# InMemoryVectorStore > **Class** in `langchain_aws` 📖 [View in docs](https://reference.langchain.com/python/langchain-aws/vectorstores/inmemorydb/base/InMemoryVectorStore) InMemoryVectorStore vector database. To use, you should have the `redis` python package installed for AWS MemoryDB: ```bash pip install redis ``` Once running, you can connect to the MemoryDB server with the following url schemas: - redis://: # simple connection - redis://:@: # connection with authentication - rediss://: # connection with SSL - rediss://:@: # connection with SSL and auth Examples: The following examples show various ways to use the Redis VectorStore with LangChain. ## Signature ```python InMemoryVectorStore( self, redis_url: str, index_name: str, embedding: Embeddings, index_schema: Optional[Union[Dict[str, ListOfDict], str, os.PathLike]] = None, vector_schema: Optional[Dict[str, Union[str, int]]] = None, relevance_score_fn: Optional[Callable[[float], float]] = None, key_prefix: Optional[str] = None, **kwargs: Any = {}, ) ``` ## Description **For all the following examples assume we have the following imports:** ```python from langchain_aws.vectorstores import InMemoryVectorStore ``` Initialize, create index, and load Documents: ```python from langchain_aws.vectorstores import InMemoryVectorStore rds = InMemoryVectorStore.from_documents( documents, # a list of Document objects from loaders or created embeddings, # an Embeddings object redis_url="redis://cluster_endpoint:6379", ) ``` Initialize, create index, and load Documents with metadata: ```python rds = InMemoryVectorStore.from_texts( texts, # a list of strings metadata, # a list of metadata dicts embeddings, # an Embeddings object redis_url="redis://cluster_endpoint:6379", ) ``` Initialize, create index, and load Documents with metadata and return keys ```python rds, keys = InMemoryVectorStore.from_texts_return_keys( texts, # a list of strings metadata, # a list of metadata dicts embeddings, # an Embeddings object redis_url="redis://cluster_endpoint:6379", ) ``` For use cases where the index needs to stay alive, you can initialize with an index name such that it's easier to reference later ```python rds = InMemoryVectorStore.from_texts( texts, # a list of strings metadata, # a list of metadata dicts embeddings, # an Embeddings object index_name="my-index", redis_url="redis://cluster_endpoint:6379", ) ``` Initialize and connect to an existing index (from above) ```python # must pass in schema and key_prefix from another index existing_rds = InMemoryVectorStore.from_existing_index( embeddings, # an Embeddings object index_name="my-index", schema=rds.schema, # schema dumped from another index key_prefix=rds.key_prefix, # key prefix from another index redis_url="redis://username:password@cluster_endpoint:6379", ) ``` Advanced examples: Custom vector schema can be supplied to change the way that MemoryDB creates the underlying vector schema. This is useful for production use cases where you want to optimize the vector schema for your use case. ex. using HNSW instead of FLAT (knn) which is the default ```python vector_schema = { "algorithm": "HNSW" } rds = InMemoryVectorStore.from_texts( texts, # a list of strings metadata, # a list of metadata dicts embeddings, # an Embeddings object vector_schema=vector_schema, redis_url="redis://cluster_endpoint:6379", ) ``` Custom index schema can be supplied to change the way that the metadata is indexed. This is useful for you would like to use the hybrid querying (filtering) capability of MemoryDB. By default, this implementation will automatically generate the index schema according to the following rules: - All strings are indexed as text fields - All numbers are indexed as numeric fields - All lists of strings are indexed as tag fields (joined by langchain_aws.vectorstores.inmemorydb.constants.INMEMORYDB_TAG_SEPARATOR) - All None values are not indexed but still stored in MemoryDB these are not retrievable through the interface here, but the raw MemoryDB client can be used to retrieve them. - All other types are not indexed To override these rules, you can pass in a custom index schema like the following ```yaml tag: - name: credit_score text: - name: user - name: job ``` Typically, the `credit_score` field would be a text field since it's a string, however, we can override this behavior by specifying the field type as shown with the yaml config (can also be a dictionary) above and the code below. ```python rds = InMemoryVectorStore.from_texts( texts, # a list of strings metadata, # a list of metadata dicts embeddings, # an Embeddings object index_schema="path/to/index_schema.yaml", # can also be a dictionary redis_url="redis://cluster_endpoint:6379", ) ``` When connecting to an existing index where a custom schema has been applied, it's important to pass in the same schema to the `from_existing_index` method. Otherwise, the schema for newly added samples will be incorrect and metadata will not be returned. ## Extends - `VectorStore` ## Constructors ```python __init__( self, redis_url: str, index_name: str, embedding: Embeddings, index_schema: Optional[Union[Dict[str, ListOfDict], str, os.PathLike]] = None, vector_schema: Optional[Dict[str, Union[str, int]]] = None, relevance_score_fn: Optional[Callable[[float], float]] = None, key_prefix: Optional[str] = None, **kwargs: Any = {}, ) ``` | Name | Type | |------|------| | `redis_url` | `str` | | `index_name` | `str` | | `embedding` | `Embeddings` | | `index_schema` | `Optional[Union[Dict[str, ListOfDict], str, os.PathLike]]` | | `vector_schema` | `Optional[Dict[str, Union[str, int]]]` | | `relevance_score_fn` | `Optional[Callable[[float], float]]` | | `key_prefix` | `Optional[str]` | ## Properties - `DEFAULT_VECTOR_SCHEMA` - `index_name` - `client` - `relevance_score_fn` - `key_prefix` - `embeddings` - `schema` ## Methods - [`from_texts_return_keys()`](https://reference.langchain.com/python/langchain-aws/vectorstores/inmemorydb/base/InMemoryVectorStore/from_texts_return_keys) - [`from_texts()`](https://reference.langchain.com/python/langchain-aws/vectorstores/inmemorydb/base/InMemoryVectorStore/from_texts) - [`from_existing_index()`](https://reference.langchain.com/python/langchain-aws/vectorstores/inmemorydb/base/InMemoryVectorStore/from_existing_index) - [`write_schema()`](https://reference.langchain.com/python/langchain-aws/vectorstores/inmemorydb/base/InMemoryVectorStore/write_schema) - [`delete()`](https://reference.langchain.com/python/langchain-aws/vectorstores/inmemorydb/base/InMemoryVectorStore/delete) - [`drop_index()`](https://reference.langchain.com/python/langchain-aws/vectorstores/inmemorydb/base/InMemoryVectorStore/drop_index) - [`add_texts()`](https://reference.langchain.com/python/langchain-aws/vectorstores/inmemorydb/base/InMemoryVectorStore/add_texts) - [`as_retriever()`](https://reference.langchain.com/python/langchain-aws/vectorstores/inmemorydb/base/InMemoryVectorStore/as_retriever) - [`similarity_search_with_score()`](https://reference.langchain.com/python/langchain-aws/vectorstores/inmemorydb/base/InMemoryVectorStore/similarity_search_with_score) - [`similarity_search()`](https://reference.langchain.com/python/langchain-aws/vectorstores/inmemorydb/base/InMemoryVectorStore/similarity_search) - [`similarity_search_by_vector()`](https://reference.langchain.com/python/langchain-aws/vectorstores/inmemorydb/base/InMemoryVectorStore/similarity_search_by_vector) - [`max_marginal_relevance_search()`](https://reference.langchain.com/python/langchain-aws/vectorstores/inmemorydb/base/InMemoryVectorStore/max_marginal_relevance_search) --- [View source on GitHub](https://github.com/langchain-ai/langchain-aws/blob/2f5e41cef9442ec840c0d8401e34dea74b061ba0/libs/aws/langchain_aws/vectorstores/inmemorydb/base.py#L70)