Open In Colab

DocArray InMemory Vector Store#

DocArrayInMemoryVectorStore is a document index provided by Docarray that stores documents in memory. It is a great starting point for small datasets, where you may not want to launch a database server.

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

%pip install llama-index-vector-stores-docarray
!pip install llama-index
import os
import sys
import logging
import textwrap

import warnings


# stop huggingface warnings
os.environ["TOKENIZERS_PARALLELISM"] = "false"

# Uncomment to see debug logs
# logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

from llama_index.core import (
from llama_index.vector_stores.docarray import DocArrayInMemoryVectorStore
from IPython.display import Markdown, display
import os

os.environ["OPENAI_API_KEY"] = "<your openai key>"

Download Data

!mkdir -p 'data/paul_graham/'
!wget '' -O 'data/paul_graham/paul_graham_essay.txt'
# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
    "Document ID:",
    "Document Hash:",
Document ID: 1c21062a-50a3-4133-a0b1-75f837a953e5 Document Hash: 77ae91ab542f3abb308c4d7c77c9bc4c9ad0ccd63144802b7cbe7e1bb3a4094e

Initialization and indexing#

from llama_index.core import StorageContext

vector_store = DocArrayInMemoryVectorStore()
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = GPTVectorStoreIndex.from_documents(
    documents, storage_context=storage_context


# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(textwrap.fill(str(response), 100))
Token indices sequence length is longer than the specified maximum sequence length for this model (1830 > 1024). Running this sequence through the model will result in indexing errors
 Growing up, the author wrote short stories, programmed on an IBM 1401, and nagged his father to buy
him a TRS-80 microcomputer. He wrote simple games, a program to predict how high his model rockets
would fly, and a word processor. He also studied philosophy in college, but switched to AI after
becoming bored with it. He then took art classes at Harvard and applied to art schools, eventually
attending RISD.
response = query_engine.query("What was a hard moment for the author?")
print(textwrap.fill(str(response), 100))
 A hard moment for the author was when he realized that the AI programs of the time were a hoax and
that there was an unbridgeable gap between what they could do and actually understanding natural
language. He had invested a lot of time and energy into learning about AI and was disappointed to
find out that it was not going to get him the results he had hoped for.

Querying with filters#

from llama_index.core.schema import TextNode

nodes = [
        text="The Shawshank Redemption",
            "author": "Stephen King",
            "theme": "Friendship",
        text="The Godfather",
            "director": "Francis Ford Coppola",
            "theme": "Mafia",
            "director": "Christopher Nolan",
from llama_index.core import StorageContext

vector_store = DocArrayInMemoryVectorStore()
storage_context = StorageContext.from_defaults(vector_store=vector_store)

index = GPTVectorStoreIndex(nodes, storage_context=storage_context)
from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters

filters = MetadataFilters(
    filters=[ExactMatchFilter(key="theme", value="Mafia")]

retriever = index.as_retriever(filters=filters)
retriever.retrieve("What is inception about?")
[NodeWithScore(node=Node(text='director: Francis Ford Coppola\ntheme: Mafia\n\nThe Godfather', doc_id='41c99963-b200-4ce6-a9c4-d06ffeabdbc5', embedding=None, doc_hash='b770e43e6a94854a22dc01421d3d9ef6a94931c2b8dbbadf4fdb6eb6fbe41010', extra_info=None, node_info=None, relationships={<DocumentRelationship.SOURCE: '1'>: 'None'}), score=0.7681788983417586)]