Skip to main content

llama-index retrievers Vectorize.io integration

Project description

LlamaIndex Retrievers Integration: Vectorize

Vectorize RAG-as-a-Service handles the messy, hard parts of AI development, so you can focus on building your applications.

Installation

pip install llama-index-retrievers-vectorize

Usage

from llama_index.retrievers.vectorize import VectorizeRetriever

retriever = VectorizeRetriever(
    api_token="...",
    organization="...",
    pipeline_id="...",
)
retriever.retrieve("query")

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

llama_index_retrievers_vectorize-0.2.0.tar.gz (4.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

File details

Details for the file llama_index_retrievers_vectorize-0.2.0.tar.gz.

File metadata

File hashes

Hashes for llama_index_retrievers_vectorize-0.2.0.tar.gz
Algorithm Hash digest
SHA256 0d34b6f6ed71e4fe6fd8fdc9bbda7579e253ddb524f9d3d74df333adb9d0b4aa
MD5 e742067c6d76e686c37ea8b293feeb13
BLAKE2b-256 33ae8aa3073fc1eb0f6476a86d375cad36570e0d7006ef8ef5270d1cf09362e5

See more details on using hashes here.

File details

Details for the file llama_index_retrievers_vectorize-0.2.0-py3-none-any.whl.

File metadata

File hashes

Hashes for llama_index_retrievers_vectorize-0.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 cea3ab7cb1b09fc914ad6d7989c28c9cf5f8faa11049a48162203b282bbadc7e
MD5 1c1df69102020c5443386d5c22932dcd
BLAKE2b-256 7426904e9157ba7aba3e464432f9bb2c394984eec73c7a1a9dbaa21896d91de6

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page