From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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322 lines
14 KiB
322 lines
14 KiB
{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "dfe37963-1af6-44fc-a841-8e462443f5e6", |
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"metadata": {}, |
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"source": [ |
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"## Expert Knowledge Worker\n", |
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"\n", |
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"### A question answering agent that is an expert knowledge worker\n", |
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"### To be used by employees of Insurellm, an Insurance Tech company\n", |
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"### The agent needs to be accurate and the solution should be low cost.\n", |
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"\n", |
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"This project will use RAG (Retrieval Augmented Generation) to ensure our question/answering assistant has high accuracy." |
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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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"id": "ba2779af-84ef-4227-9e9e-6eaf0df87e77", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# imports\n", |
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"\n", |
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"import os\n", |
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"import glob\n", |
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"from dotenv import load_dotenv\n", |
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"import gradio as gr" |
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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": 2, |
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"id": "802137aa-8a74-45e0-a487-d1974927d7ca", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# imports for langchain\n", |
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"\n", |
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"from langchain.document_loaders import DirectoryLoader, TextLoader\n", |
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"from langchain.text_splitter import CharacterTextSplitter" |
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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": "58c85082-e417-4708-9efe-81a5d55d1424", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# price is a factor for our company, so we're going to use a low cost model\n", |
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"\n", |
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"MODEL = \"gpt-4o-mini\"\n", |
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"db_name = \"vector_db\"" |
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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": "ee78efcb-60fe-449e-a944-40bab26261af", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Load environment variables in a file called .env\n", |
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"\n", |
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"load_dotenv()\n", |
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"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')" |
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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": "730711a9-6ffe-4eee-8f48-d6cfb7314905", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Read in documents using LangChain's loaders\n", |
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"# Take everything in all the sub-folders of our knowledgebase\n", |
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"\n", |
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"folders = glob.glob(\"knowledge-base/*\")\n", |
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"\n", |
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"documents = []\n", |
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"for folder in folders:\n", |
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" doc_type = os.path.basename(folder)\n", |
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" loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader)\n", |
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" folder_docs = loader.load()\n", |
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" for doc in folder_docs:\n", |
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" doc.metadata[\"doc_type\"] = doc_type\n", |
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" documents.append(doc)" |
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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": 6, |
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"id": "252f17e9-3529-4e81-996c-cfa9f08e75a8", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/plain": [ |
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"31" |
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] |
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}, |
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"execution_count": 6, |
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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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"len(documents)" |
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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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"id": "7e8decb0-d9b0-4d51-8402-7a6174d22159", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/plain": [ |
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"Document(metadata={'source': 'knowledge-base/employees/Maxine Thompson.md', 'doc_type': 'employees'}, page_content=\"# HR Record\\n\\n# Maxine Thompson\\n\\n## Summary\\n- **Date of Birth:** January 15, 1991 \\n- **Job Title:** Data Engineer \\n- **Location:** Austin, Texas \\n\\n## Insurellm Career Progression\\n- **January 2017 - October 2018**: **Junior Data Engineer** \\n * Maxine joined Insurellm as a Junior Data Engineer, focusing primarily on ETL processes and data integration tasks. She quickly learned Insurellm's data architecture, collaborating with other team members to streamline data workflows. \\n- **November 2018 - December 2020**: **Data Engineer** \\n * In her new role, Maxine expanded her responsibilities to include designing comprehensive data models and improving data quality measures. Though she excelled in technical skills, communication issues with non-technical teams led to some project delays. \\n- **January 2021 - Present**: **Senior Data Engineer** \\n * Maxine was promoted to Senior Data Engineer after successfully leading a pivotal project that improved data retrieval times by 30%. She now mentors junior engineers and is involved in strategic data initiatives, solidifying her position as a valued asset at Insurellm. She was recognized as Insurellm Innovator of the year in 2023, receiving the prestiguous IIOTY 2023 award. \\n\\n## Annual Performance History\\n- **2017**: *Meets Expectations* \\n Maxine showed potential in her role but struggled with initial project deadlines. Her adaptability and willingness to learn made positive impacts on her team. \\n\\n- **2018**: *Exceeds Expectations* \\n Maxine improved significantly, becoming a reliable team member with strong problem-solving skills. She took on leadership in a project that automated data entry processes. \\n\\n- **2019**: *Needs Improvement* \\n During this year, difficult personal circumstances affected Maxine's performance. She missed key deadlines and had several communication issues with stakeholders. \\n\\n- **2020**: *Meets Expectations* \\n Maxine focused on regaining her footing and excelling with technical skills. She was stable, though not standout, in her contributions. Feedback indicated a need for more proactivity. \\n\\n- **2021**: *Exceeds Expectations* \\n Maxine spearheaded the transition to a new data warehousing solution, significantly enhancing Insurellm’s data analytics capabilities. This major achievement bolstered her reputation within the company. \\n\\n- **2022**: *Outstanding* \\n Maxine continued her upward trajectory, successfully implementing machine learning algorithms to predict customer behavior, which was well-received by the leadership team and improved client satisfaction. \\n\\n- **2023**: *Exceeds Expectations* \\n Maxine has taken on mentoring responsibilities and is leading a cross-functional team for data governance initiatives, showcasing her leadership and solidifying her role at Insurellm. \\n\\n## Compensation History\\n- **2017**: $70,000 (Junior Data Engineer) \\n- **2018**: $75,000 (Junior Data Engineer) \\n- **2019**: $80,000 (Data Engineer) \\n- **2020**: $84,000 (Data Engineer) \\n- **2021**: $95,000 (Senior Data Engineer) \\n- **2022**: $110,000 (Senior Data Engineer) \\n- **2023**: $120,000 (Senior Data Engineer) \\n\\n## Other HR Notes\\n- Maxine participated in various company-sponsored trainings related to big data technologies and cloud infrastructure. \\n- She was recognized for her contributions with the “Insurellm Innovator Award” in 2022. \\n- Maxine is currently involved in the women-in-tech initiative and participates in mentorship programs to guide junior employees. \\n- Future development areas include improving her stakeholder communication skills to ensure smoother project transitions and collaboration. \")" |
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] |
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}, |
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"execution_count": 9, |
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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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"documents[24]" |
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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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"id": "7310c9c8-03c1-4efc-a104-5e89aec6db1a", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stderr", |
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"output_type": "stream", |
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"text": [ |
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"Created a chunk of size 1088, which is longer than the specified 1000\n" |
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] |
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} |
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], |
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"source": [ |
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", |
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"chunks = text_splitter.split_documents(documents)" |
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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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"id": "cd06e02f-6d9b-44cc-a43d-e1faa8acc7bb", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/plain": [ |
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"123" |
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] |
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}, |
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"execution_count": 11, |
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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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"len(chunks)" |
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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": 15, |
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"id": "d2562754-9052-4aae-92c1-37236435ea06", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/plain": [ |
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"Document(metadata={'source': 'knowledge-base/products/Markellm.md', 'doc_type': 'products'}, page_content='- **User-Friendly Interface**: Designed with user experience in mind, Markellm features an intuitive interface that allows consumers to easily browse and compare various insurance offerings from multiple providers.\\n\\n- **Real-Time Quotes**: Consumers can receive real-time quotes from different insurance companies, empowering them to make informed decisions quickly without endless back-and-forth communication.\\n\\n- **Customized Recommendations**: Based on user profiles and preferences, Markellm provides personalized insurance recommendations, ensuring consumers find the right coverage at competitive rates.\\n\\n- **Secure Transactions**: Markellm prioritizes security, employing robust encryption methods to ensure that all transactions and data exchanges are safe and secure.\\n\\n- **Customer Support**: Our dedicated support team is always available to assist both consumers and insurers throughout the process, providing guidance and answering any questions that may arise.')" |
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] |
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}, |
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"execution_count": 15, |
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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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"chunks[6]" |
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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": 16, |
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"id": "2c54b4b6-06da-463d-bee7-4dd456c2b887", |
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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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"Document types found: employees, contracts, company, products\n" |
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] |
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} |
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], |
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"source": [ |
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"doc_types = set(chunk.metadata['doc_type'] for chunk in chunks)\n", |
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"print(f\"Document types found: {', '.join(doc_types)}\")" |
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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": 19, |
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"id": "128c73f7-f149-4904-a554-8140941fce0c", |
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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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"page_content='## Support\n", |
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"\n", |
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"1. **Customer Support**: Velocity Auto Solutions will have access to Insurellm’s customer support team via email or chatbot, available 24/7. \n", |
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"2. **Technical Maintenance**: Regular maintenance and updates to the Carllm platform will be conducted by Insurellm, with any downtime communicated in advance. \n", |
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"3. **Training & Resources**: Initial training sessions will be provided for Velocity Auto Solutions’ staff to ensure effective use of the Carllm suite. Regular resources and documentation will be made available online.\n", |
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"\n", |
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"---\n", |
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"\n", |
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"**Accepted and Agreed:** \n", |
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"**For Velocity Auto Solutions** \n", |
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"Signature: _____________________ \n", |
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"Name: John Doe \n", |
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"Title: CEO \n", |
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"Date: _____________________ \n", |
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"\n", |
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"**For Insurellm** \n", |
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"Signature: _____________________ \n", |
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"Name: Jane Smith \n", |
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"Title: VP of Sales \n", |
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"Date: _____________________' metadata={'source': 'knowledge-base/contracts/Contract with Velocity Auto Solutions for Carllm.md', 'doc_type': 'contracts'}\n", |
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"_________\n", |
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"page_content='3. **Regular Updates:** Insurellm will offer ongoing updates and enhancements to the Homellm platform, including new features and security improvements.\n", |
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"\n", |
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"4. **Feedback Implementation:** Insurellm will actively solicit feedback from GreenValley Insurance to ensure Homellm continues to meet their evolving needs.\n", |
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"\n", |
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"---\n", |
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"\n", |
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"**Signatures:**\n", |
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"\n", |
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"_________________________________ \n", |
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"**[Name]** \n", |
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"**Title**: CEO \n", |
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"**Insurellm, Inc.**\n", |
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"\n", |
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"_________________________________ \n", |
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"**[Name]** \n", |
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"**Title**: COO \n", |
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"**GreenValley Insurance, LLC** \n", |
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"\n", |
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"---\n", |
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"\n", |
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"This agreement represents the complete understanding of both parties regarding the use of the Homellm product and supersedes any prior agreements or communications.' metadata={'source': 'knowledge-base/contracts/Contract with GreenValley Insurance for Homellm.md', 'doc_type': 'contracts'}\n", |
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"_________\n", |
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"page_content='# Avery Lancaster\n", |
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"\n", |
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"## Summary\n", |
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"- **Date of Birth**: March 15, 1985 \n", |
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"- **Job Title**: Co-Founder & Chief Executive Officer (CEO) \n", |
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"- **Location**: San Francisco, California \n", |
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"\n", |
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"## Insurellm Career Progression\n", |
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"- **2015 - Present**: Co-Founder & CEO \n", |
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" Avery Lancaster co-founded Insurellm in 2015 and has since guided the company to its current position as a leading Insurance Tech provider. Avery is known for her innovative leadership strategies and risk management expertise that have catapulted the company into the mainstream insurance market. \n", |
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"\n", |
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"- **2013 - 2015**: Senior Product Manager at Innovate Insurance Solutions \n", |
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" Before launching Insurellm, Avery was a leading Senior Product Manager at Innovate Insurance Solutions, where she developed groundbreaking insurance products aimed at the tech sector.' metadata={'source': 'knowledge-base/employees/Avery Lancaster.md', 'doc_type': 'employees'}\n", |
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"_________\n" |
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] |
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} |
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], |
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"source": [ |
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"for chunk in chunks:\n", |
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" if 'CEO' in chunk.page_content:\n", |
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" print(chunk)\n", |
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" print(\"_________\")" |
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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": "6965971c-fb97-482c-a497-4e81a0ac83df", |
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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": "Python 3 (ipykernel)", |
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"language": "python", |
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"name": "python3" |
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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": "3.11.10" |
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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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