Blocks
Pinecone
Use Pinecone vector database
Store, search, and retrieve vector embeddings using Pinecone's specialized vector database. Generate embeddings from text and perform semantic similarity searches with customizable filtering options.
Block Preview
Pinecone
Use Pinecone vector database
Usage
- Add the block to your workflow and connect it to the upstream step.
- Configure any required credentials or tokens in the inputs.
- Fill in required inputs and optional parameters for the run.
- Run a test execution, inspect outputs, and iterate before deploying.
- Deploy the pinecone block with monitoring enabled in production.
Inputs (UI)
Operation
dropdownLayout: full
Options: Generate Embeddings, Upsert Text, Search With Text, Search With Vector
Model
dropdownLayout: full
Condition: operation = "generate"
Options: multilingual-e5-large, llama-text-embed-v2, pinecone-sparse-english-v0
Text Inputs
long-inputPlaceholder: [{"text": "Your text here"}]
Layout: full
Condition: operation = "generate"
Index Host
short-inputPlaceholder: https://index-name-abc123.svc.project-id.pinecone.io
Layout: full
Condition: operation = "upsert_text"
Namespace
short-inputPlaceholder: default
Layout: full
Condition: operation = "upsert_text"
Records
long-inputPlaceholder: {"_id": "rec1", "text": "Apple's first product, the Apple I, was released in 1976.", "category": "product"} {"_id": "rec2", "chunk_text": "Apples are a great source of dietary fiber.", "category": "nutrition"}
Layout: full
Condition: operation = "upsert_text"
Index Host
short-inputPlaceholder: https://index-name-abc123.svc.project-id.pinecone.io
Layout: full
Condition: operation = "search_text"
Namespace
short-inputPlaceholder: default
Layout: full
Condition: operation = "search_text"
Search Query
long-inputPlaceholder: Enter text to search for
Layout: full
Condition: operation = "search_text"
Top K Results
short-inputPlaceholder: 10
Layout: full
Condition: operation = "search_text"
Fields to Return
long-inputPlaceholder: ["category", "text"]
Layout: full
Condition: operation = "search_text"
Filter
long-inputPlaceholder: {"category": "product"}
Layout: full
Condition: operation = "search_text"
Rerank Options
long-inputPlaceholder: {"model": "bge-reranker-v2-m3", "rank_fields": ["text"], "top_n": 2}
Layout: full
Condition: operation = "search_text"
Index Host
short-inputPlaceholder: https://index-name-abc123.svc.project-id.pinecone.io
Layout: full
Condition: operation = "fetch"
Namespace
short-inputPlaceholder: Namespace
Layout: full
Condition: operation = "fetch"
Vector IDs
long-inputPlaceholder: ["vec1", "vec2"]
Layout: full
Condition: operation = "fetch"
Index Host
short-inputPlaceholder: https://index-name-abc123.svc.project-id.pinecone.io
Layout: full
Condition: operation = "search_vector"
Namespace
short-inputPlaceholder: default
Layout: full
Condition: operation = "search_vector"
Query Vector
long-inputPlaceholder: [0.1, 0.2, 0.3, ...]
Layout: full
Condition: operation = "search_vector"
Top K Results
short-inputPlaceholder: 10
Layout: full
Condition: operation = "search_vector"
Options
checkbox-listLayout: full
Condition: operation = "search_vector"
Options: Include Values, Include Metadata
API Key
short-inputPlaceholder: Your Pinecone API key
Layout: full
Inputs (API)
operation
stringRequired
apiKey
stringRequired
indexHost
stringOptional
namespace
stringOptional
model
stringOptional
inputs
jsonOptional
parameters
jsonOptional
records
jsonOptional
searchQuery
stringOptional
topK
stringOptional
fields
jsonOptional
filter
jsonOptional
rerank
jsonOptional
ids
jsonOptional
vector
jsonOptional
includeValues
booleanOptional
includeMetadata
booleanOptional
Outputs
Primary response type:
{
"matches": "json",
"upsertedCount": "number",
"data": "json",
"model": "string",
"vector_type": "string",
"usage": "json"
}