> ## Documentation Index > Fetch the complete documentation index at: https://docs.langchain.com/llms.txt > Use this file to discover all available pages before exploring further. # Amazon memorydb integration > Integrate with the Amazon memorydb vector store using LangChain Python. > [Vector Search](https://docs.aws.amazon.com/memorydb/latest/devguide/vector-search.html/) introduction and langchain integration guide. ## What is Amazon MemoryDB? MemoryDB is compatible with Redis OSS, a popular open source data store, enabling you to quickly build applications using the same flexible and friendly Redis OSS data structures, APIs, and commands that they already use today. With MemoryDB, all of your data is stored in memory, which enables you to achieve microsecond read and single-digit millisecond write latency and high throughput. MemoryDB also stores data durably across multiple Availability Zones (AZs) using a Multi-AZ transactional log to enable fast failover, database recovery, and node restarts. ## Vector search for MemoryDB Vector search for MemoryDB extends the functionality of MemoryDB. Vector search can be used in conjunction with existing MemoryDB functionality. Applications that do not use vector search are unaffected by its presence. Vector search is available in all Regions that MemoryDB is available. You can use your existing MemoryDB data or Redis OSS API to build machine learning and generative AI use cases, such as retrieval-augmented generation, anomaly detection, document retrieval, and real-time recommendations. * Indexing of multiple fields in Redis hashes and `JSON` * Vector similarity search (with `HNSW` (ANN) or `FLAT` (KNN)) * Vector Range Search (e.g. find all vectors within a radius of a query vector) * Incremental indexing without performance loss ## Setting up ### Install Redis Python client `Redis-py` is a python client that can be used to connect to MemoryDB ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -qU redis langchain-aws ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_aws.embeddings import BedrockEmbeddings embeddings = BedrockEmbeddings() ``` ### MemoryDB connection Valid Redis Url schemas are: 1. `redis://` - Connection to Redis cluster, unencrypted 2. `rediss://` - Connection to Redis cluster, with TLS encryption More information about additional connection parameters can be found in the [redis-py documentation](https://redis-py.readthedocs.io/en/stable/connections.html). ### Sample data First we will describe some sample data so that the various attributes of the Redis vector store can be demonstrated. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} metadata = [ { "user": "john", "age": 18, "job": "engineer", "credit_score": "high", }, { "user": "derrick", "age": 45, "job": "doctor", "credit_score": "low", }, { "user": "nancy", "age": 94, "job": "doctor", "credit_score": "high", }, { "user": "tyler", "age": 100, "job": "engineer", "credit_score": "high", }, { "user": "joe", "age": 35, "job": "dentist", "credit_score": "medium", }, ] texts = ["foo", "foo", "foo", "bar", "bar"] index_name = "users" ``` ### Create MemoryDB vector store The InMemoryVectorStore instance can be initialized using the below methods * `InMemoryVectorStore.__init__` - Initialize directly * `InMemoryVectorStore.from_documents` - Initialize from a list of `LangChain.docstore.Document` objects * `InMemoryVectorStore.from_texts` - Initialize from a list of texts (optionally with metadata) * `InMemoryVectorStore.from_existing_index` - Initialize from an existing MemoryDB index ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_aws.vectorstores.inmemorydb import InMemoryVectorStore vds = InMemoryVectorStore.from_texts( embeddings, redis_url="rediss://cluster_endpoint:6379/ssl=True ssl_cert_reqs=none", ) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} vds.index_name ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 'users' ``` ## Querying There are multiple ways to query the `InMemoryVectorStore` implementation based on what use case you have: * `similarity_search`: Find the most similar vectors to a given vector. * `similarity_search_with_score`: Find the most similar vectors to a given vector and return the vector distance * `similarity_search_limit_score`: Find the most similar vectors to a given vector and limit the number of results to the `score_threshold` * `similarity_search_with_relevance_scores`: Find the most similar vectors to a given vector and return the vector similarities * `max_marginal_relevance_search`: Find the most similar vectors to a given vector while also optimizing for diversity ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} results = vds.similarity_search("foo") print(results[0].page_content) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} foo ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # with scores (distances) results = vds.similarity_search_with_score("foo", k=5) for result in results: print(f"Content: {result[0].page_content} --- Score: {result[1]}") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} Content: foo --- Score: 0.0 Content: foo --- Score: 0.0 Content: foo --- Score: 0.0 Content: bar --- Score: 0.1566 Content: bar --- Score: 0.1566 ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # limit the vector distance that can be returned results = vds.similarity_search_with_score("foo", k=5, distance_threshold=0.1) for result in results: print(f"Content: {result[0].page_content} --- Score: {result[1]}") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} Content: foo --- Score: 0.0 Content: foo --- Score: 0.0 Content: foo --- Score: 0.0 ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # with scores results = vds.similarity_search_with_relevance_scores("foo", k=5) for result in results: print(f"Content: {result[0].page_content} --- Similarity: {result[1]}") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} Content: foo --- Similarity: 1.0 Content: foo --- Similarity: 1.0 Content: foo --- Similarity: 1.0 Content: bar --- Similarity: 0.8434 Content: bar --- Similarity: 0.8434 ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # you can also add new documents as follows new_document = ["baz"] new_metadata = [{"user": "sam", "age": 50, "job": "janitor", "credit_score": "high"}] # both the document and metadata must be lists vds.add_texts(new_document, new_metadata) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} ['doc:users:b9c71d62a0a34241a37950b448dafd38'] ``` ## MemoryDB as retriever Here we go over different options for using the vector store as a retriever. There are three different search methods we can use to do retrieval. By default, it will use semantic similarity. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} query = "foo" results = vds.similarity_search_with_score(query, k=3, return_metadata=True) for result in results: print("Content:", result[0].page_content, " --- Score: ", result[1]) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} Content: foo --- Score: 0.0 Content: foo --- Score: 0.0 Content: foo --- Score: 0.0 ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} retriever = vds.as_retriever(search_type="similarity", search_kwargs={"k": 4}) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} docs = retriever.invoke(query) docs ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [Document(page_content='foo', metadata={'id': 'doc:users_modified:988ecca7574048e396756efc0e79aeca', 'user': 'john', 'job': 'engineer', 'credit_score': 'high', 'age': '18'}), Document(page_content='foo', metadata={'id': 'doc:users_modified:009b1afeb4084cc6bdef858c7a99b48e', 'user': 'derrick', 'job': 'doctor', 'credit_score': 'low', 'age': '45'}), Document(page_content='foo', metadata={'id': 'doc:users_modified:7087cee9be5b4eca93c30fbdd09a2731', 'user': 'nancy', 'job': 'doctor', 'credit_score': 'high', 'age': '94'}), Document(page_content='bar', metadata={'id': 'doc:users_modified:01ef6caac12b42c28ad870aefe574253', 'user': 'tyler', 'job': 'engineer', 'credit_score': 'high', 'age': '100'})] ``` There is also the `similarity_distance_threshold` retriever which allows the user to specify the vector distance ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} retriever = vds.as_retriever( search_type="similarity_distance_threshold", search_kwargs={"k": 4, "distance_threshold": 0.1}, ) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} docs = retriever.invoke(query) docs ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [Document(page_content='foo', metadata={'id': 'doc:users_modified:988ecca7574048e396756efc0e79aeca', 'user': 'john', 'job': 'engineer', 'credit_score': 'high', 'age': '18'}), Document(page_content='foo', metadata={'id': 'doc:users_modified:009b1afeb4084cc6bdef858c7a99b48e', 'user': 'derrick', 'job': 'doctor', 'credit_score': 'low', 'age': '45'}), Document(page_content='foo', metadata={'id': 'doc:users_modified:7087cee9be5b4eca93c30fbdd09a2731', 'user': 'nancy', 'job': 'doctor', 'credit_score': 'high', 'age': '94'})] ``` Lastly, the `similarity_score_threshold` allows the user to define the minimum score for similar documents ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} retriever = vds.as_retriever( search_type="similarity_score_threshold", search_kwargs={"score_threshold": 0.9, "k": 10}, ) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} retriever.invoke("foo") ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [Document(page_content='foo', metadata={'id': 'doc:users_modified:988ecca7574048e396756efc0e79aeca', 'user': 'john', 'job': 'engineer', 'credit_score': 'high', 'age': '18'}), Document(page_content='foo', metadata={'id': 'doc:users_modified:009b1afeb4084cc6bdef858c7a99b48e', 'user': 'derrick', 'job': 'doctor', 'credit_score': 'low', 'age': '45'}), Document(page_content='foo', metadata={'id': 'doc:users_modified:7087cee9be5b4eca93c30fbdd09a2731', 'user': 'nancy', 'job': 'doctor', 'credit_score': 'high', 'age': '94'})] ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} retriever.invoke("foo") ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [Document(page_content='foo', metadata={'id': 'doc:users:8f6b673b390647809d510112cde01a27', 'user': 'john', 'job': 'engineer', 'credit_score': 'high', 'age': '18'}), Document(page_content='bar', metadata={'id': 'doc:users:93521560735d42328b48c9c6f6418d6a', 'user': 'tyler', 'job': 'engineer', 'credit_score': 'high', 'age': '100'}), Document(page_content='foo', metadata={'id': 'doc:users:125ecd39d07845eabf1a699d44134a5b', 'user': 'nancy', 'job': 'doctor', 'credit_score': 'high', 'age': '94'}), Document(page_content='foo', metadata={'id': 'doc:users:d6200ab3764c466082fde3eaab972a2a', 'user': 'derrick', 'job': 'doctor', 'credit_score': 'low', 'age': '45'})] ``` ## Delete index To delete your entries you have to address them by their keys. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # delete the indices too InMemoryVectorStore.drop_index( index_name="users", delete_documents=True, redis_url="redis://localhost:6379" ) InMemoryVectorStore.drop_index( index_name="users_modified", delete_documents=True, redis_url="redis://localhost:6379", ) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} True ``` ***
[Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers. [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/vectorstores/memorydb.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).