# Index and query vectors ```json metadata { "schema_version": 2, "title": "Index and query vectors", "description": "Learn how to index and query vector embeddings with Redis", "categories": ["docs","develop","stack","oss","rs","rc","oss","kubernetes","clients"], "topics": ["Redis Search","JSON","hash","vectors"], "relatedPages": ["/develop/clients/redis-py/queryjson","/develop/clients/redis-py/vecsets","/develop/ai/search-and-query"], "scope": "example", "tableOfContents": {"sections":[{"id":"initialize","title":"Initialize"},{"id":"create-the-index","title":"Create the index"},{"children":[{"id":"bulk-import-with-himport","title":"Bulk import with HIMPORT"}],"id":"add-data","title":"Add data"},{"id":"run-a-query","title":"Run a query"},{"id":"differences-with-json-documents","title":"Differences with JSON documents"},{"id":"learn-more","title":"Learn more"}]} , "codeExamples": [{"codetabsId":"home_query_vec-stepimport","description":"Foundational: Import required libraries for vector embeddings, Redis operations, and search functionality","difficulty":"beginner","id":"import","languages":[{"clientId":"redis-py","clientName":"redis-py","id":"Python","langId":"python","panelId":"panel_Python_home_query_vec-stepimport"}]},{"codetabsId":"home_query_vec-stepmodel","description":"Foundational: Initialize a SentenceTransformer model to generate vector embeddings from text","difficulty":"beginner","id":"model","languages":[{"clientId":"redis-py","clientName":"redis-py","id":"Python","langId":"python","panelId":"panel_Python_home_query_vec-stepmodel"}]},{"codetabsId":"home_query_vec-stepconnect","description":"Foundational: Connect to a Redis server and clean up existing vector indexes","difficulty":"beginner","id":"connect","languages":[{"clientId":"redis-py","clientName":"redis-py","id":"Python","langId":"python","panelId":"panel_Python_home_query_vec-stepconnect"}]},{"codetabsId":"home_query_vec-stepcreate_index","description":"Foundational: Create a vector search index for hash documents with HNSW algorithm and L2 distance metric","difficulty":"intermediate","id":"create_index","languages":[{"clientId":"redis-py","clientName":"redis-py","id":"Python","langId":"python","panelId":"panel_Python_home_query_vec-stepcreate_index"}]},{"codetabsId":"home_query_vec-stepadd_data","description":"Foundational: Store hash documents with vector embeddings generated from text content","difficulty":"beginner","id":"add_data","languages":[{"clientId":"redis-py","clientName":"redis-py","id":"Python","langId":"python","panelId":"panel_Python_home_query_vec-stepadd_data"}]},{"buildsUpon":["add_data"],"codetabsId":"home_query_vec-stephimport_data","description":"Bulk ingestion: Import hash documents with vector embeddings as compact hashes using HIMPORT PREPARE and a pipeline of HIMPORT SET calls","difficulty":"intermediate","id":"himport_data","languages":[{"clientId":"redis-py","clientName":"redis-py","id":"Python","langId":"python","panelId":"panel_Python_home_query_vec-stephimport_data"}]},{"codetabsId":"home_query_vec-stepquery","description":"Vector similarity search: Find semantically similar documents by comparing query embeddings with indexed vectors using L2 distance","difficulty":"intermediate","id":"query","languages":[{"clientId":"redis-py","clientName":"redis-py","id":"Python","langId":"python","panelId":"panel_Python_home_query_vec-stepquery"}]},{"codetabsId":"home_query_vec-stepjson_index","description":"Foundational: Create a vector search index for JSON documents with JSON paths and field aliases","difficulty":"intermediate","id":"json_index","languages":[{"clientId":"redis-py","clientName":"redis-py","id":"Python","langId":"python","panelId":"panel_Python_home_query_vec-stepjson_index"}]},{"codetabsId":"home_query_vec-stepjson_data","description":"Foundational: Store JSON documents with vector embeddings as lists (different from hash binary format)","difficulty":"beginner","id":"json_data","languages":[{"clientId":"redis-py","clientName":"redis-py","id":"Python","langId":"python","panelId":"panel_Python_home_query_vec-stepjson_data"}]},{"codetabsId":"home_query_vec-stepjson_query","description":"Vector similarity search: Query JSON documents using vector embeddings with field aliases for simplified syntax","difficulty":"intermediate","id":"json_query","languages":[{"clientId":"redis-py","clientName":"redis-py","id":"Python","langId":"python","panelId":"panel_Python_home_query_vec-stepjson_query"}]}] } ``` ## Code Examples Legend The code examples below show how to perform the same operations in different programming languages and client libraries: - **Redis CLI**: Command-line interface for Redis - **C# (Synchronous)**: StackExchange.Redis synchronous client - **C# (Asynchronous)**: StackExchange.Redis asynchronous client - **Go**: go-redis client - **Java (Synchronous - Jedis)**: Jedis synchronous client - **Java (Asynchronous - Lettuce)**: Lettuce asynchronous client - **Java (Reactive - Lettuce)**: Lettuce reactive/streaming client - **JavaScript (Node.js)**: node-redis client - **PHP**: Predis client - **Python**: redis-py client - **Rust (Synchronous)**: redis-rs synchronous client - **Rust (Asynchronous)**: redis-rs asynchronous client Each code example demonstrates the same basic operation across different languages. The specific syntax and patterns vary based on the language and client library, but the underlying Redis commands and behavior remain consistent. --- [Redis Search](https://redis.io/docs/latest/develop/ai/search-and-query) lets you index vector fields in [hash](https://redis.io/docs/latest/develop/data-types/hashes) or [JSON](https://redis.io/docs/latest/develop/data-types/json) objects (see the [Vectors](https://redis.io/docs/latest/develop/ai/search-and-query/vectors) reference page for more information). Among other things, vector fields can store *text embeddings*, which are AI-generated vector representations of the semantic information in pieces of text. The [vector distance](https://redis.io/docs/latest/develop/ai/search-and-query/vectors#distance-metrics) between two embeddings indicates how similar they are semantically. By comparing the similarity of an embedding generated from some query text with embeddings stored in hash or JSON fields, Redis can retrieve documents that closely match the query in terms of their meaning. The example below uses the [`sentence-transformers`](https://pypi.org/project/sentence-transformers/) library to generate vector embeddings to store and index with Redis Search. The code is first demonstrated for hash documents with a separate section to explain the [differences with JSON documents](#differences-with-json-documents). > [!NOTE] > From [v6.0.0](https://github.com/redis/redis-py/releases/tag/v6.0.0) onwards, > `redis-py` uses query dialect 2 by default. > Redis Search methods such as [`ft().search()`](https://redis.io/docs/latest/commands/ft.search) > will explicitly request this dialect, overriding the default set for the server. > See > [Query dialects](https://redis.io/docs/latest/develop/ai/search-and-query/advanced-concepts/dialects) > for more information. ## Initialize Install [`redis-py`](https://redis.io/docs/latest/develop/clients/redis-py) if you have not already done so. Also, install `sentence-transformers` with the following command: ```bash pip install sentence-transformers ``` In a new Python source file, start by importing the required classes: Foundational: Import required libraries for vector embeddings, Redis operations, and search functionality **Difficulty:** Beginner **Available in:** Python ##### Python ```python from sentence_transformers import SentenceTransformer from redis.commands.search.query import Query from redis.commands.search.field import TextField, TagField, VectorField from redis.commands.search.index_definition import IndexDefinition, IndexType from redis.commands.json.path import Path import numpy as np import redis ``` The first of these imports is the `SentenceTransformer` class, which generates an embedding from a section of text. Here, we create an instance of `SentenceTransformer` that uses the [`all-MiniLM-L6-v2`](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model for the embeddings. This model generates vectors with 384 dimensions, regardless of the length of the input text, but note that the input is truncated to 256 tokens (see [Word piece tokenization](https://huggingface.co/learn/nlp-course/en/chapter6/6) at the [Hugging Face](https://huggingface.co/) docs to learn more about the way tokens are related to the original text). Foundational: Initialize a SentenceTransformer model to generate vector embeddings from text **Difficulty:** Beginner **Available in:** Python ##### Python ```python model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2") ``` ## Create the index Connect to Redis and delete any index previously created with the name `vector_idx`. (The `dropindex()` call throws an exception if the index doesn't already exist, which is why you need the `try: except:` block.) Foundational: Connect to a Redis server and clean up existing vector indexes **Difficulty:** Beginner **Available in:** Python ##### Python ```python r = redis.Redis(decode_responses=True) try: r.ft("vector_idx").dropindex(True) except redis.exceptions.ResponseError: pass ``` Next, create the index. The schema in the example below specifies hash objects for storage and includes three fields: the text content to index, a [tag](https://redis.io/docs/latest/develop/ai/search-and-query/advanced-concepts/tags) field to represent the "genre" of the text, and the embedding vector generated from the original text content. The `embedding` field specifies [HNSW](https://redis.io/docs/latest/develop/ai/search-and-query/vectors#hnsw-index) indexing, the [L2](https://redis.io/docs/latest/develop/ai/search-and-query/vectors#distance-metrics) vector distance metric, `Float32` values to represent the vector's components, and 384 dimensions, as required by the `all-MiniLM-L6-v2` embedding model. Foundational: Create a vector search index for hash documents with HNSW algorithm and L2 distance metric **Difficulty:** Intermediate **Available in:** Python ##### Python ```python schema = ( TextField("content"), TagField("genre"), VectorField("embedding", "HNSW", { "TYPE": "FLOAT32", "DIM": 384, "DISTANCE_METRIC":"L2" }) ) r.ft("vector_idx").create_index( schema, definition=IndexDefinition( prefix=["doc:"], index_type=IndexType.HASH ) ) ``` ## Add data You can now supply the data objects, which will be indexed automatically when you add them with [`hset()`](https://redis.io/docs/latest/commands/hset), as long as you use the `doc:` prefix specified in the index definition. Use the `model.encode()` method of `SentenceTransformer` as shown below to create the embedding that represents the `content` field. The `astype()` option that follows the `model.encode()` call specifies that we want a vector of `float32` values. The `tobytes()` option encodes the vector components together as a single binary string. Use the binary string representation when you are indexing hashes or running a query (but use a list of `float` for [JSON documents](#differences-with-json-documents)). Foundational: Store hash documents with vector embeddings generated from text content **Difficulty:** Beginner **Available in:** Python ##### Python ```python content = "That is a very happy person" r.hset("doc:0", mapping={ "content": content, "genre": "persons", "embedding": model.encode(content).astype(np.float32).tobytes(), }) content = "That is a happy dog" r.hset("doc:1", mapping={ "content": content, "genre": "pets", "embedding": model.encode(content).astype(np.float32).tobytes(), }) content = "Today is a sunny day" r.hset("doc:2", mapping={ "content": content, "genre": "weather", "embedding": model.encode(content).astype(np.float32).tobytes(), }) ``` ### Bulk import with HIMPORT From Redis Open Source 8.10, you can also add the documents with the [`HIMPORT`](https://redis.io/docs/latest/commands/himport) command family. This creates the same hashes as `hset()`, but lets Redis store them as [compact hashes](https://redis.io/docs/latest/develop/data-types/hashes#compact-hashes), which use less memory when many keys share the same field names. Redis Search indexes these hashes in the same way. Declare the field names once with [`himport_prepare()`](https://redis.io/docs/latest/commands/himport-prepare), then create each document with [`himport_set()`](https://redis.io/docs/latest/commands/himport-set), passing only its values, in the same order as the field names. Use a [pipeline](https://redis.io/docs/latest/develop/clients/redis-py/transpipe) to send all the imports in one round trip: Bulk ingestion: Import hash documents with vector embeddings as compact hashes using HIMPORT PREPARE and a pipeline of HIMPORT SET calls **Difficulty:** Intermediate **Builds upon:** add_data **Available in:** Python ##### Python ```python r.himport_prepare("doc", ["content", "genre", "embedding"]) docs = [ ("doc:0", "That is a very happy person", "persons"), ("doc:1", "That is a happy dog", "pets"), ("doc:2", "Today is a sunny day", "weather"), ] with r.pipeline(transaction=False) as pipe: for key, content, genre in docs: pipe.himport_set(key, "doc", [ content, genre, model.encode(content).astype(np.float32).tobytes(), ]) pipe.execute() ``` `himport_set()` replaces any existing key, so running this code after the `hset()` calls leaves you with the same three documents. > [!NOTE] > `HIMPORT` requires `redis-py` v8.1.0 or later, and `redis-py` marks its > support as experimental, so the API might change. Redis Cloud and > Redis Software don't support `HIMPORT`. ## Run a query After you have created the index and added the data, you are ready to run a query. To do this, you must create another embedding vector from your chosen query text. Redis calculates the similarity between the query vector and each embedding vector in the index as it runs the query. It then ranks the results in order of this numeric similarity value. The code below creates the query embedding using `model.encode()`, as with the indexing, and passes it as a parameter when the query executes (see [Vector search](https://redis.io/docs/latest/develop/ai/search-and-query/query/vector-search) for more information about using query parameters with embeddings). Vector similarity search: Find semantically similar documents by comparing query embeddings with indexed vectors using L2 distance **Difficulty:** Intermediate **Available in:** Python ##### Python ```python q = Query( "*=>[KNN 3 @embedding $vec AS vector_distance]" ).return_field("vector_distance").return_field("content").dialect(2) query_text = "That is a happy person" res = r.ft("vector_idx").search( q, query_params={ "vec": model.encode(query_text).astype(np.float32).tobytes() } ) print(res) ``` The code is now ready to run, but note that it may take a while to complete when you run it for the first time (which happens because `sentence-transformers` must download the `all-MiniLM-L6-v2` model data before it can generate the embeddings). When you run the code, it outputs the following result object (slightly formatted here for clarity): ``` Result{ 3 total, docs: [ Document { 'id': 'doc:0', 'payload': None, 'vector_distance': '0.114169985056', 'content': 'That is a very happy person' }, Document { 'id': 'doc:1', 'payload': None, 'vector_distance': '0.610845386982', 'content': 'That is a happy dog' }, Document { 'id': 'doc:2', 'payload': None, 'vector_distance': '1.48624813557', 'content': 'Today is a sunny day' } ] } ``` The exact distances may differ slightly on your system. Note that the results are ordered according to the value of the `vector_distance` field, with the lowest distance indicating the greatest similarity to the query. As you would expect, the result for `doc:0` with the content text *"That is a very happy person"* is the result that is most similar in meaning to the query text *"That is a happy person"*. ## Differences with JSON documents Indexing JSON documents is similar to hash indexing, but there are some important differences. JSON allows much richer data modelling with nested fields, so you must supply a [path](https://redis.io/docs/latest/develop/data-types/json/path) in the schema to identify each field you want to index. However, you can declare a short alias for each of these paths (using the `as_name` keyword argument) to avoid typing it in full for every query. Also, you must specify `IndexType.JSON` when you create the index. The code below shows these differences, but the index is otherwise very similar to the one created previously for hashes: Foundational: Create a vector search index for JSON documents with JSON paths and field aliases **Difficulty:** Intermediate **Available in:** Python ##### Python ```python schema = ( TextField("$.content", as_name="content"), TagField("$.genre", as_name="genre"), VectorField( "$.embedding", "HNSW", { "TYPE": "FLOAT32", "DIM": 384, "DISTANCE_METRIC": "L2" }, as_name="embedding" ) ) r.ft("vector_json_idx").create_index( schema, definition=IndexDefinition( prefix=["jdoc:"], index_type=IndexType.JSON ) ) ``` Use [`json().set()`](https://redis.io/docs/latest/commands/json.set) to add the data instead of [`hset()`](https://redis.io/docs/latest/commands/hset). The dictionaries that specify the fields have the same structure as the ones used for `hset()` but `json().set()` receives them in a positional argument instead of the `mapping` keyword argument. An important difference with JSON indexing is that the vectors are specified using lists instead of binary strings. Generate the list using the `tolist()` method instead of `tobytes()` as you would with a hash. Foundational: Store JSON documents with vector embeddings as lists (different from hash binary format) **Difficulty:** Beginner **Available in:** Python ##### Python ```python content = "That is a very happy person" r.json().set("jdoc:0", Path.root_path(), { "content": content, "genre": "persons", "embedding": model.encode(content).astype(np.float32).tolist(), }) content = "That is a happy dog" r.json().set("jdoc:1", Path.root_path(), { "content": content, "genre": "pets", "embedding": model.encode(content).astype(np.float32).tolist(), }) content = "Today is a sunny day" r.json().set("jdoc:2", Path.root_path(), { "content": content, "genre": "weather", "embedding": model.encode(content).astype(np.float32).tolist(), }) ``` The query is almost identical to the one for the hash documents. This demonstrates how the right choice of aliases for the JSON paths can save you having to write complex queries. An important thing to notice is that the vector parameter for the query is still specified as a binary string (using the `tobytes()` method), even though the data for the `embedding` field of the JSON was specified as a list. Vector similarity search: Query JSON documents using vector embeddings with field aliases for simplified syntax **Difficulty:** Intermediate **Available in:** Python ##### Python ```python q = Query( "*=>[KNN 3 @embedding $vec AS vector_distance]" ).return_field("vector_distance").return_field("content").dialect(2) query_text = "That is a happy person" res = r.ft("vector_json_idx").search( q, query_params={ "vec": model.encode(query_text).astype(np.float32).tobytes() } ) print(repr(res)) ``` Apart from the `jdoc:` prefixes for the keys, the result from the JSON query is the same as for hash: ``` Result{ 3 total, docs: [ Document { 'id': 'jdoc:0', 'payload': None, 'vector_distance': '0.114169985056', 'content': 'That is a very happy person' }, . . . ``` ## Learn more See [Vector search](https://redis.io/docs/latest/develop/ai/search-and-query/query/vector-search) for more information about the indexing options, distance metrics, and query format for vectors.