DeepSeek V4 Pro FIM API: Complete Missing Code

A function is almost finished. Its signature is already correct, the surrounding module is stable, and one block still needs an implementation.

Sending the entire file to a coding agent can create unnecessary work for this task. You may receive a rewritten function, additional explanations, or changes outside the section you intended to edit.

The DeepSeek V4 Pro FIM API offers a more focused request format. You provide the text before a gap and the text after it. The model generates a candidate for the missing section.

This tutorial builds that workflow in Python, checks the returned completion, and saves a candidate file for review. It also explains the compatibility checks required when connecting through an AI API gateway.

The endpoint matters here. A successful DeepSeek chat request does not establish that the same service supports fill-in-the-middle completion.

1. Understand the prefix, middle, and suffix

FIM means “fill in the middle.”

The input has two boundaries:

• The prefix contains the source text before the insertion point.
• The suffix contains the source text after the insertion point.

The response supplies the proposed middle.

Your application reconstructs the candidate using:

candidate = prefix + generated_middle + suffix

This makes the insertion boundary explicit. The client can preserve the surrounding text without asking the model to reproduce it.

Consider a utility function that clips a value to a permitted range. You already know its name, arguments, and expected behavior. You want the model to fill its body while leaving the following module content intact.

That is a useful FIM task because the change is local and its behavior can be checked independently.

A request to redesign authentication across several services has a different scope. It needs repository context, coordinated changes, and broader verification.

2. Check the documented FIM endpoint

DeepSeek documents FIM as a Beta feature. The official request uses:

POST https://api.deepseek.com/beta/completions

The current reference lists deepseek-flash and deepseek-v4-pro as supported model identifiers. The FIM guide states a 4K maximum completion length, and the model capability table identifies FIM as a non-thinking feature.

Use the dedicated DeepSeek FIM guide when checking these requirements.

Do not construct the request by copying a Chat Completions payload and changing its URL. FIM uses prompt and suffix; a chat request uses a conversation structure.

Likewise, the generated text is returned through a completion response. Your parser should read the response shape documented for this endpoint.

If a service only documents /chat/completions, ask whether FIM is available before building an editor integration around it.

3. Verify an API relay as a complete route

For an API gateway, confirm four items together:

An API relay may expose an upstream feature through a different path. It may also support a model for chat while omitting another endpoint.

For 4SAPI, check the current integration documentation and account model availability before selecting the FIM route. This tutorial does not assume that every DeepSeek channel exposes the Beta endpoint.

Keep the endpoint, key, and model in one configuration. Accidentally combining an upstream credential with a gateway URL creates a different integration problem from an invalid completion request.

Once those values are confirmed, the client below can use either the documented upstream endpoint or an explicitly supported gateway endpoint.

4. Define a small, testable completion task

Start with a function whose requirements are clear:

def clamp(value, lower, upper):
    """Raise ValueError for reversed bounds; otherwise clamp value."""

The required behavior is straightforward:

• Values below the lower bound return the lower bound.
• Values above the upper bound return the upper bound.
• Values inside the range remain unchanged.
• Reversed bounds raise ValueError.

These requirements give you a useful acceptance test. “Write good code” would provide a much weaker target.

For the suffix, include the real code immediately after the gap. Its purpose is to show where the insertion must stop and what follows it.

Avoid sending a large repository when a function signature, its contract, and nearby context are enough. Add context when the task requires it, rather than using maximum context as the default.

5. Build the Python request

Install the HTTP dependency:

python -m pip install requests

Configure these environment variables:

FIM_ENDPOINT
FIM_API_KEY
FIM_MODEL

For direct DeepSeek access, the documented endpoint is:

https://api.deepseek.com/beta/completions

For a gateway, use its confirmed endpoint and model identifier. Keep the API key out of the source file.

Save the following as complete_gap.py:

import json
import os
import tempfile
from pathlib import Path

import requests

endpoint = os.environ["FIM_ENDPOINT"]
api_key = os.environ["FIM_API_KEY"]
model = os.environ["FIM_MODEL"]

prefix = (
    "def clamp(value, lower, upper):\n"
    '    """Raise ValueError for reversed bounds; '
    'otherwise clamp value."""\n'
)

suffix = "\n\nEXAMPLE_BOUNDS = (0, 100)\n"

response = requests.post(
    endpoint,
    headers={
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json",
    },
    json={
        "model": model,
        "prompt": prefix,
        "suffix": suffix,
        "max_tokens": 512,
        "stream": False,
    },
    timeout=(10, 90),
    allow_redirects=False,
)

if response.status_code != 200:
    raise SystemExit(
        f"Request failed with HTTP {response.status_code}. "
        "Inspect the service error before retrying."
    )

payload = response.json()
choices = payload.get("choices")

if not isinstance(choices, list) or len(choices) != 1:
    raise SystemExit("Expected exactly one completion choice.")

choice = choices[0]

if choice.get("finish_reason") != "stop":
    raise SystemExit(
        "Completion was not accepted: "
        f"finish_reason={choice.get('finish_reason')!r}"
    )

middle = choice.get("text")

if not isinstance(middle, str) or not middle.strip():
    raise SystemExit("The completion did not contain usable text.")

candidate = prefix + middle + suffix

# Compile the source to check syntax. This does not execute it.
compile(candidate, "<fim-candidate>", "exec")

output_dir = Path(tempfile.mkdtemp(prefix="fim-review-"))
candidate_path = output_dir / "candidate.py"
candidate_path.write_text(candidate, encoding="utf-8")

record = {
    "requested_model": model,
    "returned_model": payload.get("model"),
    "response_id": payload.get("id"),
    "finish_reason": choice.get("finish_reason"),
    "usage": payload.get("usage"),
    "candidate_file": str(candidate_path),
}

(output_dir / "request-record.json").write_text(
    json.dumps(record, indent=2),
    encoding="utf-8",
)

print(f"Candidate saved for review: {candidate_path}")

The script sends one request. It saves the generated candidate in a new directory and leaves your project file untouched.

It also preserves a small request record. That record helps connect a candidate with the model route and response that produced it.

6. Preserve whitespace at the insertion boundary

Code completion is sensitive to whitespace.

The example uses middle.strip() only to detect an empty response. It inserts the original middle string into the candidate.

Do not replace that assignment with:

middle = choice["text"].strip()

Doing so can remove indentation or newlines that belong to the generated code.

The same principle applies to the suffix. If you normalize its leading whitespace or append extra indentation, you can create a syntax error that did not exist in the returned completion.

For an editor integration, record the exact prefix and suffix used for the request. Before applying the completion, confirm that the document still has the same surrounding text. A response generated for an earlier cursor position should not be inserted into a changed buffer.

7. Separate syntax checks from behavior checks

A candidate that compiles may still be wrong.

It could return the upper bound for every input, ignore reversed bounds, or implement a slightly different interpretation of the requested behavior.

After reviewing the generated code, test it against a small behavior matrix:

These are requirements for this example, not measurements of DeepSeek performance.

For a real application, include the edge cases that matter to your contract. Decide how the function should handle decimals, unsupported types, missing values, or other domain-specific inputs.

Do not let the model silently decide those product requirements during code completion.

8. Interpret an incomplete response correctly

The FIM API reference documents several finish reasons.

The example only accepts stop for automatic progression to syntax checking. Other outcomes need investigation.

A response ending because of a length limit may contain a plausible first half of a function. Appending the suffix can still create a candidate that is incomplete or semantically incorrect.

Increasing the output allowance may help when the intended implementation legitimately needs more space. It will not fix an unsuitable task boundary or a route that does not implement FIM correctly.

Preserve the failed request details and adjust one variable at a time. Repeatedly changing the model, prompt, endpoint, and output allowance makes the cause harder to identify.

9. Diagnose common integration failures

An “OpenAI-compatible” label is not a complete capability contract. Compatibility can differ by endpoint and parameter.

Capture the HTTP status and provider response identifier when troubleshooting. Never include the authorization header in an issue report or shared screenshot.

10. Evaluate an editor workflow before expanding it

Use a small collection of representative gaps from your own codebase.

Include different cases: a simple function body, a branch inside an existing function, a typed method, and a completion that must fit immediately before another declaration.

For each case, record:

• Whether the candidate compiled.
• Whether it passed the relevant behavior checks.
• Whether a developer accepted it.
• How much manual editing it required.
• How long the request took.
• Whether the document changed before the response arrived.

Acceptance rate is more useful than counting generated characters. A longer completion may create more cleanup work.

Also distinguish API latency from the complete user experience. Time spent waiting for an editor debounce, validating the response, and reviewing the candidate affects perceived responsiveness.

11. Know when to use an agent instead

FIM works best when the insertion point and surrounding context are already defined.

For cross-file changes, dependency investigation, or a task that requires navigating a repository, an agent workflow may fit better.

The existing DeepSeek V4 Pro with Claude Code guide covers that broader integration direction.

You can use both patterns in the same development process. A completion feature can handle small local edits, while an agent handles tasks that require exploration and coordinated changes.

Choose the workflow according to the job rather than expecting one request format to solve every coding problem.

Frequently asked questions

Is FIM the same as asking a chat model to finish code?

The desired result can look similar, but the request contract differs. FIM explicitly supplies the text before and after the missing section.

Can I use the normal DeepSeek chat URL?

Use the documented FIM endpoint. A successful chat request does not prove that a FIM request will work on the same path.

Does syntax validation make the generated code safe and correct?

No. It checks whether the source can be compiled. Behavior, dependencies, permissions, and application requirements need separate review.

Can an API gateway support chat but not FIM?

Yes. Verify the specific endpoint and parameters rather than inferring support from the model name alone.

Start with one verified completion route

Open the 4SAPI console, register or sign in, and confirm whether your account has a DeepSeek route that supports FIM.

If it does, create a dedicated test key and run one bounded completion through the confirmed endpoint. Review the candidate, check its behavior, and retain the request record before connecting the workflow to an editor.

That first successful, reviewed insertion gives you a concrete integration result to build on.