Vector Stores

Lotus supports multiple vector store backends for efficient semantic indexing and search. This document describes how to use and configure the available vector stores, including Qdrant, Faiss, and Weaviate.

Supported Vector Stores

QdrantVS
FaissVS
WeaviateVS

QdrantVS

Installation

Install the Qdrant client and Lotus with Qdrant support:

pip install qdrant-client lotus[qdrant]

Running Qdrant

You can run Qdrant locally using Docker:

docker run -p 6333:6333 -p 6334:6334 \
    -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \
    qdrant/qdrant

Example Usage

import pandas as pd
from qdrant_client import QdrantClient
import lotus
from lotus.models import LiteLLMRM  # or SentenceTransformersRM
from lotus.vector_store import QdrantVS

# Start Qdrant server before running this code
client = QdrantClient(url="http://localhost:6333")
rm = LiteLLMRM(model="text-embedding-3-small")
vs = QdrantVS(client)
lotus.settings.configure(rm=rm, vs=vs)

data = {"Course Name": ["Machine Learning 101", "Introduction to Cooking"]}
df = pd.DataFrame(data)
df = df.sem_index("Course Name", "my_qdrant_index")
result = df.sem_search("Course Name", "Find the course about machine learning", K=1)
print(result)

FaissVS

Installation

pip install faiss-cpu lotus

Example Usage

import pandas as pd
import lotus
from lotus.models import LiteLLMRM
from lotus.vector_store import FaissVS

rm = LiteLLMRM(model="text-embedding-3-small")
vs = FaissVS()
lotus.settings.configure(rm=rm, vs=vs)

data = {"Course Name": ["Machine Learning 101", "Introduction to Cooking"]}
df = pd.DataFrame(data)
df = df.sem_index("Course Name", "my_faiss_index")
result = df.sem_search("Course Name", "Find the course about machine learning", K=1)
print(result)

WeaviateVS

Installation

pip install weaviate-client lotus[weaviate]

Running Weaviate

You can run Weaviate locally using Docker:

docker run -p 8080:8080 -p 50051:50051 cr.weaviate.io/semitechnologies/weaviate:1.29.1

Example Usage

import pandas as pd
import weaviate
import lotus
from lotus.models import LiteLLMRM
from lotus.vector_store import WeaviateVS

client = weaviate.Client("http://localhost:8080")
rm = LiteLLMRM(model="text-embedding-3-small")
vs = WeaviateVS(client)
lotus.settings.configure(rm=rm, vs=vs)

data = {"Course Name": ["Machine Learning 101", "Introduction to Cooking"]}
df = pd.DataFrame(data)
df = df.sem_index("Course Name", "my_weaviate_index")
result = df.sem_search("Course Name", "Find the course about machine learning", K=1)
print(result)