+
+
+The steps are:
+
+1. **Chunk** your source documents into manageable pieces with overlap.
+2. **Embed** each chunk using a Perplexity embedding model.
+3. **Index** the embeddings for similarity search.
+4. **Query** by embedding the user question with the same model.
+5. **Retrieve** the top-k most similar chunks.
+6. **Generate** an answer by passing the retrieved context to the Agent API.
+
+## Prerequisites
+
+Install the Perplexity SDK:
+
+
+ 
+
+
+## What the sandbox does here
+
+- **Runs the analysis as code, not from memory.** Like a code interpreter, the
+ sandbox lets the model solve a quantitative task by writing and running Python
+ instead of guessing. The mention counts and share-of-voice percentages come
+ from code it actually executed over the search results — so the numbers are
+ real, not plausible-sounding. The script enforces this: it checks the response
+ contains a `sandbox_results` item and refuses the result otherwise, so the
+ model can't skip the tool and return invented counts.
+- **Searches the web in the same run.** The sandbox can reach Perplexity search
+ from inside the run, so the model pulls the articles itself and classifies them
+ in the same request — no separate scraping step, no glue code, no extra tool to
+ wire up.
+- **Returns a real file with zero setup on your side.** matplotlib and the
+ runtime live in the sandbox; the model renders the chart, shares it with
+ `share_file`, and you download the `.pdf` from the response by id. One request
+ in, one file out — nothing to install or host locally. A plain chat completion
+ would only return text.
+
+## Without the sandbox
+
+To build the same report yourself, you'd stand up a runtime: a machine with
+Python and matplotlib, the search and classification code, and somewhere to
+execute it and capture the file. With the `sandbox` tool the model writes and
+runs that code server-side and hands back the finished PDF — nothing to install,
+host, or keep running — and it adapts the code to whatever the search returns
+instead of you maintaining a rigid pipeline.
+
+## Installation
+
+Keep the project files in the same directory:
+`competitor_buzz_tracker.py`, `observability.py` (imported by the script),
+`requirements.txt`, and your `basket.yaml`.
+
+1. Install the dependencies — the [Perplexity Python SDK](https://github.com/ppl-ai/perplexity-python),
+ PyYAML (to read the basket config), and Pydantic (for the response schema).
+ They're pinned in `requirements.txt`:
+
+```text requirements.txt
+perplexityai==0.38.0
+PyYAML==6.0.2
+pydantic==2.13.4
+```
+
+```bash
+pip install -r requirements.txt
+```
+
+2. Set your Perplexity API key:
+
+```bash
+export PERPLEXITY_API_KEY="your-api-key-here"
+```
+
+The SDK reads the key from this environment variable.
+
+