Milvus Vector Store¶
In this notebook we are going to show a quick demo of using the MilvusVectorStore.
If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.
%pip install llama-index-vector-stores-milvus
%pip install llama-index
import logging
import sys
# Uncomment to see debug logs
# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Document
from llama_index.vector_stores.milvus import MilvusVectorStore
import textwrap
Setup OpenAI¶
Lets first begin by adding the openai api key. This will allow us to access openai for embeddings and to use chatgpt.
import openai
openai.api_key = "sk-***********"
Download Data
! mkdir -p 'data/paul_graham/'
! wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'
Generate our data¶
With our LLM set, lets start using the Milvus Index. As a first example, lets generate a document from the file found in the data/paul_graham/
folder. In this folder there is a single essay from Paul Graham titled What I Worked On
. To generate the documents we will use the SimpleDirectoryReader.
# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
print("Document ID:", documents[0].doc_id)
Document ID: 11c3a6fe-799e-4e40-8122-2339936c2722
Create an index across the data¶
Now that we have a document, we can can create an index and insert the document. For the index we will use a GPTMilvusIndex. GPTMilvusIndex takes in a few arguments:
uri (str, optional)
: The URI to connect to, comes in the form of "https://address:port" if using Milvus or Zilliz Cloud service, or "path/to/local/milvus.db" if using a lite local Milvus. Defaults to "./milvus_llamaindex.db".token (str, optional)
: The token for log in. Empty if not using rbac, if using rbac it will most likely be "username:password". Defaults to "".collection_name (str, optional)
: The name of the collection where data will be stored. Defaults to "llamalection".dim (int, optional)
: The dimension of the embeddings. If it is not provided, collection creation will be done on first insert. Defaults to None.embedding_field (str, optional)
: The name of the embedding field for the collection, defaults to DEFAULT_EMBEDDING_KEY.doc_id_field (str, optional)
: The name of the doc_id field for the collection, defaults to DEFAULT_DOC_ID_KEY.similarity_metric (str, optional)
: The similarity metric to use, currently supports IP and L2. Defaults to "IP".consistency_level (str, optional)
: Which consistency level to use for a newly created collection. Defaults to "Strong".overwrite (bool, optional)
: Whether to overwrite existing collection with same name. Defaults to False.text_key (str, optional)
: What key text is stored in in the passed collection. Used when bringing your own collection. Defaults to None.index_config (dict, optional)
: The configuration used for building the Milvus index. Defaults to None.search_config (dict, optional)
: The configuration used for searching the Milvus index. Note that this must be compatible with the index type specified by index_config. Defaults to None.
Please note that Milvus Lite requires
pymilvus>=2.4.2
.
# Create an index over the documnts
from llama_index.core import StorageContext
vector_store = MilvusVectorStore(
uri="./milvus_demo.db", dim=1536, overwrite=True
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context
)
Query the data¶
Now that we have our document stored in the index, we can ask questions against the index. The index will use the data stored in itself as the knowledge base for chatgpt.
query_engine = index.as_query_engine()
response = query_engine.query("What did the author learn?")
print(textwrap.fill(str(response), 100))
The author learned about programming on early computers like the IBM 1401 using Fortran, the limitations of early computing technology, the transition to microcomputers, and the excitement of having a personal computer like the TRS-80. Additionally, the author explored different academic paths, initially planning to study philosophy but eventually switching to AI due to a lack of interest in philosophy courses. Later on, the author pursued art education, attending RISD and the Accademia di Belli Arti in Florence, where they encountered a different approach to teaching art.
response = query_engine.query("What was a hard moment for the author?")
print(textwrap.fill(str(response), 100))
Dealing with the stress and challenges related to managing Hacker News was a difficult moment for the author.
This next test shows that overwriting removes the previous data.
vector_store = MilvusVectorStore(
uri="./milvus_demo.db", dim=1536, overwrite=True
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
[Document(text="The number that is being searched for is ten.")],
storage_context,
)
query_engine = index.as_query_engine()
res = query_engine.query("Who is the author?")
print("Res:", res)
Res: The author is the individual who created the content or work in question.
The next test shows adding additional data to an already existing index.
del index, vector_store, storage_context, query_engine
vector_store = MilvusVectorStore(uri="./milvus_demo.db", overwrite=False)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context
)
query_engine = index.as_query_engine()
res = query_engine.query("What is the number?")
print("Res:", res)
Res: The number is ten.
res = query_engine.query("Who is the author?")
print("Res:", res)
Res: Paul Graham