Milvus - Vector Store

Use Milvus as a vector store for RAG.

Quick Start

You need three things:

1. A Milvus instance (cloud or self-hosted)
2. An embedding model (to convert your queries to vectors)
3. A Milvus collection with vector fields

Usage

SDK

Basic Search

from litellm import vector_stores
import os

# Set your credentials
os.environ["MILVUS_API_KEY"] = "your-milvus-api-key"
os.environ["MILVUS_API_BASE"] = "https://your-milvus-instance.milvus.io"

# Search the vector store
response = vector_stores.search(
    vector_store_id="my-collection-name",  # Your Milvus collection name
    query="What is the capital of France?",
    custom_llm_provider="milvus",
    litellm_embedding_model="azure/text-embedding-3-large",
    litellm_embedding_config={
        "api_base": "your-embedding-endpoint",
        "api_key": "your-embedding-api-key",
        "api_version": "2025-09-01"
    },
    milvus_text_field="book_intro",  # Field name that contains text content
    api_key=os.getenv("MILVUS_API_KEY"),
)

print(response)

Async Search

from litellm import vector_stores

response = await vector_stores.asearch(
    vector_store_id="my-collection-name",
    query="What is the capital of France?",
    custom_llm_provider="milvus",
    litellm_embedding_model="azure/text-embedding-3-large",
    litellm_embedding_config={
        "api_base": "your-embedding-endpoint",
        "api_key": "your-embedding-api-key",
        "api_version": "2025-09-01"
    },
    milvus_text_field="book_intro",
    api_key=os.getenv("MILVUS_API_KEY"),
)

print(response)

Advanced Options

from litellm import vector_stores

response = vector_stores.search(
    vector_store_id="my-collection-name",
    query="What is the capital of France?",
    custom_llm_provider="milvus",
    litellm_embedding_model="azure/text-embedding-3-large",
    litellm_embedding_config={
        "api_base": "your-embedding-endpoint",
        "api_key": "your-embedding-api-key",
    },
    milvus_text_field="book_intro",
    api_key=os.getenv("MILVUS_API_KEY"),
    # Milvus-specific parameters
    limit=10,  # Number of results to return
    offset=0,  # Pagination offset
    dbName="default",  # Database name
    annsField="book_intro_vector",  # Vector field name
    outputFields=["id", "book_intro", "title"],  # Fields to return
    filter='book_id > 0',  # Metadata filter expression
    searchParams={"metric_type": "L2", "params": {"nprobe": 10}},  # Search parameters
)

print(response)

PROXY

Setup Config

Add this to your config.yaml:

vector_store_registry:
  - vector_store_name: "milvus-knowledgebase"
    litellm_params:
        vector_store_id: "my-collection-name"
        custom_llm_provider: "milvus"
        api_key: os.environ/MILVUS_API_KEY
        api_base: https://your-milvus-instance.milvus.io
        litellm_embedding_model: "azure/text-embedding-3-large"
        litellm_embedding_config:
            api_base: https://your-endpoint.cognitiveservices.azure.com/
            api_key: os.environ/AZURE_API_KEY
            api_version: "2025-09-01"
        milvus_text_field: "book_intro"
        # Optional Milvus parameters
        annsField: "book_intro_vector"
        limit: 10

Start Proxy

litellm --config /path/to/config.yaml

Search via API

curl -X POST 'http://0.0.0.0:4000/v1/vector_stores/my-collection-name/search' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-d '{
  "query": "What is the capital of France?"
}'

Required Parameters

Parameter	Type	Description
vector_store_id	string	Your Milvus collection name
custom_llm_provider	string	Set to "milvus"
litellm_embedding_model	string	Model to generate query embeddings (e.g., "azure/text-embedding-3-large")
litellm_embedding_config	dict	Config for the embedding model (api_base, api_key, api_version)
milvus_text_field	string	Field name in your collection that contains text content
api_key	string	Your Milvus API key (or set MILVUS_API_KEY env var)
api_base	string	Your Milvus API base URL (or set MILVUS_API_BASE env var)

Optional Parameters

Parameter	Type	Description
dbName	string	Database name (default: "default")
annsField	string	Vector field name to search (default: "book_intro_vector")
limit	integer	Maximum number of results to return
offset	integer	Pagination offset
filter	string	Filter expression for metadata filtering
groupingField	string	Field to group results by
outputFields	list	List of fields to return in results
searchParams	dict	Search parameters like metric type and search parameters
partitionNames	list	List of partition names to search
consistencyLevel	string	Consistency level for the search

Supported Features

Feature	Status	Notes
Logging	✅ Supported	Full logging support available
Guardrails	❌ Not Yet Supported	Guardrails are not currently supported for vector stores
Cost Tracking	✅ Supported	Cost is $0 for Milvus searches
Unified API	✅ Supported	Call via OpenAI compatible /v1/vector_stores/search endpoint
Passthrough	✅ Supported	Use native Milvus API format

Response Format

The response follows the standard LiteLLM vector store format:

{
  "object": "vector_store.search_results.page",
  "search_query": "What is the capital of France?",
  "data": [
    {
      "score": 0.95,
      "content": [
        {
          "text": "Paris is the capital of France...",
          "type": "text"
        }
      ],
      "file_id": null,
      "filename": null,
      "attributes": {
        "id": "123",
        "title": "France Geography"
      }
    }
  ]
}

Passthrough API (Native Milvus Format)

Use this to allow developers to create and search vector stores using the native Milvus API format, without giving them the Milvus credentials.

This is for the proxy only.

Admin Flow

1. Add the vector store to LiteLLM

model_list:  
  - model_name: embedding-model
    litellm_params:
      model: azure/text-embedding-3-large
      api_base: https://your-endpoint.cognitiveservices.azure.com/
      api_key: os.environ/AZURE_API_KEY
      api_version: "2025-09-01"

vector_store_registry:
  - vector_store_name: "milvus-store"
    litellm_params:
      vector_store_id: "can-be-anything" # vector store id can be anything for the purpose of passthrough api
      custom_llm_provider: "milvus"
      api_key: os.environ/MILVUS_API_KEY
      api_base: https://your-milvus-instance.milvus.io

general_settings:
    database_url: "postgresql://user:password@host:port/database"
    master_key: "sk-1234"

Add your vector store credentials to LiteLLM.

2. Start the proxy

litellm --config /path/to/config.yaml

# RUNNING on http://0.0.0.0:4000

3. Create a virtual index

curl -L -X POST 'http://0.0.0.0:4000/v1/indexes' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-d '{ 
    "index_name": "dall-e-6",
    "litellm_params": {
        "vector_store_index": "real-collection-name",
        "vector_store_name": "milvus-store"
    }
}'

This is a virtual index, which the developer can use to create and search vector stores.

4. Create a key with the vector store permissions

curl -L -X POST 'http://0.0.0.0:4000/key/generate' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-d '{
    "allowed_vector_store_indexes": [{"index_name": "dall-e-6", "index_permissions": ["write", "read"]}],
    "models": ["embedding-model"]
}'

Give the key access to the virtual index and the embedding model.

Expected response

{
    "key": "sk-my-virtual-key"
}

Developer Flow

1. Create a collection with schema

Note: Use the /milvus endpoint for the passthrough api that uses the milvus provider in your config.

from milvus_rest_client import MilvusRESTClient, DataType
import random
import time

# Configuration
uri = "http://0.0.0.0:4000/milvus"  # IMPORTANT: Use the '/milvus' endpoint for passthrough
token = "sk-my-virtual-key"
collection_name = "dall-e-6"  # Virtual index name

# Initialize client
milvus_client = MilvusRESTClient(uri=uri, token=token)
print(f"Connected to DB: {uri} successfully")

# Check if the collection exists and drop if it does
check_collection = milvus_client.has_collection(collection_name)
if check_collection:
    milvus_client.drop_collection(collection_name)
    print(f"Dropped the existing collection {collection_name} successfully")

# Define schema
dim = 64  # Vector dimension

print("Start to create the collection schema")
schema = milvus_client.create_schema()
schema.add_field(
    "book_id", DataType.INT64, is_primary=True, description="customized primary id"
)
schema.add_field("word_count", DataType.INT64, description="word count")
schema.add_field(
    "book_intro", DataType.FLOAT_VECTOR, dim=dim, description="book introduction"
)

# Prepare index parameters
print("Start to prepare index parameters with default AUTOINDEX")
index_params = milvus_client.prepare_index_params()
index_params.add_index("book_intro", metric_type="L2")

# Create collection
print(f"Start to create example collection: {collection_name}")
milvus_client.create_collection(
    collection_name, schema=schema, index_params=index_params
)
collection_property = milvus_client.describe_collection(collection_name)
print("Collection details: %s" % collection_property)

2. Insert data into the collection

# Insert data with customized ids
nb = 1000
insert_rounds = 2
start = 0  # first primary key id
total_rt = 0  # total response time for insert

print(
    f"Start to insert {nb*insert_rounds} entities into example collection: {collection_name}"
)
for i in range(insert_rounds):
    vector = [random.random() for _ in range(dim)]
    rows = [
        {"book_id": i, "word_count": random.randint(1, 100), "book_intro": vector}
        for i in range(start, start + nb)
    ]
    t0 = time.time()
    milvus_client.insert(collection_name, rows)
    ins_rt = time.time() - t0
    start += nb
    total_rt += ins_rt
print(f"Insert completed in {round(total_rt, 4)} seconds")

# Flush the collection
print("Start to flush")
start_flush = time.time()
milvus_client.flush(collection_name)
end_flush = time.time()
print(f"Flush completed in {round(end_flush - start_flush, 4)} seconds")

3. Search the collection

# Search configuration
nq = 3  # Number of query vectors
search_params = {"metric_type": "L2", "params": {"level": 2}}
limit = 2  # Number of results to return

# Perform searches
for i in range(5):
    search_vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
    t0 = time.time()
    results = milvus_client.search(
        collection_name,
        data=search_vectors,
        limit=limit,
        search_params=search_params,
        anns_field="book_intro",
    )
    t1 = time.time()
    print(f"Search {i} results: {results}")
    print(f"Search {i} latency: {round(t1-t0, 4)} seconds")

Complete Example

Here's a full working example:

from milvus_rest_client import MilvusRESTClient, DataType
import random
import time

# ----------------------------
# 🔐 CONFIGURATION
# ----------------------------
uri = "http://0.0.0.0:4000/milvus"  # IMPORTANT: Use the '/milvus' endpoint
token = "sk-my-virtual-key"
collection_name = "dall-e-6"  # Your virtual index name

# ----------------------------
# 📋 STEP 1 — Initialize Client
# ----------------------------
milvus_client = MilvusRESTClient(uri=uri, token=token)
print(f"✅ Connected to DB: {uri} successfully")

# ----------------------------
# 🗑️  STEP 2 — Drop Existing Collection (if needed)
# ----------------------------
check_collection = milvus_client.has_collection(collection_name)
if check_collection:
    milvus_client.drop_collection(collection_name)
    print(f"🗑️  Dropped the existing collection {collection_name} successfully")

# ----------------------------
# 📐 STEP 3 — Create Collection Schema
# ----------------------------
dim = 64  # Vector dimension

print("📐 Creating the collection schema")
schema = milvus_client.create_schema()
schema.add_field(
    "book_id", DataType.INT64, is_primary=True, description="customized primary id"
)
schema.add_field("word_count", DataType.INT64, description="word count")
schema.add_field(
    "book_intro", DataType.FLOAT_VECTOR, dim=dim, description="book introduction"
)

# ----------------------------
# 🔍 STEP 4 — Create Index
# ----------------------------
print("🔍 Preparing index parameters with default AUTOINDEX")
index_params = milvus_client.prepare_index_params()
index_params.add_index("book_intro", metric_type="L2")

# ----------------------------
# 🏗️  STEP 5 — Create Collection
# ----------------------------
print(f"🏗️  Creating collection: {collection_name}")
milvus_client.create_collection(
    collection_name, schema=schema, index_params=index_params
)
collection_property = milvus_client.describe_collection(collection_name)
print(f"✅ Collection created: {collection_property}")

# ----------------------------
# 📤 STEP 6 — Insert Data
# ----------------------------
nb = 1000
insert_rounds = 2
start = 0
total_rt = 0

print(f"📤 Inserting {nb*insert_rounds} entities into collection")
for i in range(insert_rounds):
    vector = [random.random() for _ in range(dim)]
    rows = [
        {"book_id": i, "word_count": random.randint(1, 100), "book_intro": vector}
        for i in range(start, start + nb)
    ]
    t0 = time.time()
    milvus_client.insert(collection_name, rows)
    ins_rt = time.time() - t0
    start += nb
    total_rt += ins_rt
print(f"✅ Insert completed in {round(total_rt, 4)} seconds")

# ----------------------------
# 💾 STEP 7 — Flush Collection
# ----------------------------
print("💾 Flushing collection")
start_flush = time.time()
milvus_client.flush(collection_name)
end_flush = time.time()
print(f"✅ Flush completed in {round(end_flush - start_flush, 4)} seconds")

# ----------------------------
# 🔍 STEP 8 — Search
# ----------------------------
nq = 3
search_params = {"metric_type": "L2", "params": {"level": 2}}
limit = 2

print(f"🔍 Performing {5} search operations")
for i in range(5):
    search_vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
    t0 = time.time()
    results = milvus_client.search(
        collection_name,
        data=search_vectors,
        limit=limit,
        search_params=search_params,
        anns_field="book_intro",
    )
    t1 = time.time()
    print(f"✅ Search {i} results: {results}")
    print(f"   Search {i} latency: {round(t1-t0, 4)} seconds")

How It Works

When you search:

1. LiteLLM converts your query to a vector using the embedding model you specified
2. It sends the vector to your Milvus instance via the /v2/vectordb/entities/search endpoint
3. Milvus finds the most similar documents in your collection using vector similarity search
4. Results come back with distance scores

The embedding model can be any model supported by LiteLLM - Azure OpenAI, OpenAI, Bedrock, etc.