Dragonfly and Vector Store¶

In this notebook we are going to show a quick demo of using the Dragonfly with Vector Store.

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

In [ ]:

%pip install -U llama-index llama-index-vector-stores-redis llama-index-embeddings-cohere llama-index-embeddings-openai

%pip install -U llama-index llama-index-vector-stores-redis llama-index-embeddings-cohere llama-index-embeddings-openai

In [ ]:

import os
import getpass
import sys
import logging
import textwrap
import warnings

warnings.filterwarnings("ignore")

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

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.redis import RedisVectorStore

import os import getpass import sys import logging import textwrap import warnings warnings.filterwarnings("ignore") logging.basicConfig(stream=sys.stdout, level=logging.INFO) from llama_index.core import VectorStoreIndex, SimpleDirectoryReader from llama_index.vector_stores.redis import RedisVectorStore

Start Dragonfly¶

The easiest way to start Dragonfly is using the Dragonfly docker image or quickly signing up for a Dragonfly Cloud demo instance.

To follow every step of this tutorial, launch the image as follows:

docker run -d -p 6379:6379 --name dragonfly docker.dragonflydb.io/dragonflydb/dragonfly

Setup OpenAI¶

Lets first begin by adding the openai api key. This will allow us to access openai for embeddings and to use chatgpt.

In [ ]:

oai_api_key = getpass.getpass("OpenAI API Key:")
os.environ["OPENAI_API_KEY"] = oai_api_key

oai_api_key = getpass.getpass("OpenAI API Key:") os.environ["OPENAI_API_KEY"] = oai_api_key

Download Data

In [ ]:

!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'

--2025-06-30 14:41:20--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 75042 (73K) [text/plain]
Saving to: ‘data/paul_graham/paul_graham_essay.txt’

data/paul_graham/pa 100%[===================>]  73.28K  --.-KB/s    in 0.04s   

2025-06-30 14:41:20 (2.00 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]

Read in a dataset¶

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

In [ ]:

# load documents
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
print(
    "Document ID:",
    documents[0].id_,
    "Document Filename:",
    documents[0].metadata["file_name"],
)

# load documents documents = SimpleDirectoryReader("./data/paul_graham").load_data() print( "Document ID:", documents[0].id_, "Document Filename:", documents[0].metadata["file_name"], )

Document ID: a5cae17c-27eb-411e-8967-fb6ef98bcdcf Document Filename: paul_graham_essay.txt

Initialize the default vector store¶

Now we have our documents prepared, we can initialize the vector store with default settings. This will allow us to store our vectors in Dragonfly and create an index for real-time search.

In [ ]:

from llama_index.core import StorageContext
from redis import Redis

# create a client connection
redis_client = Redis.from_url("redis://localhost:6379")

# create the vector store wrapper
vector_store = RedisVectorStore(redis_client=redis_client, overwrite=True)

# load storage context
storage_context = StorageContext.from_defaults(vector_store=vector_store)

# build and load index from documents and storage context
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

from llama_index.core import StorageContext from redis import Redis # create a client connection redis_client = Redis.from_url("redis://localhost:6379") # create the vector store wrapper vector_store = RedisVectorStore(redis_client=redis_client, overwrite=True) # load storage context storage_context = StorageContext.from_defaults(vector_store=vector_store) # build and load index from documents and storage context index = VectorStoreIndex.from_documents( documents, storage_context=storage_context )

14:41:29 llama_index.vector_stores.redis.base INFO   Using default RedisVectorStore schema.
14:41:31 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
14:41:31 llama_index.vector_stores.redis.base INFO   Added 22 documents to index llama_index

Query the default vector store¶

Now that we have our data stored in the index, we can ask questions against the index.

The index will use the data as the knowledge base for an LLM. The default setting for as_query_engine() utilizes OpenAI embeddings and GPT as the language model. Therefore, an OpenAI key is required unless you opt for a customized or local language model.

Below we will test searches against out index and then full RAG with an LLM.

In [ ]:

query_engine = index.as_query_engine()
retriever = index.as_retriever()

query_engine = index.as_query_engine() retriever = index.as_retriever()

In [ ]:

result_nodes = retriever.retrieve("What did the author learn?")
for node in result_nodes:
    print(node)

result_nodes = retriever.retrieve("What did the author learn?") for node in result_nodes: print(node)

14:41:40 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
14:41:40 llama_index.vector_stores.redis.base INFO   Querying index llama_index with query *=>[KNN 2 @vector $vector AS vector_distance] RETURN 5 id doc_id text _node_content vector_distance SORTBY vector_distance ASC DIALECT 2 LIMIT 0 2
14:41:40 llama_index.vector_stores.redis.base INFO   Found 2 results for query with id ['llama_index/vector_f12d31cc-d154-4ae2-9511-81a1e0b2c185', 'llama_index/vector_a67c3af9-14cc-45fd-a2dd-142753a61d79']
Node ID: f12d31cc-d154-4ae2-9511-81a1e0b2c185
Text: What I Worked On  February 2021  Before college the two main
things I worked on, outside of school, were writing and programming. I
didn't write essays. I wrote what beginning writers were supposed to
write then, and probably still are: short stories. My stories were
awful. They had hardly any plot, just characters with strong feelings,
which I ...
Score:  0.819

Node ID: a67c3af9-14cc-45fd-a2dd-142753a61d79
Text: In the summer of 2016 we moved to England. We wanted our kids to
see what it was like living in another country, and since I was a
British citizen by birth, that seemed the obvious choice. We only
meant to stay for a year, but we liked it so much that we still live
there. So most of Bel was written in England.  In the fall of 2019,
Bel was final...
Score:  0.815

In [ ]:

response = query_engine.query("What did the author learn?")
print(textwrap.fill(str(response), 100))

response = query_engine.query("What did the author learn?") print(textwrap.fill(str(response), 100))

14:41:44 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
14:41:44 llama_index.vector_stores.redis.base INFO   Querying index llama_index with query *=>[KNN 2 @vector $vector AS vector_distance] RETURN 5 id doc_id text _node_content vector_distance SORTBY vector_distance ASC DIALECT 2 LIMIT 0 2
14:41:44 llama_index.vector_stores.redis.base INFO   Found 2 results for query with id ['llama_index/vector_f12d31cc-d154-4ae2-9511-81a1e0b2c185', 'llama_index/vector_a67c3af9-14cc-45fd-a2dd-142753a61d79']
14:41:45 httpx INFO   HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
The author learned that philosophy courses in college were boring to him, leading him to switch his
focus to studying AI.

In [ ]:

result_nodes = retriever.retrieve("What was a hard moment for the author?")
for node in result_nodes:
    print(node)

result_nodes = retriever.retrieve("What was a hard moment for the author?") for node in result_nodes: print(node)

14:41:47 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
14:41:47 llama_index.vector_stores.redis.base INFO   Querying index llama_index with query *=>[KNN 2 @vector $vector AS vector_distance] RETURN 5 id doc_id text _node_content vector_distance SORTBY vector_distance ASC DIALECT 2 LIMIT 0 2
14:41:47 llama_index.vector_stores.redis.base INFO   Found 2 results for query with id ['llama_index/vector_8c02f420-3cfc-4da6-859b-97469872ef46', 'llama_index/vector_f12d31cc-d154-4ae2-9511-81a1e0b2c185']
Node ID: 8c02f420-3cfc-4da6-859b-97469872ef46
Text: HN was no doubt good for YC, but it was also by far the biggest
source of stress for me. If all I'd had to do was select and help
founders, life would have been so easy. And that implies that HN was a
mistake. Surely the biggest source of stress in one's work should at
least be something close to the core of the work. Whereas I was like
someone ...
Score:  0.804

Node ID: f12d31cc-d154-4ae2-9511-81a1e0b2c185
Text: What I Worked On  February 2021  Before college the two main
things I worked on, outside of school, were writing and programming. I
didn't write essays. I wrote what beginning writers were supposed to
write then, and probably still are: short stories. My stories were
awful. They had hardly any plot, just characters with strong feelings,
which I ...
Score:  0.802

In [ ]:

response = query_engine.query("What was a hard moment for the author?")
print(textwrap.fill(str(response), 100))

response = query_engine.query("What was a hard moment for the author?") print(textwrap.fill(str(response), 100))

14:41:51 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
14:41:51 llama_index.vector_stores.redis.base INFO   Querying index llama_index with query *=>[KNN 2 @vector $vector AS vector_distance] RETURN 5 id doc_id text _node_content vector_distance SORTBY vector_distance ASC DIALECT 2 LIMIT 0 2
14:41:51 llama_index.vector_stores.redis.base INFO   Found 2 results for query with id ['llama_index/vector_8c02f420-3cfc-4da6-859b-97469872ef46', 'llama_index/vector_f12d31cc-d154-4ae2-9511-81a1e0b2c185']
14:41:52 httpx INFO   HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
Dealing with urgent problems related to Hacker News (HN) was a significant source of stress for the
author.

In [ ]:

index.vector_store.delete_index()

index.vector_store.delete_index()

14:41:55 llama_index.vector_stores.redis.base INFO   Deleting index llama_index

Use a custom index schema¶

In most use cases, you need the ability to customize the underling index configuration and specification. For example, this is handy in order to define specific metadata filters you wish to enable.

With Dragonfly, this is as simple as defining an index schema object (from file or dict) and passing it through to the vector store client wrapper.

For this example, we will:

1. switch the embedding model to Cohere
2. add an additional metadata field for the document updated_at timestamp
3. index the existing file_name metadata field

In [ ]:

from llama_index.core.settings import Settings
from llama_index.embeddings.cohere import CohereEmbedding

# set up Cohere Key
co_api_key = getpass.getpass("Cohere API Key:")

Settings.embed_model = CohereEmbedding(api_key=co_api_key)

from llama_index.core.settings import Settings from llama_index.embeddings.cohere import CohereEmbedding # set up Cohere Key co_api_key = getpass.getpass("Cohere API Key:") Settings.embed_model = CohereEmbedding(api_key=co_api_key)

In [ ]:

from redisvl.schema import IndexSchema


custom_schema = IndexSchema.from_dict(
    {
        # customize basic index specs
        "index": {
            "name": "paul_graham",
            "prefix": "essay",
            "key_separator": ":",
        },
        # customize fields that are indexed
        "fields": [
            # required fields for llamaindex
            {"type": "tag", "name": "id"},
            {"type": "tag", "name": "doc_id"},
            {"type": "text", "name": "text"},
            # custom metadata fields
            {"type": "numeric", "name": "updated_at"},
            {"type": "tag", "name": "file_name"},
            # custom vector field definition for cohere embeddings
            {
                "type": "vector",
                "name": "vector",
                "attrs": {
                    "dims": 1024,
                    "algorithm": "hnsw",
                    "distance_metric": "cosine",
                },
            },
        ],
    }
)

from redisvl.schema import IndexSchema custom_schema = IndexSchema.from_dict( { # customize basic index specs "index": { "name": "paul_graham", "prefix": "essay", "key_separator": ":", }, # customize fields that are indexed "fields": [ # required fields for llamaindex {"type": "tag", "name": "id"}, {"type": "tag", "name": "doc_id"}, {"type": "text", "name": "text"}, # custom metadata fields {"type": "numeric", "name": "updated_at"}, {"type": "tag", "name": "file_name"}, # custom vector field definition for cohere embeddings { "type": "vector", "name": "vector", "attrs": { "dims": 1024, "algorithm": "hnsw", "distance_metric": "cosine", }, }, ], } )

In [ ]:

custom_schema.index

custom_schema.index

Out[ ]:

IndexInfo(name='paul_graham', prefix='essay', key_separator=':', storage_type=<StorageType.HASH: 'hash'>)

In [ ]:

custom_schema.fields

custom_schema.fields

Out[ ]:

{'id': TagField(name='id', type=<FieldTypes.TAG: 'tag'>, path=None, attrs=TagFieldAttributes(sortable=False, separator=',', case_sensitive=False, withsuffixtrie=False)),
 'doc_id': TagField(name='doc_id', type=<FieldTypes.TAG: 'tag'>, path=None, attrs=TagFieldAttributes(sortable=False, separator=',', case_sensitive=False, withsuffixtrie=False)),
 'text': TextField(name='text', type=<FieldTypes.TEXT: 'text'>, path=None, attrs=TextFieldAttributes(sortable=False, weight=1, no_stem=False, withsuffixtrie=False, phonetic_matcher=None)),
 'updated_at': NumericField(name='updated_at', type=<FieldTypes.NUMERIC: 'numeric'>, path=None, attrs=NumericFieldAttributes(sortable=False)),
 'file_name': TagField(name='file_name', type=<FieldTypes.TAG: 'tag'>, path=None, attrs=TagFieldAttributes(sortable=False, separator=',', case_sensitive=False, withsuffixtrie=False)),
 'vector': HNSWVectorField(name='vector', type='vector', path=None, attrs=HNSWVectorFieldAttributes(dims=1024, algorithm=<VectorIndexAlgorithm.HNSW: 'HNSW'>, datatype=<VectorDataType.FLOAT32: 'FLOAT32'>, distance_metric=<VectorDistanceMetric.COSINE: 'COSINE'>, initial_cap=None, m=16, ef_construction=200, ef_runtime=10, epsilon=0.01))}

In [ ]:

from datetime import datetime


def date_to_timestamp(date_string: str) -> int:
    date_format: str = "%Y-%m-%d"
    return int(datetime.strptime(date_string, date_format).timestamp())


# iterate through documents and add new field
for document in documents:
    document.metadata["updated_at"] = date_to_timestamp(
        document.metadata["last_modified_date"]
    )

from datetime import datetime def date_to_timestamp(date_string: str) -> int: date_format: str = "%Y-%m-%d" return int(datetime.strptime(date_string, date_format).timestamp()) # iterate through documents and add new field for document in documents: document.metadata["updated_at"] = date_to_timestamp( document.metadata["last_modified_date"] )

In [ ]:

vector_store = RedisVectorStore(
    schema=custom_schema,  # provide customized schema
    redis_client=redis_client,
    overwrite=True,
)

storage_context = StorageContext.from_defaults(vector_store=vector_store)

# build and load index from documents and storage context
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

vector_store = RedisVectorStore( schema=custom_schema, # provide customized schema redis_client=redis_client, overwrite=True, ) storage_context = StorageContext.from_defaults(vector_store=vector_store) # build and load index from documents and storage context index = VectorStoreIndex.from_documents( documents, storage_context=storage_context )

14:42:26 httpx INFO   HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK"
14:42:26 httpx INFO   HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK"
14:42:27 httpx INFO   HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK"
14:42:27 llama_index.vector_stores.redis.base INFO   Added 22 documents to index paul_graham

Query the vector store and filter on metadata¶

Now that we have additional metadata indexed in Dragonfly, let's try some queries with filters.

In [ ]:

from llama_index.core.vector_stores import (
    MetadataFilters,
    MetadataFilter,
    ExactMatchFilter,
)

retriever = index.as_retriever(
    similarity_top_k=3,
    filters=MetadataFilters(
        filters=[
            ExactMatchFilter(key="file_name", value="paul_graham_essay.txt"),
            MetadataFilter(
                key="updated_at",
                value=date_to_timestamp("2023-01-01"),
                operator=">=",
            ),
            MetadataFilter(
                key="text",
                value="learn",
                operator="text_match",
            ),
        ],
        condition="and",
    ),
)

from llama_index.core.vector_stores import ( MetadataFilters, MetadataFilter, ExactMatchFilter, ) retriever = index.as_retriever( similarity_top_k=3, filters=MetadataFilters( filters=[ ExactMatchFilter(key="file_name", value="paul_graham_essay.txt"), MetadataFilter( key="updated_at", value=date_to_timestamp("2023-01-01"), operator=">=", ), MetadataFilter( key="text", value="learn", operator="text_match", ), ], condition="and", ), )

In [ ]:

result_nodes = retriever.retrieve("What did the author learn?")

for node in result_nodes:
    print(node)

result_nodes = retriever.retrieve("What did the author learn?") for node in result_nodes: print(node)

14:42:37 httpx INFO   HTTP Request: POST https://api.cohere.com/v2/embed "HTTP/1.1 200 OK"
14:42:37 llama_index.vector_stores.redis.base INFO   Querying index paul_graham with query ((@file_name:{paul_graham_essay\.txt} @updated_at:[1672524000 +inf]) @text:(learn))=>[KNN 3 @vector $vector AS vector_distance] RETURN 5 id doc_id text _node_content vector_distance SORTBY vector_distance ASC DIALECT 2 LIMIT 0 3
14:42:37 llama_index.vector_stores.redis.base INFO   Found 3 results for query with id ['essay:30148f62-13c6-4edb-b09f-1cf3054c5c98', 'essay:054f9488-83c7-4bf6-a408-9ef17eea0446', 'essay:608adb71-a995-489d-81dc-0deab7bbe656']
Node ID: 30148f62-13c6-4edb-b09f-1cf3054c5c98
Text: If he even knew about the strange classes I was taking, he never
said anything.  So now I was in a PhD program in computer science, yet
planning to be an artist, yet also genuinely in love with Lisp hacking
and working away at On Lisp. In other words, like many a grad student,
I was working energetically on multiple projects that were not my
the...
Score:  0.404

Node ID: 054f9488-83c7-4bf6-a408-9ef17eea0446
Text: I wanted to go back to RISD, but I was now broke and RISD was
very expensive, so I decided to get a job for a year and then return
to RISD the next fall. I got one at a company called Interleaf, which
made software for creating documents. You mean like Microsoft Word?
Exactly. That was how I learned that low end software tends to eat
high end so...
Score:  0.396

Node ID: 608adb71-a995-489d-81dc-0deab7bbe656
Text: All that seemed left for philosophy were edge cases that people
in other fields felt could safely be ignored.  I couldn't have put
this into words when I was 18. All I knew at the time was that I kept
taking philosophy courses and they kept being boring. So I decided to
switch to AI.  AI was in the air in the mid 1980s, but there were two
things...
Score:  0.394

Deleting documents or index completely¶

Sometimes it may be useful to delete documents or the entire index. This can be done using the delete and delete_index methods.

In [ ]:

document_id = documents[0].doc_id
document_id

document_id = documents[0].doc_id document_id

In [ ]:

print("Number of documents before deleting", redis_client.dbsize())
vector_store.delete(document_id)
print("Number of documents after deleting", redis_client.dbsize())

print("Number of documents before deleting", redis_client.dbsize()) vector_store.delete(document_id) print("Number of documents after deleting", redis_client.dbsize())

However, the index still exists (with no associated documents).

In [ ]:

vector_store.index_exists()

vector_store.index_exists()

In [ ]:

# now lets delete the index entirely
# this will delete all the documents and the index
vector_store.delete_index()

# now lets delete the index entirely # this will delete all the documents and the index vector_store.delete_index()

In [ ]:

print("Number of documents after deleting", redis_client.dbsize())

print("Number of documents after deleting", redis_client.dbsize())