MyScale Vector Store¶

In this notebook we are going to show a quick demo of using the MyScaleVectorStore.

If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

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%pip install llama-index-vector-stores-myscale

%pip install llama-index-vector-stores-myscale

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!pip install llama-index

!pip install llama-index

Creating a MyScale Client¶

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import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

import logging import sys logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

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from os import environ
import clickhouse_connect

environ["OPENAI_API_KEY"] = "sk-*"

# initialize client
client = clickhouse_connect.get_client(
    host="YOUR_CLUSTER_HOST",
    port=8443,
    username="YOUR_USERNAME",
    password="YOUR_CLUSTER_PASSWORD",
)

from os import environ import clickhouse_connect environ["OPENAI_API_KEY"] = "sk-*" # initialize client client = clickhouse_connect.get_client( host="YOUR_CLUSTER_HOST", port=8443, username="YOUR_USERNAME", password="YOUR_CLUSTER_PASSWORD", )

Load documents, build and store the VectorStoreIndex with MyScaleVectorStore¶

Here we will use a set of Paul Graham essays to provide the text to turn into embeddings, store in a MyScaleVectorStore and query to find context for our LLM QnA loop.

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from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.myscale import MyScaleVectorStore
from IPython.display import Markdown, display

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader from llama_index.vector_stores.myscale import MyScaleVectorStore from IPython.display import Markdown, display

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# load documents
documents = SimpleDirectoryReader("../data/paul_graham").load_data()
print("Document ID:", documents[0].doc_id)
print("Number of Documents: ", len(documents))

# load documents documents = SimpleDirectoryReader("../data/paul_graham").load_data() print("Document ID:", documents[0].doc_id) print("Number of Documents: ", len(documents))

Document ID: a5f2737c-ed18-4e5d-ab9a-75955edb816d
Number of Documents:  1

Download Data

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!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'

!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'

You can process your files individually using SimpleDirectoryReader:

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loader = SimpleDirectoryReader("./data/paul_graham/")
documents = loader.load_data()
for file in loader.input_files:
    print(file)
    # Here is where you would do any preprocessing

loader = SimpleDirectoryReader("./data/paul_graham/") documents = loader.load_data() for file in loader.input_files: print(file) # Here is where you would do any preprocessing

../data/paul_graham/paul_graham_essay.txt

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# initialize with metadata filter and store indexes
from llama_index.core import StorageContext

for document in documents:
    document.metadata = {"user_id": "123", "favorite_color": "blue"}
vector_store = MyScaleVectorStore(myscale_client=client)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

# initialize with metadata filter and store indexes from llama_index.core import StorageContext for document in documents: document.metadata = {"user_id": "123", "favorite_color": "blue"} vector_store = MyScaleVectorStore(myscale_client=client) storage_context = StorageContext.from_defaults(vector_store=vector_store) index = VectorStoreIndex.from_documents( documents, storage_context=storage_context )

Query Index¶

Now MyScale vector store supports filter search and hybrid search

You can learn more about query_engine and retriever.

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import textwrap

from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters

# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine(
    filters=MetadataFilters(
        filters=[
            ExactMatchFilter(key="user_id", value="123"),
        ]
    ),
    similarity_top_k=2,
    vector_store_query_mode="hybrid",
)
response = query_engine.query("What did the author learn?")
print(textwrap.fill(str(response), 100))

import textwrap from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters # set Logging to DEBUG for more detailed outputs query_engine = index.as_query_engine( filters=MetadataFilters( filters=[ ExactMatchFilter(key="user_id", value="123"), ] ), similarity_top_k=2, vector_store_query_mode="hybrid", ) response = query_engine.query("What did the author learn?") print(textwrap.fill(str(response), 100))

Clear All Indexes¶

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for document in documents:
    index.delete_ref_doc(document.doc_id)

for document in documents: index.delete_ref_doc(document.doc_id)