n", | ||
| "# each call_llm will be a child span\n", | ||
| "@trace(name=\"Get Chapters\")\n", | ||
| "def get_chapters(course_outline: Dict[str, str]):\n", | ||
| " chapter_outlines = course_outline.get(\"chapters\", [])\n", | ||
| " chapters = [\n", | ||
| " get_function_call_or_content(\n", | ||
| " call_llm(\n", | ||
| " messages=[{\"role\": \"user\", \"content\": f\"Generate a chapter outline on {chapter.get('name')}, with description {chapter.get('description')}\"}],\n", | ||
| " name=f\"Create Chapter {idx}\",\n", | ||
| " )\n", | ||
| " )\n", | ||
| " for idx, chapter in enumerate(chapter_outlines, start=1)\n", | ||
| " ]\n", | ||
| " return chapters[:LIMIT]\n", | ||
| "\n", | ||
| "\n", | ||
| "@trace\n", | ||
| "def get_sections(chapter: Dict[str, str]):\n", | ||
| " chapter_name = chapter.get(\"name\", \"section\")\n", | ||
| " # I want the trace name to be dynamic based on the chapter param,\n", | ||
| " # so I can use Parea trace_insert helper method to add dynamic data\n", | ||
| " trace_insert({\"trace_name\": f\"Get {chapter_name} Sections\"})\n", | ||
| "\n", | ||
| " section_outlines = chapter.get(\"sections\", [])\n", | ||
| " sections = [\n", | ||
| " get_function_call_or_content(\n", | ||
| " call_llm(\n", | ||
| " messages=[{\"role\": \"user\", \"content\": f\"\"\"Generate a section outline on {section['name']}, with description {section['description']}\"\"\"}],\n", | ||
| " name=f\"Create Section {idx}\",\n", | ||
| " )\n", | ||
| " )\n", | ||
| " for idx, section in enumerate(section_outlines, start=1)\n", | ||
| " ]\n", | ||
| " return sections[:LIMIT]" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "outputs": [], | ||
| "source": [ | ||
| "@trace(name=\"Get Sections\")\n", | ||
| "def get_all_sections(chapters: List[Dict[str, str]]):\n", | ||
| " return [get_sections(chapter) for chapter in chapters]\n", | ||
| "\n", | ||
| "\n", | ||
| "@trace\n", | ||
| "def run_creation(topic: str):\n", | ||
| " trace_insert({\"trace_name\": f\"Course on {topic}\"})\n", | ||
| " course_outline = get_course(topic)\n", | ||
| " chapters = get_chapters(course_outline)\n", | ||
| " sections = get_all_sections(chapters)\n", | ||
| " return sections\n", | ||
| "\n", | ||
| "\n", | ||
| "@trace(name=\"Generate Courses for Topics\")\n", | ||
| "def main(topics):\n", | ||
| " for topic in topics:\n", | ||
| " print(f\"\\n New Topic: {topic} \\n\")\n", | ||
| " run_creation(topic)" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "outputs": [], | ||
| "source": [ | ||
| "main([\"Artificial Intelligence\", \"Machine Learning\", \"Deep Learning\"])" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "### Trace Log View in UI\n", | ||
| "\n", | ||
| "" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "outputs": [], | ||
| "source": [], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| } | ||
| ], | ||
| "nbformat": 4, | ||
| "nbformat_minor": 0, | ||
| "metadata": { | ||
| "deepnote": {}, | ||
| "orig_nbformat": 2, | ||
| "deepnote_notebook_id": "27ae7c4c406747b5b5936b15081cdaec", | ||
| "deepnote_execution_queue": [], | ||
| "kernelspec": { | ||
| "name": "python3", | ||
| "language": "python", | ||
| "display_name": "Python 3 (ipykernel)" | ||
| } | ||
| } | ||
| } |
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