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ci: regenerated with OpenAPI Doc 1.0.0, Speakeay CLI 1.82.5
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‎README.md

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s = airbyte.Airbyte(
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security=shared.Security(
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bearer_auth="",
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basic_auth=shared.SchemeBasicAuth(
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password="",
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username="",
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),
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),
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)
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‎RELEASES.md

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### Generated
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- [python v0.35.3] .
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### Releases
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- [PyPI v0.35.3] https://pypi.org/project/airbyte-api/0.35.3 - .
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- [PyPI v0.35.3] https://pypi.org/project/airbyte-api/0.35.3 - .
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## 2023-09-12 16:21:57
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### Changes
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Based on:
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- OpenAPI Doc 1.0.0
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- Speakeasy CLI 1.82.5 (2.108.3) https://github.com/speakeasy-api/speakeasy
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### Generated
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- [python v0.35.4] .
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### Releases
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- [PyPI v0.35.4] https://pypi.org/project/airbyte-api/0.35.4 - .

‎USAGE.md

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s = airbyte.Airbyte(
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security=shared.Security(
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bearer_auth="",
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basic_auth=shared.SchemeBasicAuth(
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password="",
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username="",
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),
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),
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)
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‎docs/models/shared/destinationbigquery.md

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‎docs/models/shared/destinationmilvus.md

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# DestinationMilvus
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The values required to configure the destination.
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## Fields
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| Field | Type | Required | Description |
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| ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------- |
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| `destination_type` | [DestinationMilvusMilvus](../../models/shared/destinationmilvusmilvus.md) | :heavy_check_mark: | N/A |
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| `embedding` | *Any* | :heavy_check_mark: | Embedding configuration |
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| `indexing` | [DestinationMilvusIndexing](../../models/shared/destinationmilvusindexing.md) | :heavy_check_mark: | Indexing configuration |
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| `processing` | [DestinationMilvusProcessingConfigModel](../../models/shared/destinationmilvusprocessingconfigmodel.md) | :heavy_check_mark: | N/A |

‎docs/models/shared/destinationmilvusembeddingcohere.md

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# DestinationMilvusEmbeddingCohere
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Use the Cohere API to embed text.
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## Fields
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| Field | Type | Required | Description |
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| ------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
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| `cohere_key` | *str* | :heavy_check_mark: | N/A |
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| `mode` | [Optional[DestinationMilvusEmbeddingCohereMode]](../../models/shared/destinationmilvusembeddingcoheremode.md) | :heavy_minus_sign: | N/A |

‎docs/models/shared/destinationmilvusembeddingcoheremode.md

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# DestinationMilvusEmbeddingCohereMode
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## Values
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| Name | Value |
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| -------- | -------- |
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| `COHERE` | cohere |

‎docs/models/shared/destinationmilvusembeddingfake.md

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# DestinationMilvusEmbeddingFake
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Use a fake embedding made out of random vectors with 1536 embedding dimensions. This is useful for testing the data pipeline without incurring any costs.
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## Fields
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| Field | Type | Required | Description |
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| --------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- |
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| `mode` | [Optional[DestinationMilvusEmbeddingFakeMode]](../../models/shared/destinationmilvusembeddingfakemode.md) | :heavy_minus_sign: | N/A |

‎docs/models/shared/destinationmilvusembeddingfakemode.md

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# DestinationMilvusEmbeddingFakeMode
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## Values
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| Name | Value |
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| ------ | ------ |
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| `FAKE` | fake |

‎docs/models/shared/destinationmilvusembeddingfromfield.md

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# DestinationMilvusEmbeddingFromField
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Use a field in the record as the embedding. This is useful if you already have an embedding for your data and want to store it in the vector store.
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## Fields
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| Field | Type | Required | Description | Example |
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| ------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- |
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| `dimensions` | *int* | :heavy_check_mark: | The number of dimensions the embedding model is generating | 1536 |
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| `field_name` | *str* | :heavy_check_mark: | Name of the field in the record that contains the embedding | embedding |
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| `mode` | [Optional[DestinationMilvusEmbeddingFromFieldMode]](../../models/shared/destinationmilvusembeddingfromfieldmode.md) | :heavy_minus_sign: | N/A | |

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