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feat: Qdrant support (#1228)
* feat: Qdrant support * Update private_gpt/components/vector_store/vector_store_component.py
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@@ -248,6 +248,34 @@ llama_new_context_with_model: total VRAM used: 4857.93 MB (model: 4095.05 MB, co
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AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 0 | VSX = 0 |
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```
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#### Vectorstores
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PrivateGPT supports [Chroma](https://www.trychroma.com/), [Qdrant](https://qdrant.tech/) as vectorstore providers. Chroma being the default.
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To enable Qdrant, set the `vectorstore.database` property in the `settings.yaml` file to `qdrant` and install the `qdrant` extra.
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```bash
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poetry install --extras qdrant
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```
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By default Qdrant tries to connect to an instance at `http://localhost:3000`.
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Qdrant settings can be configured by setting values to the `qdrant` propery in the `settings.yaml` file.
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The available configuration options are:
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| Field | Description |
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|--------------|-------------|
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| location | If `:memory:` - use in-memory Qdrant instance.<br>If `str` - use it as a `url` parameter.|
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| url | Either host or str of 'Optional[scheme], host, Optional[port], Optional[prefix]'.<br> Eg. `http://localhost:6333` |
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| port | Port of the REST API interface. Default: `6333` |
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| grpc_port | Port of the gRPC interface. Default: `6334` |
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| prefer_grpc | If `true` - use gRPC interface whenever possible in custom methods. |
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| https | If `true` - use HTTPS(SSL) protocol.|
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| api_key | API key for authentication in Qdrant Cloud.|
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| prefix | If set, add `prefix` to the REST URL path.<br>Example: `service/v1` will result in `http://localhost:6333/service/v1/{qdrant-endpoint}` for REST API.|
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| timeout | Timeout for REST and gRPC API requests.<br>Default: 5.0 seconds for REST and unlimited for gRPC |
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| host | Host name of Qdrant service. If url and host are not set, defaults to 'localhost'.|
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| path | Persistence path for QdrantLocal. Eg. `local_data/private_gpt/qdrant`|
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#### Known issues and Troubleshooting
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Execution of LLMs locally still has a lot of sharp edges, specially when running on non Linux platforms.
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