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chore(sync): mirror docs from openclaw/openclaw@775ed36c16

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openclaw-docs-sync[bot] 2026-04-27 10:28:16 +00:00
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.openclaw-sync/source.json
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"syncedAt": "2026-04-27T10:25:48.709Z"
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454c34daa3f5f66a97f6a701968756a77a110fe611e013b0245fe6a9ef274997 config-baseline.json
56edd542252c0ec8b3005dcddcf083a568d5e7700f7675c509c2963e36a4597c config-baseline.core.json
6bd751bdfd55d644e8a83c575c4db8f67efae00df772c71553b7e7673c5ccc13 config-baseline.json
2bf9795740688c55b8ac50ecbfe954c3d3b0dce0a17b08e89a9385d56e197bee config-baseline.core.json
07963db49502132f26db396c56b36e018b110e6c55a68b3cb012d3ec96f43901 config-baseline.channel.json
f14d1d609ce93893f3bbd6c533251d30328f4deed5cf06da7cb2c9208147dc7a config-baseline.plugin.json
ed65cefbef96f034ce2b73069d9d5bacc341a43489ff9b20a34d40956b877f79 config-baseline.plugin.json

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docs/concepts/memory-search.md
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For local embeddings with no API key, install the optional `node-llama-cpp`
runtime package next to OpenClaw and use `provider: "local"`.
Some OpenAI-compatible embedding endpoints require asymmetric labels such as
`input_type: "query"` for searches and `input_type: "document"` or `"passage"`
for indexed chunks. Configure those with `memorySearch.queryInputType` and
`memorySearch.documentInputType`; see the [Memory configuration reference](/reference/memory-config#provider-specific-config).
## Supported providers
| Provider | ID | Needs API key | Notes |

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docs/providers/openai.md
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| Realtime voice | Voice Call `realtime.provider: "openai"` / Control UI Talk | Yes |
| Embeddings | memory embedding provider | Yes |
## Memory embeddings
OpenClaw can use OpenAI, or an OpenAI-compatible embedding endpoint, for
`memory_search` indexing and query embeddings:
```json5
{
agents: {
defaults: {
memorySearch: {
provider: "openai",
model: "text-embedding-3-small",
},
},
},
}
```
For OpenAI-compatible endpoints that require asymmetric embedding labels, set
`queryInputType` and `documentInputType` under `memorySearch`. OpenClaw forwards
those as provider-specific `input_type` request fields: query embeddings use
`queryInputType`; indexed memory chunks and batch indexing use
`documentInputType`. See the [Memory configuration reference](/reference/memory-config#provider-specific-config) for the full example.
## Getting started
Choose your preferred auth method and follow the setup steps.

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Changing model or `outputDimensionality` triggers an automatic full reindex.
</Warning>
</Accordion>
<Accordion title="OpenAI-compatible input types">
OpenAI-compatible embedding endpoints can opt into provider-specific `input_type` request fields. This is useful for asymmetric embedding models that require different labels for query and document embeddings.
| Key | Type | Default | Description |
| ------------------- | -------- | ------- | ------------------------------------------------------- |
| `inputType` | `string` | unset | Shared `input_type` for query and document embeddings |
| `queryInputType` | `string` | unset | Query-time `input_type`; overrides `inputType` |
| `documentInputType` | `string` | unset | Index/document `input_type`; overrides `inputType` |
```json5
{
agents: {
defaults: {
memorySearch: {
provider: "openai",
remote: {
baseUrl: "https://embeddings.example/v1",
apiKey: "env:EMBEDDINGS_API_KEY",
},
model: "asymmetric-embedder",
queryInputType: "query",
documentInputType: "passage",
},
},
},
}
```
Changing these values affects embedding cache identity for provider batch indexing and should be followed by a memory reindex when the upstream model treats the labels differently.
</Accordion>
<Accordion title="Bedrock">
Bedrock uses the AWS SDK default credential chain — no API keys needed. If OpenClaw runs on EC2 with a Bedrock-enabled instance role, just set the provider and model: