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You can add DeepSeek to a server-side application with an API key, the OpenAI-compatible base URL https://api.deepseek.com, and the POST /chat/completions operation. Configure a compatible SDK or send HTTPS yourself, then set stream: true when you want incremental output instead of one completed response. Keep the key on your server, parse streaming server-sent events (SSE) until data: [DONE], and verify model names and parameters against DeepSeek’s current documentation before deployment.
This guide shows the complete integration pattern, including authentication, Python and JavaScript clients, raw HTTP, request fields, streaming, JSON mode, error handling, and production safeguards.
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What do you need before making a DeepSeek API request?
You need a DeepSeek account with an API key, a trusted server environment in which to store that key, and an HTTP client or compatible SDK. The examples use the OpenAI-format interface because DeepSeek documents compatibility with OpenAI-style clients. Your application can be written in Python, JavaScript, or another language that can send HTTPS requests.
- Credential: create an API key in DeepSeek’s developer console before calling the API.
- Secret storage: place the key in an environment variable or a managed secret store, never in source control or a browser/mobile bundle.
- Base URL: use
https://api.deepseek.comfor the OpenAI-compatible format. - Operation: send a
POSTrequest to/chat/completions, either directly or through an SDK method that builds this route. - Model: select a model identifier that is listed as available when you deploy; the current page lists
deepseek-flashanddeepseek-v4-pro, but names and capabilities can change.
The API reference uses HTTP Bearer authentication. In practice, the SDK adds an Authorization: Bearer ... header for you; a raw HTTP client must add that header explicitly.
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How should you authenticate and protect the API key?
Authentication is a Bearer token sent over HTTPS. Read the key at runtime from a server-side secret and pass it to your client library or request header.
Environment-variable setup
Set a variable such as DEEPSEEK_API_KEY in your deployment platform’s secret configuration. A local shell example is:
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export DEEPSEEK_API_KEY='your-key-from-deepseek'Do not commit a real value to a repository, print it in logs, include it in error responses, or embed it in JavaScript delivered to a browser. A public client can be inspected by anyone, so a key shipped there can be copied and used outside your application. Instead, let the browser call your own authenticated endpoint and have that server endpoint call DeepSeek.
Server-side request header
If you are not using an SDK, send:
Authorization: Bearer $DEEPSEEK_API_KEY
Content-Type: application/jsonUse a secret manager when your hosting platform provides one. Restrict who can read the secret, rotate it when staff or systems change, and keep separate keys for development and production so a test leak does not expose production usage.
Which base URL and endpoint should you call?
For the OpenAI-compatible API format, set the base URL to https://api.deepseek.com and call POST /chat/completions. The resulting full URL is https://api.deepseek.com/chat/completions. DeepSeek also documents an Anthropic-compatible base URL, https://api.deepseek.com/anthropic; use that when your application is built around the Anthropic-compatible interface and follow the current route and schema documented for that format.
| Integration choice | Base URL | Client and schema considerations |
|---|---|---|
| OpenAI-compatible | https://api.deepseek.com |
OpenAI-compatible SDK configuration; chat completions use POST /chat/completions. |
| Anthropic-compatible | https://api.deepseek.com/anthropic |
Use an Anthropic-compatible client and the route and request schema specified in the current DeepSeek documentation for that format. |
Do not mix a client’s native provider URL with DeepSeek’s URL accidentally. The base URL, authentication method, endpoint path, and request schema must belong to the same compatibility format.
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How do you configure the Python SDK?
DeepSeek’s quickstart demonstrates configuring an OpenAI-compatible Python client with a custom base URL. Install a current version of the SDK, expose the key as an environment variable, and create the client on your server:
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ.get("DEEPSEEK_API_KEY"),
base_url="https://api.deepseek.com",
)
response = client.chat.completions.create(
model="deepseek-flash",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"},
],
)
print(response.choices[0].message.content)The required request concepts are a model and a messages array. A normal, non-streaming call resolves to one response object, from which the assistant text is read at response.choices[0].message.content in the SDK shape shown above.
Check the installed SDK’s release notes for any changes to constructor arguments or response objects. The model identifier in this example is illustrative of the current quickstart; availability and supported parameters are time-sensitive.
How do you configure a JavaScript or TypeScript SDK?
The same configuration pattern works in a server-side JavaScript or TypeScript process: supply the DeepSeek base URL and read the key from process.env.
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.DEEPSEEK_API_KEY,
baseURL: "https://api.deepseek.com",
});
const response = await client.chat.completions.create({
model: "deepseek-flash",
messages: [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "Hello!" }
]
});
console.log(response.choices[0].message.content);Run this code only in a trusted server, serverless function, or edge function where environment variables are not exposed to the client bundle. Your web or mobile front end should call your own endpoint, which validates the user request, applies limits, and invokes DeepSeek.
How can you call the endpoint with raw HTTP?
Raw HTTP is useful when your language has no compatible SDK or when you need complete control over headers and streaming. This curl request sends a non-streaming chat completion:
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curl https://api.deepseek.com/chat/completions
-H "Authorization: Bearer $DEEPSEEK_API_KEY"
-H "Content-Type: application/json"
-d '{
"model": "deepseek-flash",
"messages": [
{"role": "user", "content": "Explain dependency injection in two sentences."}
]
}'Expect an HTTP error status and a provider error body when authentication, validation, quota, or server processing fails. Your application should record the status and a request identifier if one is returned, while removing the API key and sensitive prompt content from logs.
Which request fields matter for chat completions?
The chat-completions request requires model and messages. The reference also documents controls including stream, thinking, reasoning_effort, and max_tokens. Accept only parameters supported by the model you have selected.
| Field | Purpose | Implementation note |
|---|---|---|
model |
Selects the deployed model. | Model IDs and capabilities can change; confirm them in the live model documentation. |
messages |
Provides conversation turns such as system, user, and assistant messages. |
Keep the array in the order needed to establish context and instructions. |
stream |
Chooses incremental SSE output instead of one completed body. | Set to true only when your client consumes events. |
thinking |
Requests the documented thinking behavior where supported. | Verify model support and returned fields before relying on it. |
reasoning_effort |
Controls reasoning effort where the selected model supports it. | Unsupported combinations may be rejected; treat this as model-dependent. |
max_tokens |
Places an upper bound on generated output tokens. | A low limit can truncate ordinary text or structured JSON. |
response_format |
Requests a response format such as JSON mode. | For JSON mode, use {"type":"json_object"} and instruct the model to output JSON. |
Keep prompts and limits aligned with the selected model’s context limit. A request can be accepted yet still produce incomplete output if the generation limit is reached.
How do streaming responses work?
Set stream: true and treat the response as a sequence of data-only server-sent events, not as one final JSON document. Each event can contain a partial message delta. Append or render the delta as it arrives, then stop when the server sends data: [DONE].
Python streaming pattern
The OpenAI-compatible Python SDK exposes an iterable stream in the documented pattern:
stream = client.chat.completions.create(
model="deepseek-flash",
messages=[{"role": "user", "content": "Explain this briefly."}],
stream=True,
)
for chunk in stream:
delta = chunk.choices[0].delta.content
if delta:
print(delta, end="", flush=True)A production implementation should not assume every chunk contains text. Some chunks can carry metadata, role information, finish information, or usage, so check that the choices collection and content value exist before appending.
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Including usage data
The endpoint reference documents stream_options.include_usage for streaming requests. Use it only together with stream: true; usage is supplied in the final chunk as described by the reference. Design your parser to accept a final chunk whose text delta is empty while still extracting usage fields.
stream = client.chat.completions.create(
model="deepseek-flash",
messages=[{"role": "user", "content": "Give three short tips."}],
stream=True,
stream_options={"include_usage": True},
)
for chunk in stream:
if getattr(chunk, "choices", None):
delta = chunk.choices[0].delta
if getattr(delta, "content", None):
print(delta.content, end="", flush=True)
usage = getattr(chunk, "usage", None)
if usage:
save_usage(usage)The exact object attributes vary by SDK version. Inspect the current library’s event types rather than assuming that the Python example’s attribute names are universal.
Relaying a stream to a browser
If a browser needs token-by-token output, create a server endpoint that opens the DeepSeek stream and forwards events or normalized text to the browser. Authenticate the user session before opening the upstream request, enforce a per-user limit, and close the upstream connection when the browser disconnects. This architecture keeps the reusable DeepSeek key private while still giving the user responsive output.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should an SSE parser handle chunks and termination?
SSE data may arrive split across network packets, so read from a buffer and process complete event boundaries rather than assuming one network read equals one event. For each event, remove the data: prefix, ignore keep-alive lines if present, and parse JSON data chunks. A literal [DONE] data line terminates the stream.
- Open the HTTPS response and verify a successful status before parsing events.
- Accumulate bytes until the SSE event delimiter is received.
- Extract each
data:line and trim the single optional space after the colon. - If the payload is
[DONE], mark the response complete and stop reading. - Otherwise parse the JSON payload, append any non-empty text delta, and record finish or usage metadata.
- If the connection closes before
[DONE], mark the generation incomplete and decide whether a safe retry is possible.
Do not concatenate raw JSON event strings and parse them only at the end; that defeats streaming and can fail when a connection is interrupted. Also avoid displaying partial sensitive content in server logs.
How do you request JSON output safely?
Use response_format with type: "json_object" and explicitly instruct the model in the prompt to produce JSON. JSON mode controls the requested format, but it does not guarantee that your application’s schema is complete or that the output will not be truncated.
response = client.chat.completions.create(
model="deepseek-flash",
messages=[
{"role": "system", "content": "Return only a JSON object with keys title and summary."},
{"role": "user", "content": "Summarize this text: ..."}
],
response_format={"type": "json_object"},
max_tokens=300,
)After receiving the response, parse it with your language’s JSON parser, validate required keys and data types, and reject or repair invalid application data. If the token limit is reached, the JSON can be cut off; increase the limit or shorten the prompt and retry under an idempotency policy that your application controls.
What errors and operational failures should you plan for?
Separate configuration errors from transient transport failures so that retries do not amplify a bad request.
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- Authentication failure: verify the environment variable is present, has no accidental whitespace, and belongs to the intended account. Replace a revoked key rather than retrying indefinitely.
- Invalid request: check the model name, required messages, parameter spelling, and whether an option is supported by that model.
- Timeout or connection reset: set a bounded connect and read timeout, cancel abandoned streams, and retry only when repeating the request is acceptable.
- Rate or quota response: apply exponential backoff with jitter, cap concurrent requests, and return a clear “try again” response to your user.
- Partial stream: preserve the text already delivered, mark the message incomplete, and offer a controlled retry rather than silently presenting it as final.
- Malformed structured output: validate JSON and schema before storing or executing any model-generated values.
Use a request correlation ID in your own logs. Record latency, status, model ID, token usage when available, and failure category, but redact API keys, authorization headers, and personal data in prompts.
Which models and prices should you use?
The current model page lists deepseek-flash and deepseek-v4-pro, along with context limits, supported features, and token rates. These are volatile service details: model names, capabilities, limits, and prices may change, and the pricing page states that prices can vary. Check the live DeepSeek model and pricing documentation immediately before deployment and again when you update a production model.
Do not hard-code a model name in multiple services. Put it in configuration, validate it during startup or deployment, and expose a controlled feature flag so you can move to a replacement model without editing application logic. Budgeting should use the provider’s current input and output token rates for the exact model and account region or billing arrangement you use; no permanent price should be inferred from an older example.
What is a production-ready deployment checklist?
- Create a DeepSeek API key and store it in a server-side secret manager.
- Configure the OpenAI-compatible client with
https://api.deepseek.com, or select the documented Anthropic-compatible base URL for that integration. - Set the model ID through deployment configuration and verify its current availability.
- Send a small non-streaming request to confirm authentication, routing, and message formatting.
- Add request timeouts, status handling, bounded retries, and concurrency limits.
- For streaming, parse event boundaries, handle empty metadata chunks, and stop on
data: [DONE]. - If forwarding to a browser, authenticate your user endpoint and never expose the provider key.
- Validate JSON-mode output against an application schema before using it.
- Monitor latency, errors, incomplete streams, and usage while redacting secrets.
- Recheck the live API reference whenever you change SDK versions, model IDs, parameters, or pricing assumptions.
Frequently Asked Questions
Can I call the DeepSeek API directly from a React or mobile app?
You should not place a reusable DeepSeek key in a React bundle or mobile binary. Put the key on a server endpoint, authenticate your users there, and return only the response data or a controlled stream to the client.
How can I test authentication without generating a long answer?
Send a short message with a small output limit from a server environment and inspect the HTTP status and response shape. This isolates key, base-URL, and model configuration problems before you add streaming or complex prompts.
Does streaming reduce the number of tokens billed?
Streaming changes delivery timing, not the amount of text the model generates. Usage accounting follows the provider’s current model and token rules; request usage metadata when supported and verify it against the live pricing documentation.
What should my application show if the stream ends unexpectedly?
Keep the partial text but label it incomplete, log the interruption category, and offer the user a retry action. Do not treat a connection close without the terminator as a confirmed final answer.
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