Top 5 Vector Databases to Use for RAG (Retrieval-Augmented Generation) in 2025

Retrieval-Augmented Generation (RAG) has become a crucial approach in building applications that combine the generative power of large language models (LLMs) with factual and domain-specific knowledge retrieval. At its core, RAG relies on vector databases to store and query embeddings, enabling it to retrieve contextually relevant data efficiently.

Why Does RAG Use Vector Databases?

Traditional databases are optimized for structured data or keyword-based searches. However, RAG operates on embeddings—dense numerical representations of data generated by models like OpenAI's embeddings or Sentence Transformers. These embeddings capture semantic meaning, enabling similarity searches through vector operations rather than exact matches.

Vector databases are designed for such operations, offering functionalities like:

• Efficient k-Nearest Neighbors (k-NN) search: To find the most similar vectors.
• Scalability: For handling millions or even billions of vectors.
• Integration with AI workflows: APIs and frameworks that align well with machine learning pipelines.

Here’s a breakdown of the top vector databases for RAG, along with simple integration examples.

1. Pinecone

Pinecone offers a fully managed, scalable, and high-performance vector database. Its simplicity, support for hybrid search, and tight integration with machine learning workflows make it a go-to option for RAG.

Features:

• Real-time updates with no downtime.
• High-dimensional vector search at scale.
• Integrations with Python, LangChain, and more.

Code Example:

from pinecone import init, Index

init(api_key="your-api-key", environment="us-west1-gcp")
index = Index("my-index")

vectors = [("id1", [0.1, 0.2, 0.3]), ("id2", [0.4, 0.5, 0.6])]
index.upsert(vectors)

query_result = index.query([0.1, 0.2, 0.3], top_k=2)
print(query_result)

2. Weaviate

Weaviate is an open-source vector database with strong support for metadata filtering and modular vector search. Its RESTful API makes it accessible for a variety of applications.

Features:

• Built-in support for multiple vectorization modules.
• Schema-based approach for structured and unstructured data.
• Handles both text and multi-modal data.

Code Example:

import weaviate

client = weaviate.Client("http://localhost:8080")

schema = {
    "classes": [{
        "class": "Document",
        "vectorizer": "text2vec-transformers"
    }]
}

client.schema.create(schema)
client.data_object.create({"content": "Hello world!"}, "Document")

near_text = {"concepts": ["Hello"]}
response = client.query.get("Document", ["content"]).with_near_text(near_text).do()
print(response)

3. Milvus

Milvus is a feature-rich open-source vector database designed for scalability and high-performance search. It supports billions of vectors, making it ideal for large-scale RAG systems.

Features:

• GPU acceleration for faster searches.
• Distributed architecture for horizontal scaling.
• Extensive API and SDK support.

Code Example:

from pymilvus import connections, Collection

connections.connect("default", host="localhost", port="19530")
collection = Collection(name="example_collection")

data = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]
ids = [1, 2]
collection.insert({"embeddings": data, "ids": ids})

results = collection.search(data=[0.1, 0.2, 0.3], anns_field="embeddings", param={"metric_type": "L2", "params": {"nprobe": 10}}, limit=2)
print(results)

4. Qdrant

Qdrant is an open-source, user-friendly vector search engine with a focus on ease of use and metadata-rich search capabilities. Its API-first design suits developers looking to quickly prototype and scale.

Features:

• Rich filtering with metadata.
• Horizontal scalability and durability.
• Support for large language model embeddings.

Code Example:

import qdrant_client

client = qdrant_client.QdrantClient(host="localhost", port=6333)
client.recreate_collection("my_collection", vector_size=3)

vectors = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]
client.upload_collection("my_collection", records=vectors)

query_vector = [0.1, 0.2, 0.3]
response = client.search("my_collection", query_vector, top=2)
print(response)

5. Chroma

Chroma is a lightweight vector database optimized for simplicity and ease of integration with Python-based AI tools. It’s particularly popular in LangChain-based workflows.

I typically use Chroma for prototyping RAG workflows.

Features:

• Minimalistic and Python-first.
• Ideal for small-to-medium scale projects.
• Tight integration with popular ML frameworks.

Code Example:

import chromadb

client = chromadb.Client()
collection = client.create_collection("my_collection")

collection.add(
    ids=["doc1", "doc2"],
    embeddings=[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]],
    metadatas=[{"category": "text"}, {"category": "image"}]
)

results = collection.query(query_embeddings=[[0.1, 0.2, 0.3]], n_results=1)
print(results)

Conclusion

Vector databases are indispensable for building Retrieval-Augmented Generation systems, empowering them to efficiently retrieve and manage the embeddings required for high-performance contextual responses. Whether you’re looking for scalability (Milvus), simplicity (Chroma), or metadata handling (Weaviate), there’s a vector database suited to your needs.

Evaluate your project requirements and choose the database that aligns with your scaling, budget, and development constraints.