RAG Sample - 4. Build Embeddings and Insert to DB

Building embeddings

I followed two sources for embeddings:

1. Building an Agentic RAG locally with Milvus, Ollama and LangGraph and the associated code langgraph-rag-agent-local.ipynb
2. The "Build RAG with Milvus | Milvus Documentation" tutorial

This is where I had my first surprise: Building embeddings is not necessarily a feature of the vector DB, and requires an embedding model. Fortunately, Ollama provides such models, and langchain has python bindings.

The first step is to create a OllamaEmbeddings object:

ollama_emb = OllamaEmbeddings(model="mxbai-embed-large:latest", base_url="http://10.10.0.10:11434")

I used mxbai because it's larger ™, but I've seen other tutorials that use nomic just as well. mxbai generates embeddings with a size of 1024 (and you need it for the schema). I did a test with llama3, and it generated a 4096 item-long embedding. I guess a TODO is to see if it's worth using larger models for this stuff..

Once the embedding object has been created, we can embed documents (note a document is a bunch of text -- chunk):

ollama_emb.embed_documents(chunks)

My code looks like this:

from langchain_community.embeddings import OllamaEmbeddings

ollama_emb = OllamaEmbeddings(model="mxbai-embed-large:latest", base_url="http://10.0.0.35:11434")


def emb_text(text):
    return (
        ollama_emb.embed_documents([text])[0]
    )

def emb_chunks(chunks):
    return (
        ollama_emb.embed_documents(chunks)
    )

if __name__ == "__main__":
    test_embedding = emb_text("This is a test, from other texts")
    embedding_dim = len(test_embedding)
    print(test_embedding)
    print(embedding_dim)

It has the following characteristics:

• emb_text generates embeddings for a single string
• emb_chunks generates embeddings for an array of strings, one for each string
• the embedding model is hardcoded, but it'll probably end up in a dotenv file
• The main tries to embed a simple string and prints the embeddings vector and its length. As noted above, mxbai generates 1024-wide vectors.

Insert into the database

My current code looks like this:

uri = "http://10.10.0.10:19350"
collection_name = "my_data"
client = MilvusClient(uri=uri)

data = generate_embeddings(doc)

client.insert(collection_name, data)

connections.connect(client=client)
utility.wait_for_index_building_complete(
  collection_name=collection_name,
  index_name="_embeddings",
)
print(client.describe_collection(collection_name))

It:

1. Builds a MilvusClient object
2. Generates the embeddings for the sample document
3. Inserts the data into the collection
4. Waits for the index to be built
5. Prints collection statistics

Stay tuned for more!