--- title: Dragonfly and Vector Store | Developer Documentation --- 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 🦙. ``` %pip install -U llama-index llama-index-vector-stores-redis llama-index-embeddings-cohere llama-index-embeddings-openai ``` ``` 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](https://www.dragonflydb.io/cloud) demo instance. To follow every step of this tutorial, launch the image as follows: Terminal window ``` 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. ``` oai_api_key = getpass.getpass("OpenAI API Key:") os.environ["OPENAI_API_KEY"] = oai_api_key ``` Download Data ``` !mkdir -p 'data/paul_graham/' !wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/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/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. ``` # 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. ``` 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. ``` query_engine = index.as_query_engine() retriever = index.as_retriever() ``` ``` 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 ``` ``` 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. ``` ``` 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 ``` ``` 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. ``` ``` 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](https://cohere.com/) 2. add an additional metadata field for the document `updated_at` timestamp 3. index the existing `file_name` metadata field ``` 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 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", }, }, ], } ) ``` ``` custom_schema.index ``` ``` IndexInfo(name='paul_graham', prefix='essay', key_separator=':', storage_type=) ``` ``` custom_schema.fields ``` ``` {'id': TagField(name='id', type=, path=None, attrs=TagFieldAttributes(sortable=False, separator=',', case_sensitive=False, withsuffixtrie=False)), 'doc_id': TagField(name='doc_id', type=, path=None, attrs=TagFieldAttributes(sortable=False, separator=',', case_sensitive=False, withsuffixtrie=False)), 'text': TextField(name='text', type=, path=None, attrs=TextFieldAttributes(sortable=False, weight=1, no_stem=False, withsuffixtrie=False, phonetic_matcher=None)), 'updated_at': NumericField(name='updated_at', type=, path=None, attrs=NumericFieldAttributes(sortable=False)), 'file_name': TagField(name='file_name', type=, 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=, datatype=, distance_metric=, initial_cap=None, m=16, ef_construction=200, ef_runtime=10, epsilon=0.01))} ``` ``` 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"] ) ``` ``` 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. ``` 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", ), ) ``` ``` 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. ``` document_id = documents[0].doc_id document_id ``` ``` 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). ``` vector_store.index_exists() ``` ``` # now lets delete the index entirely # this will delete all the documents and the index vector_store.delete_index() ``` ``` print("Number of documents after deleting", redis_client.dbsize()) ```