Pinecone
Use Pinecone vector database
Integrate Pinecone into the workflow. Can generate embeddings, upsert text, search with text, fetch vectors, and search with vectors.
Generate embeddings from text using Pinecone
| Parameter | Type | Required | Description |
|---|
model | string | Yes | No description |
inputs | array | Yes | No description |
apiKey | string | Yes | No description |
| Parameter | Type | Description |
|---|
data | array | Generated embeddings data with values and vector type |
model | string | Model used for generating embeddings |
vector_type | string | Type of vector generated (dense/sparse) |
usage | object | Usage statistics for embeddings generation |
| Parameter | Type | Required | Description |
|---|
indexHost | string | Yes | Full Pinecone index host URL (e.g., "https://my-index-abc123.svc.pinecone.io"\) |
namespace | string | Yes | Namespace to upsert records into (e.g., "documents", "embeddings") |
records | array | Yes | Record or array of records to upsert, each containing _id, text, and optional metadata |
apiKey | string | Yes | No description |
| Parameter | Type | Description |
|---|
statusText | string | Status of the upsert operation |
| Parameter | Type | Required | Description |
|---|
indexHost | string | Yes | Full Pinecone index host URL (e.g., "https://my-index-abc123.svc.pinecone.io"\) |
namespace | string | No | Namespace to search in (e.g., "documents", "embeddings") |
searchQuery | string | Yes | No description |
topK | string | No | Number of results to return (e.g., "10", "25") |
fields | array | No | No description |
filter | object | No | Filter to apply to the search (e.g., {"category": "tech", "year": {"$gte": 2020}}) |
rerank | object | No | No description |
apiKey | string | Yes | No description |
| Parameter | Type | Description |
|---|
matches | array | Search results with ID, score, and metadata |
↳ id | string | Vector ID |
↳ score | number | Similarity score |
↳ metadata | object | Associated metadata |
usage | object | Usage statistics including tokens, read units, and rerank units |
↳ total_tokens | number | Total tokens used for embedding |
↳ read_units | number | Read units consumed |
↳ rerank_units | number | Rerank units used |
| Parameter | Type | Required | Description |
|---|
indexHost | string | Yes | Full Pinecone index host URL (e.g., "https://my-index-abc123.svc.pinecone.io"\) |
namespace | string | No | Namespace to search in (e.g., "documents", "embeddings") |
vector | array | Yes | No description |
topK | number | No | No description |
filter | object | No | Filter to apply to the search (e.g., {"category": "tech", "year": {"$gte": 2020}}) |
includeValues | boolean | No | No description |
includeMetadata | boolean | No | No description |
apiKey | string | Yes | No description |
| Parameter | Type | Description |
|---|
matches | array | Vector search results with ID, score, values, and metadata |
namespace | string | Namespace where the search was performed |
| Parameter | Type | Required | Description |
|---|
indexHost | string | Yes | Full Pinecone index host URL (e.g., "https://my-index-abc123.svc.pinecone.io"\) |
ids | array | Yes | Array of vector IDs to fetch (e.g., ["vec-001", "vec-002"]) |
namespace | string | No | Namespace to fetch vectors from (e.g., "documents", "embeddings") |
apiKey | string | Yes | No description |
| Parameter | Type | Description |
|---|
matches | array | Fetched vectors with ID, values, metadata, and score |
↳ id | string | Vector ID |
↳ values | array | Vector values |
↳ metadata | object | Associated metadata |
↳ score | number | Match score (1.0 for exact matches) |
data | array | Vector data with values and vector type |
↳ values | array | Vector values |
↳ vector_type | string | Vector type (dense/sparse) |
usage | object | Usage statistics including total read units |
↳ total_tokens | number | Read units consumed |
