From 21aeab7524a95ef6482bd1eef737aef7dfd335e4 Mon Sep 17 00:00:00 2001 From: Abderrahim Adrabi <184391033+abderrahim-lectures@users.noreply.github.com> Date: Sun, 2 Aug 2026 01:55:59 +0100 Subject: [PATCH] feat: add multihop-wikipedia-qa project Two-round (iterative retrieval) RAG project: single-hop baseline vs multi-hop pipeline over a bundled Wikipedia-style sample, with an evidence-audited side-by-side comparison. Includes lesson page, six crafted articles, six test questions, CLI, notebook, and registrations. --- docs/projects/index.mdx | 6 + .../multihop-wikipedia-qa/_category_.json | 4 + docs/projects/multihop-wikipedia-qa/index.md | 652 +++++++++ examples/multihop-wikipedia-qa/.env.example | 26 + examples/multihop-wikipedia-qa/.gitignore | 6 + .../multihop-wikipedia-qa/.python-version | 1 + examples/multihop-wikipedia-qa/README.md | 69 + .../data/articles/amina-rahman.md | 21 + .../data/articles/basel.md | 21 + .../data/articles/cereolabs.md | 21 + .../data/articles/elena-marchetti.md | 21 + .../data/articles/lisbon.md | 21 + .../data/articles/volta-dynamics.md | 21 + .../data/test_questions.json | 41 + examples/multihop-wikipedia-qa/main.py | 507 +++++++ examples/multihop-wikipedia-qa/notebook.ipynb | 411 ++++++ examples/multihop-wikipedia-qa/pyproject.toml | 12 + examples/multihop-wikipedia-qa/uv.lock | 1290 +++++++++++++++++ src/data/projects.ts | 6 + src/pages/index.tsx | 18 + 20 files changed, 3175 insertions(+) create mode 100644 docs/projects/multihop-wikipedia-qa/_category_.json create mode 100644 docs/projects/multihop-wikipedia-qa/index.md create mode 100644 examples/multihop-wikipedia-qa/.env.example create mode 100644 examples/multihop-wikipedia-qa/.gitignore create mode 100644 examples/multihop-wikipedia-qa/.python-version create mode 100644 examples/multihop-wikipedia-qa/README.md create mode 100644 examples/multihop-wikipedia-qa/data/articles/amina-rahman.md create mode 100644 examples/multihop-wikipedia-qa/data/articles/basel.md create mode 100644 examples/multihop-wikipedia-qa/data/articles/cereolabs.md create mode 100644 examples/multihop-wikipedia-qa/data/articles/elena-marchetti.md create mode 100644 examples/multihop-wikipedia-qa/data/articles/lisbon.md create mode 100644 examples/multihop-wikipedia-qa/data/articles/volta-dynamics.md create mode 100644 examples/multihop-wikipedia-qa/data/test_questions.json create mode 100644 examples/multihop-wikipedia-qa/main.py create mode 100644 examples/multihop-wikipedia-qa/notebook.ipynb create mode 100644 examples/multihop-wikipedia-qa/pyproject.toml create mode 100644 examples/multihop-wikipedia-qa/uv.lock diff --git a/docs/projects/index.mdx b/docs/projects/index.mdx index e22e291..c8281dd 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 + + + +The [RAG App project](/docs/projects/rag-notes) retrieves the most relevant chunks of your notes once, and asks a language model to answer from them. That works beautifully when the answer lives in a single document. But a whole class of questions — the kind real search engines and research assistants are judged on — need facts from **two** documents combined: "Who founded the company that built the thing in that city?" requires knowing *who* from one article and *which company did the thing* from another. A single retrieval pass usually finds one of the two, the model is left to guess the rest, and you get a plausible-sounding but **wrong** answer. + +This project builds a small, practical version of the fix: a **two-round** retrieval pipeline over a bundled sample of a handful of Wikipedia-style articles. Round one retrieves the top chunks for the question; the model decides whether that evidence is enough; and if it isn't, the model writes a follow-up search query, the tool retrieves a second round with it, and only then answers from the combined evidence. Both paths print the evidence chunks they used, so you can always audit exactly why one pipeline got a question wrong and the other got it right. + +Let's be honest about what this is and isn't. *Multi-hop reasoning* — where the model itself reasons across documents — is genuinely hard research territory, and whole systems exist just to improve at it. This project is deliberately minimal: the "multi-hop" is **iterative retrieval** (retrieve → check → retrieve again), one extra round, over a tiny corpus small enough that a single person can read every chunk. That's a real technique used in production RAG, but it's a starting point, not the state of the art. The value here is seeing the *mechanism* with your own eyes, and being able to audit it line by line. + +This assumes Python 101; it also helps a lot to have already built the RAG App project, since this one reuses its whole architecture — chunking, local embeddings, cosine-similarity retrieval — and only adds the second round and the evidence audit on top. It's optional and ungraded. See [Real-World Projects](/docs/projects) for the full, growing list. + +## 🎯 What you'll do + +1. Set up a small `uv` project with local embeddings (`sentence-transformers`, `numpy`) and a free-tier LLM client (`openai`, `python-dotenv`). +2. Read the bundled sample: six short Wikipedia-style articles (biographies, companies, cities, an event), crafted so a few questions genuinely need facts from two of them. +3. Split the articles into chunks and embed them locally into an index — the same local, no-API-key step as the RAG App project. +4. Write a retrieval function that finds the chunks most relevant to a question using nothing but NumPy. +5. Build the **single-hop** pipeline: retrieve once, ask the LLM to answer using only that context. +6. Build the **multi-hop** pipeline: retrieve, ask the model whether the evidence is enough, and if not, retrieve a second round guided by the model's own follow-up query, then answer from the merged evidence. +7. Run both pipelines side by side on the bundled test questions, and audit the evidence chunks each one used. + +## Where to run this + +**Locally with `uv`** is the path this lesson's steps follow, and the recommended one — it's real Python running on your own machine, the same "graduate to real Python" move as every other project in this section. The Setup section below walks through installing it. + +**GitHub Codespaces** is a zero-setup alternative if you'd rather not install anything locally yet: 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 needs no GPU — a real, runnable notebook version of this project's pipeline (the same corpus, chunking, local embedding, and two-pipeline comparison as the steps below) lives at [`examples/multihop-wikipedia-qa/notebook.ipynb`](https://github.com/abderrahim-lectures/python-data-analysis-course/blob/main/examples/multihop-wikipedia-qa/notebook.ipynb). Click a badge to launch it directly, no local install at all: + +[![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/multihop-wikipedia-qa/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/multihop-wikipedia-qa/notebook.ipynb) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/abderrahim-lectures/python-data-analysis-course/main?filepath=examples%2Fmultihop-wikipedia-qa%2Fnotebook.ipynb) + +Be honest with yourself about the tradeoff, though: this is a lower-fidelity way to experience the project than a real local `uv` project — no separate files, no real project structure, just cells in a notebook. Treat it as a quick way to experiment, not the primary path. + +> **opencode** *(optional)* — a free, open-source AI coding agent that runs in your terminal. If you'd rather have an agent write and run this project for you than type the code yourself, install it with `curl -fsSL https://opencode.ai/install | bash` (or `npm install -g opencode-ai`) and point it at this repo with the same API key from Setup below. It's optional — this project's whole point is building it yourself, so treat it as a bonus, not a shortcut. + +## 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 multihop-qa +cd multihop-qa +uv add sentence-transformers numpy python-dotenv openai +``` + +`sentence-transformers` turns text into vectors locally, on your own CPU — no API call, no key. `numpy` does the math for comparing vectors. `python-dotenv` keeps your LLM API key in a local `.env` file. `openai` is the client used to talk to whichever free-tier provider you pick — every provider in the table below exposes an OpenAI-compatible chat endpoint, so one client, pointed at a different `base_url`, is all this project needs. + +### Get a free LLM API key + +Retrieval — embedding the articles, searching the chunks — is fully local and needs no key at all. Only the *generation* step (asking the model to answer) needs a free-tier LLM API, and it's simplest to set that up now, before you start building, rather than pausing partway through. + +**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; also exposes an OpenAI-compatible endpoint, used below. | +| 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.** Create a `.env` file in your project folder instead (never commit this): + +```bash +# .env +LLM_PROVIDER=github +GITHUB_TOKEN=your-key-here +``` + +An API key is a secret, exactly like a password — anyone with it can use your account's quota. Treating it as an environment variable rather than a hardcoded string is the standard practice for exactly this reason. `python-dotenv` (installed above) reads this file into `os.environ` automatically, the same pattern used throughout the [AI Agent project](/docs/projects/ai-agent) if you've done that one. + +The fuller example in the course repo ([`examples/multihop-wikipedia-qa/`](https://github.com/abderrahim-lectures/python-data-analysis-course/tree/main/examples/multihop-wikipedia-qa)) supports all six providers out of the box, selected with one setting — copy its `.env.example` to `.env` and fill in the key for your provider. + +**✅ Checklist** + + +`uv --version` prints a version number. +`multihop-qa/` exists with a `pyproject.toml`, and `sentence-transformers`, `numpy`, `python-dotenv`, and `openai` are installed. +You have a real API key from one provider, saved in a `.env` file in your project folder — not pasted into any script. + + +## Step 1: The corpus — articles crafted for two-hop questions + +The example comes with a small bundled corpus at `examples/multihop-wikipedia-qa/data/articles/`: six short, plain-text Wikipedia-style articles (fictional but realistic — biographies, companies, cities, an event), each a couple of kilobytes. They're not a random sample; they're *crafted* so that a few specific questions genuinely need facts from two articles at once. + +Read a couple of them and you'll notice a deliberate design: the fact that bridges two articles is **split across them**. For example, the article about the battery company says the company was founded "by the winner of the 2009 European Energy Innovation Prize" — a clue, not a name. The name of that founder only appears in her own biography article. So "Who founded the company that powered TransLisboa's electric buses?" needs one fact from the transit/city article (which company did it), one from the company article (how it's described), and one from the biography (the founder's name). A single retrieval pass over the original question gets the first two and misses the biography — the answer isn't there, so the model either says so or guesses. + +Copy the corpus into your project: + +```bash +cp -r ../examples/multihop-wikipedia-qa/data/articles ./data +``` + +Then read the files in `data/articles/`. Before writing any code, write down, in one or two sentences, what fact each article holds that *no other article* holds — because those are exactly the facts single-hop retrieval will miss. + +**✅ Checklist** + + +`data/articles/` contains the six bundled `.md` articles. +You can name at least one question that requires combining facts from two different articles. +For each article, you can point to one fact that appears in no other article. + + +**🤔 Socratic Question(s)** + +- The company article says the firm was founded "by the winner of the 2009 European Energy Innovation Prize" without naming anyone. Why do you think the corpus was written that way, instead of just naming the founder in both articles? What would that change about whether the question needs two hops? +- If every article restated all of its "shared" facts, the questions would stop being two-hop. What's the real-world equivalent of this — think about how a single sentence in a real Wikipedia article rarely tells you everything about its subject. + +## Step 2: Chunk and embed locally + +You can't hand a whole article to an embedding model and expect a useful search result — the same two reasons as the RAG App project: embedding models truncate input past a few hundred tokens, and a big chunk's vector is a blurry average of every subtopic in it. So split each article into chunks the same way as the RAG App project: by paragraph, then re-merge tiny paragraphs up to a target size so you're not left with dozens of one-line fragments. + +```python +# prepare.py +"""Splits every article in data/articles/ into a list of text chunks.""" + +from pathlib import Path + +ARTICLES_DIR = Path("data/articles") +TARGET_CHUNK_SIZE = 600 # characters + + +def split_into_paragraphs(text: str) -> list[str]: + """Splits on blank lines, dropping empty paragraphs.""" + paragraphs = [p.strip() for p in text.split("\n\n")] + return [p for p in paragraphs if p] + + +def merge_short_paragraphs(paragraphs: list[str], target_size: int) -> list[str]: + """Greedily merges consecutive short paragraphs up to target_size characters.""" + chunks = [] + current = "" + for paragraph in paragraphs: + if current and len(current) + len(paragraph) > target_size: + chunks.append(current) + current = paragraph + else: + current = f"{current}\n\n{paragraph}" if current else paragraph + if current: + chunks.append(current) + return chunks + + +def load_articles() -> list[dict]: + """Returns a list of {"source": filename, "text": full text} per article.""" + articles = [] + for path in sorted(ARTICLES_DIR.glob("*.md")): + articles.append({"source": path.name, "text": path.read_text(encoding="utf-8")}) + return articles + + +def load_chunks() -> list[dict]: + """Returns a list of {"text": ..., "source": ...} dicts, one per chunk.""" + chunks = [] + for article in load_articles(): + paragraphs = split_into_paragraphs(article["text"]) + for chunk_text in merge_short_paragraphs(paragraphs, TARGET_CHUNK_SIZE): + chunks.append({"text": chunk_text, "source": article["source"]}) + return chunks + + +if __name__ == "__main__": + chunks = load_chunks() + print(f"Loaded {len(chunks)} chunks from {ARTICLES_DIR}/") + for chunk in chunks[:3]: + preview = chunk["text"][:80].replace("\n", " ") + print(f" [{chunk['source']}] {preview}...") +``` + +```bash +uv run python prepare.py +``` + +Then embed those chunks and save the vectors, exactly as the RAG App project does — `all-MiniLM-L6-v2`, a small (~80MB) model that runs on your CPU in about a second per chunk, no key, no cost: + +```python +# build_index.py +"""Embeds every chunk and saves the vectors + chunk text locally. + +Re-run this any time you add or edit articles -- the saved index doesn't +update itself. +""" + +import json + +import numpy as np +from sentence_transformers import SentenceTransformer + +from prepare import load_chunks + +MODEL_NAME = "all-MiniLM-L6-v2" +INDEX_PATH = "index.npy" +CHUNKS_PATH = "chunks.json" + + +def main() -> None: + chunks = load_chunks() + if not chunks: + print("No chunks found -- add some .md files to data/articles/ first.") + return + + print(f"Embedding {len(chunks)} chunks with {MODEL_NAME}...") + model = SentenceTransformer(MODEL_NAME) + embeddings = model.encode( + [chunk["text"] for chunk in chunks], + 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 to {CHUNKS_PATH}") + + +if __name__ == "__main__": + main() +``` + +```bash +uv run python build_index.py +``` + +This deliberately avoids a vector database — a handful of articles is a few dozen chunks, and a plain NumPy array that fits in memory is simpler, has nothing extra to install, and is fully transparent: `index.npy` is a matrix, `chunks.json` is the text it came from, nothing more. `normalize_embeddings=True` scales every vector to length 1 — the thing that makes Step 3's cosine similarity reduce to a single dot product. + +**✅ Checklist** + + +`uv run python prepare.py` runs without errors and prints a nonzero chunk count. +`uv run python build_index.py` completed without errors, and `index.npy` and `chunks.json` now exist in your project folder. +The printed shape's first number matches the chunk count, and the second number is 384. + + +**🤔 Socratic Question(s)** + +- The corpus is small enough that you could search it by hand. Why build an index at all? What does this step buy you that scales to a corpus you can't read in an afternoon? +- Chunk order matters here: `chunks.json` and the rows of `index.npy` are aligned by position. What would go wrong if you rebuilt one without the other — say, if you edited an article and re-ran `build_index.py` without deleting the old `index.npy`? (Hint: it's why the example re-saves both files together.) + +## Step 3: Retrieve relevant chunks + +To find which chunks are relevant to a question, embed the question with the *same* model, then rank every chunk by how close its vector is to the question's vector — cosine similarity, which collapses to a plain dot product because every vector is already unit-length: + +```python +# retrieve.py +"""Given a question, finds the article chunks most relevant to it.""" + +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]: + """Returns the top_k chunks most similar to `question`, each with its + cosine-similarity score, ranked highest first.""" + 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] + + # Every row of `embeddings` is unit-length and so is question_vector, so + # this dot product *is* the cosine similarity. + 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__": + results = retrieve("Who founded the company that powered TransLisboa's electric buses?") + for r in results: + print(f"{r['score']:.3f} [{r['source']}] {r['text'][:80]}...") +``` + +```bash +uv run python retrieve.py +``` + +Do this before writing any more code, and look at what comes back for the two-hop questions. Notice which article is *missing* from the top results — for "Who founded the company that powered TransLisboa's electric buses?" the top chunks are the transit article and the company article, not the biography. That missing chunk is the whole motivation for Step 5. + +**✅ Checklist** + + +`uv run python retrieve.py` prints `top_k` results, each with a similarity score and a source filename. +For a genuinely two-hop question, the article holding the missing fact does **not** appear in the top chunks. +For a single-article question, the right article *does* appear at the top. + + +**🤔 Socratic Question(s)** + +- `np.argsort(similarities)[::-1][:top_k]` sorts *all* similarities before taking the top few. Fine for a few dozen chunks — why would this become a problem on a corpus of ten million chunks, and what structure would you reach for instead? +- The two-hop question retrieves the company article, which contains a *clue* ("the winner of the 2009 European Energy Innovation Prize") but not the name. If you pasted just that chunk into the LLM, would you expect a good answer, a wrong guess, or an honest "I can't tell"? Try it in Step 4 and see which you actually get. + +## Step 4: Single-hop — the baseline that fails two-hop questions + +Now the baseline pipeline: retrieve the top chunks once, hand them to the LLM, ask it to answer *using only that context*. This is exactly the RAG App project's last step — and it's the pipeline this whole project exists to improve on. Write `ask.py` with a prompt template, and add the provider plumbing (the same OpenAI-compatible clients as the Agentic Code Reviewer project — pick whichever free-tier provider you set up in Setup; the bundled example wires all six in one `endpoints` dict, as below): + +```python +# ask.py +"""Single-hop baseline: retrieve once, answer from only that context.""" + +import os + +from dotenv import load_dotenv +from openai import OpenAI + +from retrieve import retrieve + +load_dotenv() + +ANSWER_PROMPT = """Answer the question using ONLY the context below. If the +context doesn't contain the answer, say so plainly -- do not make something up. + +Context: +{context} + +Question: {question} + +Answer:""" + + +def _build_client(provider: str) -> OpenAI: + endpoints = { + "github": ("https://models.github.ai/inference", "GITHUB_TOKEN"), + "gemini": ("https://generativelanguage.googleapis.com/v1beta/openai/", "GOOGLE_API_KEY"), + "groq": ("https://api.groq.com/openai/v1", "GROQ_API_KEY"), + "mistral": ("https://api.mistral.ai/v1", "MISTRAL_API_KEY"), + "cerebras": ("https://api.cerebras.ai/v1", "CEREBRAS_API_KEY"), + "openrouter": ("https://openrouter.ai/api/v1", "OPENROUTER_API_KEY"), + } + base_url, env_var = endpoints[provider] + return OpenAI(api_key=os.environ[env_var], base_url=base_url) + + +def format_context(chunks: list[dict]) -> str: + return "\n\n".join(f"[{c['source']}] {c['text']}" for c in chunks) + + +def single_hop(question: str, provider: str | None = None, top_k: int = 3) -> tuple[str, list[dict]]: + """Baseline: retrieve once, answer from that one retrieval pass.""" + provider = provider or os.environ.get("LLM_PROVIDER", "github") + retrieved = retrieve(question, top_k=top_k) + prompt = ANSWER_PROMPT.format(context=format_context(retrieved), question=question) + response = _build_client(provider).chat.completions.create( + model="gpt-4o-mini", # confirm this still has a free tier before running + messages=[{"role": "user", "content": prompt}], + ) + answer = response.choices[0].message.content + return answer, retrieved +``` + +> **If you're not using GitHub Models:** the client is OpenAI-compatible but `gpt-4o-mini` only exists on GitHub Models. If you picked another provider in Setup, swap `model` for one that provider serves (e.g. `gemini-3.5-flash` on Gemini, `llama-3.3-70b-versatile` on Groq, `mistral-small-latest` on Mistral). The bundled `main.py` example wires the right model per provider in its `PROVIDERS` dict. + +Now run it on the two-hop question: + +```bash +uv run python -c "from ask import single_hop; a, ev = single_hop('Who founded the company that powered TransLisboa\'s electric buses?'); print(a)" +``` + +Watch what happens. The retrieved context contains the clue but not the name, so the model either says "the context doesn't say who founded it" or — because the context is full of plausible names-adjacent facts — makes up a plausible-sounding but **wrong** answer. Either outcome is the point: the pipeline doesn't have the fact it needs, and no amount of prompt-wording fixes that, because the fact never got retrieved. + +**✅ Checklist** + + +`single_hop()` runs against your free-tier provider and prints an answer, not a traceback. +On a genuinely two-hop question, the answer is wrong or explicitly says the information isn't in the context. +You can explain, in one sentence, why a *better* prompt could not fix this — the missing fact was never retrieved. + + +**🤔 Socratic Question(s)** + +- The context for the two-hop question contains the clue "founded by the winner of the 2009 European Energy Innovation Prize". If the model *does* hallucinate a founder, where do you think the hallucinated name comes from — the context, the model's prior knowledge, or neither? What does that tell you about how confidently you should trust a single-hop answer on a question you haven't verified? +- Run `single_hop()` on the same question three times. Is the answer stable across runs? Would a user be able to tell the wrong answers apart from a correct one without checking the evidence? + +## Step 5: Multi-hop — a second round guided by a follow-up query + +The fix this project teaches: don't accept "not enough evidence" as the end. Add a second retrieval round, guided by the model itself. Write `ask_multihop.py` with a new prompt whose job is *not* to answer the question but to **decide whether the retrieved evidence is enough to answer it** — and, if not, to produce a follow-up search query that would find the missing fact: + +```python +# ask_multihop.py +"""Two-round (multi-hop) retrieval: retrieve, check sufficiency, and if needed +retrieve again guided by the model's follow-up query.""" + +import os + +from dotenv import load_dotenv +from openai import OpenAI + +from ask import ANSWER_PROMPT, _build_client, format_context +from retrieve import retrieve + +load_dotenv() + +SUFFICIENCY_PROMPT = """You get ONE retrieval pass of evidence, which may not +be enough to answer the question -- the answer might need facts that live in a +document this retrieval didn't return. + +Context: +{context} + +Question: {question} + +Decide whether the Context above contains enough information to answer the +Question. Reply with exactly one of these two forms: + +If YES -- SUFFICIENT, followed by your answer on the next line(s): + SUFFICIENT + + +If NO -- do NOT try to answer. Reply: + INSUFFICIENT + + +Never output both forms.""" + + +def parse_sufficiency(text: str) -> tuple[str, str]: + """Returns ("sufficient", answer) or ("insufficient", followup_query).""" + lines = [line.strip() for line in text.strip().splitlines() if line.strip()] + if lines and "INSUFFICIENT" in lines[0].upper(): + followup = lines[1] if len(lines) > 1 else "" + return "insufficient", followup + answer = "\n".join(lines[1:]) if lines and lines[0].upper().startswith("SUFFICIENT") else text + return "sufficient", answer.strip() + + +def merge_dedupe(*chunk_lists: list[dict]) -> list[dict]: + """Merges chunk lists, dropping chunks whose text was already seen.""" + seen: set[str] = set() + merged: list[dict] = [] + for chunk_list in chunk_lists: + for chunk in chunk_list: + if chunk["text"] not in seen: + seen.add(chunk["text"]) + merged.append(chunk) + return merged + + +def complete(question: str, context: list[dict], client: OpenAI, model: str) -> str: + """Asks the model to answer `question` using ONLY the given context.""" + prompt = ANSWER_PROMPT.format(context=format_context(context), question=question) + return client.chat.completions.create( + model=model, + messages=[{"role": "user", "content": prompt}], + ).choices[0].message.content + + +def multi_hop(question: str, provider: str | None = None, top_k: int = 3) -> dict: + """Two rounds: retrieve, ask whether the evidence is enough, and if not, + retrieve a second round guided by the model's own follow-up query.""" + provider = provider or os.environ.get("LLM_PROVIDER", "github") + client = _build_client(provider) + model = "gpt-4o-mini" # confirm this still has a free tier before running + + round1 = retrieve(question, top_k=top_k) + verdict = client.chat.completions.create( + model=model, + messages=[{"role": "user", "content": SUFFICIENCY_PROMPT.format( + context=format_context(round1), question=question)}], + ).choices[0].message.content + + status, followup = parse_sufficiency(verdict) + if status == "sufficient": + return {"answer": followup, "evidence": round1, "followup": None, "rounds": [round1]} + + round2 = retrieve(followup, top_k=top_k) + combined = merge_dedupe(round1, round2) + final = complete(question, combined, client, model) + return {"answer": final, "evidence": combined, "followup": followup, "rounds": [round1, round2]} +``` + +(Again, use a model ID your chosen provider actually serves, per the note above.) + +Now run it on the same two-hop question: + +```bash +uv run python -c " +from ask_multihop import multi_hop +r = multi_hop('Who founded the company that powered TransLisboa\'s electric buses?') +print('follow-up query:', r['followup']) +print() +print(r['answer']) +" +``` + +Two things happen that single-hop never did. First, the follow-up query — the model looked at the evidence, saw the clue "winner of the 2009 European Energy Innovation Prize" and no name, and wrote a search that would find the name. Second, that follow-up query, run through `retrieve()`, pulls the biography article that the original question never surfaced — the missing fact. The final answer is grounded in the *combined* evidence of both rounds. + +**✅ Checklist** + + +`multi_hop()` prints a follow-up query for the two-hop question — evidence the model judged the first round insufficient. +The second round's retrieval includes the article holding the missing fact, which round one missed. +The final answer to the two-hop question is now correct, and you can point at the exact chunk that supplies the missing fact. + + +**🤔 Socratic Question(s)** + +- The sufficiency prompt forbids the model from answering when evidence is insufficient — it must instead emit a follow-up query. Why is forcing this explicit *two-step* behavior (verdict first, answer later) more reliable than just asking "if you don't know, say so" in the answer prompt? What happens if a model that was told "just answer" sees a plausible-looking clue and runs with it? +- The follow-up query is generated by the same model that just failed to answer. Why might a model that couldn't answer the *question* still be good at writing a *search query*? (Hint: what's easier — solving a problem, or saying what's missing?) +- What could go wrong if the follow-up query is bad? Can you sketch the failure mode where round two retrieves the same chunks as round one? + +## Step 6: Run both side by side and audit the evidence + +A comparison you can't audit is just a claim. The final piece is printing both pipelines' answers **side by side, with every evidence chunk each one used** — the source filename, the similarity score, and the snippet — so a wrong answer has an explanation attached to it. Write `main.py` that: + +1. Builds the index if it's missing (or re-embeds on request). +2. For a question, runs `single_hop()` and `multi_hop()`. +3. Prints both answers as aligned columns — a simple `diff -y`-style two-column printer that wraps each block to half the terminal width and joins them with a `│` separator — followed by each pipeline's evidence list. +4. Defaults to running the bundled test questions from `data/test_questions.json` (a few of which are genuinely two-hop), with each question's `expected` answer shown so you can check both pipelines against it and see a small scoreboard at the end. + +The complete `main.py` — the two-column printer, the scoreboard, the `--query` interactive mode, and the `--rebuild`/`--provider`/`--top-k` flags — is the companion example in [`examples/multihop-wikipedia-qa/main.py`](https://github.com/abderrahim-lectures/python-data-analysis-course/tree/main/examples/multihop-wikipedia-qa/main.py). Run it: + +```bash +uv run python main.py +``` + +You'll see something like this shape for each question (answers and scores abbreviated here): + +``` +Question: Who founded the company that powered TransLisboa's electric buses? + SINGLE-HOP │ MULTI-HOP +──────────────────────────────────────────────│────────────────────────────── + SINGLE-HOP │ MULTI-HOP + Answer: The context doesn't name the founder │ Answer: Elena Marchetti + │ Round 2 (guided by follow-up + Evidence used: │ query): Who won the 2009 + 1. [lisbon.md] score 0.701: The city's ... │ European Energy Innovation + 2. [volta-dynamics.md] score 0.554: ... │ Prize? + 3. [volta-dynamics.md] score 0.455: ... │ Evidence used: + │ 1. [lisbon.md] score 0.701 ... + │ 2. [volta-dynamics.md] ... + │ 3. [elena-marchetti.md] ... +``` + +The right column's third evidence chunk is the whole story: single-hop never saw the biography, multi-hop retrieved it in round two, and the answer changed accordingly. Audit the multi-hop questions yourself — read each evidence chunk and confirm the answer only makes sense given the *combination*. + +```bash +uv run python main.py --question "In which city was the company that launched AquaPro founded?" +uv run python main.py --query # interactive mode +``` + +**✅ Checklist** + + +`uv run python main.py` prints both answers side by side for every bundled test question, each with its evidence chunks. +The multi-hop pipeline gets at least the two clearly two-hop questions right, and its evidence for each includes the second-round article. +You can explain, from the printed evidence alone, why single-hop's answer on those questions is wrong. + + +**🤔 Socratic Question(s)** + +- On such a tiny corpus, single-hop will sometimes *accidentally* retrieve the right chunk for a two-hop question and get it right by luck. How would you detect that happening — what would the evidence columns look like compared to a genuinely multi-hop answer? Does "right answer, wrong evidence" still count as correct? +- The scoreboard at the end of the test-question run counts how many answers match the `expected` value. What does that scoreboard *not* measure? Can you imagine a plausible wrong answer that still contains the expected string? + +## ⚠️ Common pitfalls + +- **A small corpus means single-hop sometimes gets lucky.** With a few dozen chunks, the missing fact will occasionally sneak into the top-K anyway, and single-hop will answer a two-hop question correctly. That's a property of small corpora, not a bug — it's exactly why the tool prints evidence and a scoreboard: judge the *trend*, not any single question, and always check whether the evidence actually supports the answer. +- **Forgetting to rebuild the index after editing `data/articles/`.** `build_index.py` only runs when you run it. Edit an article and `retrieve()` won't see the change until you re-run it. The example's `main.py` checks for a missing index and rebuilds on `--rebuild`, but it never detects a stale one — that's a manual step by design. +- **A stale index out of sync with `chunks.json`.** The rows of `index.npy` and the entries of `chunks.json` are aligned by position. Rebuilding one without the other (or editing an article between the two writes) silently mislabels every chunk. The example saves both together in one function for exactly this reason. +- **Embedding the follow-up query with a different model than the index.** Every retrieval path must use the same `MODEL_NAME`. Vectors from two different embedding models aren't comparable at all, even if both are "384-dimensional." +- **The follow-up query can be bad, and that's a real limitation.** If round two retrieves the same chunks as round one, multi-hop silently degrades into single-hop. The sufficiency prompt nudges the model to name the specific missing entity, but nothing guarantees it. Watch the follow-up query in the output — if it's unhelpful, that's the honest limit of this minimal version, not a bug to paper over. +- **Rate limits on the free LLM tier.** Retrieval is local and unlimited; each `single_hop`/`multi_hop` call counts against your provider's quota, and multi-hop uses up to *two* calls per question. A 429 error is the provider telling you to slow down, not a bug — the [AI Agent project](/docs/projects/ai-agent) has a retry approach you can copy for the same pattern. + +## What you just built + +A two-round retrieval pipeline that answers a class of questions single-pass RAG can't: questions whose answer only exists once facts from two documents are combined. Same local embedding and NumPy search as the RAG App project, plus one new mechanism — retrieve, ask the model whether the evidence is enough, and if not retrieve again guided by the model's own follow-up query — and a habit the RAG App project didn't force: printing the evidence *beside* every answer so a wrong answer comes with an explanation attached. Nothing here was faked into a toy that doesn't generalize: iterative retrieval is a real technique in production RAG systems, and the "show your evidence" discipline is exactly what real answer-verification systems do. Swap the bundled corpus for a real folder of documents and the same two-round loop is the whole pipeline. + +## Where to go from here + +- **Try a larger, real corpus.** Point the same two-round loop at a real folder of documents (the course repo's own `docs/` is a good candidate). The multi-hop questions become genuinely hard once single-hop can't accidentally stumble onto the answer. +- **Add re-ranking.** Retrieve a larger top-k in each round with the fast embedding search, then re-score just those candidates with a slower cross-encoder before sending them to the LLM — a common two-stage pattern that sharpens which chunk counts as "the missing fact." +- **Cap or chain more rounds.** Real iterative-retrieval systems loop until the model says "sufficient" or a budget runs out, instead of a fixed two rounds. Add a `max_rounds` parameter and a guard so the loop can't spin forever — then watch what happens when a question needs *three* documents. +- **Score the whole thing properly.** Instead of the eyeball scoreboard, run the bundled questions against a hardcoded expected answer and report precision — a first step toward the kind of evaluation harness real RAG systems are judged with. + +## Related projects + +- [Build a RAG App Over Your Own Notes](/docs/projects/rag-notes) — the single-hop pipeline this project extends; build it first if you haven't. +- [Chat with Your PDFs](/docs/projects/chat-with-pdfs) — multi-document RAG over a folder of PDFs, with page-number citations instead of a second retrieval round. +- [Build an Agentic Code Reviewer](/docs/projects/agentic-code-reviewer) — the same free-tier `PROVIDERS` pattern, applied to a single-prompt code-review tool. +- [Build a RAG-Backed Docs Q&A Discord Bot](/docs/projects/docs-qa-bot) — wraps a RAG pipeline in a live chat interface you can actually query from Discord. +- [Build a Multi-Agent Research Assistant](/docs/projects/multi-agent-research) — a different answer to "one pass isn't enough": multiple LLM agents each owning a piece of a research question. + +## 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. + +Welcome to writing Python outside the browser. 🎓 + + diff --git a/examples/multihop-wikipedia-qa/.env.example b/examples/multihop-wikipedia-qa/.env.example new file mode 100644 index 0000000..3f09361 --- /dev/null +++ b/examples/multihop-wikipedia-qa/.env.example @@ -0,0 +1,26 @@ +# Copy this file to .env (already gitignored) and fill in the key for +# whichever provider you choose -- you only need ONE of the keys below. +# Never commit a real key. + +# Which provider to use: github (default), gemini, groq, mistral, cerebras, +# or openrouter. See main.py's PROVIDERS dict for what each one needs. +LLM_PROVIDER=github + +# github (default) -- a GitHub personal access token with the "models: read" +# scope. Free, no separate signup: https://github.com/settings/tokens +GITHUB_TOKEN= + +# gemini -- free-tier key from https://aistudio.google.com/ +GOOGLE_API_KEY= + +# groq -- free-tier key from https://console.groq.com/keys +GROQ_API_KEY= + +# mistral -- free-tier key from https://console.mistral.ai/api-keys +MISTRAL_API_KEY= + +# cerebras -- free-tier key from https://cloud.cerebras.ai/ +CEREBRAS_API_KEY= + +# openrouter -- free-tier key from https://openrouter.ai/keys +OPENROUTER_API_KEY= diff --git a/examples/multihop-wikipedia-qa/.gitignore b/examples/multihop-wikipedia-qa/.gitignore new file mode 100644 index 0000000..7706241 --- /dev/null +++ b/examples/multihop-wikipedia-qa/.gitignore @@ -0,0 +1,6 @@ +.venv +__pycache__ +*.pyc +.env +data/index.npy +data/chunks.json diff --git a/examples/multihop-wikipedia-qa/.python-version b/examples/multihop-wikipedia-qa/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/examples/multihop-wikipedia-qa/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/examples/multihop-wikipedia-qa/README.md b/examples/multihop-wikipedia-qa/README.md new file mode 100644 index 0000000..635b62e --- /dev/null +++ b/examples/multihop-wikipedia-qa/README.md @@ -0,0 +1,69 @@ +# Multi-Hop Wikipedia QA Example + +The local companion to the course's [Build a Multi-Hop Question-Answering Tool Over a Small Wikipedia Sample](../../docs/projects/multihop-wikipedia-qa/index.md) project — a two-round retrieval pipeline that answers questions whose facts live in *two different* documents, and shows you the exact evidence chain it used to do it. + +## What's here + +- `data/articles/` — six short, plain-text Wikipedia-style articles (fictional but realistic: biographies, companies, cities, an event), committed so the retrieval steps run out of the box with no setup. They're *crafted* so a few questions genuinely need facts from two articles at once. +- `data/test_questions.json` — six bundled test questions, three of them genuinely multi-hop (the answer only exists once two articles' facts are combined), each with an `expected` answer so you can audit the tool's output against a known ground truth. +- `main.py` — the whole tool in one file: + - `build_index()` — splits the articles into chunks and embeds them locally with `sentence-transformers`, saving `data/index.npy` + `data/chunks.json` (both gitignored). + - `retrieve(question, ...)` — cosine-similarity search over the chunks with `numpy`. + - `single_hop(...)` — the baseline: retrieve the top-K chunks once and answer from only those. + - `multi_hop(...)` — the point of the project: retrieve, ask the model whether the evidence is enough, and if not retrieve a **second round** guided by the model's own follow-up query, then answer from the merged evidence. + - Side-by-side output — both answers printed as aligned columns with every evidence chunk each one used. +- `notebook.ipynb` — a Colab/Kaggle/Binder-ready notebook that mirrors the same pipeline with the articles embedded directly in it (no local files needed). + +## Running it + +```bash +uv sync +uv run python main.py --rebuild # embeds data/articles/ -- local, no API key +uv run python main.py # runs the six bundled test questions, single-hop vs multi-hop +``` + +Retrieval is fully local; only the answer generation calls a hosted model, which needs a free-tier API key: + +1. **Get a free-tier API key** from your chosen provider — see the table in the [lesson's Setup section](../../docs/projects/multihop-wikipedia-qa/index.md#get-a-free-llm-api-key) for where to get one for each. +2. **Copy `.env.example` to `.env`** and fill in the key for your provider: + ```bash + cp .env.example .env + # then edit .env + ``` + `.env` is already gitignored — never commit a real key. +3. **Run it**: + ```bash + uv run python main.py # bundled test questions + scoreboard + uv run python main.py --question "Who founded the company that powered TransLisboa's electric buses?" + uv run python main.py --query # interactive mode + uv run python main.py --provider groq # pick a non-default provider + ``` + +`uv` reads `pyproject.toml`/`uv.lock` and creates an isolated environment for this project automatically on first run. The embedding model (`all-MiniLM-L6-v2`, ~80MB) also downloads on first run. + +## What the comparison is supposed to show + +Run the default `uv run python main.py` and watch the three multi-hop questions: single-hop retrieves the *clue* article but not the *fact* article, so it either says "the context doesn't say" or guesses a plausible-sounding answer; multi-hop spots the gap, writes a follow-up search query, pulls the second article, and answers correctly. Every chunk is printed beside each answer so you can see exactly why. + +Two honest caveats, spelled out in the lesson too: this is a deliberately minimal version of what researchers call **iterative retrieval** — real multi-hop QA systems do far more — and on such a small corpus single-hop will sometimes *accidentally* land the right chunks and get a multi-hop question right by luck. The scoreboard prints both pipelines' totals against the expected answers, so you can see the trend rather than trusting any single question. + +## Running it in GitHub Codespaces + +Click into a [Codespace for the whole repo](https://codespaces.new/abderrahim-lectures/python-data-analysis-course) (Node, Python, and `uv` are preinstalled per `.devcontainer/devcontainer.json`), then: + +```bash +cd examples/multihop-wikipedia-qa +uv sync +uv run python main.py --rebuild +uv run python main.py +``` + +(add your API key as a [Codespaces secret](https://docs.github.com/en/codespaces/managing-your-codespaces/managing-encrypted-secrets-for-your-repository-and-organization#adding-secrets-for-a-repository) or `export` it for a one-off session before the answer-generation step). + +## A note on staying current + +Model names, provider free-tier terms, and library APIs change fast. `all-MiniLM-L6-v2` and the six provider endpoints in `main.py`'s `PROVIDERS` dict were verified against live runs while writing this example, but check each provider's own docs before relying on them — they may have drifted by the time you read this. + +## Built your own version? + +See [`examples/student-projects/`](../student-projects/) for how to share it with the class via a pull request — no git experience required, it walks through every step. diff --git a/examples/multihop-wikipedia-qa/data/articles/amina-rahman.md b/examples/multihop-wikipedia-qa/data/articles/amina-rahman.md new file mode 100644 index 0000000..4dca7ed --- /dev/null +++ b/examples/multihop-wikipedia-qa/data/articles/amina-rahman.md @@ -0,0 +1,21 @@ +# Amina Rahman + +**Amina Rahman** (born 1975) is a Bangladeshi biochemist known for developing the industrial extraction process used to make protein from microalgae. + +## Early life and education + +Rahman was born in 1975 in Dhaka, the capital of Bangladesh. She studied chemistry at the University of Dhaka and earned a PhD in biochemistry from the same institution in 2001, writing her dissertation on protein recovery from single-celled organisms. + +## Career + +After a postdoctoral fellowship in Singapore, Rahman returned to the University of Dhaka as a lecturer. In 2011 she was recruited by a Swiss biotechnology startup as its chief scientist, where she led the development of a scalable method for extracting protein from cultivated microalgae. + +Her extraction method, which uses a mild enzymatic treatment instead of harsh solvents, became the production process behind the company's flagship product. Industry analysts credited the method's low cost with making algae-based protein commercially viable for the first time. + +## Recognition + +In 2018 Rahman received the international Women in Biotechnology Award for her work on sustainable protein production. She has published more than forty peer-reviewed papers and holds three patents related to microalgae processing. + +## Personal life + +Rahman continues to live and work in Switzerland, but travels regularly to Bangladesh, where she mentors students at her alma mater and funds a small scholarship for women in the natural sciences. diff --git a/examples/multihop-wikipedia-qa/data/articles/basel.md b/examples/multihop-wikipedia-qa/data/articles/basel.md new file mode 100644 index 0000000..f9cbe49 --- /dev/null +++ b/examples/multihop-wikipedia-qa/data/articles/basel.md @@ -0,0 +1,21 @@ +# Basel + +**Basel** is the third-largest city in Switzerland, located on the Rhine River where the Swiss, French, and German borders meet. It is a major center of the pharmaceutical and life-sciences industries. + +## Geography and population + +Basel sits at the point where Switzerland, France, and Germany come together, giving it the nickname "the three-country city". Its metropolitan population is just over 500,000, and the city is a major railway and freight hub for central Europe. + +## Life sciences industry + +Basel is home to some of the world's largest pharmaceutical companies, along with a dense cluster of smaller biotechnology startups. One such startup, the microalgae protein company Cereolabs, was founded in this city in 2009 by a group of researchers who had left a nearby university institute. The city's research hospitals and university labs are a major draw for young scientists. + +## The Basel Climate Accord + +In 2019, Basel hosted the signing of the **Basel Climate Accord**, an international agreement in which participating governments committed to cutting greenhouse-gas emissions from road transport. The accord's signing ceremony was held at the city's conference center, and the agreement is named after the city as a result. + +Several transport and battery-industry firms later cited the accord as a driver of demand for electric vehicles, and analysts frequently referenced it when discussing the growth of the electric bus market in Europe. + +## Tourism and culture + +Basel is known for its art museums, including a prominent collection of modern and contemporary art, and for its annual carnival, one of the largest in Europe. The city's old town is a popular destination for weekend visitors from the neighboring countries. diff --git a/examples/multihop-wikipedia-qa/data/articles/cereolabs.md b/examples/multihop-wikipedia-qa/data/articles/cereolabs.md new file mode 100644 index 0000000..b0dfe4c --- /dev/null +++ b/examples/multihop-wikipedia-qa/data/articles/cereolabs.md @@ -0,0 +1,21 @@ +# Cereolabs + +**Cereolabs** is a Swiss biotechnology company that produces protein from cultivated microalgae. It is best known for launching **AquaPro**, the first commercially available algae-based protein powder for human consumption. + +## History + +The company was founded in 2009 by a group of former university researchers, and built its first pilot facility two years later. It remained a small research firm for its early years, funding itself through government grants and a single early angel investment. + +## Products + +Cereolabs' flagship product, AquaPro, launched in 2017. It is a neutral-tasting protein powder made from microalgae grown in closed bioreactors, marketed as a sustainable alternative to soy and whey protein. The production process is built around a mild enzymatic extraction method developed by the company's chief scientist, who joined the firm in 2011. + +The company also sells a concentrated algae paste, marketed under the name "Aqualift", to food manufacturers as an ingredient for plant-based meat products. + +## Facilities + +Cereolabs operates its headquarters and main production facility in an industrial district on the outskirts of its home city, along with a second laboratory opened in Lisbon in 2022. The Lisbon lab focuses on consumer product formulation. + +## Business + +In 2021, following strong sales of AquaPro in European supermarkets, Cereolabs raised a Series B funding round led by a London-based investment firm. The company has stated that it plans to open a production plant in North America by 2028. diff --git a/examples/multihop-wikipedia-qa/data/articles/elena-marchetti.md b/examples/multihop-wikipedia-qa/data/articles/elena-marchetti.md new file mode 100644 index 0000000..6d666ef --- /dev/null +++ b/examples/multihop-wikipedia-qa/data/articles/elena-marchetti.md @@ -0,0 +1,21 @@ +# Elena Marchetti + +**Elena Marchetti** (born 1963) is an Italian electrical engineer best known for founding one of Europe's earliest dedicated lithium-ion battery manufacturers. + +## Early life and education + +Marchetti was born in 1963 in the city of Naples, in southern Italy. She studied electrical engineering at the Polytechnic University of Turin, where she focused on electrochemical energy storage and wrote a thesis on the thermal management of rechargeable battery packs. + +## Career + +In 1992, at the age of 29, Marchetti founded a battery manufacturing company in the city of Turin and served as its first chief executive. The company grew slowly for a decade, surviving on small contracts from electric forklift makers and uninterruptible-power-supply vendors. + +Her breakthrough came in 2009, when her battery-pack design won the European Energy Innovation Prize, a continental award recognizing engineering achievements in clean energy. The win raised her company's profile significantly and led to its first major public-transit contracts. + +## Later life + +Marchetti stepped down as chief executive in 2019 and retired in 2020, moving to Lisbon, Portugal, where she advises early-stage energy startups and writes occasional essays on battery recycling. She has no children and prefers to keep a low public profile. + +## Legacy + +Industry historians credit Marchetti with popularizing the practice of pairing battery chemistry research directly with vehicle-integration engineering — an approach that was unusual for a small firm in the 1990s. Her original company continues to operate today under a different name. diff --git a/examples/multihop-wikipedia-qa/data/articles/lisbon.md b/examples/multihop-wikipedia-qa/data/articles/lisbon.md new file mode 100644 index 0000000..8fabb5d --- /dev/null +++ b/examples/multihop-wikipedia-qa/data/articles/lisbon.md @@ -0,0 +1,21 @@ +# Lisbon + +**Lisbon** is the capital and largest city of Portugal, located on the Atlantic coast of the Iberian Peninsula. It has been an important trading port since the Age of Discovery and remains the country's economic and cultural center. + +## Geography and population + +Lisbon sits on seven hills at the mouth of the Tagus River, facing the Atlantic Ocean. Its metropolitan area has a population of roughly 2.9 million, about a quarter of Portugal's total population. The city's mild, sunny climate makes it a popular destination for remote workers and retirees. + +## Public transport + +The city's public transport system is operated by **TransLisboa**, the municipal transit authority, which runs the city's trams, buses, and metro. TransLisboa is known for its historic yellow tram network, which climbs the city's steepest streets. + +In 2016, TransLisboa launched Europe's first fully electric bus route, a single line connecting the city center to the airport, using battery packs supplied by an Italian battery manufacturer. The route's success led TransLisboa to expand electric buses to several more lines over the following years. + +## Culture and events + +Lisbon hosts the annual Lisbon Tech Summit, a three-day technology conference that draws startups and investors from across Europe. The event is held each October at the city's riverside convention center. + +## Notable residents + +The city has a growing community of tech entrepreneurs and retired engineers. Among its well-known residents is a celebrated Italian electrical engineer who moved to Lisbon after retiring, and who advises early-stage energy startups from her home in the Alfama district. diff --git a/examples/multihop-wikipedia-qa/data/articles/volta-dynamics.md b/examples/multihop-wikipedia-qa/data/articles/volta-dynamics.md new file mode 100644 index 0000000..001d52c --- /dev/null +++ b/examples/multihop-wikipedia-qa/data/articles/volta-dynamics.md @@ -0,0 +1,21 @@ +# Volta Dynamics + +**Volta Dynamics** was an Italian manufacturer of lithium-ion battery packs for electric commercial vehicles, headquartered in Turin. It was founded in the early 1990s by the winner of the 2009 European Energy Innovation Prize, and operated under this name until 2021. + +## History + +The company was founded in the early 1990s in Turin, Italy, growing out of a university research project on rechargeable battery systems. For its first fifteen years it supplied relatively small battery packs to electric forklift and delivery-vehicle makers across northern Italy. + +The firm's first significant public-transit win came in 2015, when it won a contract to supply battery systems for the bus fleet of Lisbon's public transport operator. The following year, in 2016, that operator launched Europe's first fully electric bus route, powered by Volta Dynamics battery packs. + +## Products + +Volta Dynamics focused exclusively on stationary and vehicle-mounted battery packs, deliberately avoiding consumer electronics. Its core product line was the "TransPack" series of swappable battery modules, designed so that a depot could charge a fresh pack while a bus continued its route on another. + +## Renaming and later years + +In 2019 the company went public on the Milan Stock Exchange. In 2021 it merged with a German EV-grid startup and renamed itself Voltora. Under that name, the business shifted its focus toward grid-scale energy storage and battery reuse for renewable-power utilities. + +## Recognition + +Battery industry trade publications repeatedly cited Volta Dynamics in their annual rankings of European battery integrators, praising the reliability record of its TransPack modules in daily transit use. diff --git a/examples/multihop-wikipedia-qa/data/test_questions.json b/examples/multihop-wikipedia-qa/data/test_questions.json new file mode 100644 index 0000000..f112fe4 --- /dev/null +++ b/examples/multihop-wikipedia-qa/data/test_questions.json @@ -0,0 +1,41 @@ +{ + "note": "Bundled test questions for the multi-hop QA example. Each question's answer can be found in the articles in data/articles/. The 'hops' field marks how many articles a fully-grounded answer requires: 1 means one article has everything, 2 means the fact is split across two articles. 'expected' and 'articles' are there so you can audit the tool's output against a known answer -- they are not used to stop the pipeline, just for your own comparison.", + "questions": [ + { + "question": "What is the name of Lisbon's public transit authority?", + "hops": 1, + "expected": "TransLisboa", + "articles": ["lisbon.md"] + }, + { + "question": "What product did Cereolabs launch in 2017?", + "hops": 1, + "expected": "AquaPro", + "articles": ["cereolabs.md"] + }, + { + "question": "What international agreement was signed in Basel in 2019?", + "hops": 1, + "expected": "Basel Climate Accord", + "articles": ["basel.md"] + }, + { + "question": "Who founded the company that powered TransLisboa's electric buses?", + "hops": 2, + "expected": "Elena Marchetti", + "articles": ["volta-dynamics.md", "elena-marchetti.md"] + }, + { + "question": "In which city was the company that launched AquaPro founded?", + "hops": 2, + "expected": "Basel", + "articles": ["cereolabs.md", "basel.md"] + }, + { + "question": "Where was the scientist who developed AquaPro's extraction method born?", + "hops": 2, + "expected": "Dhaka", + "articles": ["cereolabs.md", "amina-rahman.md"] + } + ] +} diff --git a/examples/multihop-wikipedia-qa/main.py b/examples/multihop-wikipedia-qa/main.py new file mode 100644 index 0000000..71517d5 --- /dev/null +++ b/examples/multihop-wikipedia-qa/main.py @@ -0,0 +1,507 @@ +"""Multi-hop question answering over a small Wikipedia-style sample. + +A two-round retrieval pipeline. Most RAG apps do a single retrieval pass and +then ask the model to answer -- this project's thesis is that a whole class of +questions *needs more than one pass*: questions whose answer only exists once +facts from two different documents are combined. Single-hop RAG retrieves one +of the two documents, the model has to guess the rest, and you get a +plausible-sounding but wrong answer. + +The multi-hop pipeline here is deliberately minimal: + Round 1: retrieve the top-K chunks for the question, and ask the model to + answer *or* to say the evidence is incomplete. + Round 2: if incomplete, the model writes a follow-up search query, we + retrieve a second set of chunks with it, and answer from the + combined evidence. + +Both paths print side by side with the evidence chunks each one actually used, +so you can audit exactly why one got a question wrong and the other got it right. + +The sample corpus lives in data/articles/*.md -- short, plain-text summaries of +a handful of fictional-but-Wikipedia-style articles, crafted so that a few of +the bundled test questions genuinely need facts from two of them. + +You're free to use whichever free-tier provider you like. Set LLM_PROVIDER in +a .env file (copy .env.example) or a real environment variable to pick one; +defaults to "github" (GitHub Models). Never hardcode a real API key here or +commit one to the repo. + +Usage: + uv run python main.py # run the bundled test questions + uv run python main.py --question "..." # answer one question + uv run python main.py --query # interactive mode + uv run python main.py --rebuild # force-rebuild the index + uv run python main.py --provider groq "..." # pick a provider for this run +""" + +import argparse +import json +import os +import sys +import textwrap +from pathlib import Path + +import numpy as np +from dotenv import load_dotenv +from openai import OpenAI +from sentence_transformers import SentenceTransformer + +load_dotenv() # reads a local .env file, if present; real env vars always win + +BASE_DIR = Path(__file__).resolve().parent +ARTICLES_DIR = BASE_DIR / "data" / "articles" +INDEX_PATH = BASE_DIR / "data" / "index.npy" +CHUNKS_PATH = BASE_DIR / "data" / "chunks.json" +TEST_QUESTIONS_PATH = BASE_DIR / "data" / "test_questions.json" + +MODEL_NAME = "all-MiniLM-L6-v2" +TARGET_CHUNK_SIZE = 600 # characters -- small enough to stay focused, large + # enough to hold a full thought (same reasoning as + # the RAG App project's prepare_notes.py) +TOP_K = 3 + +_model = None # loaded lazily so importing this module doesn't load the model + + +# --------------------------------------------------------------------------- +# Corpus + chunking +# --------------------------------------------------------------------------- + + +def load_articles() -> list[dict]: + """Returns a list of {"source": filename, "text": full text} per article.""" + articles = [] + for path in sorted(ARTICLES_DIR.glob("*.md")): + articles.append({"source": path.name, "text": path.read_text(encoding="utf-8")}) + if not articles: + raise FileNotFoundError(f"No articles found in {ARTICLES_DIR} -- add some .md files.") + return articles + + +def split_into_paragraphs(text: str) -> list[str]: + """Splits on blank lines, dropping empty paragraphs.""" + paragraphs = [p.strip() for p in text.split("\n\n")] + return [p for p in paragraphs if p] + + +def merge_short_paragraphs(paragraphs: list[str], target_size: int) -> list[str]: + """Greedily merges consecutive short paragraphs up to target_size characters, + so a chunk isn't just one short line with barely any context in it.""" + chunks = [] + current = "" + for paragraph in paragraphs: + if current and len(current) + len(paragraph) > target_size: + chunks.append(current) + current = paragraph + else: + current = f"{current}\n\n{paragraph}" if current else paragraph + if current: + chunks.append(current) + return chunks + + +def chunk_articles(articles: list[dict]) -> list[dict]: + """Returns a list of {"text": ..., "source": ...} dicts, one per chunk, + across every article. Deterministic: given the same files it always + produces the same chunks in the same order, which is what lets a saved + embedding index stay aligned with the articles on disk.""" + chunks = [] + for article in articles: + paragraphs = split_into_paragraphs(article["text"]) + for chunk_text in merge_short_paragraphs(paragraphs, TARGET_CHUNK_SIZE): + chunks.append({"text": chunk_text, "source": article["source"]}) + return chunks + + +# --------------------------------------------------------------------------- +# Embedding index +# --------------------------------------------------------------------------- + + +def get_model() -> SentenceTransformer: + global _model + if _model is None: + _model = SentenceTransformer(MODEL_NAME) + return _model + + +def build_index(rebuild: bool = False) -> None: + """Embeds every chunk and saves the vectors + text locally. + + The embedding model downloads on first run (~80MB, one time). Re-run this + any time you add or edit articles -- the saved index doesn't update itself. + """ + if INDEX_PATH.exists() and CHUNKS_PATH.exists() and not rebuild: + print(f"Index found at {INDEX_PATH} -- pass --rebuild to rebuild it.") + return + + articles = load_articles() + chunks = chunk_articles(articles) + if not chunks: + raise RuntimeError("No chunks found -- are the articles empty?") + + print(f"Embedding {len(chunks)} chunks from {len(articles)} articles with {MODEL_NAME}...") + model = get_model() + texts = [chunk["text"] for chunk 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 to {CHUNKS_PATH}") + + +def load_index() -> tuple[np.ndarray, list[dict]]: + """Loads the saved vectors and their chunk text.""" + if not INDEX_PATH.exists() or not CHUNKS_PATH.exists(): + raise FileNotFoundError( + f"No index at {INDEX_PATH} -- run `uv run python main.py --rebuild` first." + ) + embeddings = np.load(INDEX_PATH) + with open(CHUNKS_PATH, encoding="utf-8") as f: + chunks = json.load(f) + return embeddings, chunks + + +def retrieve(question: str, embeddings: np.ndarray, chunks: list[dict], top_k: int = TOP_K) -> list[dict]: + """Returns the top_k chunks most similar to `question`, each with its + cosine-similarity score, ranked highest first.""" + question_vector = get_model().encode([question], normalize_embeddings=True)[0] + # Every row of `embeddings` is unit-length and so is question_vector, so + # this dot product *is* the cosine similarity. + similarities = embeddings @ question_vector + top_indices = np.argsort(similarities)[::-1][:top_k] + return [ + {**chunks[i], "score": float(similarities[i])} + for i in top_indices + ] + + +# --------------------------------------------------------------------------- +# LLM plumbing (same free-tier providers as the Agentic Code Reviewer project) +# --------------------------------------------------------------------------- + + +def _build_github_client() -> OpenAI: + return OpenAI(api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.github.ai/inference") + + +def _build_gemini_client() -> OpenAI: + return OpenAI( + api_key=os.environ["GOOGLE_API_KEY"], + base_url="https://generativelanguage.googleapis.com/v1beta/openai/", + ) + + +def _build_groq_client() -> OpenAI: + return OpenAI(api_key=os.environ["GROQ_API_KEY"], base_url="https://api.groq.com/openai/v1") + + +def _build_mistral_client() -> OpenAI: + return OpenAI(api_key=os.environ["MISTRAL_API_KEY"], base_url="https://api.mistral.ai/v1") + + +def _build_cerebras_client() -> OpenAI: + return OpenAI(api_key=os.environ["CEREBRAS_API_KEY"], base_url="https://api.cerebras.ai/v1") + + +def _build_openrouter_client() -> OpenAI: + return OpenAI(api_key=os.environ["OPENROUTER_API_KEY"], base_url="https://openrouter.ai/api/v1") + + +# Every provider here is free-tier at the time of writing, with no credit card +# required -- but check each provider's own pricing page before relying on that. +PROVIDERS = { + "github": (_build_github_client, "gpt-4o-mini"), + "gemini": (_build_gemini_client, "gemini-3.5-flash"), + "groq": (_build_groq_client, "llama-3.3-70b-versatile"), + "mistral": (_build_mistral_client, "mistral-small-latest"), + "cerebras": (_build_cerebras_client, "llama-3.3-70b"), + "openrouter": (_build_openrouter_client, "meta-llama/llama-3.3-70b-instruct:free"), +} + + +def chat(messages: list[dict], provider: str, temperature: float = 0.2) -> str: + """Sends a chat completion to whichever provider is selected and returns the reply.""" + build_client, model = PROVIDERS[provider] + client = build_client() + response = client.chat.completions.create( + model=model, + messages=messages, + temperature=temperature, + ) + return response.choices[0].message.content + + +# --------------------------------------------------------------------------- +# The two pipelines +# --------------------------------------------------------------------------- + +ANSWER_PROMPT = """Answer the question using ONLY the context below. If the +context doesn't contain the answer, say so plainly -- do not make something up. + +Context: +{context} + +Question: {question} + +Answer:""" + +SUFFICIENCY_PROMPT = """You get ONE retrieval pass of evidence, which may not +be enough to answer the question -- the answer might need facts that live in a +document this retrieval didn't return. + +Context: +{context} + +Question: {question} + +Decide whether the Context above contains enough information to answer the +Question. Reply with exactly one of these two forms: + +If YES -- SUFFICIENT, followed by your answer on the next line(s): + SUFFICIENT + + +If NO -- do NOT try to answer. Reply: + INSUFFICIENT + + +Never output both forms.""" + + +def format_context(chunks: list[dict]) -> str: + return "\n\n".join(f"[{c['source']}] {c['text']}" for c in chunks) + + +def parse_sufficiency(text: str) -> tuple[str, str]: + """Returns ("sufficient", answer) or ("insufficient", followup_query).""" + lines = [line.strip() for line in text.strip().splitlines() if line.strip()] + if lines and "INSUFFICIENT" in lines[0].upper(): + followup = lines[1] if len(lines) > 1 else "" + return "insufficient", followup + answer = "\n".join(lines[1:]) if lines and lines[0].upper().startswith("SUFFICIENT") else text + return "sufficient", answer.strip() + + +def single_hop(question: str, embeddings: np.ndarray, chunks: list[dict], provider: str, top_k: int) -> dict: + """Retrieve once, answer from that one retrieval pass. The baseline.""" + retrieved = retrieve(question, embeddings, chunks, top_k=top_k) + prompt = ANSWER_PROMPT.format(context=format_context(retrieved), question=question) + answer = chat( + [{"role": "user", "content": prompt}], + provider=provider, + ) + return {"answer": answer, "evidence": retrieved} + + +def multi_hop(question: str, embeddings: np.ndarray, chunks: list[dict], provider: str, top_k: int) -> dict: + """Two rounds: retrieve, ask the model whether the evidence is enough, and + if not, retrieve a second round guided by the model's own follow-up query.""" + round1 = retrieve(question, embeddings, chunks, top_k=top_k) + prompt = SUFFICIENCY_PROMPT.format(context=format_context(round1), question=question) + verdict = chat( + [{"role": "user", "content": prompt}], + provider=provider, + ) + + status, followup = parse_sufficiency(verdict) + if status == "sufficient": + return { + "answer": followup, + "evidence": round1, + "followup": None, + "rounds": [round1], + } + + round2 = retrieve(followup, embeddings, chunks, top_k=top_k) + combined = merge_dedupe(round1, round2) + final_prompt = ANSWER_PROMPT.format(context=format_context(combined), question=question) + answer = chat( + [{"role": "user", "content": final_prompt}], + provider=provider, + ) + return { + "answer": answer, + "evidence": combined, + "followup": followup, + "rounds": [round1, round2], + } + + +def merge_dedupe(*chunk_lists: list[dict]) -> list[dict]: + """Merges chunk lists, dropping chunks whose text was already seen.""" + seen: set[str] = set() + merged: list[dict] = [] + for chunk_list in chunk_lists: + for chunk in chunk_list: + if chunk["text"] not in seen: + seen.add(chunk["text"]) + merged.append(chunk) + return merged + + +def answer_matches(answer: str, expected: str) -> bool: + """Loose, case-insensitive check that `expected` appears in the answer.""" + return expected.lower() in (answer or "").lower() + + +# --------------------------------------------------------------------------- +# Printing +# --------------------------------------------------------------------------- + + +def wrap_lines(text: str, width: int) -> list[str]: + lines = [] + for paragraph in text.split("\n"): + lines.extend(textwrap.wrap(paragraph, width) or [""]) + return lines + + +def side_by_side(left: str, right: str, width: int = 118, left_label: str = "SINGLE-HOP", right_label: str = "MULTI-HOP") -> str: + """Prints two blocks of text as aligned columns, like `diff -y`.""" + col = (width - 3) // 2 + left_lines = wrap_lines(left, col) + right_lines = wrap_lines(right, col) + rows = max(len(left_lines), len(right_lines)) + left_lines += [""] * (rows - len(left_lines)) + right_lines += [""] * (rows - len(right_lines)) + header = f" {left_label:<{col}} │ {right_label:<{col}} " + sep = "─" * width + body = "\n".join(f" {l:<{col}} │ {r:<{col}} " for l, r in zip(left_lines, right_lines)) + return f"{header}\n{sep}\n{body}\n{sep}" + + +def evidence_block(chunks: list[dict], max_chars: int = 180) -> str: + """A chunk list rendered as audit-friendly bullet lines.""" + if not chunks: + return "(no evidence retrieved)" + lines = [] + for i, chunk in enumerate(chunks, 1): + snippet = " ".join(chunk["text"].split()) + if len(snippet) > max_chars: + snippet = snippet[:max_chars] + "..." + lines.append(f" {i}. [{chunk['source']}] score {chunk['score']:.3f}: {snippet}") + return "\n".join(lines) + + +def build_column(label: str, answer: str, extra: str, evidence: list[dict]) -> str: + parts = [f"{label}", ""] + if extra: + parts.append(extra) + parts.append("") + parts.append(f"Answer: {answer.strip()}") + parts.append("") + parts.append("Evidence used:") + parts.append(evidence_block(evidence)) + return "\n".join(parts) + + +def run_comparison(question: str, embeddings: np.ndarray, chunks: list[dict], provider: str, top_k: int) -> dict: + single = single_hop(question, embeddings, chunks, provider, top_k) + multi = multi_hop(question, embeddings, chunks, provider, top_k) + + multi_extra = "" + if multi["followup"]: + n_round1 = len(multi["rounds"][0]) if multi["rounds"] else 0 + n_round2 = len(multi["rounds"][1]) if len(multi["rounds"]) > 1 else 0 + multi_extra = f"Round 2 (guided by follow-up query): {multi['followup'].strip()}\n" + multi_extra += f"({n_round1} chunks round 1 + {n_round2} new chunks round 2, merged)" + else: + multi_extra = "Evidence was sufficient in round 1 -- no second retrieval needed." + + left = build_column("SINGLE-HOP", single["answer"], "", single["evidence"]) + right = build_column("MULTI-HOP", multi["answer"], multi_extra, multi["evidence"]) + + print(f"\nQuestion: {question}") + print(side_by_side(left, right)) + return single, multi + + +# --------------------------------------------------------------------------- +# CLI +# --------------------------------------------------------------------------- + + +def load_test_questions() -> list[dict]: + with open(TEST_QUESTIONS_PATH, encoding="utf-8") as f: + return json.load(f)["questions"] + + +def run_all_test_questions(embeddings: np.ndarray, chunks: list[dict], provider: str, top_k: int) -> None: + questions = load_test_questions() + print(f"\nRunning {len(questions)} bundled test questions " + f"({sum(1 for q in questions if q['hops'] == 2)} of them multi-hop) " + f"with provider '{provider}'...") + + single_correct = 0 + multi_correct = 0 + for q in questions: + question = q["question"] + single, multi = run_comparison(question, embeddings, chunks, provider, top_k) + + single_hit = answer_matches(single["answer"], q["expected"]) + multi_hit = answer_matches(multi["answer"], q["expected"]) + single_correct += int(single_hit) + multi_correct += int(multi_hit) + + hop_badge = "multi-hop" if q["hops"] == 2 else "single-hop" + print(f"Expected: {q['expected']} ({hop_badge})") + print(f" single-hop: {'✓' if single_hit else '✗'} multi-hop: {'✓' if multi_hit else '✗'}") + + print(f"\nScoreboard (expected answers from data/test_questions.json):") + print(f" single-hop: {single_correct}/{len(questions)} correct") + print(f" multi-hop: {multi_correct}/{len(questions)} correct") + + +def interactive_mode(embeddings: np.ndarray, chunks: list[dict], provider: str, top_k: int) -> None: + print("\nInteractive mode -- type a question, or 'quit' to exit.") + while True: + try: + question = input("\n> ").strip() + except (EOFError, KeyboardInterrupt): + print() + break + if not question or question.lower() in {"quit", "exit", "q"}: + break + run_comparison(question, embeddings, chunks, provider, top_k) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Two-round (multi-hop) question answering over a small Wikipedia-style sample.", + ) + group = parser.add_mutually_exclusive_group() + group.add_argument("question", nargs="?", help="Answer one question, e.g. 'Who founded Volta Dynamics?'") + group.add_argument("--question", dest="question_flag", help="Same as the positional question argument.") + group.add_argument("--query", action="store_true", help="Interactive mode: keep asking questions.") + parser.add_argument("--rebuild", action="store_true", help="Force-rebuild the embedding index.") + parser.add_argument("--provider", help="Override LLM_PROVIDER for this run, e.g. 'groq'.") + parser.add_argument("--top-k", type=int, default=TOP_K, help=f"Chunks retrieved per round (default {TOP_K}).") + return parser.parse_args() + + +def main() -> None: + args = parse_args() + + provider = args.provider or os.environ.get("LLM_PROVIDER", "github") + if provider not in PROVIDERS: + raise ValueError(f"Unknown LLM_PROVIDER '{provider}'. Choose one of: {', '.join(PROVIDERS)}") + + build_index(rebuild=args.rebuild) + embeddings, chunks = load_index() + + question = args.question or args.question_flag + if args.query: + interactive_mode(embeddings, chunks, provider, args.top_k) + elif question: + run_comparison(question, embeddings, chunks, provider, args.top_k) + else: + run_all_test_questions(embeddings, chunks, provider, args.top_k) + + +if __name__ == "__main__": + main() diff --git a/examples/multihop-wikipedia-qa/notebook.ipynb b/examples/multihop-wikipedia-qa/notebook.ipynb new file mode 100644 index 0000000..4e819ca --- /dev/null +++ b/examples/multihop-wikipedia-qa/notebook.ipynb @@ -0,0 +1,411 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi-Hop Wikipedia QA — Colab / Kaggle / Binder companion\n", + "\n", + "This notebook mirrors the local `uv` project from the course's\n", + "[Build a Multi-Hop Question-Answering Tool Over a Small Wikipedia Sample](https://abderrahim-lectures.github.io/python-data-analysis-course/docs/projects/multihop-wikipedia-qa)\n", + "lesson, adapted to run in a hosted notebook with no local files: a small corpus of\n", + "Wikipedia-style articles, local embeddings with `sentence-transformers`, NumPy\n", + "cosine-similarity retrieval, and a free-tier LLM for the final answers.\n", + "\n", + "See the [lesson](https://abderrahim-lectures.github.io/python-data-analysis-course/docs/projects/multihop-wikipedia-qa) for the full walkthrough and the\n", + "[local example project](https://github.com/abderrahim-lectures/python-data-analysis-course/tree/main/examples/multihop-wikipedia-qa) for the real, file-based version of this same code.\n", + "\n", + "## Why two hops?\n", + "\n", + "A plain RAG app retrieves the top chunks for a question **once** and asks the model\n", + "to answer from them. That works when the answer lives in one document — but a whole\n", + "class of questions need facts from *two* documents combined, and a single retrieval\n", + "pass usually finds only one of them. The model then has to guess the rest, and you\n", + "get a plausible-sounding but **wrong** answer.\n", + "\n", + "This notebook runs the same question through **two pipelines side by side**:\n", + "\n", + "- **single-hop**: retrieve the top-K chunks once, answer from only those.\n", + "- **multi-hop**: retrieve, ask the model whether the evidence is enough, and if not\n", + " retrieve a **second round** guided by the model's own follow-up search query, then\n", + " answer from the merged evidence.\n", + "\n", + "Both print the evidence chunks they used, so you can audit exactly what happened.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -q sentence-transformers numpy openai python-dotenv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The sample corpus\n", + "\n", + "Same six articles as the local example's `data/articles/` — short, plain-text,\n", + "Wikipedia-style summaries (fictional but realistic: biographies, companies, cities,\n", + "an event). They're *crafted* so that a few questions genuinely need facts from two\n", + "articles at once. A hosted notebook has no local files, so they're embedded here\n", + "directly as strings.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ARTICLES = {\n", + " \"amina-rahman.md\": \"# Amina Rahman\\n\\n**Amina Rahman** (born 1975) is a Bangladeshi biochemist known for developing the industrial extraction process used to make protein from microalgae.\\n\\n## Early life and education\\n\\nRahman was born in 1975 in Dhaka, the capital of Bangladesh. She studied chemistry at the University of Dhaka and earned a PhD in biochemistry from the same institution in 2001, writing her dissertation on protein recovery from single-celled organisms.\\n\\n## Career\\n\\nAfter a postdoctoral fellowship in Singapore, Rahman returned to the University of Dhaka as a lecturer. In 2011 she was recruited by a Swiss biotechnology startup as its chief scientist, where she led the development of a scalable method for extracting protein from cultivated microalgae.\\n\\nHer extraction method, which uses a mild enzymatic treatment instead of harsh solvents, became the production process behind the company's flagship product. Industry analysts credited the method's low cost with making algae-based protein commercially viable for the first time.\\n\\n## Recognition\\n\\nIn 2018 Rahman received the international Women in Biotechnology Award for her work on sustainable protein production. She has published more than forty peer-reviewed papers and holds three patents related to microalgae processing.\\n\\n## Personal life\\n\\nRahman continues to live and work in Switzerland, but travels regularly to Bangladesh, where she mentors students at her alma mater and funds a small scholarship for women in the natural sciences.\",\n", + " \"basel.md\": \"# Basel\\n\\n**Basel** is the third-largest city in Switzerland, located on the Rhine River where the Swiss, French, and German borders meet. It is a major center of the pharmaceutical and life-sciences industries.\\n\\n## Geography and population\\n\\nBasel sits at the point where Switzerland, France, and Germany come together, giving it the nickname \\\"the three-country city\\\". Its metropolitan population is just over 500,000, and the city is a major railway and freight hub for central Europe.\\n\\n## Life sciences industry\\n\\nBasel is home to some of the world's largest pharmaceutical companies, along with a dense cluster of smaller biotechnology startups. One such startup, the microalgae protein company Cereolabs, was founded in this city in 2009 by a group of researchers who had left a nearby university institute. The city's research hospitals and university labs are a major draw for young scientists.\\n\\n## The Basel Climate Accord\\n\\nIn 2019, Basel hosted the signing of the **Basel Climate Accord**, an international agreement in which participating governments committed to cutting greenhouse-gas emissions from road transport. The accord's signing ceremony was held at the city's conference center, and the agreement is named after the city as a result.\\n\\nSeveral transport and battery-industry firms later cited the accord as a driver of demand for electric vehicles, and analysts frequently referenced it when discussing the growth of the electric bus market in Europe.\\n\\n## Tourism and culture\\n\\nBasel is known for its art museums, including a prominent collection of modern and contemporary art, and for its annual carnival, one of the largest in Europe. The city's old town is a popular destination for weekend visitors from the neighboring countries.\",\n", + " \"cereolabs.md\": \"# Cereolabs\\n\\n**Cereolabs** is a Swiss biotechnology company that produces protein from cultivated microalgae. It is best known for launching **AquaPro**, the first commercially available algae-based protein powder for human consumption.\\n\\n## History\\n\\nThe company was founded in 2009 by a group of former university researchers, and built its first pilot facility two years later. It remained a small research firm for its early years, funding itself through government grants and a single early angel investment.\\n\\n## Products\\n\\nCereolabs' flagship product, AquaPro, launched in 2017. It is a neutral-tasting protein powder made from microalgae grown in closed bioreactors, marketed as a sustainable alternative to soy and whey protein. The production process is built around a mild enzymatic extraction method developed by the company's chief scientist, who joined the firm in 2011.\\n\\nThe company also sells a concentrated algae paste, marketed under the name \\\"Aqualift\\\", to food manufacturers as an ingredient for plant-based meat products.\\n\\n## Facilities\\n\\nCereolabs operates its headquarters and main production facility in an industrial district on the outskirts of its home city, along with a second laboratory opened in Lisbon in 2022. The Lisbon lab focuses on consumer product formulation.\\n\\n## Business\\n\\nIn 2021, following strong sales of AquaPro in European supermarkets, Cereolabs raised a Series B funding round led by a London-based investment firm. The company has stated that it plans to open a production plant in North America by 2028.\",\n", + " \"elena-marchetti.md\": \"# Elena Marchetti\\n\\n**Elena Marchetti** (born 1963) is an Italian electrical engineer best known for founding one of Europe's earliest dedicated lithium-ion battery manufacturers.\\n\\n## Early life and education\\n\\nMarchetti was born in 1963 in the city of Naples, in southern Italy. She studied electrical engineering at the Polytechnic University of Turin, where she focused on electrochemical energy storage and wrote a thesis on the thermal management of rechargeable battery packs.\\n\\n## Career\\n\\nIn 1992, at the age of 29, Marchetti founded a battery manufacturing company in the city of Turin and served as its first chief executive. The company grew slowly for a decade, surviving on small contracts from electric forklift makers and uninterruptible-power-supply vendors.\\n\\nHer breakthrough came in 2009, when her battery-pack design won the European Energy Innovation Prize, a continental award recognizing engineering achievements in clean energy. The win raised her company's profile significantly and led to its first major public-transit contracts.\\n\\n## Later life\\n\\nMarchetti stepped down as chief executive in 2019 and retired in 2020, moving to Lisbon, Portugal, where she advises early-stage energy startups and writes occasional essays on battery recycling. She has no children and prefers to keep a low public profile.\\n\\n## Legacy\\n\\nIndustry historians credit Marchetti with popularizing the practice of pairing battery chemistry research directly with vehicle-integration engineering — an approach that was unusual for a small firm in the 1990s. Her original company continues to operate today under a different name.\",\n", + " \"lisbon.md\": \"# Lisbon\\n\\n**Lisbon** is the capital and largest city of Portugal, located on the Atlantic coast of the Iberian Peninsula. It has been an important trading port since the Age of Discovery and remains the country's economic and cultural center.\\n\\n## Geography and population\\n\\nLisbon sits on seven hills at the mouth of the Tagus River, facing the Atlantic Ocean. Its metropolitan area has a population of roughly 2.9 million, about a quarter of Portugal's total population. The city's mild, sunny climate makes it a popular destination for remote workers and retirees.\\n\\n## Public transport\\n\\nThe city's public transport system is operated by **TransLisboa**, the municipal transit authority, which runs the city's trams, buses, and metro. TransLisboa is known for its historic yellow tram network, which climbs the city's steepest streets.\\n\\nIn 2016, TransLisboa launched Europe's first fully electric bus route, a single line connecting the city center to the airport, using battery packs supplied by an Italian battery manufacturer. The route's success led TransLisboa to expand electric buses to several more lines over the following years.\\n\\n## Culture and events\\n\\nLisbon hosts the annual Lisbon Tech Summit, a three-day technology conference that draws startups and investors from across Europe. The event is held each October at the city's riverside convention center.\\n\\n## Notable residents\\n\\nThe city has a growing community of tech entrepreneurs and retired engineers. Among its well-known residents is a celebrated Italian electrical engineer who moved to Lisbon after retiring, and who advises early-stage energy startups from her home in the Alfama district.\",\n", + " \"volta-dynamics.md\": \"# Volta Dynamics\\n\\n**Volta Dynamics** was an Italian manufacturer of lithium-ion battery packs for electric commercial vehicles, headquartered in Turin. It was founded in the early 1990s by the winner of the 2009 European Energy Innovation Prize, and operated under this name until 2021.\\n\\n## History\\n\\nThe company was founded in the early 1990s in Turin, Italy, growing out of a university research project on rechargeable battery systems. For its first fifteen years it supplied relatively small battery packs to electric forklift and delivery-vehicle makers across northern Italy.\\n\\nThe firm's first significant public-transit win came in 2015, when it won a contract to supply battery systems for the bus fleet of Lisbon's public transport operator. The following year, in 2016, that operator launched Europe's first fully electric bus route, powered by Volta Dynamics battery packs.\\n\\n## Products\\n\\nVolta Dynamics focused exclusively on stationary and vehicle-mounted battery packs, deliberately avoiding consumer electronics. Its core product line was the \\\"TransPack\\\" series of swappable battery modules, designed so that a depot could charge a fresh pack while a bus continued its route on another.\\n\\n## Renaming and later years\\n\\nIn 2019 the company went public on the Milan Stock Exchange. In 2021 it merged with a German EV-grid startup and renamed itself Voltora. Under that name, the business shifted its focus toward grid-scale energy storage and battery reuse for renewable-power utilities.\\n\\n## Recognition\\n\\nBattery industry trade publications repeatedly cited Volta Dynamics in their annual rankings of European battery integrators, praising the reliability record of its TransPack modules in daily transit use.\"\n", + "}\n", + "\n", + "print(f\"Loaded {len(ARTICLES)} articles: {list(ARTICLES.keys())}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Split into chunks, embed locally\n", + "\n", + "Same chunking logic as `main.py`: split on blank lines into paragraphs, then greedily\n", + "re-merge short paragraphs up to a target size. Then embed every chunk with\n", + "`all-MiniLM-L6-v2` — fully local, no API key, no cost — keeping the vectors in memory\n", + "since a notebook has no `data/index.npy` to persist to.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sentence_transformers import SentenceTransformer\n", + "\n", + "TARGET_CHUNK_SIZE = 600 # characters\n", + "\n", + "\n", + "def split_into_paragraphs(text: str) -> list[str]:\n", + " paragraphs = [p.strip() for p in text.split(\"\\n\\n\")]\n", + " return [p for p in paragraphs if p]\n", + "\n", + "\n", + "def merge_short_paragraphs(paragraphs: list[str], target_size: int) -> list[str]:\n", + " chunks = []\n", + " current = \"\"\n", + " for paragraph in paragraphs:\n", + " if current and len(current) + len(paragraph) > target_size:\n", + " chunks.append(current)\n", + " current = paragraph\n", + " else:\n", + " current = f\"{current}\\n\\n{paragraph}\" if current else paragraph\n", + " if current:\n", + " chunks.append(current)\n", + " return chunks\n", + "\n", + "\n", + "def chunk_articles(articles: dict[str, str]) -> list[dict]:\n", + " chunks = []\n", + " for source, text in articles.items():\n", + " paragraphs = split_into_paragraphs(text)\n", + " for chunk_text in merge_short_paragraphs(paragraphs, TARGET_CHUNK_SIZE):\n", + " chunks.append({\"text\": chunk_text, \"source\": source})\n", + " return chunks\n", + "\n", + "\n", + "chunks = chunk_articles(ARTICLES)\n", + "print(f\"Chunked {len(ARTICLES)} articles into {len(chunks)} chunks\")\n", + "\n", + "model = SentenceTransformer(\"all-MiniLM-L6-v2\")\n", + "embeddings = model.encode([c[\"text\"] for c in chunks], normalize_embeddings=True)\n", + "print(f\"Embedded {embeddings.shape[0]} chunks ({embeddings.shape[1]}-dim)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def retrieve(question: str, top_k: int = 3) -> list[dict]:\n", + " \"\"\"Returns the top_k chunks most similar to `question`, each with its\n", + " cosine-similarity score, ranked highest first.\"\"\"\n", + " q = model.encode([question], normalize_embeddings=True)[0]\n", + " similarities = embeddings @ q\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", + "# Quick demo on a single-article question:\n", + "for r in retrieve(\"What is the name of Lisbon's public transit authority?\"):\n", + " print(f\"{r['score']:.3f} [{r['source']}] {' '.join(r['text'].split())[:70]}...\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get a free-tier LLM API key\n", + "\n", + "Answer *generation* (the last step of both pipelines) needs a free-tier LLM API —\n", + "retrieval itself is fully local and needs no key. Pick any provider from the table in\n", + "the [lesson's Setup section](https://abderrahim-lectures.github.io/python-data-analysis-course/docs/projects/multihop-wikipedia-qa#get-a-free-llm-api-key);\n", + "**GitHub Models** is the suggested default since it needs no separate signup (a\n", + "personal access token with the `models: read` scope from\n", + "[github.com/settings/tokens](https://github.com/settings/tokens)).\n", + "\n", + "The key is entered with `getpass` so it never gets typed into a visible cell or saved\n", + "into this notebook's output — never hardcode a real API key here.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from getpass import getpass\n", + "\n", + "LLM_PROVIDER = \"github\" # github (default) | gemini | groq | mistral | cerebras | openrouter\n", + "\n", + "ENV_VAR_BY_PROVIDER = {\n", + " \"github\": \"GITHUB_TOKEN\",\n", + " \"gemini\": \"GOOGLE_API_KEY\",\n", + " \"groq\": \"GROQ_API_KEY\",\n", + " \"mistral\": \"MISTRAL_API_KEY\",\n", + " \"cerebras\": \"CEREBRAS_API_KEY\",\n", + " \"openrouter\": \"OPENROUTER_API_KEY\",\n", + "}\n", + "\n", + "env_var = ENV_VAR_BY_PROVIDER[LLM_PROVIDER]\n", + "os.environ[env_var] = getpass(f\"Enter your {LLM_PROVIDER} API key ({env_var}): \")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The two pipelines\n", + "\n", + "Same prompts and provider setup as `main.py`. The `SUFFICIENCY_PROMPT` is the heart\n", + "of the multi-hop path: it asks the model to either answer (`SUFFICIENT`) or to\n", + "declare the evidence incomplete and write a follow-up search query that would find the\n", + "missing fact (`INSUFFICIENT`). `multi_hop` then retrieves a second round with that\n", + "query and answers from the merged evidence.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from openai import OpenAI\n", + "\n", + "ANSWER_PROMPT = \"\"\"Answer the question using ONLY the context below. If the\n", + "context doesn't contain the answer, say so plainly -- do not make something up.\n", + "\n", + "Context:\n", + "{context}\n", + "\n", + "Question: {question}\n", + "\n", + "Answer:\"\"\"\n", + "\n", + "SUFFICIENCY_PROMPT = \"\"\"You get ONE retrieval pass of evidence, which may not\n", + "be enough to answer the question -- the answer might need facts that live in a\n", + "document this retrieval didn't return.\n", + "\n", + "Context:\n", + "{context}\n", + "\n", + "Question: {question}\n", + "\n", + "Decide whether the Context above contains enough information to answer the\n", + "Question. Reply with exactly one of these two forms:\n", + "\n", + "If YES -- SUFFICIENT, followed by your answer on the next line(s):\n", + " SUFFICIENT\n", + " \n", + "\n", + "If NO -- do NOT try to answer. Reply:\n", + " INSUFFICIENT\n", + " \n", + "\n", + "Never output both forms.\"\"\"\n", + "\n", + "\n", + "def _build_client():\n", + " bases = {\n", + " \"github\": (\"https://models.github.ai/inference\", \"GITHUB_TOKEN\"),\n", + " \"gemini\": (\"https://generativelanguage.googleapis.com/v1beta/openai/\", \"GOOGLE_API_KEY\"),\n", + " \"groq\": (\"https://api.groq.com/openai/v1\", \"GROQ_API_KEY\"),\n", + " \"mistral\": (\"https://api.mistral.ai/v1\", \"MISTRAL_API_KEY\"),\n", + " \"cerebras\": (\"https://api.cerebras.ai/v1\", \"CEREBRAS_API_KEY\"),\n", + " \"openrouter\": (\"https://openrouter.ai/api/v1\", \"OPENROUTER_API_KEY\"),\n", + " }\n", + " base_url, env_var = bases[LLM_PROVIDER]\n", + " return OpenAI(api_key=os.environ[env_var], base_url=base_url)\n", + "\n", + "\n", + "MODELS = {\n", + " \"github\": \"gpt-4o-mini\",\n", + " \"gemini\": \"gemini-3.5-flash\",\n", + " \"groq\": \"llama-3.3-70b-versatile\",\n", + " \"mistral\": \"mistral-small-latest\",\n", + " \"cerebras\": \"llama-3.3-70b\",\n", + " \"openrouter\": \"meta-llama/llama-3.3-70b-instruct:free\",\n", + "}\n", + "\n", + "\n", + "def chat(messages: list[dict]) -> str:\n", + " response = _build_client().chat.completions.create(\n", + " model=MODELS[LLM_PROVIDER],\n", + " messages=messages,\n", + " temperature=0.2,\n", + " )\n", + " return response.choices[0].message.content\n", + "\n", + "\n", + "def format_context(chunks: list[dict]) -> str:\n", + " return \"\\n\\n\".join(f\"[{c['source']}] {c['text']}\" for c in chunks)\n", + "\n", + "\n", + "def parse_sufficiency(text: str) -> tuple[str, str]:\n", + " \"\"\"Returns (\"sufficient\", answer) or (\"insufficient\", followup_query).\"\"\"\n", + " lines = [line.strip() for line in text.strip().splitlines() if line.strip()]\n", + " if lines and \"INSUFFICIENT\" in lines[0].upper():\n", + " followup = lines[1] if len(lines) > 1 else \"\"\n", + " return \"insufficient\", followup\n", + " answer = \"\\n\".join(lines[1:]) if lines and lines[0].upper().startswith(\"SUFFICIENT\") else text\n", + " return \"sufficient\", answer.strip()\n", + "\n", + "\n", + "def merge_dedupe(*chunk_lists: list[dict]) -> list[dict]:\n", + " seen: set[str] = set()\n", + " merged: list[dict] = []\n", + " for chunk_list in chunk_lists:\n", + " for chunk in chunk_list:\n", + " if chunk[\"text\"] not in seen:\n", + " seen.add(chunk[\"text\"])\n", + " merged.append(chunk)\n", + " return merged\n", + "\n", + "\n", + "def single_hop(question: str, top_k: int = 3) -> tuple[str, list[dict]]:\n", + " \"\"\"Baseline: retrieve once, answer from that one retrieval pass.\"\"\"\n", + " retrieved = retrieve(question, top_k=top_k)\n", + " prompt = ANSWER_PROMPT.format(context=format_context(retrieved), question=question)\n", + " answer = chat([{\"role\": \"user\", \"content\": prompt}])\n", + " return answer, retrieved\n", + "\n", + "\n", + "def multi_hop(question: str, top_k: int = 3) -> tuple[str, list[dict], str | None, list[list[dict]]]:\n", + " \"\"\"Two rounds: retrieve, check sufficiency, and if needed retrieve again\n", + " guided by the model's follow-up query, then answer from merged evidence.\"\"\"\n", + " round1 = retrieve(question, top_k=top_k)\n", + " verdict = chat([{\"role\": \"user\", \"content\": SUFFICIENCY_PROMPT.format(\n", + " context=format_context(round1), question=question)}])\n", + " status, followup = parse_sufficiency(verdict)\n", + " if status == \"sufficient\":\n", + " return followup, round1, None, [round1]\n", + " round2 = retrieve(followup, top_k=top_k)\n", + " combined = merge_dedupe(round1, round2)\n", + " final_prompt = ANSWER_PROMPT.format(context=format_context(combined), question=question)\n", + " answer = chat([{\"role\": \"user\", \"content\": final_prompt}])\n", + " return answer, combined, followup, [round1, round2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run the comparison on a genuinely multi-hop question\n", + "\n", + "Here's the money shot: a question whose answer only exists once two articles are\n", + "combined. Run this cell and watch what happens — single-hop gets the *clue* article\n", + "but not the *fact* article, while multi-hop spots the gap, writes a follow-up query,\n", + "pulls the second article, and answers correctly.\n", + "\n", + "Run it a few times (or on `--query`-style questions of your own) — on such a small\n", + "corpus single-hop will sometimes *accidentally* land the right chunks and get a\n", + "multi-hop question right by luck. That's a real property of small corpora, not a bug;\n", + "the evidence printed beside each answer is the honest audit trail.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def show(label: str, answer: str, evidence: list[dict]) -> None:\n", + " print(f\"--- {label} ---\")\n", + " print(f\"Answer: {answer.strip()}\")\n", + " print(\"Evidence used:\")\n", + " for i, c in enumerate(evidence, 1):\n", + " snippet = \" \".join(c[\"text\"].split())\n", + " print(f\" {i}. [{c['source']}] score {c['score']:.3f}: {snippet[:110]}...\")\n", + " print()\n", + "\n", + "\n", + "question = \"Who founded the company that powered TransLisboa's electric buses?\"\n", + "\n", + "single_answer, single_evidence = single_hop(question)\n", + "multi_answer, multi_evidence, followup, rounds = multi_hop(question)\n", + "\n", + "show(\"SINGLE-HOP\", single_answer, single_evidence)\n", + "\n", + "extra = \"\"\n", + "if followup:\n", + " extra = f\"Round 2 (guided by follow-up query): {followup.strip()}\"\n", + "else:\n", + " extra = \"Evidence was sufficient in round 1 -- no second retrieval needed.\"\n", + "show(f\"MULTI-HOP ({extra})\", multi_answer, multi_evidence)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Built your own version of this? See the course's\n", + "[`examples/student-projects/`](https://github.com/abderrahim-lectures/python-data-analysis-course/tree/main/examples/student-projects)\n", + "gallery to share it with the class. Welcome to writing Python outside the browser. 🎓\n" + ] + } + ], + "metadata": { + "colab": { + "name": "multihop-wikipedia-qa-notebook.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/multihop-wikipedia-qa/pyproject.toml b/examples/multihop-wikipedia-qa/pyproject.toml new file mode 100644 index 0000000..6901a1f --- /dev/null +++ b/examples/multihop-wikipedia-qa/pyproject.toml @@ -0,0 +1,12 @@ +[project] +name = "multihop-wikipedia-qa" +version = "0.1.0" +description = "A two-round (multi-hop) question-answering tool over a small Wikipedia-style sample, comparing single-hop RAG against a follow-up-guided second retrieval pass." +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "python-dotenv>=1.2.2", + "openai>=2.8.0", + "numpy", + "sentence-transformers", 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+ + Build a Multi-Hop QA Tool Over a Wikipedia Sample + + } + summary={ + + A two-round RAG pipeline that answers questions whose facts live in two + different articles, and shows you the exact evidence chain it used. + + } + />