How Tessell for Milvus works?

Fully managed Milvus vector database service with enterprise-grade data management, performance optimized for production, cost-optimized for development, and all with your own security and compliance posture.

Store, index, and manage massive embedding vectors generated by deep neural networks and other machine learning (ML) models. Tessell for Milvus is highly resilient and reliable.

Where can vector databases help?

Retrieval Augmented Generation (RAG)

Build LLM apps with foundational LLM model using RAG.

Recommender System

Get personalized recommendations based on user profiles, behaviors, and queries, identifying vectors aligned with their interests. 

Text/Semantic Search

Allow search engines to return results semantically similar to the query, even if they don't contain the exact keywords.

Image Similarity Search

Explore visually akin images within an extensive repository of image libraries.

Question Answering System

Build a chatbot for interactive question-answering that automatically responds to user queries.

Build LLM apps with Tessell for Milvus

Build your LLM apps in minutes with Tessell for Milvus. Tessell manages and provisions your vector databases, seamlessly integrates with popular LLM frameworks, ensuring smooth data flow and efficient model training, and powers powerful vector search capabilities for lightning-fast retrieval of similar text, images, or code within your LLM app.

1
2from pymilvus import connections, db
3from pymilvus import CollectionSchema, FieldSchema, DataType
4
5#Create client connection to Tessell for Milvus
6
7TESSELL_MILVUS_URI = "xxxxxxx.tessell.com:19530"
8
9conn = connections.connect(
10  		alias="default",
11  		uri=TESSELL_MILVUS_URI,
12  		token="root:Milvus",
13		)
14
15db = conn.create_database('tessell_articles')
16
17article_id = FieldSchema(
18  name="article_id",
19  dtype=DataType.INT64,
20  is_primary=True,
21)
22article_title = FieldSchema(
23  name="article_title",
24  dtype=DataType.VARCHAR,
25  max_length=500,
26  # The default value will be used if this field is left empty during data inserts or upserts.
27  # The data type of `default_value` must be the same as that specified in `dtype`.
28  default_value="Unknown"
29)
30
31article_content= FieldSchema(
32  name="article_title",
33  dtype=DataType.VARCHAR,
34  max_length=10000,
35  # The default value will be used if this field is left empty during data inserts or upserts.
36  # The data type of `default_value` must be the same as that specified in `dtype`.
37  default_value="Unknown"
38)
39embeddings = FieldSchema(
40  name="book_intro",
41  dtype=DataType.FLOAT_VECTOR,
42  dim=786
43)
44schema = CollectionSchema(
45  fields=[article_id, article_title, article_content, embeddings],
46  description="Tessell articles knowledge basae",
47  enable_dynamic_field=True
48)
49collection_name = "tessell_articles"
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from pymilvus import connections, db from pymilvus import CollectionSchema, FieldSchema, DataType #Create client connection to Tessell for Milvus TESSELL_MILVUS_URI = "xxxxxxx.tessell.com:19530" conn = connections.connect( alias="default", uri=TESSELL_MILVUS_URI, token="root:Milvus", ) db = conn.create_database('tessell_articles') article_id = FieldSchema( name="article_id", dtype=DataType.INT64, is_primary=True, ) article_title = FieldSchema( name="article_title", dtype=DataType.VARCHAR, max_length=500, # The default value will be used if this field is left empty during data inserts or upserts. # The data type of `default_value` must be the same as that specified in `dtype`. default_value="Unknown" ) article_content= FieldSchema( name="article_title", dtype=DataType.VARCHAR, max_length=10000, # The default value will be used if this field is left empty during data inserts or upserts. # The data type of `default_value` must be the same as that specified in `dtype`. default_value="Unknown" ) embeddings = FieldSchema( name="book_intro", dtype=DataType.FLOAT_VECTOR, dim=786 ) schema = CollectionSchema( fields=[article_id, article_title, article_content, embeddings], description="Tessell articles knowledge basae", enable_dynamic_field=True ) collection_name = "tessell_articles"

Integrates seamlessly with your tech stack

What is it like running Milvus on Tessell?

Ease of Consumption

Deploy fully-managed Milvus database based on choice of cluster shapes and cloud.

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Select Cloud

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Choose cluster shape

  • Depending upon how many vectors you want to store.
  • Chose from micro to large shape cluster based on requirements.
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Deploy Cluster

Self-healing and self-managed. 99.99% uptime

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Build on the Milvus strength with reliability & resiliency built-in.

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99.99% uptime SLA, and zero data corruption.

Collection & Indexes Lifecycle management

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Support for database indexing and collection lifecycle management through Tessell on Milvus.

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Powerful, flexible support for embeddings generated by  LLMs.

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Lightning-fast queries on any size data set

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Cost-effective storage of vectors.