Comparing Open Source Vector Databases for RAG Pipelines

Retrieval-Augmented Generation (RAG) pipelines have emerged as a transformative approach to enhancing the accuracy and contextual relevance of large language models (LLMs). At the heart of these pipelines lie vector databases, which enable the efficient storage, retrieval, and comparison of high-dimensional vector embeddings derived from text, images, and other data modalities. As we step into 2025, the demand for robust, scalable, and open-source vector databases has surged, driven by the need to build cost-effective and customizable RAG systems.

This comprehensive guide explores the latest open-source vector databases tailored for RAG pipelines in 2025. We will delve into their features, performance metrics, scalability, and integration capabilities, empowering you to make an informed decision for your AI-driven applications. Whether you are a data scientist, AI engineer, or tech enthusiast, this guide will provide the insights you need to optimize your RAG workflows.

The Role of Vector Databases in RAG Pipelines

Before diving into the comparison, it’s essential to understand the pivotal role vector databases play in RAG pipelines. RAG pipelines augment the capabilities of LLMs by retrieving relevant contextual information from a knowledge base before generating responses. This process involves:

1. Embedding Generation: Converting text or other data into dense vector representations using models like BERT, Sentence-BERT, or custom embedders.
2. Vector Storage: Storing these embeddings in a vector database optimized for fast similarity searches.
3. Retrieval: Querying the database to fetch the most semantically similar vectors to the user’s input.
4. Augmentation: Supplying the retrieved context to the LLM to generate accurate and contextually rich responses.

Embedding Generation

Embedding generation is the first step in the RAG pipeline, where text or other data is converted into dense vector representations. These embeddings capture the semantic meaning of the data, allowing for efficient similarity searches. For example, consider the following sentence:

"The quick brown fox jumps over the lazy dog."

Using a model like Sentence-BERT, this sentence can be converted into a 384-dimensional vector. Each dimension represents a different aspect of the sentence's meaning, such as the presence of animals, the action of jumping, and the context of the sentence.

Example: Generating Embeddings with Sentence-BERT

Let's walk through an example of generating embeddings using the sentence-transformers library. In this example, we'll use the all-MiniLM-L6-v2 model to generate embeddings for a set of sentences.

1. Install the required library:

pip install sentence-transformers

2. Generate embeddings:

from sentence_transformers import SentenceTransformer

# Load the model
model = SentenceTransformer('all-MiniLM-L6-v2')

# Define the sentences
sentences = [
    "The quick brown fox jumps over the lazy dog.",
    "A fast animal leaps over a sleeping canine.",
    "The fox is known for its speed and agility."
]

# Generate embeddings
embeddings = model.encode(sentences)

print(embeddings.shape)  # Output: (3, 384)

In this example, we first load the all-MiniLM-L6-v2 model using the SentenceTransformer class. We then define a list of sentences and generate embeddings for each sentence using the encode method. The encode method returns a NumPy array of shape (n, d), where n is the number of sentences and d is the dimensionality of the embeddings. In this case, the embeddings are 384-dimensional vectors.

Vector Storage

Once the embeddings are generated, they need to be stored in a vector database. Vector databases are optimized for storing and retrieving high-dimensional vectors efficiently. They use specialized data structures like inverted indices, tree-based structures, or graph-based structures to enable fast similarity searches.

Example: Storing Embeddings in Milvus

Let's walk through an example of storing embeddings in Milvus. In this example, we'll use the pymilvus library to connect to a Milvus instance and store the embeddings generated in the previous example.

1. Install the required library:

pip install pymilvus

2. Store embeddings in Milvus:

from pymilvus import connections, CollectionSchema, FieldSchema, DataType, Collection

# Connect to Milvus
connections.connect("default", host="localhost", port="19530")

# Define the schema
fields = [
    FieldSchema(name="id", dtype=DataType.INT64, is_primary=True),
    FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=384)
]
schema = CollectionSchema(fields, description="RAG embeddings")

# Create the collection
collection = Collection("rag_embeddings", schema)

# Insert the embeddings
data = [
    [0, 1, 2],
    embeddings.tolist()
]
collection.insert(data)

In this example, we first connect to a Milvus instance using the connections.connect method. We then define a schema for the collection using the CollectionSchema class. The schema specifies the fields in the collection, including the primary key and the vector field. We then create the collection using the Collection class and insert the embeddings using the insert method. The insert method takes a list of data, where each element in the list corresponds to a field in the schema.

Retrieval

Retrieval is the process of querying the vector database to fetch the most semantically similar vectors to the user’s input. This is typically done using a similarity metric like cosine similarity, Euclidean distance, or dot product. The similarity metric measures the distance between the query vector and the stored vectors, with smaller distances indicating higher similarity.

Example: Retrieving Embeddings from Milvus

Let's walk through an example of retrieving embeddings from Milvus. In this example, we'll use the pymilvus library to query the Milvus instance and retrieve the most similar vectors to a given query.

1. Query the Milvus instance:

from pymilvus import utility

# Define the query vector
query = "What is the capital of France?"
query_embedding = model.encode(query)

# Search for similar vectors
search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
results = collection.search(
    data=[query_embedding.tolist()],
    anns_field="embedding",
    param=search_params,
    limit=3
)

print(results)

In this example, we first define a query vector using the encode method of the SentenceTransformer class. We then search for similar vectors in the Milvus instance using the search method. The search method takes the query vector, the name of the vector field, the search parameters, and the limit on the number of results. The search parameters specify the distance metric and the number of probes to use for the search. The search method returns a list of results, where each result contains the ID of the vector and the distance to the query vector.

Augmentation

Augmentation is the final step in the RAG pipeline, where the retrieved context is supplied to the LLM to generate accurate and contextually rich responses. The LLM uses the retrieved context to generate a response that is not only accurate but also contextually relevant to the user’s input.

Example: Augmenting Responses with LangChain

Let's walk through an example of augmenting responses using LangChain. In this example, we'll use the langchain library to build a RAG pipeline that retrieves relevant context from a vector database and generates a response using an LLM.

1. Install the required libraries:

pip install langchain pymilvus sentence-transformers

2. Build the RAG pipeline:

from langchain.vectorstores import Milvus
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.llms import HuggingFaceHub
from langchain.chains import RetrievalQA

# Initialize the Milvus vector store
embedding = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
vectorstore = Milvus(
    embedding=embding,
    collection_name="rag_embeddings",
    connection_args={"host": "localhost", "port": "19530"}
)

# Initialize the LLM
llm = HuggingFaceHub(repo_id="google/flan-t5-large", model_kwargs={"temperature": 0.5, "max_length": 512})

# Build the RAG pipeline
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

# Generate a response
query = "What is the capital of France?"
response = qa_chain.run(query)
print(response)

In this example, we first initialize the Milvus vector store using the Milvus class. We then initialize the LLM using the HuggingFaceHub class. We then build the RAG pipeline using the RetrievalQA class. The RetrievalQA class takes the LLM, the chain type, and the retriever as input. The chain type specifies the type of chain to use, such as "stuff", "map_reduce", or "refine". The retriever is responsible for retrieving the relevant context from the vector database. We then generate a response to the query using the run method of the RetrievalQA class.

Challenges in RAG Pipelines

While RAG pipelines offer significant advantages over traditional LLMs, they also come with their own set of challenges. One of the most significant challenges is the semantic gap—where embeddings may align topically but miss exact relevance. This can impact retrieval accuracy, sometimes dropping below 60% in certain use cases. To address this challenge, researchers are exploring new techniques for generating more accurate embeddings and improving the retrieval process.

Another challenge is the scalability of RAG pipelines. As the size of the knowledge base grows, the time and computational resources required to generate and store embeddings also increase. This can make it difficult to scale RAG pipelines to large-scale applications. To address this challenge, researchers are exploring new techniques for compressing embeddings and optimizing the retrieval process.

Example: Addressing the Semantic Gap with Hybrid Search

One way to address the semantic gap is to use hybrid search, which combines vector similarity with keyword-based search. Hybrid search can improve retrieval accuracy by combining the strengths of both approaches. For example, a hybrid search can retrieve documents that are both semantically similar to the query and contain the exact keywords in the query.

Let's walk through an example of implementing hybrid search using Weaviate. In this example, we'll use the weaviate-client library to query the Weaviate instance and retrieve documents using hybrid search.

1. Install the required library:

pip install weaviate-client

2. Query the Weaviate instance:

import weaviate
from weaviate import Client

# Connect to Weaviate
client = weaviate.Client("http://localhost:8080")

# Define the query
query = "What is the capital of France?"
query_embedding = model.encode(query)

# Perform hybrid search
results = client.query.get("Document", ["text"]).with_near_text({"concepts": [query], "certainty": 0.7}).with_near_vector({"vector": query_embedding.tolist(), "certainty": 0.7}).do()

print(results)

In this example, we first connect to a Weaviate instance using the Client class. We then define a query and generate an embedding for the query using the encode method of the SentenceTransformer class. We then perform hybrid search using the with_near_text and with_near_vector methods. The with_near_text method retrieves documents that are semantically similar to the query, while the with_near_vector method retrieves documents that are similar to the query embedding. The certainty parameter specifies the minimum similarity score for the results. The do method executes the query and returns the results.

Top Open-Source Vector Databases for RAG in 2025

As of 2025, several open-source vector databases have risen to prominence, each offering unique strengths for RAG applications. Below, we compare the most widely adopted options:

1. Milvus

Overview: Developed by Zilliz, Milvus is a cloud-native vector database designed for large-scale similarity search and AI applications. It supports hybrid search (combining vector and scalar filtering) and offers robust scalability.

Key Features:

• High Performance: Optimized for low-latency searches even with billions of vectors. Milvus uses a combination of inverted indices and tree-based structures to enable fast similarity searches. For example, Milvus can retrieve the most similar vectors to a given query in milliseconds, even when the database contains billions of vectors.
• Scalability: Supports horizontal scaling with distributed architecture. Milvus can be deployed on a cluster of machines, allowing for linear scalability as the size of the dataset grows. For example, if you have a dataset of 1 billion vectors, you can deploy Milvus on a cluster of 10 machines, each storing 100 million vectors.
• Integration: Compatible with popular frameworks like LangChain, LlamaIndex, and Hugging Face. Milvus provides APIs and client libraries for seamless integration with these frameworks. For example, you can use the Milvus Python client to store and retrieve vectors in a LangChain RAG pipeline.
• Hybrid Search: Combines vector similarity with metadata filtering for precise retrieval. Milvus allows you to filter the results of a similarity search based on metadata, such as the date of a document or the author of a piece of text. For example, you can retrieve the most similar vectors to a given query that were generated from documents published in the last year.

Use Case: Ideal for enterprise-level RAG applications requiring high throughput and scalability, such as recommendation systems and semantic search engines. For example, an e-commerce company can use Milvus to build a recommendation system that retrieves the most relevant products to a user's search query based on the product descriptions and user behavior.

Limitations: Requires tuning for optimal performance, and setup can be complex for beginners. Milvus provides a range of configuration options for tuning the performance of the database, such as the size of the index and the number of shards. However, tuning these options can be complex and may require a deep understanding of the underlying data structures and algorithms.

2. Qdrant

Overview: Qdrant is a lightweight, fast, and user-friendly vector search engine written in Rust. It is designed for real-time similarity search and supports advanced features like payload indexing and dynamic schema.

Key Features:

• Speed: Optimized for low-latency searches with minimal computational overhead. Qdrant uses a combination of inverted indices and tree-based structures to enable fast similarity searches. For example, Qdrant can retrieve the most similar vectors to a given query in microseconds, making it ideal for real-time applications.
• Flexibility: Supports dynamic schema changes and customizable distance metrics. Qdrant allows you to add, remove, or modify the fields in the database schema at runtime. For example, you can add a new field to the schema to store the author of a document after the database has been created. Qdrant also supports a range of distance metrics, such as cosine similarity, Euclidean distance, and dot product, allowing you to choose the metric that best suits your application.
• API-First Design: Offers a RESTful API and client libraries for seamless integration. Qdrant provides a RESTful API for storing and retrieving vectors, as well as client libraries for Python, JavaScript, and Go. For example, you can use the Qdrant Python client to store and retrieve vectors in a LangChain RAG pipeline.
• Cloud and On-Premise: Available as a managed cloud service or self-hosted solution. Qdrant can be deployed on a cloud provider like AWS, Google Cloud, or Azure, or self-hosted on your own infrastructure. For example, you can deploy Qdrant on a cluster of machines in your data center to ensure low-latency access to the database.

Use Case: Perfect for startups and small-to-medium enterprises (SMEs) looking for a balance between performance and ease of use. It’s also well-suited for applications requiring real-time updates, such as chatbots and personalized search. For example, a chatbot can use Qdrant to retrieve the most relevant responses to a user's query based on the chat history and user behavior.

Limitations: May not scale as efficiently as Milvus for extremely large datasets. Qdrant is designed for low-latency searches and may not scale as efficiently as Milvus for datasets containing billions of vectors. However, Qdrant can be deployed on a cluster of machines to improve scalability.

3. Chroma

Overview: Chroma is an open-source embedding database designed specifically for AI applications, including RAG pipelines. It is lightweight, easy to deploy, and optimized for developer productivity.

Key Features:

• Simplicity: Minimal setup required, making it ideal for prototyping and small-scale applications. Chroma can be installed and configured in minutes, allowing you to quickly prototype a RAG pipeline. For example, you can use Chroma to build a simple RAG pipeline for a small-scale application like a personal assistant.
• LangChain Integration: Works seamlessly with LangChain, a popular RAG framework. Chroma provides a LangChain integration that allows you to store and retrieve vectors in a LangChain RAG pipeline. For example, you can use the Chroma integration to store the embeddings of a set of documents and retrieve the most relevant documents for a given query.
• In-Memory and Persistent Storage: Supports both in-memory operations and disk persistence. Chroma can store vectors in memory for fast access or persist them to disk for durability. For example, you can store the embeddings of a set of documents in memory for fast access during development and persist them to disk for durability in production.
• Filtering Capabilities: Allows metadata-based filtering to refine search results. Chroma allows you to filter the results of a similarity search based on metadata, such as the date of a document or the author of a piece of text. For example, you can retrieve the most similar vectors to a given query that were generated from documents published in the last year.

Use Case: Best suited for developers and researchers who need a quick and easy-to-use vector database for experimental or small-scale RAG projects. For example, a researcher can use Chroma to build a RAG pipeline for a small-scale experiment, such as evaluating the performance of different embedding models.

Limitations: Not designed for large-scale production environments with billions of vectors. Chroma is designed for small-scale applications and may not scale as efficiently as Milvus or Qdrant for datasets containing billions of vectors. However, Chroma can be deployed on a cluster of machines to improve scalability.

4. Weaviate

Overview: Weaviate is an open-source vector search engine that combines vector search with structured data storage. It supports hybrid search, graph traversal, and modular design for extensibility.

Key Features:

• Hybrid Search: Combines vector search with keyword-based BM25 for improved accuracy. Weaviate uses a combination of vector search and BM25 to retrieve the most relevant documents for a given query. For example, Weaviate can retrieve the most relevant documents for a query that contains both keywords and semantic information, such as "What is the capital of France and its history?"
• GraphQL API: Offers a flexible and intuitive query language for data retrieval. Weaviate provides a GraphQL API for querying the database, allowing you to retrieve vectors and metadata in a flexible and intuitive way. For example, you can use the GraphQL API to retrieve the most similar vectors to a given query and the metadata associated with those vectors, such as the title and author of a document.
• Modular Architecture: Supports custom vectorizers and pluggable modules for extensibility. Weaviate provides a modular architecture that allows you to customize the database to suit your application. For example, you can use a custom vectorizer to generate embeddings for a specific type of data, such as images or audio.
• Scalability: Designed for both small and large-scale deployments. Weaviate can be deployed on a single machine for small-scale applications or on a cluster of machines for large-scale applications. For example, you can deploy Weaviate on a cluster of machines to ensure low-latency access to the database for a large-scale application like a recommendation system.

Use Case: Ideal for applications requiring a blend of semantic and structured search, such as knowledge graphs, enterprise search, and complex RAG systems. For example, an enterprise search application can use Weaviate to retrieve the most relevant documents for a given query based on both the semantic information and the metadata associated with the documents.

Limitations: Requires familiarity with GraphQL for advanced queries, which may have a learning curve. Weaviate provides a GraphQL API for querying the database, which may have a learning curve for developers who are not familiar with GraphQL. However, Weaviate provides extensive documentation and examples to help developers get started.

5. FAISS (Facebook AI Similarity Search)

Overview: Developed by Meta, FAISS is a library for efficient similarity search and clustering of dense vectors. While not a full-fledged database, it is often integrated into RAG pipelines for its high-performance search capabilities.

Key Features:

• Speed: Optimized for fast similarity search using GPU acceleration. FAISS uses a combination of inverted indices and tree-based structures to enable fast similarity searches. For example, FAISS can retrieve the most similar vectors to a given query in microseconds, making it ideal for real-time applications.
• Memory Efficiency: Supports compressed indexing to reduce memory usage. FAISS provides a range of indexing options, such as flat indexing and IVF (Inverted File) indexing, that allow you to trade off between memory usage and search speed. For example, you can use IVF indexing to reduce the memory usage of the database while maintaining fast search speeds.
• Integration: Works well with Python-based AI stacks and can be combined with other databases for persistence. FAISS provides a Python API for storing and retrieving vectors, as well as client libraries for other languages like C++ and Java. For example, you can use the FAISS Python API to store and retrieve vectors in a LangChain RAG pipeline.

Use Case: Best for research and experimental projects where raw search speed is critical. Often used in conjunction with other databases like Chroma or Milvus for production RAG systems. For example, a researcher can use FAISS to build a RAG pipeline for a small-scale experiment, such as evaluating the performance of different embedding models.

Limitations: Lacks built-in persistence and advanced features like hybrid search or metadata filtering. FAISS is designed for in-memory operations and does not provide built-in persistence or advanced features like hybrid search or metadata filtering. However, FAISS can be combined with other databases like Chroma or Milvus to provide persistence and advanced features.

Comparative Analysis: Which Vector Database Should You Choose?

To help you select the best vector database for your RAG pipeline, we’ve summarized the key attributes of each option in the table below:

Integration with RAG Frameworks

A critical consideration when choosing a vector database is its compatibility with RAG frameworks like LangChain, LlamaIndex, and Haystack. These frameworks simplify the development of RAG pipelines by providing pre-built components for data ingestion, embedding generation, retrieval, and evaluation.

LangChain: The Gold Standard for RAG in 2025

LangChain remains the most popular open-source framework for building RAG systems in 2025. It offers seamless integration with all the vector databases discussed above, including:

• Milvus: Via the Milvus wrapper for vector storage and retrieval.
• Qdrant: Through the QdrantVectorStore class.
• Chroma: Using the Chroma integration for persistent storage.
• Weaviate: Supported via the Weaviate vector store.
• FAISS: Integrated through the FAISS wrapper for in-memory operations.

LangChain’s modular design allows developers to mix and match components, making it easier to experiment with different vector databases and retrieval strategies. Additionally, LangChain provides tools for evaluating RAG performance, ensuring that your pipeline meets accuracy and latency requirements.

Example: Building a RAG Pipeline with LangChain and Milvus

Let's walk through an example of building a RAG pipeline using LangChain and Milvus. In this example, we'll use the sentence-transformers library to generate embeddings and Milvus to store and retrieve the embeddings.

1. Install the required libraries:

pip install langchain milvus sentence-transformers

2. Generate embeddings:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-MiniLM-L6-v2')
documents = ["The capital of France is Paris.", "Paris is known for the Eiffel Tower."]
embeddings = model.encode(documents)

3. Store embeddings in Milvus:

from pymilvus import connections, CollectionSchema, FieldSchema, DataType, Collection

# Connect to Milvus
connections.connect("default", host="localhost", port="19530")

# Define the schema
fields = [
    FieldSchema(name="id", dtype=DataType.INT64, is_primary=True),
    FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=384)
]
schema = CollectionSchema(fields, description="RAG embeddings")

# Create the collection
collection = Collection("rag_embeddings", schema)

# Insert the embeddings
data = [
    [0, 1],
    embeddings.tolist()
]
collection.insert(data)

4. Retrieve embeddings using LangChain:

from langchain.vectorstores import Milvus
from langchain.embeddings import SentenceTransformerEmbeddings

# Initialize the Milvus vector store
embedding = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
vectorstore = Milvus(
    embedding=embedding,
    collection_name="rag_embeddings",
    connection_args={"host": "localhost", "port": "19530"}
)

# Retrieve the most similar documents to a query
query = "What is the capital of France?"
docs = vectorstore.similarity_search(query)
print(docs)

In this example, we first generate embeddings for a set of documents using the sentence-transformers library. We then store the embeddings in Milvus using the pymilvus library. Finally, we use LangChain to retrieve the most similar documents to a given query.

Performance Benchmarks and Evaluation

Selecting a vector database isn’t just about features—it’s also about performance. Key metrics to evaluate include:

1. Query Latency: The time taken to retrieve the nearest neighbors for a given query.
2. Throughput: The number of queries the database can handle per second.
3. Recall: The ability to retrieve all relevant vectors for a query.
4. Scalability: How well the database performs as the dataset grows.

Benchmark Results (2025)

Based on recent benchmarks and community feedback:

• Milvus excels in scalability and throughput, making it the top choice for large-scale deployments. For example, Milvus can handle millions of queries per second with low latency, making it ideal for enterprise-scale applications.
• Qdrant leads in low-latency searches, ideal for real-time applications. For example, Qdrant can retrieve the most similar vectors to a given query in microseconds, making it ideal for real-time applications like chatbots.
• Chroma offers the fastest setup and is perfect for prototyping. For example, Chroma can be installed and configured in minutes, allowing you to quickly prototype a RAG pipeline.
• Weaviate provides the best hybrid search capabilities, combining vector and keyword-based retrieval. For example, Weaviate can retrieve the most relevant documents for a query that contains both keywords and semantic information, such as "What is the capital of France and its history?"
• FAISS delivers the lowest query latency for in-memory operations but lacks persistence. For example, FAISS can retrieve the most similar vectors to a given query in microseconds, making it ideal for research and experimental projects.

For a detailed evaluation of your RAG pipeline, consider using tools like Deepchecks or Ragas, which provide metrics for accuracy, latency, and retrieval quality.

Example: Evaluating RAG Performance with Ragas

Let's walk through an example of evaluating the performance of a RAG pipeline using Ragas. In this example, we'll use the ragas library to evaluate the accuracy and latency of a RAG pipeline built with LangChain and Milvus.

1. Install the required libraries:

pip install ragas langchain milvus sentence-transformers

2. Build the RAG pipeline:

from langchain.vectorstores import Milvus
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.llms import HuggingFaceHub
from langchain.chains import RetrievalQA

# Initialize the Milvus vector store
embedding = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
vectorstore = Milvus(
    embedding=embedding,
    collection_name="rag_embeddings",
    connection_args={"host": "localhost", "port": "19530"}
)

# Initialize the LLM
llm = HuggingFaceHub(repo_id="google/flan-t5-large", model_kwargs={"temperature": 0.5, "max_length": 512})

# Build the RAG pipeline
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

3. Evaluate the RAG pipeline:

from ragas import evaluate
from ragas.metrics import (
    answer_relevancy,
    faithfulness,
    context_relevancy,
    context_recall,
    context_precision,
    answer_correctness,
    latency
)

# Define the evaluation dataset
dataset = [
    {"query": "What is the capital of France?", "answer": "The capital of France is Paris."},
    {"query": "What is Paris known for?", "answer": "Paris is known for the Eiffel Tower."}
]

# Evaluate the RAG pipeline
results = evaluate(
    dataset,
    metrics=[
        answer_relevancy,
        faithfulness,
        context_relevancy,
        context_recall,
        context_precision,
        answer_correctness,
        latency
    ],
    chain=qa_chain
)

print(results)

In this example, we first build a RAG pipeline using LangChain and Milvus. We then use the ragas library to evaluate the accuracy and latency of the RAG pipeline. The evaluate function takes a dataset of queries and answers, a list of metrics to evaluate, and the RAG pipeline to evaluate. The function returns a dictionary of results, including the accuracy and latency of the RAG pipeline.

Future Trends in Vector Databases for RAG

As we look beyond 2025, several trends are shaping the future of vector databases in RAG pipelines:

1. Embedding-Free RAG: Emerging architectures are exploring alternatives to vector embeddings, focusing on reasoning, structure, and interpretability to address semantic gaps in retrieval. For example, researchers are exploring the use of graph-based representations to capture the relationships between concepts in a knowledge base, allowing for more accurate and interpretable retrieval.
2. Multi-Modal Retrieval: Vector databases are evolving to support not just text but also images, audio, and video, enabling richer RAG applications. For example, a RAG pipeline can retrieve relevant images or videos in response to a user's query, providing a more immersive and engaging experience.
3. Federated Vector Databases: Decentralized and privacy-preserving vector databases are gaining traction for applications in healthcare and finance. For example, a federated vector database can store and retrieve vectors from multiple sources while preserving the privacy of the data, allowing for more secure and compliant applications.
4. Automated Optimization: AI-driven tuning of vector databases to optimize retrieval accuracy and performance dynamically. For example, a vector database can use reinforcement learning to optimize the indexing strategy based on the query patterns, improving the retrieval accuracy and performance over time.

---: Choosing the Right Vector Database for Your RAG Pipeline

Selecting the right vector database for your RAG pipeline depends on your specific use case, scalability needs, and performance requirements. Here’s a quick decision guide:

• For enterprise-scale applications: Choose Milvus for its scalability and robustness.
• For real-time, low-latency applications: Opt for Qdrant or FAISS.
• For prototyping and small projects: Chroma is the easiest to set up and use.
• For hybrid search and structured data: Weaviate offers the most flexibility.

Regardless of your choice, integrating your vector database with a RAG framework like LangChain will streamline development and ensure your pipeline is both efficient and effective.

Final Thoughts

The landscape of open-source vector databases for RAG pipelines is vibrant and rapidly evolving. By staying informed about the latest advancements and understanding the strengths and limitations of each option, you can build RAG systems that are not only powerful but also tailored to your unique requirements. As AI continues to advance, the role of vector databases in enabling accurate, context-aware responses will only grow, making them an indispensable tool for the future of AI-driven applications.