Retrieval

Search with Intelligent Insights

TaskingAI's Retrieval, a versatile vector database, enhances your applications with AI-driven comprehension across diverse data formats.
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Experience enhanced search capabilities that deliver accurate and contextually relevant results, supported by real-time queries and dynamic index updates for fresh insights.

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GitHub Repo

Vector-Based Retrieval: Elevating Data Intelligence

Document Splitter

Efficiently segments and transforms documents, ensuring optimal data utilization for retrieval.

Text Embedding

Provides a unified platform with a wide range of embedding providers for creating semantically-rich text embeddings, ensuring swift content identification.

Versatile Vector Stores

Supports a wide array of vector stores, providing flexibility in data storage and retrieval.

Simplify Data Retrieval

Transform the way your language models interact with external data. TaskingAI's Retrieval module offers an intuitive and straightforward approach to augment your AI's capabilities.

See documentation

import taskingai collection = taskingai.retrieval.create_collection( embedding_model_id="YOU_MODEL_ID", capacity=1000, )

record = taskingai.retrieval.create_record( collection_id=collection.collection_id, content="Machine learning is a subfield of artificial intelligence...", text_splitter=TokenTextSplitter(chunk_size=200, overlap_size=20), )

chunks = taskingai.retrieval.query_chunks( collection_id=collection.collection_id, query_text="Basketball", top_k=2 )

Smart Retrieval Interface

Connect and enhance your data querying processes with TaskingAI's Smart Retrieval Interface. Tailored for enterprises, it revolutionizes data extraction by providing real-time search capabilities to enhance

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Utilize TaskingAI to integrate Large Language Models with AI applications, realizing your AI vision.