server: /v1/responses (partial) (#18486)

* from previous PR

* Make instruction(system) as first message

* Convert [input_message] (text/image/file)

* Rename convert_responses_to_chatcmpl(body) -> response_body

* Initial tool call support

* Erase instructions field from chatcmpl body

* Feed reasoning texts to chat template

* Use std::vector instead of opaque json array

* Make output_item.added events consistent

* Move `server_task_result_cmpl_partial::update` from header to source

* Match ID of output_item.added and .done events

* Add function_call only if there is no "fc_" prefix

* Add function call output at non-streaming API

* Test if ID is persistent

* Add doc

* Fix style - use trailing comma

* Rewrite state management

* catch up with upstream/master

* Fix style - "type" is the first item of SSE data

* Explicitly check "instructions" from response_body

* Make lambdas static

* Check if reasoning content exists

* Add `oai_resp_id` to task_result_state(also initialized at ctor), server_task_result_cmpl_partial, and server_task_result_cmpl_final

* Reject `input_file` since it is not supported by chatcmpl

* Add "fc_" prefix to non-straming function call id as coderabbit pointed out

---------

Co-authored-by: openingnow <>

tools/server/README.md

@@ -6,7 +6,7 @@ Set of LLM REST APIs and a web UI to interact with llama.cpp.
 
 **Features:**
  * LLM inference of F16 and quantized models on GPU and CPU
- * [OpenAI API](https://github.com/openai/openai-openapi) compatible chat completions and embeddings routes
+ * [OpenAI API](https://github.com/openai/openai-openapi) compatible chat completions, responses, and embeddings routes
  * [Anthropic Messages API](https://docs.anthropic.com/en/api/messages) compatible chat completions
  * Reranking endpoint (https://github.com/ggml-org/llama.cpp/pull/9510)
  * Parallel decoding with multi-user support
@@ -1267,6 +1267,49 @@ This provides information on the performance of the server. It also allows calcu
 
 The total number of tokens in context is equal to `prompt_n + cache_n + predicted_n`
 
+### POST `/v1/responses`: OpenAI-compatible Responses API
+
+*Options:*
+
+See [OpenAI Responses API documentation](https://platform.openai.com/docs/api-reference/responses).
+
+*Examples:*
+
+You can use either Python `openai` library with appropriate checkpoints:
+
+```python
+import openai
+
+client = openai.OpenAI(
+    base_url="http://localhost:8080/v1", # "http://<Your api-server IP>:port"
+    api_key = "sk-no-key-required"
+)
+
+response = client.responses.create(
+  model="gpt-4.1",
+  instructions="You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests.",
+  input="Write a limerick about python exceptions"
+)
+
+print(response.output_text)
+```
+
+... or raw HTTP requests:
+
+```shell
+curl http://localhost:8080/v1/responses \
+-H "Content-Type: application/json" \
+-H "Authorization: Bearer no-key" \
+-d '{
+"model": "gpt-4.1",
+"instructions": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests.",
+"input": "Write a limerick about python exceptions"
+}'
+```
+
+This endpoint works by converting Responses request into Chat Completions request.
+
+
 ### POST `/v1/embeddings`: OpenAI-compatible embeddings API
 
 This endpoint requires that the model uses a pooling different than type `none`. The embeddings are normalized using the Eucledian norm.