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58 lines
2.7 KiB
Plaintext
58 lines
2.7 KiB
Plaintext
---
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title: "Configure Models (Models)"
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---
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## LangBot Models
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LangBot Models is an official model service provided by LangBot. When you initialize a local instance with your LangBot Space account, available models will be automatically added to your instance without any configuration. You will receive a certain amount of free quota to get started quickly.
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<img src="/images/zh/deploy/models/langbot_models.png" alt="langbot_models" />
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For specific available models, please check [LangBot Space](https://space.langbot.app/models).
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## Custom Models
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You can also add models from other sources.
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### LLM Model
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Models will be used by pipelines to process messages. The first model you configure will be set as the model for the default pipeline.
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You can add multiple models, and then select which model to use in the pipeline.
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<img width="400px" src="/images/zh/deploy/models/model_config.png" alt="model_config" />
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Enter these four parameters: `Model Name`, `Model Provider`, `Request URL`, and `API Key`, then submit.
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For model capabilities, please choose according to the specific model characteristics:
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- Visual Capability: Needs to be enabled to recognize images
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- Function Calling: Needs to be enabled to use Agent tools in conversations
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### Embedding Model
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Embedding models are used to compute vector representations of messages. If you need to use knowledge bases, please configure this model.
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<img width="400px" src="/images/zh/deploy/models/embedding_model.png" alt="embedding_model" />
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Enter these four parameters: `Model Name`, `Model Provider`, `Request URL`, and `API Key`, then submit. After that, please configure the knowledge base to use this model.
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### Using seekdb Built-in Embedding Model (Zero Configuration)
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The system has integrated the official embedding model provided by seekdb, no parameters required.
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- On the "Embedding Model" page, select "seekdb-built-in";
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- Click "Save" to use it immediately;
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- Then select this model in your knowledge base to take effect.
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### Rerank Model
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Rerank models re-score RAG retrieval results to significantly improve the precision of knowledge base answers. When a knowledge base returns multiple candidate documents, the rerank model uses a cross-encoder to precisely score each document's relevance to the query, selecting only the most relevant content.
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Enter `Model Name` and select `Model Provider`, then submit. After that, select this model in your pipeline's "AI" configuration.
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<Tip>
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Rerank models are optional. Without one, knowledge base results are used in their original retrieval order. With a rerank model configured, results are re-scored and only the top-K most relevant results are kept.
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</Tip>
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