"rating": 7.7, "director": "Steven Spielberg", "genre": "science fiction"},    ),    Document(        page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",        metadata={"year": 2010, "director": "Christopher Nolan", "genre": "science fiction", "rating": 8.2},    ),    Document(        page_content="A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea",        metadata={"year": 2006, "director": "Satoshi Kon", "genre": "science fiction", "rating": 8.6},    ),    Document(        page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them",        metadata={"year": 2019, "director": "Greta Gerwig", "genre": "drama", "rating": 8.3},    ),    Document(        page_content="Toys come alive and have a blast doing so",        metadata={"year": 1995, "director": "John Lasseter", "genre": "animated", "rating": 9.1,},    ),    Document(        page_content="Three men walk into the Zone, three men walk out of the Zone",        metadata={            "year": 1979,            "rating": 9.9,            "director": "Andrei Tarkovsky",            "genre": "science fiction",        },    ),]index_schema = {    "tag": [{"name": "genre"}],    "text": [{"name": "director"}],    "numeric": [{"name": "year"}, {"name": "rating"}],}vectorstore = Redis.from_documents(    docs,     embeddings,     redis_url="redis://localhost:6379",    index_name="movie_reviews",    index_schema=index_schema,)    `index_schema` does not match generated metadata schema.    If you meant to manually override the schema, please ignore this message.    index_schema: {'tag': [{'name': 'genre'}], 'text': [{'name': 'director'}], 'numeric': [{'name': 'year'}, {'name': 'rating'}]}    generated_schema: {'text': [{'name': 'director'}, {'name': 'genre'}], 'numeric': [{'name': 'year'}, {'name': 'rating'}], 'tag': []}    Creating our self-querying retriever‚ÄãNow we can instantiate
Redis is an open-source key-value store that can be used as a cache, message broker, database, vector database and more.
Redis is an open-source key-value store that can be used as a cache, message broker, database, vector database and more. ->: "rating": 7.7, "director": "Steven Spielberg", "genre": "science fiction"},    ),    Document(        page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",        metadata={"year": 2010, "director": "Christopher Nolan", "genre": "science fiction", "rating": 8.2},    ),    Document(        page_content="A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea",        metadata={"year": 2006, "director": "Satoshi Kon", "genre": "science fiction", "rating": 8.6},    ),    Document(        page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them",        metadata={"year": 2019, "director": "Greta Gerwig", "genre": "drama", "rating": 8.3},    ),    Document(        page_content="Toys come alive and have a blast doing so",        metadata={"year": 1995, "director": "John Lasseter", "genre": "animated", "rating": 9.1,},    ),    Document(        page_content="Three men walk into the Zone, three men walk out of the Zone",        metadata={            "year": 1979,            "rating": 9.9,            "director": "Andrei Tarkovsky",            "genre": "science fiction",        },    ),]index_schema = {    "tag": [{"name": "genre"}],    "text": [{"name": "director"}],    "numeric": [{"name": "year"}, {"name": "rating"}],}vectorstore = Redis.from_documents(    docs,     embeddings,     redis_url="redis://localhost:6379",    index_name="movie_reviews",    index_schema=index_schema,)    `index_schema` does not match generated metadata schema.    If you meant to manually override the schema, please ignore this message.    index_schema: {'tag': [{'name': 'genre'}], 'text': [{'name': 'director'}], 'numeric': [{'name': 'year'}, {'name': 'rating'}]}    generated_schema: {'text': [{'name': 'director'}, {'name': 'genre'}], 'numeric': [{'name': 'year'}, {'name': 'rating'}], 'tag': []}    Creating our self-querying retriever‚ÄãNow we can instantiate