DeepSeek Generative AI with Weaviate
Weaviate's integration with DeepSeek's API allows you to access their generative models' capabilities directly from Weaviate.
Configure a Weaviate 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.
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.
v1.36.19, v1.37.10, and v1.38.2The generative-deepseek module is available from v1.36.19 on the v1.36 line, v1.37.10 on the v1.37 line, and v1.38.2 on the v1.38 line. Earlier patch releases on these lines do not include it.
For Weaviate Cloud (WCD) users
This integration is enabled by default on Weaviate Cloud (WCD) instances.
For self-hosted users
- Check the cluster metadata to verify if the module is enabled.
- Follow the how-to configure modules guide to enable the module in Weaviate.
- To enable the module, include it in the
ENABLE_MODULESenvironment 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 to sign up and obtain an API key.
Provide the API key to Weaviate using one of the following methods:
- Set the
DEEPSEEK_APIKEYenvironment variable that is available to Weaviate. - Provide the API key at runtime, as shown in the examples below.
import weaviate
from weaviate.classes.init import Auth
import os
# Recommended: save sensitive data as environment variables
deepseek_key = os.getenv("DEEPSEEK_APIKEY")
headers = {
"X-Deepseek-Api-Key": deepseek_key,
# "X-Deepseek-Baseurl": "https://api.deepseek.com", # Optional; for providing a custom base URL
}
client = weaviate.connect_to_weaviate_cloud(
cluster_url=weaviate_url, # `weaviate_url`: your Weaviate URL
auth_credentials=Auth.api_key(weaviate_key), # `weaviate_key`: your Weaviate API key
headers=headers
)
# Work with Weaviate
client.close()
Configure collection
A collection's generative model integration configuration is mutable from v1.25.23, v1.26.8 and v1.27.1. See this section 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 for the current model names.
Configure a Weaviate index as follows to use a DeepSeek generative model:
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 for the current names, and Generative parameters for the other settings you can configure alongside it.
You can also override the model at query time.
Generative parameters
Configure the following generative parameters to customize the model behavior.
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.
Select a model at runtime
Aside from setting the default model provider when creating the collection, you can also override it at query time.
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 above.
Retrieval augmented generation
After configuring the generative AI integration, perform RAG operations, either with the single prompt or grouped task method.
Single prompt
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.
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
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.
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 and the DeepSeek API documentation.
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 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 and How-to: Manage objects guides show how to perform data operations (i.e. create, read, update, delete collections and objects within them).
- The How-to: Query & Search guides show how to perform search operations (i.e. vector, keyword, hybrid) as well as retrieval augmented generation.
References
Questions and feedback
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