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53 lines
1.6 KiB
Markdown
53 lines
1.6 KiB
Markdown
---
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title: MemGPT
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weight: 3200
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---
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# MemGPT
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[MemGPT](https://memgpt.ai/) is a system that enables LLMs to manage their own memory and overcome limited context windows to
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- Create perpetual chatbots that learn about you and change their personalities over time.
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- Create perpetual chatbots that can interface with large data stores.
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Qdrant is available as a storage backend in MemGPT for storing and semantically retrieving data.
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## Usage
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#### Installation
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To install the required dependencies, install `pymemgpt` with the `qdrant` extra.
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```sh
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pip install 'pymemgpt[qdrant]'
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```
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You can configure MemGPT to use either a Qdrant server or an in-memory instance with the `memgpt configure` command.
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#### Configuring the Qdrant server
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When you run `memgpt configure`, go through the prompts as described in the [MemGPT configuration documentation](https://memgpt.readme.io/docs/config).
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After you address several `memgpt` questions, you come to the following `memgpt` prompts:
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```console
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? Select storage backend for archival data: qdrant
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? Select Qdrant backend: server
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? Enter the Qdrant instance URI (Default: localhost:6333): https://xyz-example.eu-central.aws.cloud.qdrant.io
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```
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You can set an API key for authentication using the `QDRANT_API_KEY` environment variable.
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#### Configuring an in-memory instance
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```console
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? Select storage backend for archival data: qdrant
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? Select Qdrant backend: local
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```
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The data is persisted at the default MemGPT storage directory.
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## Further Reading
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- [MemGPT Examples][https://github.com/cpacker/MemGPT/tree/main/examples]
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- [MemGPT Documentation](https://memgpt.readme.io/docs/index).
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