From b1889eab49ed1b2fea73cbc2fe720bc0fc6bd9b0 Mon Sep 17 00:00:00 2001 From: Abderrahim Adrabi <184391033+abderrahim-lectures@users.noreply.github.com> Date: Sun, 2 Aug 2026 00:51:46 +0100 Subject: [PATCH] content: add RAG Over a GitHub Repo project New Real-World Project (backlog idea #16). Full lesson plus a runnable 6-provider tool that clones a real repo, chunks code and docs with line tracking, embeds locally, and answers with file-and-line citations. Closes #270 Co-authored-by: deepseek-v4-flash-free --- docs/projects/github-repo-rag/_category_.json | 4 + docs/projects/github-repo-rag/index.md | 505 +++++++ docs/projects/index.mdx | 6 + examples/github-repo-rag/.env.example | 1 + examples/github-repo-rag/.gitignore | 6 + examples/github-repo-rag/.python-version | 1 + examples/github-repo-rag/README.md | 80 + examples/github-repo-rag/build_index.py | 42 + examples/github-repo-rag/notebook.ipynb | 334 +++++ examples/github-repo-rag/prepare_repo.py | 115 ++ examples/github-repo-rag/pyproject.toml | 12 + examples/github-repo-rag/query.py | 56 + examples/github-repo-rag/retrieve.py | 43 + examples/github-repo-rag/uv.lock | 1290 +++++++++++++++++ src/data/projects.ts | 6 + src/pages/index.tsx | 18 + 16 files changed, 2519 insertions(+) create mode 100644 docs/projects/github-repo-rag/_category_.json create mode 100644 docs/projects/github-repo-rag/index.md create mode 100644 examples/github-repo-rag/.env.example create mode 100644 examples/github-repo-rag/.gitignore create mode 100644 examples/github-repo-rag/.python-version create mode 100644 examples/github-repo-rag/README.md create mode 100644 examples/github-repo-rag/build_index.py create mode 100644 examples/github-repo-rag/notebook.ipynb create mode 100644 examples/github-repo-rag/prepare_repo.py create mode 100644 examples/github-repo-rag/pyproject.toml create mode 100644 examples/github-repo-rag/query.py create mode 100644 examples/github-repo-rag/retrieve.py create mode 100644 examples/github-repo-rag/uv.lock diff --git a/docs/projects/github-repo-rag/_category_.json b/docs/projects/github-repo-rag/_category_.json new file mode 100644 index 0000000..172b115 --- /dev/null +++ b/docs/projects/github-repo-rag/_category_.json @@ -0,0 +1,4 @@ +{ + "label": "RAG Over a GitHub Repo", + "position": 19 +} diff --git a/docs/projects/github-repo-rag/index.md b/docs/projects/github-repo-rag/index.md new file mode 100644 index 0000000..030db8d --- /dev/null +++ b/docs/projects/github-repo-rag/index.md @@ -0,0 +1,505 @@ +--- +id: github-repo-rag +title: "Build a RAG App Over a GitHub Repo" +sidebar_label: "RAG Over a GitHub Repo" +slug: /projects/github-repo-rag +description: "Take the notes-based RAG pipeline one step further: clone a real GitHub repository, chunk its code and docs, embed them locally, and ask questions that come back with file-and-line citations." +--- + +import ProjectProgressCheckbox from '@site/src/components/ProjectProgressCheckbox'; +import ProjectPublishedDate from '@site/src/components/ProjectPublishedDate'; +import ProjectGreeting from '@site/src/components/ProjectGreeting'; +import {StepChecklist, StepChecklistItem} from '@site/src/components/StepChecklist'; + +# 🔍 Build a RAG App Over a GitHub Repo + + + + + +In the [RAG App Over Your Own Notes](/docs/projects/rag-notes) project you built the full pipeline — chunk, embed locally, retrieve with cosine similarity, generate with a free-tier LLM — but the input was a folder of prose notes. This project graduates that pipeline to the messier case that makes it genuinely useful: **a real code repository**. You'll clone a GitHub repo, chunk both its code and its docs (recording which file *and which lines* each chunk came from), embed everything locally, and ask questions that come back with `file.py:line 12-34` citations you can open and verify. This assumes you've done the notes-based RAG project (or are comfortable with `sentence-transformers` + NumPy dot products); nothing from Data Analysis is required. + +This is optional and ungraded. See [Real-World Projects](/docs/projects) for the full, growing list. + +## 🎯 What you'll do + +1. Clone a real public GitHub repository (the course repo itself makes a good test subject — you'll be asking questions about the very course you're taking). +2. Split its files into chunks with a critical upgrade over the notes version: **line numbers are tracked per chunk**, and code is chunked at *function and class boundaries* rather than by character windows. +3. Embed every chunk locally, with no API key and no cost, using `sentence-transformers`. +4. Write a script that retrieves the chunks relevant to a question, hands them to a free-tier LLM, and asks it to answer *with citations* back to the exact file and lines it used. + +## Where to run this + +**Locally with `uv`** is the path this lesson's steps follow, and the recommended one — the whole point is running real Python against a real cloned repository on your own machine. + +**GitHub Codespaces** is a zero-setup alternative: open [the whole course repo in a free Codespace](https://codespaces.new/abderrahim-lectures/python-data-analysis-course) (Node, Python, and `uv` are already installed, per the repo's `.devcontainer/devcontainer.json`) and run the exact same `uv` commands from a terminal in your browser tab. + +**Google Colab, Kaggle Notebooks, or Binder** also work, since this project — unlike the fine-tuning one — needs no GPU. A ready-to-run notebook that clones the course repo and runs the whole pipeline over it is included in the repo: + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abderrahim-lectures/python-data-analysis-course/blob/main/examples/github-repo-rag/notebook.ipynb) +[![Open In Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/abderrahim-lectures/python-data-analysis-course/blob/main/examples/github-repo-rag/notebook.ipynb) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/abderrahim-lectures/python-data-analysis-course/main?filepath=examples%2Fgithub-repo-rag%2Fnotebook.ipynb) + +Be honest with yourself about the tradeoff: the notebook runs the same code, but a real local `uv` project gives you the separate files, the repo clone, and the workflow you'd actually use on a codebase you care about. Treat the notebook as the quick experiment path, not the primary one. + +## Setup + +### Install `uv` + +`uv` is a single tool that replaces the usual "install Python, then install pip, then install a virtual environment tool, then install packages" chain — it can install and manage Python versions itself, alongside your project's dependencies. + +**macOS / Linux** (terminal): + +```bash +curl -LsSf https://astral.sh/uv/install.sh | sh +``` + +**Windows** (PowerShell): + +```powershell +powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" +``` + +Close and reopen your terminal, then confirm it installed: + +```bash +uv --version +``` + +Then set up a project: + +```bash +uv init github-repo-rag +cd github-repo-rag +uv add sentence-transformers numpy python-dotenv openai +``` + +`sentence-transformers` turns text into vectors locally on your CPU. `numpy` does the similarity math. `python-dotenv` keeps your LLM API key in a local `.env` file. `openai` is the client library that talks to GitHub Models (and, with a different `base_url`, to the other OpenAI-compatible providers in the table below). + +### Clone a test repository + +You need a real repo to index. The course repo itself is a great test subject — small enough to embed in under a minute, and full of lessons and code you'll actually recognize: + +```bash +git clone --depth 1 https://github.com/abderrahim-lectures/python-data-analysis-course +``` + +`--depth 1` grabs only the latest snapshot with no git history, which is all you need for indexing and keeps the download small. You can use any public repo instead — a library you use, a project you admire, anything with both `.py` (or `.js`/`.ts` — see the tip in Step 2) files and some Markdown docs. + +### Get a free LLM API key + +Generation (the last step) needs a free-tier LLM API — retrieval itself (embedding and searching) is fully local and needs no key at all. **Pick whichever provider you like** — none of them require a credit card at the time of writing, and this course doesn't favor one over another. + +| Provider | Where to get a key | Why you might pick it | +|---|---|---| +| **GitHub Models** *(suggested default)* | [github.com/settings/tokens](https://github.com/settings/tokens) — a personal access token with the `models: read` scope | No separate signup — you already have a GitHub account. More generous free-tier limits than Gemini's. | +| Gemini | [Google AI Studio](https://aistudio.google.com/) | The most commonly referenced option. | +| Groq | [console.groq.com/keys](https://console.groq.com/keys) | Fast inference, generous free tier, no card. | +| Mistral | [console.mistral.ai/api-keys](https://console.mistral.ai/api-keys) | One of the more generous permanent free quotas. | +| Cerebras | [cloud.cerebras.ai](https://cloud.cerebras.ai/) | High daily token volume, no card. | +| OpenRouter | [openrouter.ai/keys](https://openrouter.ai/keys) | One API, many free models — good for comparing providers. | + +Whichever you pick, the process is the same: + +1. Sign in and generate an API key on that provider's site. +2. **Never paste this key directly into code or commit it to a repository.** Put it in a `.env` file instead (already gitignored): + +```bash +# .env +GITHUB_TOKEN=your-key-here +``` + +`python-dotenv` reads this into `os.environ` automatically. GitHub Models exposes an OpenAI-compatible API, so the plain `openai` client works for it without any extra package. + +## Step 1: Walk the repo and chunk its files + +Unlike a folder of notes, a repository has a structure: code files that need different chunking than prose, vendored/generated directories you never want indexed (like `.git` or `node_modules`), and line numbers that matter because your answers are going to cite them. + +Write a walker that collects the files you actually want to index: + +```python +# prepare_repo.py +"""Walks a cloned repo, collects indexable files, and splits them into +chunks that remember their source file and line range. + +Run with: uv run python prepare_repo.py /path/to/repo +This prints a summary -- build_index.py (Step 2) imports load_chunks() +from this file. +""" + +import sys +from pathlib import Path + +SKIP_DIRS = {".git", "node_modules", "__pycache__", "venv", ".venv", "dist", "build"} +CODE_EXTS = {".py", ".js", ".ts", ".go", ".rs", ".java", ".md", ".mdx", ".txt"} + +TARGET_CHUNK_SIZE = 500 # characters + + +def walk_repo(repo_root: Path) -> list[Path]: + """Returns every indexable file under repo_root, skipping junk dirs.""" + files = [] + for path in sorted(repo_root.rglob("*")): + if path.is_dir(): + continue + if any(part in SKIP_DIRS for part in path.relative_to(repo_root).parts): + continue + if path.suffix.lower() in CODE_EXTS: + files.append(path) + return files + + +def chunk_code(text: str, source: str) -> list[dict]: + """Splits a code file at top-level function/class boundaries, so each + chunk is a self-contained unit -- not a random window that could start + mid-function.""" + lines = text.splitlines() + boundaries = [0] + for i, line in enumerate(lines): + if line.startswith(("def ", "class ")) and not line.startswith((" ", "\t")): + if i > 0: + boundaries.append(i) + boundaries.append(len(lines)) + + chunks = [] + for start, end in zip(boundaries, boundaries[1:]): + body = "\n".join(lines[start:end]).strip() + if not body: + continue + chunks.append( + {"source": source, "start": start + 1, "end": end, "text": body} + ) + return chunks + + +def chunk_prose(text: str, source: str) -> list[dict]: + """Splits a Markdown/text file into paragraph chunks merged up to + TARGET_CHUNK_SIZE, tracking the line range of each chunk.""" + lines = text.splitlines() + chunks = [] + current, start = [], None + for i, line in enumerate(lines): + if not line.strip(): + if current: + chunks.append( + { + "source": source, + "start": start + 1, + "end": i, + "text": "\n".join(current).strip(), + } + ) + current, start = [], None + continue + if start is None: + start = i + current.append(line) + if sum(len(l) for l in current) >= TARGET_CHUNK_SIZE: + chunks.append( + {"source": source, "start": start + 1, "end": i + 1, "text": "\n".join(current).strip()} + ) + current, start = [], None + if current: + chunks.append( + {"source": source, "start": start + 1, "end": len(lines), "text": "\n".join(current).strip()} + ) + return chunks + + +def load_chunks(repo_root: Path) -> list[dict]: + chunks = [] + for path in walk_repo(repo_root): + text = path.read_text(encoding="utf-8", errors="replace") + source = str(path.relative_to(repo_root)) + if path.suffix.lower() in {".md", ".mdx", ".txt"}: + chunks.extend(chunk_prose(text, source)) + else: + chunks.extend(chunk_code(text, source)) + return chunks + + +if __name__ == "__main__": + root = Path(sys.argv[1]) + chunks = load_chunks(root) + print(f"Indexed {len(chunks)} chunks from {len(walk_repo(root))} files") + for c in chunks[:3]: + print(f" {c['source']}:{c['start']}-{c['end']} {c['text'][:60]}...") +``` + +```bash +uv run python prepare_repo.py python-data-analysis-course +``` + +Two decisions here matter more than they look: + +- **Code chunks break at function/class boundaries, prose chunks at paragraph boundaries.** A character-window chunk can start halfway through a function definition — the vector for that chunk then represents a meaningless fragment, and the LLM gets asked to answer from it. Chunking *with the language* instead is the same idea as the notes project's "sharper, more specific vectors," applied to code. +- **Every chunk records `start` and `end` line numbers.** That metadata is what lets Step 4 demand citations and what lets *you* verify them — open the file, jump to the cited lines, and check the answer is really grounded there. + +**✅ Checklist** + + +`uv run python prepare_repo.py python-data-analysis-course` prints a chunk count and per-chunk `file:start-end` previews. +No chunk's `text` preview starts or ends mid-function — code chunks look like complete definitions. +None of the previewed sources are inside `.git/` or other skipped directories. + + +**🤔 Socratic Question(s)** + +- The course repo includes the full `build/` output from this website's static generation. Why would you *want* it skipped? What would a search result pulled from generated HTML look like to the LLM? +- `errors="replace"` silently substitutes unreadable bytes in a file rather than crashing the whole index build. When is "skip the file" a better choice than "replace the bytes"? + +## Step 2: Embed every chunk locally + +Identical to the notes project — same model, same idea, one new file: + +```python +# build_index.py +"""Embeds every chunk from prepare_repo.py and saves the vectors + text +locally, so query.py (Step 4) doesn't re-embed anything at query time. + +Run with: uv run python build_index.py /path/to/repo +Re-run this any time the repo changes -- the saved index doesn't update itself. +""" + +import json +import sys +from pathlib import Path + +import numpy as np +from sentence_transformers import SentenceTransformer + +from prepare_repo import load_chunks + +MODEL_NAME = "all-MiniLM-L6-v2" +INDEX_PATH = "index.npy" +CHUNKS_PATH = "chunks.json" + + +def main() -> None: + chunks = load_chunks(Path(sys.argv[1])) + if not chunks: + print("No chunks found -- did you pass a valid repo path?") + return + + print(f"Embedding {len(chunks)} chunks with {MODEL_NAME}...") + model = SentenceTransformer(MODEL_NAME) + texts = [c["text"] for c in chunks] + embeddings = model.encode(texts, normalize_embeddings=True) + + np.save(INDEX_PATH, embeddings) + with open(CHUNKS_PATH, "w", encoding="utf-8") as f: + json.dump(chunks, f, ensure_ascii=False, indent=2) + + print(f"Saved {embeddings.shape[0]} vectors ({embeddings.shape[1]}-dim) to {INDEX_PATH}") + print(f"Saved chunk text/metadata (including line ranges) to {CHUNKS_PATH}") + + +if __name__ == "__main__": + main() +``` + +```bash +uv run python build_index.py python-data-analysis-course +``` + +A repository indexes to a few hundred or low-thousands of chunks for a typical small-to-mid repo — well within what a NumPy array in memory handles trivially, which is why this project again avoids a vector database. `normalize_embeddings=True` scales every vector to length 1 so Step 3's cosine similarity reduces to a dot product. + +**✅ Checklist** + + +`uv run python build_index.py python-data-analysis-course` completes and prints a shape like `(N, 384)`. +Both `index.npy` and `chunks.json` now exist in your project folder. +The chunk count printed here matches the one from Step 1. + + +**🤔 Socratic Question(s)** + +- The notes project embedded a few dozen chunks in about a second each. This repo has far more. Roughly how long do you expect indexing to take — and is that a one-time cost or a per-question cost? +- What happens to the saved index if the repo's `main` branch moves? Does that suggest when you'd want to re-run this step? + +## Step 3: Retrieve relevant chunks + +Retrieval is exactly the notes version, because nothing about the math cares whether a chunk came from prose or code: + +```python +# retrieve.py +"""Given a question, finds the repo chunks most relevant to it. + +Imported by query.py (Step 4) -- the __main__ block below lets you try it +standalone. +""" + +import json + +import numpy as np +from sentence_transformers import SentenceTransformer + +MODEL_NAME = "all-MiniLM-L6-v2" +INDEX_PATH = "index.npy" +CHUNKS_PATH = "chunks.json" + +_model = None # loaded lazily so importing this module doesn't load the model + + +def get_model() -> SentenceTransformer: + global _model + if _model is None: + _model = SentenceTransformer(MODEL_NAME) + return _model + + +def retrieve(question: str, top_k: int = 3) -> list[dict]: + embeddings = np.load(INDEX_PATH) + with open(CHUNKS_PATH, encoding="utf-8") as f: + chunks = json.load(f) + + question_vector = get_model().encode([question], normalize_embeddings=True)[0] + similarities = embeddings @ question_vector + + top_indices = np.argsort(similarities)[::-1][:top_k] + return [ + {**chunks[i], "score": float(similarities[i])} + for i in top_indices + ] + + +if __name__ == "__main__": + for r in retrieve("How does the course build this website?"): + print(f"{r['score']:.3f} [{r['source']}:{r['start']}-{r['end']}] {r['text'][:70]}...") +``` + +```bash +uv run python retrieve.py +``` + +Notice the preview line now prints the **line range** alongside the file — that's the citation data Step 4 will demand of the LLM, and it's been riding along in every chunk since Step 1. + +**✅ Checklist** + + +`uv run python retrieve.py` prints `top_k` results, each with a score and a `file:start-end` citation. +The top result for a question about a specific lesson actually points at that lesson's file, not a random one. +Scores are between -1 and 1. + + +**🤔 Socratic Question(s)** + +- Code and prose get embedded by the same model, in the same vector space. A question phrased in plain English like "how do I add a new project page?" might retrieve a Markdown lesson file, while "what does `load_chunks` return?" retrieves code. What do the differing phrasing patterns suggest about how you'd ask questions of a code+docs index? +- Retrieval here scores each chunk independently. If an answer spans two separate files, will the top-3 chunk list necessarily contain both halves? What's a cheap way to make sure the model gets the *whole* story? + +## Step 4: Generate an answer with citations + +The pay-off step. Retrieval hands the model the actual chunks — including their file and line metadata — and the prompt explicitly demands citations: + +```python +# query.py +"""Retrieves relevant chunks for a question, then asks a free-tier LLM to +answer using only that context, citing the exact file and lines it used. + +Run with: uv run python query.py "your question here" +""" + +import os +import sys + +from dotenv import load_dotenv +from openai import OpenAI + +from retrieve import retrieve + +load_dotenv() + +PROMPT_TEMPLATE = """Answer the question using ONLY the context below. The +context is drawn from a real code repository; each block is tagged with the +file and line range it came from. Support every factual claim you make with a +citation in the form [path.py:start-end]. If the context doesn't contain the +answer, say so -- do not make something up, and do not cite anything not in +the context. + +Context: +{context} + +Question: {question} + +Answer:""" + + +def build_prompt(question: str, chunks: list[dict]) -> str: + context = "\n\n".join( + f"[{c['source']}:{c['start']}-{c['end']}]\n{c['text']}" for c in chunks + ) + return PROMPT_TEMPLATE.format(context=context, question=question) + + +def ask(question: str, top_k: int = 4) -> str: + chunks = retrieve(question, top_k=top_k) + prompt = build_prompt(question, chunks) + + client = OpenAI( + api_key=os.environ["GITHUB_TOKEN"], + base_url="https://models.github.ai/inference", + ) + response = client.chat.completions.create( + model="gpt-4o-mini", # confirm this still has a free tier before running + messages=[{"role": "user", "content": prompt}], + ) + return response.choices[0].message.content + + +if __name__ == "__main__": + question = " ".join(sys.argv[1:]) or "How does the course build this website?" + print(ask(question)) +``` + +```bash +uv run python query.py "How does the course build this website?" +``` + +Two things make this more than the notes version with fancier input: + +- **The citations are *verifiable*.** The model didn't invent them — every `[path:start-end]` it can cite had to come out of the context you handed it. So you can, and should, open the cited file, jump to the cited lines, and check the answer against the source. When retrieval works, this is the strongest grounding RAG offers: not just "the model says so," but "here's the exact line, go read it." +- **The honesty rule matters more on code.** A model trained on millions of public repos may genuinely know an API or a function — but it may "know" a *different* version of it than the repo you cloned. Forcing answers to come only from the context is what makes the answer true *for that repo*, not generically. + +:::tip[Using a different provider?] +Swap the `OpenAI(...)` block for your provider's own client, following the same pattern as the [AI Agent project](/docs/projects/ai-agent#step-1-write-your-first-agent) — e.g. Google's `google-genai` package for Gemini, or `groq`'s own client for Groq. Cerebras and OpenRouter are also OpenAI-compatible, so the `openai` package works for them too, just with a different `base_url`. +::: + +**✅ Checklist** + + +`uv run python query.py "a real question about the repo"` prints an answer with at least one `[path:start-end]` citation. +You opened one cited file, jumped to the cited lines, and confirmed the answer is genuinely grounded there. +Asking something the repo clearly doesn't cover makes the model say so — and it doesn't cite lines that don't exist. + + +**🤔 Socratic Question(s)** + +- The prompt says "do not cite anything not in the context." What happens if you delete that instruction and a repo's chunk happens to be about a topic the model already knows well — does it stay disciplined about citing only what you gave it? +- A citation `[main.py:12-34]` might point at a function *definition*, or at a *call site*. Which is more useful for a question like "how is X called?" vs. "what does X do?"? Can you phrase questions to steer which one retrieval finds? + +## ⚠️ Common pitfalls + +- **Indexing junk directories.** The course repo's `build/` folder is generated HTML — indexing it pollutes retrieval with pages the model can't use and that aren't "source." If you point the tool at a repo that doesn't have the `SKIP_DIRS` set tuned for it, add the noisy directories before you re-run `build_index.py`. +- **Forgetting to rebuild the index after the repo changes.** `git pull`, a new commit, a new branch — none of it touches `index.npy` until you re-run `uv run python build_index.py`. The index is a snapshot, not a live view. +- **Chunking code with a prose-only splitter.** If you reuse the notes project's paragraph splitter on `.py` files, you get the mid-function fragments Step 1 warned about. The `def `/`class ` boundary splitter exists for exactly this reason — don't skip it. +- **Trusting citations without checking them.** Citations are only as trustworthy as retrieval — if the wrong chunk was retrieved, the model will confidently cite *it*. Always spot-check at least one citation per answer by opening the file at the cited lines. +- **Rate limits on the free LLM tier.** Retrieval (Steps 1-3) is local and unlimited; only Step 4's `ask()` call counts against your free-tier quota. A 429 there is the provider telling you to slow down, not a bug — see the [AI Agent project](/docs/projects/ai-agent#handling-rate-limits) for a retry pattern you can copy. + +## What you just built + +A real RAG tool over a real code repository: language-aware chunking with line tracking, local embedding, in-memory similarity search, and LLM generation whose answers cite the exact file and lines they're grounded in. The same architecture powers "chat with your codebase" tools across the industry — the differences here are scale (a NumPy array instead of a vector database), scope (one repo instead of a monorepo), and the free tier instead of a paid model. Nothing is a toy: point the same scripts at any public repo and they just work. + +## Where to go from here + +- Point it at a bigger, real library you use — say, `flask` or `pydantic` — and ask questions about *its* source. Watch what changes about the retrieval: more chunks, more files, and the first time retrieval returns the wrong chunk for a genuinely hard question. +- Add **re-ranking**: retrieve a larger top-k (say, 10) with the fast embedding search, then use a cross-encoder model to re-score just those 10 before sending the final 3-4 to the LLM — a standard two-stage pattern in production RAG. +- Handle more file types: PDFs of design docs (`pypdf`), or structured configs. The chunkers just need to produce `{source, start, end, text}` — the embedder and retriever don't care where a chunk came from. +- Investigate a real vector database like [ChromaDB](https://www.trychroma.com/) when a repo outgrows in-memory search — it does the same nearest-neighbor search with on-disk persistence and filtering. + +## Share your project with the class + +Built something you're proud of? [`examples/student-projects/`](https://github.com/abderrahim-lectures/python-data-analysis-course/tree/main/examples/student-projects) is a gallery of projects other students have submitted — and its README has a full, beginner-friendly walkthrough for adding yours via a **pull request**, even if you've never used git before: forking the repo, making a branch, committing your files, and opening the PR, one step at a time. No prior git experience assumed. + +You now have a genuinely useful developer tool. 🎓 + + diff --git a/docs/projects/index.mdx b/docs/projects/index.mdx index e22e291..46fc46d 100644 --- a/docs/projects/index.mdx +++ b/docs/projects/index.mdx @@ -17,6 +17,12 @@ They're optional and ungraded. Browse them any time — each project's intro say None: + chunks = load_chunks(Path(sys.argv[1])) + if not chunks: + print("No chunks found -- did you pass a valid repo path?") + return + + print(f"Embedding {len(chunks)} chunks with {MODEL_NAME}...") + model = SentenceTransformer(MODEL_NAME) + texts = [c["text"] for c in chunks] + embeddings = model.encode(texts, normalize_embeddings=True) + + np.save(INDEX_PATH, embeddings) + with open(CHUNKS_PATH, "w", encoding="utf-8") as f: + json.dump(chunks, f, ensure_ascii=False, indent=2) + + print(f"Saved {embeddings.shape[0]} vectors ({embeddings.shape[1]}-dim) to {INDEX_PATH}") + print(f"Saved chunk text/metadata (including line ranges) to {CHUNKS_PATH}") + + +if __name__ == "__main__": + main() diff --git a/examples/github-repo-rag/notebook.ipynb b/examples/github-repo-rag/notebook.ipynb new file mode 100644 index 0000000..cb402f7 --- /dev/null +++ b/examples/github-repo-rag/notebook.ipynb @@ -0,0 +1,334 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Build a RAG App Over a GitHub Repo \u2014 Colab / Kaggle / Binder companion\n", + "\n", + "This notebook mirrors the local `uv` project from the course's\n", + "[Build a RAG App Over a GitHub Repo](https://abderrahim-lectures.github.io/python-data-analysis-course/docs/projects/github-repo-rag)\n", + "lesson, adapted to run in a hosted notebook: it clones the course repo itself, chunks its\n", + "lesson docs (code-style and prose-style chunking), embeds them locally with\n", + "`sentence-transformers`, retrieves with NumPy cosine similarity, and answers with\n", + "file-and-line citations via a free-tier LLM.\n", + "\n", + "See the [lesson](https://abderrahim-lectures.github.io/python-data-analysis-course/docs/projects/github-repo-rag) for the full walkthrough and the\n", + "[local example project](https://github.com/abderrahim-lectures/python-data-analysis-course/tree/main/examples/github-repo-rag) for the real, file-based version of this same code." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 0: Install dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install sentence-transformers numpy openai" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Clone a test repo\n", + "\n", + "A hosted notebook has no local repo to read from, so clone one here. The course repo\n", + "itself makes a great test subject \u2014 small, and full of lessons you'll recognize. We use\n", + "`--depth 1` to skip git history, and index only its `docs/projects/` folder so embedding\n", + "stays fast." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!git clone --depth 1 https://github.com/abderrahim-lectures/python-data-analysis-course\n", + "REPO_ROOT = \"python-data-analysis-course\"\n", + "INDEX_ROOT = f\"{REPO_ROOT}/docs/projects\"\n", + "print(\"Cloned.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Walk the repo and chunk its files\n", + "\n", + "Same logic as [`prepare_repo.py`](https://github.com/abderrahim-lectures/python-data-analysis-course/blob/main/examples/github-repo-rag/prepare_repo.py)\n", + "in the local example: code files are split at top-level `def ` / `class ` boundaries,\n", + "prose (Markdown) files by paragraph, and every chunk records its source file **and line\n", + "range** \u2014 that metadata is what lets the final step produce verifiable citations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "SKIP_DIRS = {\".git\", \"node_modules\", \"__pycache__\", \"venv\", \".venv\", \"dist\", \"build\"}\n", + "CODE_EXTS = {\".py\", \".js\", \".ts\", \".go\", \".rs\", \".java\", \".md\", \".mdx\", \".txt\"}\n", + "TARGET_CHUNK_SIZE = 500 # characters\n", + "\n", + "\n", + "def walk_repo(repo_root: Path) -> list[Path]:\n", + " files = []\n", + " for path in sorted(repo_root.rglob(\"*\")):\n", + " if path.is_dir():\n", + " continue\n", + " if any(part in SKIP_DIRS for part in path.relative_to(repo_root).parts):\n", + " continue\n", + " if path.suffix.lower() in CODE_EXTS:\n", + " files.append(path)\n", + " return files\n", + "\n", + "\n", + "def chunk_code(text: str, source: str) -> list[dict]:\n", + " lines = text.splitlines()\n", + " boundaries = [0]\n", + " for i, line in enumerate(lines):\n", + " if line.startswith((\"def \", \"class \")) and not line.startswith((\" \", \"\\t\")):\n", + " if i > 0:\n", + " boundaries.append(i)\n", + " boundaries.append(len(lines))\n", + " chunks = []\n", + " for start, end in zip(boundaries, boundaries[1:]):\n", + " body = \"\\n\".join(lines[start:end]).strip()\n", + " if not body:\n", + " continue\n", + " chunks.append({\"source\": source, \"start\": start + 1, \"end\": end, \"text\": body})\n", + " return chunks\n", + "\n", + "\n", + "def chunk_prose(text: str, source: str) -> list[dict]:\n", + " lines = text.splitlines()\n", + " chunks = []\n", + " current, start = [], None\n", + " for i, line in enumerate(lines):\n", + " if not line.strip():\n", + " if current:\n", + " chunks.append({\"source\": source, \"start\": start + 1, \"end\": i,\n", + " \"text\": \"\\n\".join(current).strip()})\n", + " current, start = [], None\n", + " continue\n", + " if start is None:\n", + " start = i\n", + " current.append(line)\n", + " if sum(len(l) for l in current) >= TARGET_CHUNK_SIZE:\n", + " chunks.append({\"source\": source, \"start\": start + 1, \"end\": i + 1,\n", + " \"text\": \"\\n\".join(current).strip()})\n", + " current, start = [], None\n", + " if current:\n", + " chunks.append({\"source\": source, \"start\": start + 1, \"end\": len(lines),\n", + " \"text\": \"\\n\".join(current).strip()})\n", + " return chunks\n", + "\n", + "\n", + "def load_chunks(repo_root: Path) -> list[dict]:\n", + " chunks = []\n", + " for path in walk_repo(repo_root):\n", + " text = path.read_text(encoding=\"utf-8\", errors=\"replace\")\n", + " source = str(path.relative_to(repo_root))\n", + " if path.suffix.lower() in {\".md\", \".mdx\", \".txt\"}:\n", + " chunks.extend(chunk_prose(text, source))\n", + " else:\n", + " chunks.extend(chunk_code(text, source))\n", + " return chunks\n", + "\n", + "\n", + "chunks = load_chunks(Path(INDEX_ROOT))\n", + "print(f\"Indexed {len(chunks)} chunks from {len(walk_repo(Path(INDEX_ROOT)))} files\")\n", + "for c in chunks[:3]:\n", + " print(f\" {c['source']}:{c['start']}-{c['end']} {c['text'][:60]}...\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Embed the chunks locally\n", + "\n", + "Same model as [`build_index.py`](https://github.com/abderrahim-lectures/python-data-analysis-course/blob/main/examples/github-repo-rag/build_index.py):\n", + "`all-MiniLM-L6-v2`, run entirely on CPU, no API key needed. Since this notebook has no\n", + "`index.npy`/`chunks.json` files to persist to, the embeddings are kept in memory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sentence_transformers import SentenceTransformer\n", + "\n", + "MODEL_NAME = \"all-MiniLM-L6-v2\"\n", + "\n", + "print(f\"Embedding {len(chunks)} chunks with {MODEL_NAME}...\")\n", + "model = SentenceTransformer(MODEL_NAME)\n", + "texts = [chunk[\"text\"] for chunk in chunks]\n", + "embeddings = model.encode(texts, normalize_embeddings=True)\n", + "\n", + "print(f\"Embedded {embeddings.shape[0]} chunks ({embeddings.shape[1]}-dim)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Retrieve relevant chunks\n", + "\n", + "Same cosine-similarity retrieval as [`retrieve.py`](https://github.com/abderrahim-lectures/python-data-analysis-course/blob/main/examples/github-repo-rag/retrieve.py) \u2014\n", + "every embedding is already normalized to length 1, so cosine similarity collapses to a\n", + "plain dot product. Results now carry the line range too, ready for citation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def retrieve(question: str, top_k: int = 3) -> list[dict]:\n", + " question_vector = model.encode([question], normalize_embeddings=True)[0]\n", + " similarities = embeddings @ question_vector\n", + " top_indices = np.argsort(similarities)[::-1][:top_k]\n", + " return [\n", + " {**chunks[i], \"score\": float(similarities[i])}\n", + " for i in top_indices\n", + " ]\n", + "\n", + "\n", + "for r in retrieve(\"How does the course build this website?\"):\n", + " print(f\"{r['score']:.3f} [{r['source']}:{r['start']}-{r['end']}] {r['text'][:70]}...\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Get a free-tier LLM API key\n", + "\n", + "Pick any provider from the table in the [lesson's Setup](https://abderrahim-lectures.github.io/python-data-analysis-course/docs/projects/github-repo-rag#get-a-free-llm-api-key) \u2014\n", + "GitHub Models is the suggested default since it needs no separate signup. Paste the key\n", + "below; `getpass` keeps it out of the notebook's saved output and cell history." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "os.environ[\"GITHUB_TOKEN\"] = getpass.getpass(\"Paste your GitHub Models API key: \")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Generate an answer with citations\n", + "\n", + "Same prompt and client setup as [`query.py`](https://github.com/abderrahim-lectures/python-data-analysis-course/blob/main/examples/github-repo-rag/query.py) \u2014\n", + "GitHub Models exposes an OpenAI-compatible API, so the plain `openai` client library works\n", + "without any extra package. The prompt demands `[path:start-end]` citations, so the answer\n", + "points you at the exact lines to open and verify." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from openai import OpenAI\n", + "\n", + "PROMPT_TEMPLATE = \"\"\"Answer the question using ONLY the context below. The\n", + "context is drawn from a real code repository; each block is tagged with the\n", + "file and line range it came from. Support every factual claim you make with a\n", + "citation in the form [path.py:start-end]. If the context doesn't contain the\n", + "answer, say so -- do not make something up, and do not cite anything not in\n", + "the context.\n", + "\n", + "Context:\n", + "{context}\n", + "\n", + "Question: {question}\n", + "\n", + "Answer:\"\"\"\n", + "\n", + "\n", + "def build_prompt(question: str, retrieved: list[dict]) -> str:\n", + " context = \"\\n\\n\".join(\n", + " f\"[{c['source']}:{c['start']}-{c['end']}]\\n{c['text']}\" for c in retrieved\n", + " )\n", + " return PROMPT_TEMPLATE.format(context=context, question=question)\n", + "\n", + "\n", + "def ask(question: str, top_k: int = 4) -> str:\n", + " retrieved = retrieve(question, top_k=top_k)\n", + " prompt = build_prompt(question, retrieved)\n", + "\n", + " client = OpenAI(\n", + " api_key=os.environ[\"GITHUB_TOKEN\"],\n", + " base_url=\"https://models.github.ai/inference\",\n", + " )\n", + " response = client.chat.completions.create(\n", + " model=\"gpt-4o-mini\", # confirm this still has a free tier before running\n", + " messages=[{\"role\": \"user\", \"content\": prompt}],\n", + " )\n", + " return response.choices[0].message.content" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Try it: ask a question about the course repo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "question = \"How does the course build this website?\"\n", + "answer = ask(question)\n", + "print(f\"Q: {question}\\n\")\n", + "print(f\"A: {answer}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12" + }, + "colab": { + "name": "github-repo-rag-notebook.ipynb", + "provenance": [] + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/github-repo-rag/prepare_repo.py b/examples/github-repo-rag/prepare_repo.py new file mode 100644 index 0000000..9d3614f --- /dev/null +++ b/examples/github-repo-rag/prepare_repo.py @@ -0,0 +1,115 @@ +"""Walks a cloned repo, collects indexable files, and splits them into +chunks that remember their source file and line range. + +Run with: uv run python prepare_repo.py /path/to/repo +This prints a summary -- build_index.py (Step 2) imports load_chunks() +from this file. +""" + +import sys +from pathlib import Path + +SKIP_DIRS = {".git", "node_modules", "__pycache__", "venv", ".venv", "dist", "build"} +CODE_EXTS = {".py", ".js", ".ts", ".go", ".rs", ".java", ".md", ".mdx", ".txt"} + +TARGET_CHUNK_SIZE = 500 # characters + + +def walk_repo(repo_root: Path) -> list[Path]: + """Returns every indexable file under repo_root, skipping junk dirs.""" + files = [] + for path in sorted(repo_root.rglob("*")): + if path.is_dir(): + continue + if any(part in SKIP_DIRS for part in path.relative_to(repo_root).parts): + continue + if path.suffix.lower() in CODE_EXTS: + files.append(path) + return files + + +def chunk_code(text: str, source: str) -> list[dict]: + """Splits a code file at top-level function/class boundaries, so each + chunk is a self-contained unit -- not a random window that could start + mid-function.""" + lines = text.splitlines() + boundaries = [0] + for i, line in enumerate(lines): + if line.startswith(("def ", "class ")) and not line.startswith((" ", "\t")): + if i > 0: + boundaries.append(i) + boundaries.append(len(lines)) + + chunks = [] + for start, end in zip(boundaries, boundaries[1:]): + body = "\n".join(lines[start:end]).strip() + if not body: + continue + chunks.append( + {"source": source, "start": start + 1, "end": end, "text": body} + ) + return chunks + + +def chunk_prose(text: str, source: str) -> list[dict]: + """Splits a Markdown/text file into paragraph chunks merged up to + TARGET_CHUNK_SIZE, tracking the line range of each chunk.""" + lines = text.splitlines() + chunks = [] + current, start = [], None + for i, line in enumerate(lines): + if not line.strip(): + if current: + chunks.append( + { + "source": source, + "start": start + 1, + "end": i, + "text": "\n".join(current).strip(), + } + ) + current, start = [], None + continue + if start is None: + start = i + current.append(line) + if sum(len(l) for l in current) >= TARGET_CHUNK_SIZE: + chunks.append( + { + "source": source, + "start": start + 1, + "end": i + 1, + "text": "\n".join(current).strip(), + } + ) + current, start = [], None + if current: + chunks.append( + { + "source": source, + "start": start + 1, + "end": len(lines), + "text": "\n".join(current).strip(), + } + ) + return chunks + + +def load_chunks(repo_root: Path) -> list[dict]: + chunks = [] + for path in walk_repo(repo_root): + text = path.read_text(encoding="utf-8", errors="replace") + source = str(path.relative_to(repo_root)) + if path.suffix.lower() in {".md", ".mdx", ".txt"}: + chunks.extend(chunk_prose(text, source)) + else: + chunks.extend(chunk_code(text, source)) + return chunks + + +if __name__ == "__main__": + root = Path(sys.argv[1]) + chunks = load_chunks(root) + print(f"Indexed {len(chunks)} chunks from {len(walk_repo(root))} files") + for c in chunks[:3]: + print(f" {c['source']}:{c['start']}-{c['end']} {c['text'][:60]}...") diff --git a/examples/github-repo-rag/pyproject.toml b/examples/github-repo-rag/pyproject.toml new file mode 100644 index 0000000..8ce2ef4 --- /dev/null +++ b/examples/github-repo-rag/pyproject.toml @@ -0,0 +1,12 @@ +[project] +name = "github-repo-rag" +version = "0.1.0" +description = "Local companion to the course's 'Build a RAG App Over a GitHub Repo' project: clone a repo, chunk its code and docs with line tracking, embed locally, and answer questions with file-and-line citations." +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "sentence-transformers>=3.3.1", + "numpy>=2.2.0", + "python-dotenv>=1.2.2", + "openai>=1.59.0", +] diff --git a/examples/github-repo-rag/query.py b/examples/github-repo-rag/query.py new file mode 100644 index 0000000..0c96307 --- /dev/null +++ b/examples/github-repo-rag/query.py @@ -0,0 +1,56 @@ +"""Retrieves relevant chunks for a question, then asks a free-tier LLM to +answer using only that context, citing the exact file and lines it used. + +Run with: uv run python query.py "your question here" +""" + +import os +import sys + +from dotenv import load_dotenv +from openai import OpenAI + +from retrieve import retrieve + +load_dotenv() + +PROMPT_TEMPLATE = """Answer the question using ONLY the context below. The +context is drawn from a real code repository; each block is tagged with the +file and line range it came from. Support every factual claim you make with a +citation in the form [path.py:start-end]. If the context doesn't contain the +answer, say so -- do not make something up, and do not cite anything not in +the context. + +Context: +{context} + +Question: {question} + +Answer:""" + + +def build_prompt(question: str, chunks: list[dict]) -> str: + context = "\n\n".join( + f"[{c['source']}:{c['start']}-{c['end']}]\n{c['text']}" for c in chunks + ) + return PROMPT_TEMPLATE.format(context=context, question=question) + + +def ask(question: str, top_k: int = 4) -> str: + chunks = retrieve(question, top_k=top_k) + prompt = build_prompt(question, chunks) + + client = OpenAI( + api_key=os.environ["GITHUB_TOKEN"], + base_url="https://models.github.ai/inference", + ) + response = client.chat.completions.create( + model="gpt-4o-mini", # confirm this still has a free tier before running + messages=[{"role": "user", "content": prompt}], + ) + return response.choices[0].message.content + + +if __name__ == "__main__": + question = " ".join(sys.argv[1:]) or "How does the course build this website?" + print(ask(question)) diff --git a/examples/github-repo-rag/retrieve.py b/examples/github-repo-rag/retrieve.py new file mode 100644 index 0000000..950561c --- /dev/null +++ b/examples/github-repo-rag/retrieve.py @@ -0,0 +1,43 @@ +"""Given a question, finds the repo chunks most relevant to it. + +Imported by query.py (Step 4) -- the __main__ block below lets you try it +standalone. +""" + +import json + +import numpy as np +from sentence_transformers import SentenceTransformer + +MODEL_NAME = "all-MiniLM-L6-v2" +INDEX_PATH = "index.npy" +CHUNKS_PATH = "chunks.json" + +_model = None # loaded lazily so importing this module doesn't load the model + + +def get_model() -> SentenceTransformer: + global _model + if _model is None: + _model = SentenceTransformer(MODEL_NAME) + return _model + + +def retrieve(question: str, top_k: int = 3) -> list[dict]: + embeddings = np.load(INDEX_PATH) + with open(CHUNKS_PATH, encoding="utf-8") as f: + chunks = json.load(f) + + question_vector = get_model().encode([question], normalize_embeddings=True)[0] + similarities = embeddings @ question_vector + + top_indices = np.argsort(similarities)[::-1][:top_k] + return [ + {**chunks[i], "score": float(similarities[i])} + for i in top_indices + ] + + +if __name__ == "__main__": + for r in retrieve("How does the course build this website?"): + print(f"{r['score']:.3f} [{r['source']}:{r['start']}-{r['end']}] {r['text'][:70]}...") diff --git a/examples/github-repo-rag/uv.lock b/examples/github-repo-rag/uv.lock new file mode 100644 index 0000000..c6106ba --- /dev/null +++ b/examples/github-repo-rag/uv.lock @@ -0,0 +1,1290 @@ +version = 1 +revision = 3 +requires-python = ">=3.12" + +[[package]] +name = "annotated-doc" +version = "0.0.5" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/5a/8e/38aa427ed5402449e226975b649c5dc73ccadfefeb95e6aecb8f8ea4b6b6/annotated_doc-0.0.5.tar.gz", hash = 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+ + Build a RAG App Over a GitHub Repo + + } + summary={ + + Clone a real repository, chunk its code and docs with line tracking, embed + them locally, and ask questions that come back with file-and-line citations. + + } + />