Career chat (RAG + Gradio, Jupyter notebook)

This folder is a community contribution in the same spirit as other community_contributions examples: a personal career assistant that answers in your voice using retrieval-augmented generation (RAG) and a Gradio web UI. Everything lives in one notebook so you can run and tweak it step by step.

What it is

• career_chat_rag.ipynb — The full solution:
  • Section 1 — Ingest: Read files under me/ (PDF and .txt/.md), split into chunks, embed with OpenAI embeddings, save to local vector_db/ (Chroma).
  • Section 2 — Chat: Retrieve top chunks per question, call the OpenAI chat API, and optionally use tools that append to me/leads.txt and me/unknown_questions.txt.

There is no external notification service: leads and unknown questions are appended to local files under me/.

How it works (flow)

1. Add me/summary.txt and optionally me/linkedin.pdf (or other PDFs).
2. Open career_chat_rag.ipynb from the sammyloto directory (so me/ and vector_db/ paths work).
3. Run Section 1 cells to build the index (re-run when your source files change).
4. Run Section 2 cells to start Gradio. Each message:
   • Retrieves relevant passages from vector_db/.
   • Generates a reply with the chat model, using those passages as factual context.
   • Optionally tool calls: record_user_details → me/leads.txt, record_unknown_question → me/unknown_questions.txt.

flowchart LR
  subgraph prep [Notebook section 1]
    A[me/summary.txt + PDFs] --> B[Chunk + OpenAI embeddings]
    B --> C[vector_db Chroma]
  end
  subgraph chat [Notebook section 2]
    U[User message] --> R[Retriever]
    C --> R
    R --> L[OpenAI chat + tools]
    L --> O[Reply + optional me/*.txt logs]
  end

Environment variables (OpenAI)

Create a .env in this folder or use the repo root .env with:

• OPENAI_API_KEY — Your OpenAI API key.

Optional:

• CHAT_MODEL — Defaults to gpt-4o-mini.
• EMBEDDING_MODEL — Defaults to text-embedding-3-large.

The notebook uses the official OpenAI client and LangChain’s OpenAIEmbeddings with the default OpenAI base URL (no OpenRouter).

Setup and run

cd community_contributions/sammyloto
python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r requirements.txt

If the notebook says No module named 'langchain_chroma': the kernel’s Python is not the one where you installed packages. Either select the venv as the Jupyter kernel in Cursor/VS Code, or run the first code cell in career_chat_rag.ipynb (%pip install …), which installs into the active notebook kernel.

Edit me/summary.txt and add me/linkedin.pdf if you like, then:

jupyter notebook career_chat_rag.ipynb

Or use VS Code / Cursor to open the notebook, select your interpreter, and Run All (or run section 1, then section 2). Stop the Gradio cell with the notebook Interrupt control when finished.

Customization

• Set YOUR_NAME in the notebook’s config cell.
• Adjust TOP_K, CHUNK_SIZE, and CHUNK_OVERLAP in the same area as needed.

Files you might see after use

Path	Meaning
vector_db/	Chroma database (rebuilt when you re-run the ingest cells)
me/leads.txt	Lines appended when the model records a user’s email
me/unknown_questions.txt	Questions the model could not answer from context

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Pattern: aligned with other contributions that combine LangChain Chroma RAG, OpenAI, and Gradio for a “chat as me” demo—here packaged as a single notebook.