Vector Stores Supported for Semantic Search in Code-Graph-RAG: Qdrant and Milvus Explained

Code-Graph-RAG supports both Qdrant and Milvus (including Milvus Lite) as vector store backends for semantic search, configurable via the VECTOR_STORE_BACKEND environment variable.

Code-Graph-RAG is an open-source retrieval-augmented generation framework for codebases that relies on semantic search to retrieve relevant code snippets. The project implements a unified vector store abstraction in codebase_rag/vector_store.py that supports multiple high-performance vector databases for embedding storage and similarity search. Understanding the vector stores supported for semantic search in Code-Graph-RAG helps you select the optimal backend for your infrastructure requirements.

Supported Vector Store Backends

Qdrant

Qdrant is an open-source vector database that stores embeddings in collections and executes cosine similarity search across high-dimensional vectors. According to the source code in codebase_rag/vector_store.py, the library initializes Qdrant connections through the get_qdrant_client factory function (lines 50-68) when the backend is configured for Qdrant operation.

Milvus and Milvus Lite

Milvus provides a high-performance vector engine optimized for large-scale similarity search. The Code-Graph-RAG implementation specifically detects Milvus Lite's cosine-distance quirks to ensure accurate similarity calculations. The MilvusVectorStore implementation resides in codebase_rag/vector_store.py (lines 32-35) and handles connections to both full Milvus deployments and lightweight Milvus Lite instances.

How Backend Selection Works

The system determines which vector store to use through the _selected_backend function in codebase_rag/vector_store.py (lines 30-42). This function reads the VECTOR_STORE_BACKEND setting from the project configuration—defined in codebase_rag/config.py—to instantiate the appropriate client.

The codebase_rag/constants/providers.py file defines the VectorStoreBackend enum with QDRANT and MILVUS values, which the configuration system validates against during initialization.

If the VECTOR_STORE_BACKEND environment variable does not match either "qdrant" or "milvus", the system logs a warning and disables vector store operations to prevent runtime errors.

Configuring the Vector Store Backend

Configure your backend by setting the environment variable before importing the library:

import os
os.environ["VECTOR_STORE_BACKEND"] = "qdrant"   # or "milvus"

The codebase_rag/utils/dependencies.py module verifies that optional dependencies (pymilvus or qdrant-client) are installed before attempting to initialize the selected backend.

Performing Semantic Search Operations

Once configured, the unified API abstracts backend differences. Use search_embeddings to query vectors against your indexed codebase:

from codebase_rag.vector_store import search_embeddings

# query_vec is a list[float] embedding produced by the embedder

results = search_embeddings(query_vec, top_k=5, project="my_project")

# Returns: [(node_id, similarity_score), ...]

print(results)

To store embeddings, use the batch insertion function regardless of which backend is active:

from codebase_rag.vector_store import store_embedding_batch

points = [
    (123, [0.12, 0.45, ...], "my_project.module.Class.method"),
    (124, [0.33, 0.67, ...], "my_project.module.OtherClass.func"),
]

stored = store_embedding_batch(points)
print(f"Stored {stored} embeddings")

Summary

• Dual backend support: Code-Graph-RAG supports both Qdrant and Milvus (including Milvus Lite) for semantic search operations.
• Unified interface: The search_embeddings and store_embedding_batch functions in codebase_rag/vector_store.py provide backend-agnostic vector operations.
• Configuration-driven: Set VECTOR_STORE_BACKEND to "qdrant" or "milvus" in your environment to select the active implementation.
• Dependency validation: The system checks for pymilvus or qdrant-client availability in codebase_rag/utils/dependencies.py before initializing the vector store.
• Automatic fallback: The system disables vector operations with a warning if an unsupported backend is specified.

Frequently Asked Questions

What vector stores does Code-Graph-RAG support?

Code-Graph-RAG supports Qdrant and Milvus (including Milvus Lite) as its vector store backends for semantic search. The backend is selected via the VECTOR_STORE_BACKEND environment variable, and the implementation details are handled in codebase_rag/vector_store.py.

How do I switch between Qdrant and Milvus in Code-Graph-RAG?

Set the VECTOR_STORE_BACKEND environment variable to either "qdrant" or "milvus" before running your application. The _selected_backend function in codebase_rag/vector_store.py (lines 30-42) automatically instantiates the correct client based on this setting, using get_qdrant_client for Qdrant or MilvusVectorStore for Milvus.

Does Code-Graph-RAG handle Milvus Lite differently from standard Milvus?

Yes, the codebase specifically detects Milvus Lite's cosine-distance quirks in the MilvusVectorStore implementation. While the configuration uses the same "milvus" backend identifier, the library adjusts similarity calculations to account for Lite-specific behavior during vector comparison operations.

What happens if I specify an unsupported vector store backend?

If the VECTOR_STORE_BACKEND value does not match "qdrant" or "milvus", the system logs a warning and disables vector store operations entirely. This prevents runtime errors while alerting you to configuration issues, as implemented in the _selected_backend logic within codebase_rag/vector_store.py.

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