Weaviate's integration with DeepSeek's API allows you to access their generative models' capabilities directly from Weaviate. [Configure a Weaviate collection](#configure-collection) to use a generative AI model with DeepSeek. Weaviate will perform retrieval augmented generation (RAG) using the specified model and your DeepSeek API key. More specifically, Weaviate will perform a search, retrieve the most relevant objects, and then pass them to the DeepSeek generative model to generate outputs. ![RAG integration illustration](/assets/docs/weaviate/model-providers/_includes/integration_deepseek_rag.png) :::callout{intent="info" title="Code examples are Python-only for now"} Examples for the other client languages will follow. ::: ## Requirements ### Weaviate configuration Your Weaviate instance must be configured with the DeepSeek generative AI integration (`generative-deepseek`) module. :::callout{intent="info" title="Added in `v1.36.19`, `v1.37.10`, and `v1.38.2`"} ::: :::accordion{title="For Weaviate Cloud (WCD) users"} This integration is enabled by default on Weaviate Cloud (WCD) instances. ::: :::accordion{title="For self-hosted users"} - Check the [cluster metadata](../monitoring-and-logging/status.md#cluster-metadata) to verify if the module is enabled. - Follow the [how-to configure modules](../how-to-configure-weaviate/modules.md) guide to enable the module in Weaviate. - To enable the module, include it in the `ENABLE_MODULES` environment variable available to Weaviate, e.g. `ENABLE_MODULES="generative-deepseek"` (add it to your existing comma-separated list if other modules are enabled). ::: ### API credentials You must provide a valid DeepSeek API key to Weaviate for this integration. Go to [DeepSeek](https://platform.deepseek.com/) to sign up and obtain an API key. Provide the API key to Weaviate using one of the following methods: - Set the `DEEPSEEK_APIKEY` environment variable that is available to Weaviate. - Provide the API key at runtime, as shown in the examples below. ```python # Recommended: save sensitive data as environment variables deepseek_key = os.getenv("DEEPSEEK_APIKEY") ``` ## Configure collection :::callout{intent="info" title="Generative model integration mutability"} A collection's `generative` model integration configuration is mutable from `v1.25.23`, `v1.26.8` and `v1.27.1`. See [this section](../how-to-manage-collections/generative-reranker-models.md#update-the-generative-model-integration) for details on how to update the collection configuration. ::: Always set `model` explicitly. The module's built-in default is a retired model alias, so a collection configured without a `model` points at a model that DeepSeek no longer serves. See [Available models](#available-models) for the current model names. [Configure a Weaviate index](../how-to-manage-collections/generative-reranker-models.md#specify-a-generative-model-integration) as follows to use a DeepSeek generative model: ```python {5-7} from weaviate.classes.config import Configure client.collections.create( "DemoCollection", generative_config=Configure.Generative.deepseek( model="deepseek-v4-flash" ) # Additional parameters not shown ) ``` ### Select a model Specify any current DeepSeek model name. See [Available models](#available-models) for the current names, and [Generative parameters](#generative-parameters) for the other settings you can configure alongside it. You can also [override the model at query time](#select-a-model-at-runtime). ### Generative parameters Configure the following generative parameters to customize the model behavior. ```python {5-15} from weaviate.classes.config import Configure client.collections.create( "DemoCollection", generative_config=Configure.Generative.deepseek( model="deepseek-v4-flash", # # These parameters are optional # temperature=0.7, # max_tokens=500, # frequency_penalty=0.0, # presence_penalty=0.0, # top_p=1.0, # base_url="https://api.deepseek.com", # stop=["\n\n"], ) ) ``` Weaviate checks `maxTokens` against a built-in ceiling only for the retired `deepseek-chat` and `deepseek-reasoner` aliases. For any current model, a `maxTokens` above the model's limit is accepted when you create the collection and fails later, as an error from DeepSeek at query time. For further details on model parameters, see the [DeepSeek API documentation](https://api-docs.deepseek.com/). ## Select a model at runtime Aside from setting the default model provider when creating the collection, you can also override it at query time. ```python {9-20} from weaviate.classes.config import Configure from weaviate.classes.generate import GenerativeConfig collection = client.collections.use("DemoCollection") response = collection.generate.near_text( query="A holiday film", limit=2, grouped_task="Write a tweet promoting these two movies", generative_provider=GenerativeConfig.deepseek( # # These parameters are optional model="deepseek-v4-pro", # temperature=0.7, # max_tokens=500, # frequency_penalty=0.0, # presence_penalty=0.0, # top_p=1.0, # base_url="https://api.deepseek.com", # stop=["\n\n"], ), # Additional parameters not shown ) ``` ## Header parameters You can provide the API key as well as some optional parameters at runtime through additional headers in the request. The following headers are available: - `X-Deepseek-Api-Key`: The DeepSeek API key. - `X-Deepseek-Baseurl`: The base URL to use (e.g. a proxy) instead of the default DeepSeek URL. `X-Deepseek-Api-Key` takes precedence over the `DEEPSEEK_APIKEY` environment variable. The API key is never part of the collection configuration, so if neither the header nor the environment variable is set, the request fails with `api key: no api key found`. `X-Deepseek-Baseurl` takes precedence over a `baseURL` set at query time, which in turn takes precedence over the `baseURL` in the collection configuration. If none of them are set, Weaviate uses `https://api.deepseek.com`. Provide an API root rather than a full endpoint path, because Weaviate appends `/chat/completions` to it. Provide the headers as shown in the [API credentials examples](#api-credentials) above. ## Retrieval augmented generation After configuring the generative AI integration, perform RAG operations, either with the [single prompt](#single-prompt) or [grouped task](#grouped-task) method. ### Single prompt ![Single prompt RAG integration generates individual outputs per search result](/assets/docs/weaviate/model-providers/_includes/integration_deepseek_rag_single.png) To generate text for each object in the search results, use the single prompt method. The example below generates outputs for each of the `n` search results, where `n` is specified by the `limit` parameter. When creating a single prompt query, use braces `{}` to interpolate the object properties you want Weaviate to pass on to the language model. For example, to pass on the object's `title` property, include `{title}` in the query. ```python {5-6} collection = client.collections.use("DemoCollection") response = collection.generate.near_text( query="A holiday film", # The model provider integration will automatically vectorize the query single_prompt="Translate this into French: {title}", limit=2 ) for obj in response.objects: print(obj.properties["title"]) print(f"Generated output: {obj.generated}") # Note that the generated output is per object ``` ### Grouped task ![Grouped task RAG integration generates one output for the set of search results](/assets/docs/weaviate/model-providers/_includes/integration_deepseek_rag_grouped.png) To generate one text for the entire set of search results, use the grouped task method. In other words, when you have `n` search results, the generative model generates one output for the entire group. ```python {5-6} collection = client.collections.use("DemoCollection") response = collection.generate.near_text( query="A holiday film", # The model provider integration will automatically vectorize the query grouped_task="Write a fun tweet to promote readers to check out these films.", limit=2 ) print(f"Generated output: {response.generative.text}") # Note that the generated output is per query for obj in response.objects: print(obj.properties["title"]) ``` ## References ### Available models Weaviate forwards the configured model name to DeepSeek as-is. There is no allowlist on the Weaviate side, so any current DeepSeek model name is accepted. The current model names are `deepseek-v4-flash` and `deepseek-v4-pro`. For the full list of models and pricing, see the [DeepSeek pricing page](https://api-docs.deepseek.com/quick_start/pricing) and the [DeepSeek API documentation](https://api-docs.deepseek.com/). :::callout{intent="warning" title="The module default is a retired model"} The `deepseek-chat` and `deepseek-reasoner` aliases are retired and DeepSeek no longer serves them. They previously pointed at `deepseek-v4-flash` in its non-thinking and thinking modes respectively. The built-in default model of the `generative-deepseek` module is still `deepseek-chat`, so a collection created without a `model` points at a retired alias. Set `model` on every collection you create. ::: ### Reasoning models The `generative-deepseek` module returns only the model's message content. If you use a reasoning model, its separate reasoning (chain-of-thought) output is not surfaced through the integration. Reasoning models can take much longer to respond than non-reasoning models. Weaviate applies the [`MODULES_CLIENT_TIMEOUT`](../database-configuration/overview.md#MODULES_CLIENT_TIMEOUT) environment variable to the whole request, including reading the response, and it defaults to 50 seconds. If reasoning queries time out, raise this value on your Weaviate instance. ## Further resources ### Code examples Once the integration is configured at the collection, the data management and search operations in Weaviate work identically to any other collection. See the following model-agnostic examples: - The [How-to: Manage collections](../how-to-manage-collections/index.md) and [How-to: Manage objects](../how-to-manage-objects/index.md) guides show how to perform data operations (i.e. create, read, update, delete collections and objects within them). - The [How-to: Query & Search](../how-to-query-search/index.md) guides show how to perform search operations (i.e. vector, keyword, hybrid) as well as retrieval augmented generation. ### References - [DeepSeek API documentation](https://api-docs.deepseek.com/) ## Questions and feedback Have a question or feedback? Here's how to reach us. ::::card-grid :::card{title="Community Forum" href="https://forum.weaviate.io/c/support" icon="messages-square"} Ask questions and connect with other developers on our **Community forum**. ::: :::card{title="Support" href="/guides/support-overview" icon="life-buoy"} Weaviate Cloud user or customer? Find the right channel on the **Support page**. ::: :::: ## Related pages - [Agents](./agents-index.md) - [AI-assisted Weaviate code generation](./ai-assisted-vibe-coding-index.md) - [APIs](./apis-index.md) - [Authorization and authentication](./authorization-and-authentication-index.md) - [Benchmarks](./benchmarks-index.md) - [Best practices](./best-practices-index.md) - [Client libraries](./clients-index.md) - [Client Libraries / SDKs](./client-libraries-index.md) - [Cloud](./cloud-index.md) - [Cloud account management](./cloud-account-management-index.md) # Agent Instructions This portal answers questions programmatically. To receive a synthesized, source-cited answer instead of crawling page by page, append the `?ask=` query parameter to any page URL on this site: /guides/quickstart?ask=how+do+I+authenticate Optional parameters: - `&goal=` steers the answer toward your objective (e.g. `&goal=write+a+python+client`). - `&version=