# node-red-contrib-pinecone
> Pinecone makes it easy to provide long-term memory for high-performance AI applications
Latest version **1.0.7** (published 2023-12-26) · MIT license · 0 weekly downloads
## Install
```sh
npm install node-red-contrib-pinecone
pnpm add node-red-contrib-pinecone
yarn add node-red-contrib-pinecone
bun add node-red-contrib-pinecone
```
## Health
**Score 15/100 (F)** — status: abandoned.
Positive: no vulnerabilities.
Warnings: low downloads; no types; no esm support.
Negative: abandoned; low maintenance score.
## Facts
| | |
|---|---|
| Version | 1.0.7 |
| Published | 2023-12-26 |
| First published | 2023-06-24 |
| Weekly downloads | 0 |
| License | MIT |
| TypeScript types | none |
| Module format | CommonJS |
| Dependencies | 1 |
| Unpacked size | 38.1 KB |
| Known vulnerabilities | 0 |
| Install scripts | no |
| GitHub stars | 0 |
| Author | vovajr11 |
| Maintainers | vova11 |
| Keywords | node-red, pinecone, nodejs, javascript, api, flow, npm, machine-learning, node, example, ai |
## Links
- npm: https://www.npmjs.com/package/node-red-contrib-pinecone
- Repository: https://github.com/UBOS-tech/node-red-contrib-pinecone
- Homepage: https://github.com/UBOS-tech/node-red-contrib-pinecone#readme
- Issues: https://github.com/UBOS-tech/node-red-contrib-pinecone/issues
- npm.io page: https://npm.io/package/node-red-contrib-pinecone
## Dependencies (1)
- [crypto-js](https://npm.io/package/crypto-js.md) ^4.0.0
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## Recent versions
- 1.0.7 (latest) — 2023-12-26
- 1.0.6 — 2023-12-26
- 1.0.5 — 2023-09-12
- 1.0.4 — 2023-09-12
- 1.0.3 — 2023-09-12
- 1.0.2 — 2023-09-12
- 1.0.1 — 2023-06-26
- 1.0.0 — 2023-06-24
## README
## node-red-contrib-pinecone
Pinecone makes it easy to provide long-term memory for high-performance AI applications
[](https://flows.nodered.org/node/node-red-contrib-pinecone)
[](https://www.npmjs.com/package/node-red-contrib-pinecone)
### Quick Start
Install with the built in Node-RED Palette manager or using npm:
```
npm install node-red-contrib-pinecone
```
## Properties Index Operations
```js
msg.PINECONE_API_KEY = "Your API key";
msg.PINECONE_ENVIRONMENT = "Your environment";
msg.PINECONE_BASE_URL = "https://controller.[Your environment].pinecone.io";
```
1. When `msg.actionType` is set to `list_collections`: This operation returns a list of your Pinecone collections
2. When `msg.actionType` is set to `create_collection`:
- **[Required]** `name`: [Type: string] The name of the collection to be created
- **[Required]** `source`: [Type: string] The name of the source index to be used as the source for the collection
3. When `msg.actionType` is set to `describe_collection`:
- **[Required]** `name`: [Type: string] The name of the collection
4. When `msg.actionType` is set to `delete_collection`:
- **[Required]** `name`: [Type: string] The name of the collection
5. When `msg.actionType` is set to `list_collections`: This operation returns a list of your Pinecone indexes
6. When `msg.actionType` is set to `create_index`:
- **[Required]** `name`: [Type: string] The name of the collection to be created
- **[Required]** `dimension`: [Type: integer] The dimensions of the vectors to be inserted in the index
- `metric`: [Type: string] The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'
- `pods`: [Type: integer] The number of pods for the index to use,including replicas
- `replicas`: [Type: integer] The number of replicas. Replicas duplicate your index. They provide higher availability and throughput
- `pod_type`: [Type: string] The type of pod to use. One of **s1**, **p1**, or **p2** appended with **.** and one of **x1**, **x2**, **x4**, or **x8**.
- `metadata_config`: [Type: object | null] Configuration for the behavior of Pinecone's internal metadata index. By default, all metadata is indexed; when metadata_config is present, only specified metadata fields are indexed. To specify metadata fields to index, provide a JSON object of the following form:
```json
{"indexed": ["example_metadata_field"]}
```
- `source_collection`: [Type: string] The name of the collection to create an index from
Example:
```js
msg.payload = {
name: "pinecone-index",
metric: "cosine",
pods: 1,
replicas: 1,
pod_type: "p1.x1"
}
```
7. When `msg.actionType` is set to `describe_index`:
- **[Required]** `name`: [Type: string] The name of the collection
8. When `msg.actionType` is set to `delete_index`:
- **[Required]** `name`: [Type: string] The name of the collection
9. When `msg.actionType` is set to `configure_index`:
- **[Required]** `name`: [Type: string] The name of the collection
- `replicas`: [Type: string] The desired number of replicas for the index
- `pod_type`: [Type: string] The new pod type for the index. One of **s1**, **p1**, or **p2** appended with **.** and one of **x1**, **x2**, **x4**, or **x8**.
## Properties Vector operations
```js
msg.PINECONE_API_KEY = "Your API key";
msg.PINECONE_ENVIRONMENT = "Your environment";
msg.PINECONE_BASE_URL = "https://index_name-project_id.svc.environment.pinecone.io";
```
1. When `msg.actionType` is set to `fetch`:
- **[Required]** `msg.name`: The name of the index
- **[Required]** `msg.vectors`: The vector IDs to fetch. Does not accept values containing spaces. For example:
```js
msg.payload = {
name: "pinecone-index",
vectors: ["vec1", "vec2"]
};
```
2. When `msg.actionType` is set to `query`:
- **[Required]** `name`: [Type: string] The name of the index
- **[Required]** `topK`: [Type: int64] The number of results to return for each query
- `filter`: [Type: object] The filter to apply. You can use vector metadata to limit your search. See [docs](https://www.pinecone.io/docs/metadata-filtering)
- `includeValues`: [Type: boolean] Indicates whether vector values are included in the response
- `includeMetadata`: [Type: boolean] Indicates whether metadata is included in the response as well as the ids
- `vector`: [Type: array of floats] The query vector. This should be the same length as the dimension of the index being queried. Each query() request can contain only one of the parameters id or vector.
- `sparseVector`: [Type: object] Vector sparse data. Represented as a list of indices and a list of corresponded values, which must be the same length
- `id`: [Type: string] The unique ID of the vector to be used as a query vector. Each query() request can contain only one of the parameters queries, vector, or id
Example:
```js
msg.payload = {
name: "pinecone-index",
topK: 10,
vector: [0.1,0.2,0.3],
namespace: 'example-namespace'
};
```
3. When `msg.actionType` is set to `update`:
- **[Required]** `name`: [Type: string] The name of the index
- **[Required]** `id`: [Type: string] Vector's unique id
- `values`: [Type: array of floats] Vector data
- `sparseValues`: [Type: object] Vector sparse data. Represented as a list of indices and a list of corresponded values, which must be the same length
- `setMetadata`: [Type: object] Metadata to set for the vector
- `namespace`: [Type: string] The namespace containing the vector to update
Example:
```js
msg.payload = {
name: "pinecone-index",
id:'vec1',
values: [0.1,0.2,0.3,0.4],
setMetadata: {'genre': 'drama'},
namespace: 'example-namespace'
};
```
4. When `msg.actionType` is set to `upsert`:
- **[Required]** `name`: [Type: string] The name of the index
- **[Required]** `vectors`: [Type: array of objects] An array containing the vectors to upsert. Recommended batch limit is 100 vectors
- `namespace`: [Type: string] This is the namespace name where you upsert vectors
Example:
```js
msg.payload = {
name: "pinecone-index",
vectors: [
{
id: 'vec2',
values: [0.2,0.3,0.4,0.5],
metadata: {'genre': 'action'}
}
]
};
```
5. When `msg.delete` is set to `upsert`:
- **[Required]** `name`: [Type: string] The name of the index
- **[Required]** `ids`: [Type: array of strings] Vectors to delete
- `deleteAll`: [Type: boolean] This indicates that all vectors in the index namespace should be deleted
- `namespace`: [Type: string] The namespace to delete vectors from, if applicable
- `filter`: [Type: boolean] If specified, the metadata filter here will be used to select the vectors to delete. This is mutually exclusive with specifying IDs to delete in the ids param or using delete_all=True. See [doc](https://www.pinecone.io/docs/metadata-filtering)
Example:
```js
msg.payload = {
name: "pinecone-index",
ids: ["vec1", "vec2"]
};
```
6. When `msg.delete` is set to `describeIndexStats`:
- **[Required]** `name`: [Type: string] The name of the index
- `filter`: [Type: boolean] If this parameter is present, the operation only returns statistics for vectors that satisfy the filter. See [doc](https://www.pinecone.io/docs/metadata-filtering)
### Bugs reports and feature requests
Please report any issues or feature requests at GitHub.
## License
[MIT License](https://github.com/UBOS-tech/node-red-contrib-pinecone/blob/main/LICENSE)
---
_Source: https://npm.io/package/node-red-contrib-pinecone · Machine-readable twin of the npm.io package page. Health data is recomputed on every publish._