Use Milvus as a LangChain Vector Store

This notebook shows how to use functionality related to the Milvus as a LangChain vector store.

Setup

You'll need to install langchain-milvus and other necessary dependencies.

The latest version of pymilvus comes with a local vector database Milvus Lite, good for prototyping. If you have large scale of data such as more than a million docs, we recommend setting up a more performant Milvus server on docker or kubernetes.

Initialization

Compartmentalize the data with Milvus Collections

You can store different unrelated documents in different collections within same Milvus instance to maintain the context

Here's how you can create a new vector store collection from documents:

And here is how you retrieve that stored collection

Manage vector store

Once you have created your vector store, we can interact with it by adding and deleting different items.

Add items to vector store

We can add items to our vector store by using the add_documents function.

Delete items from vector store

Query vector store

Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.

Query directly

Similarity search

Performing a simple similarity search with filtering on metadata can be done as follows:

Similarity search with score

You can also search with score:

For a full list of all the search options available when using the Milvus vector store, you can visit the API reference.

Query by turning into retriever

You can also transform the vector store into a retriever for easier usage in your chains.

Usage for Retrieval-Augmented Generation

For guides on how to use this vector store for retrieval-augmented generation (RAG), see this RAG guide.

Per-User Retrieval

When building a retrieval app, you often have to build it with multiple users in mind. This means that you may be storing data not just for one user, but for many different users, and they should not be able to see eachother’s data.

Milvus recommends using partition_key to implement multi-tenancy, here is an example.

The feature of Partition key is now not available in Milvus Lite, if you want to use it, you need to start Milvus server from docker or kubernetes.

To conduct a search using the partition key, you should include either of the following in the boolean expression of the search request:

search_kwargs={"expr": '<partition_key> == "xxxx"'}

search_kwargs={"expr": '<partition_key> == in ["xxx", "xxx"]'}

Do replace <partition_key> with the name of the field that is designated as the partition key.

Milvus changes to a partition based on the specified partition key, filters entities according to the partition key, and searches among the filtered entities.

API reference

For detailed documentation, head to the API reference: https://reference.langchain.com/python/integrations/langchain_milvus/