| name | pinecone-quickstart |
| description | Interactive Pinecone quickstart for new developers. Choose between two paths - Database (create an integrated index, upsert data, and query using Pinecone MCP + Python) or Assistant (create a Pinecone Assistant for document Q&A). Use when a user wants to get started with Pinecone for the first time or wants a guided tour of Pinecone's tools. |
Pinecone Quickstart
Welcome! This skill walks you through your first Pinecone experience using the tools available to you. In this quickstart,
you will learn how to do a simple form of semantic search over some example data.
Prerequisites
Before starting either path, verify the API key works by calling list-indexes via the Pinecone MCP. If it succeeds, proceed. If it fails, ask the user to set their key:
- Terminal:
export PINECONE_API_KEY="your-key"
- Or create a
.env file in the project root: PINECONE_API_KEY=your-key
Then retry list-indexes to confirm.
Step 0: Choose Your Path
Ask the user which path they want:
- Database – Build a vector search index. Best for developers who want to store and search embeddings. Uses the Pinecone MCP + a Python upsert script.
- Assistant – Build a document Q&A assistant. Best for users who want to upload files and ask questions with cited answers. No code required.
Path A: Database Quickstart
For each step, explain to the user what will happen. An overview is here:
- Check if MCP is set
- Create an integrated index with MCP
- Upsert sample data using the bundled script (9 sentences across productivity, health, and nature themes)
- Run a semantic search query and explore further queries
- Optionally try reranking
- Offer the complete standalone script
Step 1 – Verify MCP is Available
The prerequisite check already called list-indexes. If it succeeded, the MCP is working — proceed to Step 2.
If it failed because MCP tools were unavailable (not an auth error):
Step 2 – Create an Integrated Index
Use the MCP create-index-for-model tool to create a serverless index with integrated embeddings:
name: quickstart-skills
cloud: aws
region: us-east-1
embed:
model: llama-text-embed-v2
fieldMap:
text: chunk_text
Explain to the user what's happening:
- An integrated index uses a built-in Pinecone embedding model (
llama-text-embed-v2)
- This means you send plain text and Pinecone handles the embedding automatically
- The
field_map tells Pinecone which field in your records contains the text to embed
Wait for the index to become ready before proceeding. Waiting a few seconds is sufficient.
Step 3 – Upsert Sample Data
Run the bundled upsert script to seed the index with sample records.
If PINECONE_API_KEY is set in the environment:
uv run scripts/upsert.py --index quickstart-skills
If using a .env file:
uv run --env-file .env scripts/upsert.py --index quickstart-skills
Explain to the user what's happening:
- The script uploads 9 sample records across three themes: productivity (getting work done), health (feeling unwell), and nature (outdoors/wildlife)
- The dataset is intentionally varied so semantic search can show its value — the queries below use completely different words than the records, but the right ones still surface
- Each record has an
_id, a chunk_text field (the text that gets embedded), and a category field
- This is the same structure you'd use for your own data — just replace the records
Step 4 – Query with the MCP
Use the MCP search-records tool to run the first semantic search:
index: quickstart-skills
namespace: example-namespace
query:
topK: 3
inputs:
text: "getting things done efficiently"
Display the results in a clean table: ID, score, and chunk_text.
Explain to the user what's happening:
- Notice the query shares no keywords with the records — but it surfaces the productivity sentences
- That's semantic search: it finds meaning, not just matching words
- You sent plain text — Pinecone embedded the query using the same model as the index
Offer to explore further: Ask the user if they'd like to try another query to see the effect more clearly:
- Option A:
"feeling under the weather" — should surface the health records
- Option B:
"wildlife spotting outside" — should surface the nature records
- Option C: No thanks, move on
Run whichever query they choose and display the results the same way. If they want to try both, do both. After each result, point out which theme surfaced and why.
If they decline or are done exploring, proceed to Step 5 or offer to skip ahead to the complete script.
Step 5 – Try Reranking (Optional)
Ask the user if they want to try reranking.
If yes, use search-records again with reranking enabled:
rerank:
model: bge-reranker-v2-m3
rankFields: [chunk_text]
topN: 3
Explain: Reranking runs a second-pass model over the results to improve relevance ordering.
Step 6 – Wrap Up
Congratulate the user on completing the quickstart. Ask if they'd like a standalone Python script that does everything in one go — create index, upsert, query, and rerank.
If yes, copy it to their working directory:
cp scripts/quickstart_complete.py ./pinecone_quickstart.py
Tell the user:
- The script is at
./pinecone_quickstart.py
- Run it with:
uv run pinecone_quickstart.py
- It uses
uv inline dependencies — no separate install needed
- They can swap in their own
records list to build something real
Path B: Assistant Quickstart
Guide the user through the Pinecone Assistant workflow using the existing assistant skills:
Step 1 – Check for Documents
Before anything else, ask the user if they have files to upload. Pinecone Assistant accepts .pdf, .md, .txt, and .docx files — a single file or a folder of files both work.
If they have files: ask for the path and proceed to Step 2.
If they don't have files: offer two options:
- Generate sample docs — create a few short markdown files in
./sample-docs/ so they can complete the quickstart right now. Ask what topics they'd like (or default to: a product FAQ, a short how-to guide, and a brief company overview). Write 3 files, each 150–250 words.
- Come back later — let them know they can return once they have documents and pick up from Step 2.
Step 2 – Create an Assistant
Invoke pinecone-assistant or run (add --env-file .env if using a .env file):
uv run ../pinecone-assistant/scripts/create.py --name my-assistant
Explain: The assistant is a fully managed RAG service — upload documents, ask questions, get cited answers.
Step 3 – Upload Documents
Invoke pinecone-assistant or run (add --env-file .env if using a .env file):
uv run ../pinecone-assistant/scripts/upload.py --assistant my-assistant --source ./your-docs
Explain: Pinecone handles chunking, embedding, and indexing automatically — no configuration needed.
Step 4 – Chat with the Assistant
Invoke pinecone-assistant or run (add --env-file .env if using a .env file):
uv run ../pinecone-assistant/scripts/chat.py --assistant my-assistant --message "What are the main topics in these documents?"
Explain: Responses include citations with source file and page number.
Next Steps for Assistant
Troubleshooting
PINECONE_API_KEY not set
Terminal environments:
export PINECONE_API_KEY="your-key"
IDEs that don't inherit shell variables: create a .env file in the project root:
PINECONE_API_KEY=your-key
Then use uv run --env-file .env when running scripts. Restart your IDE/agent session after setting.
MCP tools not available
- Verify the Pinecone MCP server is configured in your IDE's MCP settings
- Check that
PINECONE_API_KEY is set before the MCP server starts
Index already exists
- The upsert script is safe to re-run — it will upsert over existing records
- Or delete and recreate: use
pc index delete -n quickstart-skills via the CLI
uv not installed
See the uv installation guide.
Further Reading
