pineconer/index.Rmd at main · bob-rietveld/pineconer · GitHub

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---
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, eval = FALSE)
```

# pineconer <img src="man/figures/logo.png" align="right" height="139" alt="" />

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An R interface to the [Pinecone](https://www.pinecone.io/) vector database API for building similarity search and RAG (Retrieval-Augmented Generation) applications.

## Overview

pineconer provides a comprehensive set of functions for working with the Pinecone vector database directly from R:

- **Index Management** - Create, configure, and manage serverless or pod-based indexes
- **Vector Operations** - Query, upsert, fetch, update, and delete vectors with filtering support
- **Inference API** - Generate embeddings and rerank results using Pinecone's hosted models
- **Assistants API** - Build RAG applications by uploading documents and chatting with AI assistants
- **Bulk Operations** - Import large datasets efficiently from cloud storage

## Installation

Install the development version from GitHub:

```{r}
# install.packages("pak")
pak::pak("bob-rietveld/pineconer")
```

## Configuration

Set your Pinecone API key in `~/.Renviron`:

```
PINECONE_API_KEY=your_api_key
```

## Quick Start

### Create an Index

```{r}
library(pineconer)

# Create a serverless index
create_index(
  name = "my-index",
  dimension = 1536,

  metric = "cosine",
  spec = list(serverless = list(cloud = "aws", region = "us-east-1"))
)
```

### Query Vectors

```{r}
# Get index info
index <- describe_index("my-index")

# Query for similar vectors
results <- vector_query(
  index = index,
  vector = my_embedding,
  top_k = 10
)
```

### Use the Assistants API

```{r}
# Create an assistant for RAG
create_assistant(name = "my-assistant")

# Upload documents
assistant_upload_file("my-assistant", "documents/report.pdf")

# Chat with your documents
response <- assistant_chat(
 assistant_name = "my-assistant",
  messages = list(
    list(role = "user", content = "Summarize the key findings")
  )
)

response$content$message$content
```

## Features

| Feature | Functions |
|---------|-----------|
| **Indexes** | `create_index()`, `list_indexes()`, `describe_index()`, `configure_index()`, `delete_index()` |
| **Vectors** | `vector_query()`, `vector_upsert()`, `vector_fetch()`, `vector_update()`, `vector_delete()` |
| **Collections** | `create_collection()`, `list_collections()`, `describe_collection()`, `delete_collection()` |
| **Inference** | `embed()`, `rerank()` |
| **Records** | `records_upsert()`, `records_search()` |
| **Assistants** | `create_assistant()`, `assistant_upload_file()`, `assistant_chat()`, `assistant_context()` |
| **Bulk Import** | `start_import()`, `describe_import()`, `list_imports()` |

## Learn More

- [Getting Started](articles/getting-started.html) - Step-by-step guide to using pineconer
- [Assistants API](articles/assistants.html) - Build RAG applications with Pinecone Assistants
- [Inference API](articles/inference.html) - Generate embeddings and rerank results
- [Function Reference](reference/index.html) - Complete API documentation

## License
MIT