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git clone gitlawb://did:key:z6Mkq5mY...iFZ5/my-project-publ...git clone gitlawb://did:key:z6Mkq5mY.../my-project-publ...2fa351d6docs: add automaton and perps launch sources16d ago| #1 | # Neptune Analytics Vector Store |
| #2 | |
| #3 | [Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/what-is-neptune-analytics.html/) is a memory-optimized graph database engine for analytics. With Neptune Analytics, you can get insights and find trends by processing large amounts of graph data in seconds, including vector search. |
| #4 | |
| #5 | |
| #6 | ## Installation |
| #7 | |
| #8 | ```bash |
| #9 | pip install mem0ai[vector_stores] |
| #10 | ``` |
| #11 | |
| #12 | ## Usage |
| #13 | |
| #14 | ```python |
| #15 | config = { |
| #16 | "vector_store": { |
| #17 | "provider": "neptune", |
| #18 | "config": { |
| #19 | "collection_name": "mem0", |
| #20 | "endpoint": f"neptune-graph://my-graph-identifier", |
| #21 | }, |
| #22 | }, |
| #23 | } |
| #24 | |
| #25 | m = Memory.from_config(config) |
| #26 | messages = [ |
| #27 | {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"}, |
| #28 | {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."}, |
| #29 | {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."}, |
| #30 | {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."} |
| #31 | ] |
| #32 | m.add(messages, user_id="alice", metadata={"category": "movies"}) |
| #33 | ``` |
| #34 | |
| #35 | ## Parameters |
| #36 | |
| #37 | Let's see the available parameters for the `neptune` config: |
| #38 | |
| #39 | | Parameter | Description | Default Value | |
| #40 | | --- | --- | --- | |
| #41 | | `collection_name` | The name of the collection to store the vectors | `mem0` | |
| #42 | | `endpoint` | Connection URL for the Neptune Analytics service | `neptune-graph://my-graph-identifier` | |
| #43 |