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Enhance README
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@andreibondarev
andreibondarev committed Jul 4, 2024
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118 changes: 53 additions & 65 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -47,129 +47,117 @@ client = Milvus::Client.new(
### Using the Collections endpoints
```ruby
# Check if the collection exists.
client.collections.has(collection_name: "book")
client.collections.has(collection_name: "example_collection")
```

```ruby
# Rename a collection.
client.collections.rename(collection_name: "book", new_collection_name: "note")
client.collections.rename(collection_name: "example_collection", new_collection_name: "example_collection")
```

```ruby
# Get collection stats
client.collections.get_stats(collection_name: "book")
client.collections.get_stats(collection_name: "example_collection")
```

```ruby
# Data types: https://github.com/patterns-ai-core/milvus/blob/main/lib/milvus/constants.rb

# Creating a new collection schema
client.collections.create(
collection_name: "book",
description: "Test book search",
auto_id: false,
collection_name: "example_collection",
description: "Book search",
auto_id: true,
fields: [
{
"fieldName": "book_id",
"description": "book id",
"isPrimary": true,
"autoID": false,
"dataType": "Int64"
fieldName: "book_id",
description: "Book ID",
isPrimary: true,
autoID: false,
dataType: "Int64"
},
{
"fieldName": "word_count",
"description": "count of words",
"isPrimary": false,
"dataType": "Int64"
fieldName: "content",
description: "Chunk Content",
dataType: "VarChar",
elementTypeParams: {
max_length: "512"
}
},
{
"fieldName": "book_intro",
"description": "embedded vector of book introduction",
"dataType": "FloatVector",
"isPrimary": false,
"elementTypeParams": {
"dim": "2"
fieldName: "vector",
description: "Chunk Embedding",
dataType: "FloatVector",
elementTypeParams: {
dim: 1536
}
}
]
)
```
```ruby
# Descrbie the collection
client.collections.describe(collection_name: "book")
client.collections.describe(collection_name: "example_collection")
```
```ruby
# Drop the collection
client.collections.drop(collection_name: "book")
client.collections.drop(collection_name: "example_collection")
```
```ruby
# Load the collection to memory before a search or a query
client.collections.load(collection_name: "book")
client.collections.load(collection_name: "example_collection")
```
```ruby
# Load status of a specific collection.
client.collections.get_load_state(collection_name: "example_collection")
```
```ruby
# List all collections in the specified database.
client.collections.list
```
```ruby
# Release a collection from memory after a search or a query to reduce memory usage
client.collections.release(collection_name: "book")
client.collections.release(collection_name: "example_collection")
```

### Inserting Data
```ruby
client.entities.insert(
collection_name: "book",
num_rows: 5, # Number of rows to be inserted. The number should be the same as the length of each field array.
fields_data: [
{
"field_name": "book_id",
"type": Milvus::DATA_TYPES["int64"],
"field": [1,2,3,4,5]
},
{
"field_name": "word_count",
"type": Milvus::DATA_TYPES["int64"],
"field": [1000,2000,3000,4000,5000]
},
{
"field_name": "book_intro",
"type": 101,
"field": [ [1,1],[2,1],[3,1],[4,1],[5,1] ]
}
collection_name: "example_collection",
data: [
{ id: 1, content: "The quick brown fox jumps over the lazy dog", vector: ([0.1]*1536) },
{ id: 2, content: "Lorem ipsum dolor sit amet, consectetur adipiscing elit", vector: ([0.2]*1536) },
{ id: 3, content: "Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua", vector: ([0.3]*1536) }
]
)
```
```ruby
# Delete the entities with the boolean expression you created
client.entities.delete(
collection_name: "book",
collection_name: "example_collection",
expression: "book_id in [0,1]"
)
```
```ruby
# Compact data manually
client.entities.compact!(
collection_id: "book"
)
# => {"status"=>{}, "compactionID"=>440928616022809499}
# Inserts new records into the database or updates existing ones.
client.entities.upsert()
```
```ruby
# Check compaction status
client.entities.compact_status(
compaction_id: 440928616022809499
)
# => {"status"=>{}, "state"=>2}
# Get specific entities by their IDs
client.entities.get()
```

### Indexes
```ruby
# Create an index
index_params = {
fieldName: "example_field",
indexType: "IVF_FLAT",
metricType: "L2",
params: { nlist: 100 }
}
index_params = [
{
metricType: "L2",
fieldName: "vector",
indexName: "vector_idx",
indexConfig: {
index_type: "AUTOINDEX"
}
}
]

client.indexes.create(
collection_name: "example_collection",
Expand Down Expand Up @@ -200,21 +188,21 @@ client.indexes.drop(
### Search, Querying & Hybrid Search
```ruby
client.entities.search(
collection_name: "recipes",
collection_name: "example_collection",
anns_field: "vectors",
data: [embedding],
filter: "id in [450847466900987454]"
)
```
```ruby
client.entities.query(
collection_name: "recipes",
collection_name: "example_collection",
filter: "id in [450847466900987455, 450847466900987454]"
)
```
```ruby
client.entities.hybrid_search(
collection_name: "recipes",
collection_name: "example_collection",
search: [{
filter: "id in [450847466900987455]",
data: [embedding],
Expand Down
35 changes: 17 additions & 18 deletions lib/milvus/constants.rb
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Expand Up @@ -2,22 +2,21 @@

module Milvus
# https://milvus.io/api-reference/pymilvus/v2.4.x/MilvusClient/Collections/DataType.md
DATA_TYPES = {
"boolean" => 1,
"int8" => 2,
"int16" => 3,
"int32" => 4,
"int64" => 5,
"float" => 10,
"double" => 11,
"string" => 20,
"varchar" => 21,
"array" => 22,
"json" => 23,
"binary_vector" => 100,
"float_vector" => 101,
"float16_vector" => 102,
"bfloat16_vector" => 103,
"sparse_float_vector" => 104
}.freeze
DATA_TYPES = [
"Boolean",
"Int8",
"Int16",
"Int32",
"Int64",
"Float",
"Double",
"VarChar",
"Array",
"Json",
"BinaryVector",
"FloatVector",
"Float16Vector",
"BFloat16Vector",
"SparseFloatVector"
].freeze
end

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