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server : add Anthropic Messages API support (#17570) 

* server : add Anthropic Messages API support

* remove -@pytest.mark.slow from tool calling/jinja tests

* server : remove unused code and slow/skip on test_anthropic_vision_base64_with_multimodal_model in test_anthropic_api.py

* server : removed redundant n field logic in anthropic_params_from_json

* server : use single error object instead of error_array in streaming response handler for /v1/chat/completions and use unordered_set instead of set in to_json_anthropic_stream()

* server : refactor Anthropic API to use OAI conversion

* make sure basic test always go first

* clean up

* clean up api key check, add test

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
This commit is contained in:
Fredrik Hultin 2025-11-28 12:57:04 +01:00 committed by GitHub
parent ff55414c42
commit ddf9f94389
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11 changed files with 1553 additions and 70 deletions
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72
tools/server/README.md
View File

@ -7,6 +7,7 @@ Set of LLM REST APIs and a simple web front end 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
* [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
* Continuous batching
@ -1352,6 +1353,77 @@ See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-r
}'
```
### POST `/v1/messages`: Anthropic-compatible Messages API
Given a list of `messages`, returns the assistant's response. Streaming is supported via Server-Sent Events. While no strong claims of compatibility with the Anthropic API spec are made, in our experience it suffices to support many apps.
*Options:*
See [Anthropic Messages API documentation](https://docs.anthropic.com/en/api/messages). Tool use requires `--jinja` flag.
`model`: Model identifier (required)
`messages`: Array of message objects with `role` and `content` (required)
`max_tokens`: Maximum tokens to generate (default: 4096)
`system`: System prompt as string or array of content blocks
`temperature`: Sampling temperature 0-1 (default: 1.0)
`top_p`: Nucleus sampling (default: 1.0)
`top_k`: Top-k sampling
`stop_sequences`: Array of stop sequences
`stream`: Enable streaming (default: false)
`tools`: Array of tool definitions (requires `--jinja`)
`tool_choice`: Tool selection mode (`{"type": "auto"}`, `{"type": "any"}`, or `{"type": "tool", "name": "..."}`)
*Examples:*
```shell
curl http://localhost:8080/v1/messages \
-H "Content-Type: application/json" \
-H "x-api-key: your-api-key" \
-d '{
"model": "gpt-4",
"max_tokens": 1024,
"system": "You are a helpful assistant.",
"messages": [
{"role": "user", "content": "Hello!"}
]
}'
```
### POST `/v1/messages/count_tokens`: Token Counting
Counts the number of tokens in a request without generating a response.
Accepts the same parameters as `/v1/messages`. The `max_tokens` parameter is not required.
*Example:*
```shell
curl http://localhost:8080/v1/messages/count_tokens \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-4",
"messages": [
{"role": "user", "content": "Hello!"}
]
}'
```
*Response:*
```json
{"input_tokens": 10}
```
## More examples
### Interactive mode

242
tools/server/server-common.cpp
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@ -725,7 +725,6 @@ std::vector<server_tokens> tokenize_input_prompts(const llama_vocab * vocab, mtm
return result;
}
//
// OAI utils
//
@ -1048,6 +1047,222 @@ json oaicompat_chat_params_parse(
return llama_params;
}
json convert_anthropic_to_oai(const json & body) {
json oai_body;
// Convert system prompt
json oai_messages = json::array();
auto system_param = json_value(body, "system", json());
if (!system_param.is_null()) {
std::string system_content;
if (system_param.is_string()) {
system_content = system_param.get<std::string>();
} else if (system_param.is_array()) {
for (const auto & block : system_param) {
if (json_value(block, "type", std::string()) == "text") {
system_content += json_value(block, "text", std::string());
}
}
}
oai_messages.push_back({
{"role", "system"},
{"content", system_content}
});
}
// Convert messages
if (!body.contains("messages")) {
throw std::runtime_error("'messages' is required");
}
const json & messages = body.at("messages");
if (messages.is_array()) {
for (const auto & msg : messages) {
std::string role = json_value(msg, "role", std::string());
if (!msg.contains("content")) {
if (role == "assistant") {
continue;
}
oai_messages.push_back(msg);
continue;
}
const json & content = msg.at("content");
if (content.is_string()) {
oai_messages.push_back(msg);
continue;
}
if (!content.is_array()) {
oai_messages.push_back(msg);
continue;
}
json tool_calls = json::array();
json converted_content = json::array();
json tool_results = json::array();
bool has_tool_calls = false;
for (const auto & block : content) {
std::string type = json_value(block, "type", std::string());
if (type == "text") {
converted_content.push_back(block);
} else if (type == "image") {
json source = json_value(block, "source", json::object());
std::string source_type = json_value(source, "type", std::string());
if (source_type == "base64") {
std::string media_type = json_value(source, "media_type", std::string("image/jpeg"));
std::string data = json_value(source, "data", std::string());
std::ostringstream ss;
ss << "data:" << media_type << ";base64," << data;
converted_content.push_back({
{"type", "image_url"},
{"image_url", {
{"url", ss.str()}
}}
});
} else if (source_type == "url") {
std::string url = json_value(source, "url", std::string());
converted_content.push_back({
{"type", "image_url"},
{"image_url", {
{"url", url}
}}
});
}
} else if (type == "tool_use") {
tool_calls.push_back({
{"id", json_value(block, "id", std::string())},
{"type", "function"},
{"function", {
{"name", json_value(block, "name", std::string())},
{"arguments", json_value(block, "input", json::object()).dump()}
}}
});
has_tool_calls = true;
} else if (type == "tool_result") {
std::string tool_use_id = json_value(block, "tool_use_id", std::string());
auto result_content = json_value(block, "content", json());
std::string result_text;
if (result_content.is_string()) {
result_text = result_content.get<std::string>();
} else if (result_content.is_array()) {
for (const auto & c : result_content) {
if (json_value(c, "type", std::string()) == "text") {
result_text += json_value(c, "text", std::string());
}
}
}
tool_results.push_back({
{"role", "tool"},
{"tool_call_id", tool_use_id},
{"content", result_text}
});
}
}
if (!converted_content.empty() || has_tool_calls) {
json new_msg = {{"role", role}};
if (!converted_content.empty()) {
new_msg["content"] = converted_content;
} else if (has_tool_calls) {
new_msg["content"] = "";
}
if (!tool_calls.empty()) {
new_msg["tool_calls"] = tool_calls;
}
oai_messages.push_back(new_msg);
}
for (const auto & tool_msg : tool_results) {
oai_messages.push_back(tool_msg);
}
}
}
oai_body["messages"] = oai_messages;
// Convert tools
if (body.contains("tools")) {
const json & tools = body.at("tools");
if (tools.is_array()) {
json oai_tools = json::array();
for (const auto & tool : tools) {
oai_tools.push_back({
{"type", "function"},
{"function", {
{"name", json_value(tool, "name", std::string())},
{"description", json_value(tool, "description", std::string())},
{"parameters", tool.contains("input_schema") ? tool.at("input_schema") : json::object()}
}}
});
}
oai_body["tools"] = oai_tools;
}
}
// Convert tool_choice
if (body.contains("tool_choice")) {
const json & tc = body.at("tool_choice");
if (tc.is_object()) {
std::string type = json_value(tc, "type", std::string());
if (type == "auto") {
oai_body["tool_choice"] = "auto";
} else if (type == "any" || type == "tool") {
oai_body["tool_choice"] = "required";
}
}
}
// Convert stop_sequences to stop
if (body.contains("stop_sequences")) {
oai_body["stop"] = body.at("stop_sequences");
}
// Handle max_tokens (required in Anthropic, but we're permissive)
if (body.contains("max_tokens")) {
oai_body["max_tokens"] = body.at("max_tokens");
} else {
oai_body["max_tokens"] = 4096;
}
// Pass through common params
for (const auto & key : {"temperature", "top_p", "top_k", "stream"}) {
if (body.contains(key)) {
oai_body[key] = body.at(key);
}
}
// Handle Anthropic-specific thinking param
if (body.contains("thinking")) {
json thinking = json_value(body, "thinking", json::object());
std::string thinking_type = json_value(thinking, "type", std::string());
if (thinking_type == "enabled") {
int budget_tokens = json_value(thinking, "budget_tokens", 10000);
oai_body["thinking_budget_tokens"] = budget_tokens;
}
}
// Handle Anthropic-specific metadata param
if (body.contains("metadata")) {
json metadata = json_value(body, "metadata", json::object());
std::string user_id = json_value(metadata, "user_id", std::string());
if (!user_id.empty()) {
oai_body["__metadata_user_id"] = user_id;
}
}
return oai_body;
}
json format_embeddings_response_oaicompat(const json & request, const json & embeddings, bool use_base64) {
json data = json::array();
int32_t n_tokens = 0;
@ -1211,7 +1426,7 @@ std::string tokens_to_output_formatted_string(const llama_context * ctx, const l
// format server-sent event (SSE), return the formatted string to send
// note: if data is a json array, it will be sent as multiple events, one per item
std::string format_sse(const json & data) {
std::string format_oai_sse(const json & data) {
std::ostringstream ss;
auto send_single = [&ss](const json & data) {
ss << "data: " <<
@ -1230,6 +1445,29 @@ std::string format_sse(const json & data) {
return ss.str();
}
std::string format_anthropic_sse(const json & data) {
std::ostringstream ss;
auto send_event = [&ss](const json & event_obj) {
if (event_obj.contains("event") && event_obj.contains("data")) {
ss << "event: " << event_obj.at("event").get<std::string>() << "\n";
ss << "data: " << safe_json_to_str(event_obj.at("data")) << "\n\n";
} else {
ss << "data: " << safe_json_to_str(event_obj) << "\n\n";
}
};
if (data.is_array()) {
for (const auto & event : data) {
send_event(event);
}
} else {
send_event(data);
}
return ss.str();
}
bool is_valid_utf8(const std::string & str) {
const unsigned char* bytes = reinterpret_cast<const unsigned char*>(str.data());
const unsigned char* end = bytes + str.length();

8
tools/server/server-common.h
View File

@ -294,6 +294,9 @@ json oaicompat_chat_params_parse(
const oaicompat_parser_options & opt,
std::vector<raw_buffer> & out_files);
// convert Anthropic Messages API format to OpenAI Chat Completions API format
json convert_anthropic_to_oai(const json & body);
// TODO: move it to server-task.cpp
json format_embeddings_response_oaicompat(const json & request, const json & embeddings, bool use_base64 = false);
@ -320,7 +323,10 @@ std::string tokens_to_output_formatted_string(const llama_context * ctx, const l
// format server-sent event (SSE), return the formatted string to send
// note: if data is a json array, it will be sent as multiple events, one per item
std::string format_sse(const json & data);
std::string format_oai_sse(const json & data);
// format Anthropic-style SSE with event types
std::string format_anthropic_sse(const json & data);
bool is_valid_utf8(const std::string & str);

23
tools/server/server-http.cpp
View File

@ -136,15 +136,22 @@ bool server_http_context::init(const common_params & params) {
return true;
}
// Check for API key in the header
auto auth_header = req.get_header_value("Authorization");
std::string prefix = "Bearer ";
if (auth_header.substr(0, prefix.size()) == prefix) {
std::string received_api_key = auth_header.substr(prefix.size());
if (std::find(api_keys.begin(), api_keys.end(), received_api_key) != api_keys.end()) {
return true; // API key is valid
// Check for API key in the Authorization header
std::string req_api_key = req.get_header_value("Authorization");
if (req_api_key.empty()) {
// retry with anthropic header
req_api_key = req.get_header_value("X-Api-Key");
}
// remove the "Bearer " prefix if needed
std::string prefix = "Bearer ";
if (req_api_key.substr(0, prefix.size()) == prefix) {
req_api_key = req_api_key.substr(prefix.size());
}
// validate the API key
if (std::find(api_keys.begin(), api_keys.end(), req_api_key) != api_keys.end()) {
return true; // API key is valid
}
// API key is invalid or not provided

304
tools/server/server-task.cpp
View File

@ -565,15 +565,17 @@ std::vector<unsigned char> completion_token_output::str_to_bytes(const std::stri
// server_task_result_cmpl_final
//
json server_task_result_cmpl_final::to_json() {
switch (oaicompat) {
case OAICOMPAT_TYPE_NONE:
switch (res_type) {
case TASK_RESPONSE_TYPE_NONE:
return to_json_non_oaicompat();
case OAICOMPAT_TYPE_COMPLETION:
case TASK_RESPONSE_TYPE_OAI_CMPL:
return to_json_oaicompat();
case OAICOMPAT_TYPE_CHAT:
case TASK_RESPONSE_TYPE_OAI_CHAT:
return stream ? to_json_oaicompat_chat_stream() : to_json_oaicompat_chat();
case TASK_RESPONSE_TYPE_ANTHROPIC:
return stream ? to_json_anthropic_stream() : to_json_anthropic();
default:
GGML_ASSERT(false && "Invalid oaicompat_type");
GGML_ASSERT(false && "Invalid task_response_type");
}
}
@ -768,19 +770,203 @@ json server_task_result_cmpl_final::to_json_oaicompat_chat_stream() {
return deltas;
}
json server_task_result_cmpl_final::to_json_anthropic() {
std::string stop_reason = "max_tokens";
if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
stop_reason = oaicompat_msg.tool_calls.empty() ? "end_turn" : "tool_use";
}
json content_blocks = json::array();
common_chat_msg msg;
if (!oaicompat_msg.empty()) {
msg = oaicompat_msg;
} else {
msg.role = "assistant";
msg.content = content;
}
if (!msg.content.empty()) {
content_blocks.push_back({
{"type", "text"},
{"text", msg.content}
});
}
for (const auto & tool_call : msg.tool_calls) {
json tool_use_block = {
{"type", "tool_use"},
{"id", tool_call.id},
{"name", tool_call.name}
};
try {
tool_use_block["input"] = json::parse(tool_call.arguments);
} catch (const std::exception &) {
tool_use_block["input"] = json::object();
}
content_blocks.push_back(tool_use_block);
}
json res = {
{"id", oaicompat_cmpl_id},
{"type", "message"},
{"role", "assistant"},
{"content", content_blocks},
{"model", oaicompat_model},
{"stop_reason", stop_reason},
{"stop_sequence", stopping_word.empty() ? nullptr : json(stopping_word)},
{"usage", {
{"input_tokens", n_prompt_tokens},
{"output_tokens", n_decoded}
}}
};
return res;
}
json server_task_result_cmpl_final::to_json_anthropic_stream() {
json events = json::array();
std::string stop_reason = "max_tokens";
if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
stop_reason = oaicompat_msg.tool_calls.empty() ? "end_turn" : "tool_use";
}
bool has_text = !oaicompat_msg.content.empty();
size_t num_tool_calls = oaicompat_msg.tool_calls.size();
bool text_block_started = false;
std::unordered_set<size_t> tool_calls_started;
for (const auto & diff : oaicompat_msg_diffs) {
if (!diff.content_delta.empty()) {
if (!text_block_started) {
events.push_back({
{"event", "content_block_start"},
{"data", {
{"type", "content_block_start"},
{"index", 0},
{"content_block", {
{"type", "text"},
{"text", ""}
}}
}}
});
text_block_started = true;
}
events.push_back({
{"event", "content_block_delta"},
{"data", {
{"type", "content_block_delta"},
{"index", 0},
{"delta", {
{"type", "text_delta"},
{"text", diff.content_delta}
}}
}}
});
}
if (diff.tool_call_index != std::string::npos) {
size_t content_block_index = (has_text ? 1 : 0) + diff.tool_call_index;
if (tool_calls_started.find(diff.tool_call_index) == tool_calls_started.end()) {
const auto & full_tool_call = oaicompat_msg.tool_calls[diff.tool_call_index];
events.push_back({
{"event", "content_block_start"},
{"data", {
{"type", "content_block_start"},
{"index", content_block_index},
{"content_block", {
{"type", "tool_use"},
{"id", full_tool_call.id},
{"name", full_tool_call.name}
}}
}}
});
tool_calls_started.insert(diff.tool_call_index);
}
if (!diff.tool_call_delta.arguments.empty()) {
events.push_back({
{"event", "content_block_delta"},
{"data", {
{"type", "content_block_delta"},
{"index", content_block_index},
{"delta", {
{"type", "input_json_delta"},
{"partial_json", diff.tool_call_delta.arguments}
}}
}}
});
}
}
}
if (has_text) {
events.push_back({
{"event", "content_block_stop"},
{"data", {
{"type", "content_block_stop"},
{"index", 0}
}}
});
}
for (size_t i = 0; i < num_tool_calls; i++) {
size_t content_block_index = (has_text ? 1 : 0) + i;
events.push_back({
{"event", "content_block_stop"},
{"data", {
{"type", "content_block_stop"},
{"index", content_block_index}
}}
});
}
events.push_back({
{"event", "message_delta"},
{"data", {
{"type", "message_delta"},
{"delta", {
{"stop_reason", stop_reason},
{"stop_sequence", stopping_word.empty() ? nullptr : json(stopping_word)}
}},
{"usage", {
{"output_tokens", n_decoded}
}}
}}
});
events.push_back({
{"event", "message_stop"},
{"data", {
{"type", "message_stop"}
}}
});
return events;
}
//
// server_task_result_cmpl_partial
//
json server_task_result_cmpl_partial::to_json() {
switch (oaicompat) {
case OAICOMPAT_TYPE_NONE:
switch (res_type) {
case TASK_RESPONSE_TYPE_NONE:
return to_json_non_oaicompat();
case OAICOMPAT_TYPE_COMPLETION:
case TASK_RESPONSE_TYPE_OAI_CMPL:
return to_json_oaicompat();
case OAICOMPAT_TYPE_CHAT:
case TASK_RESPONSE_TYPE_OAI_CHAT:
return to_json_oaicompat_chat();
case TASK_RESPONSE_TYPE_ANTHROPIC:
return to_json_anthropic();
default:
GGML_ASSERT(false && "Invalid oaicompat_type");
GGML_ASSERT(false && "Invalid task_response_type");
}
}
@ -905,7 +1091,7 @@ json server_task_result_cmpl_partial::to_json_oaicompat_chat() {
// server_task_result_embd
//
json server_task_result_embd::to_json() {
return oaicompat == OAICOMPAT_TYPE_EMBEDDING
return res_type == TASK_RESPONSE_TYPE_OAI_EMBD
? to_json_oaicompat()
: to_json_non_oaicompat();
}
@ -936,6 +1122,102 @@ json server_task_result_rerank::to_json() {
};
}
json server_task_result_cmpl_partial::to_json_anthropic() {
json events = json::array();
bool first = (n_decoded == 1);
static bool text_block_started = false;
if (first) {
text_block_started = false;
events.push_back({
{"event", "message_start"},
{"data", {
{"type", "message_start"},
{"message", {
{"id", oaicompat_cmpl_id},
{"type", "message"},
{"role", "assistant"},
{"content", json::array()},
{"model", oaicompat_model},
{"stop_reason", nullptr},
{"stop_sequence", nullptr},
{"usage", {
{"input_tokens", n_prompt_tokens},
{"output_tokens", 0}
}}
}}
}}
});
}
for (const auto & diff : oaicompat_msg_diffs) {
if (!diff.content_delta.empty()) {
if (!text_block_started) {
events.push_back({
{"event", "content_block_start"},
{"data", {
{"type", "content_block_start"},
{"index", 0},
{"content_block", {
{"type", "text"},
{"text", ""}
}}
}}
});
text_block_started = true;
}
events.push_back({
{"event", "content_block_delta"},
{"data", {
{"type", "content_block_delta"},
{"index", 0},
{"delta", {
{"type", "text_delta"},
{"text", diff.content_delta}
}}
}}
});
}
if (diff.tool_call_index != std::string::npos) {
size_t content_block_index = (text_block_started ? 1 : 0) + diff.tool_call_index;
if (!diff.tool_call_delta.name.empty()) {
events.push_back({
{"event", "content_block_start"},
{"data", {
{"type", "content_block_start"},
{"index", content_block_index},
{"content_block", {
{"type", "tool_use"},
{"id", diff.tool_call_delta.id},
{"name", diff.tool_call_delta.name}
}}
}}
});
}
if (!diff.tool_call_delta.arguments.empty()) {
events.push_back({
{"event", "content_block_delta"},
{"data", {
{"type", "content_block_delta"},
{"index", content_block_index},
{"delta", {
{"type", "input_json_delta"},
{"partial_json", diff.tool_call_delta.arguments}
}}
}}
});
}
}
}
return events;
}
//
// server_task_result_error
//

33
tools/server/server-task.h
View File

@ -27,11 +27,12 @@ enum server_task_type {
};
// TODO: change this to more generic "response_format" to replace the "format_response_*" in server-common
enum oaicompat_type {
OAICOMPAT_TYPE_NONE,
OAICOMPAT_TYPE_CHAT,
OAICOMPAT_TYPE_COMPLETION,
OAICOMPAT_TYPE_EMBEDDING,
enum task_response_type {
TASK_RESPONSE_TYPE_NONE, // llama.cpp native format
TASK_RESPONSE_TYPE_OAI_CHAT,
TASK_RESPONSE_TYPE_OAI_CMPL,
TASK_RESPONSE_TYPE_OAI_EMBD,
TASK_RESPONSE_TYPE_ANTHROPIC,
};
enum stop_type {
@ -66,9 +67,9 @@ struct task_params {
struct common_params_sampling sampling;
struct common_params_speculative speculative;
// OAI-compat fields
// response formatting
bool verbose = false;
oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
task_response_type res_type = TASK_RESPONSE_TYPE_NONE;
std::string oaicompat_model;
std::string oaicompat_cmpl_id;
common_chat_syntax oaicompat_chat_syntax;
@ -227,9 +228,9 @@ struct server_task_result_cmpl_final : server_task_result {
task_params generation_params;
// OAI-compat fields
// response formatting
bool verbose = false;
oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
task_response_type res_type = TASK_RESPONSE_TYPE_NONE;
std::string oaicompat_model;
std::string oaicompat_cmpl_id;
common_chat_msg oaicompat_msg;
@ -253,6 +254,10 @@ struct server_task_result_cmpl_final : server_task_result {
json to_json_oaicompat_chat();
json to_json_oaicompat_chat_stream();
json to_json_anthropic();
json to_json_anthropic_stream();
};
struct server_task_result_cmpl_partial : server_task_result {
@ -270,9 +275,9 @@ struct server_task_result_cmpl_partial : server_task_result {
result_timings timings;
result_prompt_progress progress;
// OAI-compat fields
// response formatting
bool verbose = false;
oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
task_response_type res_type = TASK_RESPONSE_TYPE_NONE;
std::string oaicompat_model;
std::string oaicompat_cmpl_id;
std::vector<common_chat_msg_diff> oaicompat_msg_diffs;
@ -292,6 +297,8 @@ struct server_task_result_cmpl_partial : server_task_result {
json to_json_oaicompat();
json to_json_oaicompat_chat();
json to_json_anthropic();
};
struct server_task_result_embd : server_task_result {
@ -300,8 +307,8 @@ struct server_task_result_embd : server_task_result {
int32_t n_tokens;
// OAI-compat fields
oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
// response formatting
task_response_type res_type = TASK_RESPONSE_TYPE_NONE;
virtual int get_index() override {
return index;

95
tools/server/server.cpp
View File

@ -1255,7 +1255,7 @@ struct server_context {
res->post_sampling_probs = slot.task->params.post_sampling_probs;
res->verbose = slot.task->params.verbose;
res->oaicompat = slot.task->params.oaicompat;
res->res_type = slot.task->params.res_type;
res->oaicompat_model = slot.task->params.oaicompat_model;
res->oaicompat_cmpl_id = slot.task->params.oaicompat_cmpl_id;
@ -1297,7 +1297,7 @@ struct server_context {
res->verbose = slot.task->params.verbose;
res->stream = slot.task->params.stream;
res->include_usage = slot.task->params.include_usage;
res->oaicompat = slot.task->params.oaicompat;
res->res_type = slot.task->params.res_type;
res->oaicompat_model = slot.task->params.oaicompat_model;
res->oaicompat_cmpl_id = slot.task->params.oaicompat_cmpl_id;
res->oaicompat_msg = slot.update_chat_msg(res->oaicompat_msg_diffs);
@ -1328,7 +1328,7 @@ struct server_context {
res->id = slot.task->id;
res->index = slot.task->index;
res->n_tokens = slot.task->n_tokens();
res->oaicompat = slot.task->params.oaicompat;
res->res_type = slot.task->params.res_type;
const int n_embd = llama_model_n_embd(model);
@ -2951,7 +2951,7 @@ public:
data,
files,
req.should_stop,
OAICOMPAT_TYPE_NONE); // infill is not OAI compatible
TASK_RESPONSE_TYPE_NONE); // infill is not OAI compatible
};
server_http_context::handler_t post_completions = [this](const server_http_req & req) {
@ -2962,7 +2962,7 @@ public:
body,
files,
req.should_stop,
OAICOMPAT_TYPE_NONE);
TASK_RESPONSE_TYPE_NONE);
};
server_http_context::handler_t post_completions_oai = [this](const server_http_req & req) {
@ -2973,7 +2973,7 @@ public:
body,
files,
req.should_stop,
OAICOMPAT_TYPE_COMPLETION);
TASK_RESPONSE_TYPE_OAI_CMPL);
};
server_http_context::handler_t post_chat_completions = [this](const server_http_req & req) {
@ -2988,7 +2988,38 @@ public:
body_parsed,
files,
req.should_stop,
OAICOMPAT_TYPE_CHAT);
TASK_RESPONSE_TYPE_OAI_CHAT);
};
server_http_context::handler_t post_anthropic_messages = [this](const server_http_req & req) {
std::vector<raw_buffer> files;
json body = convert_anthropic_to_oai(json::parse(req.body));
json body_parsed = oaicompat_chat_params_parse(
body,
ctx_server.oai_parser_opt,
files);
return handle_completions_impl(
SERVER_TASK_TYPE_COMPLETION,
body_parsed,
files,
req.should_stop,
TASK_RESPONSE_TYPE_ANTHROPIC);
};
server_http_context::handler_t post_anthropic_count_tokens = [this](const server_http_req & req) {
auto res = std::make_unique<server_res_generator>(ctx_server);
std::vector<raw_buffer> files;
json body = convert_anthropic_to_oai(json::parse(req.body));
json body_parsed = oaicompat_chat_params_parse(
body,
ctx_server.oai_parser_opt,
files);
json prompt = body_parsed.at("prompt");
llama_tokens tokens = tokenize_mixed(ctx_server.vocab, prompt, true, true);
res->ok({{"input_tokens", static_cast<int>(tokens.size())}});
return res;
};
// same with handle_chat_completions, but without inference part
@ -3107,11 +3138,11 @@ public:
};
server_http_context::handler_t post_embeddings = [this](const server_http_req & req) {
return handle_embeddings_impl(req, OAICOMPAT_TYPE_NONE);
return handle_embeddings_impl(req, TASK_RESPONSE_TYPE_NONE);
};
server_http_context::handler_t post_embeddings_oai = [this](const server_http_req & req) {
return handle_embeddings_impl(req, OAICOMPAT_TYPE_EMBEDDING);
return handle_embeddings_impl(req, TASK_RESPONSE_TYPE_OAI_EMBD);
};
server_http_context::handler_t post_rerank = [this](const server_http_req & req) {
@ -3262,7 +3293,7 @@ private:
const json & data,
const std::vector<raw_buffer> & files,
const std::function<bool()> & should_stop,
oaicompat_type oaicompat) {
task_response_type res_type) {
GGML_ASSERT(type == SERVER_TASK_TYPE_COMPLETION || type == SERVER_TASK_TYPE_INFILL);
auto res = std::make_unique<server_res_generator>(ctx_server);
@ -3279,7 +3310,7 @@ private:
// process prompt
std::vector<server_tokens> inputs;
if (oaicompat && ctx_server.mctx != nullptr) {
if (res_type != TASK_RESPONSE_TYPE_NONE && ctx_server.mctx != nullptr) {
// This is the case used by OAI compatible chat path with MTMD. TODO It can be moved to the path below.
inputs.push_back(process_mtmd_prompt(ctx_server.mctx, prompt.get<std::string>(), files));
} else {
@ -3301,7 +3332,7 @@ private:
task.id_slot = json_value(data, "id_slot", -1);
// OAI-compat
task.params.oaicompat = oaicompat;
task.params.res_type = res_type;
task.params.oaicompat_cmpl_id = completion_id;
// oaicompat_model is already populated by params_from_json_cmpl
@ -3352,10 +3383,14 @@ private:
}
// next responses are streamed
res->data = format_sse(first_result->to_json()); // to be sent immediately
if (res_type == TASK_RESPONSE_TYPE_ANTHROPIC) {
res->data = format_anthropic_sse(first_result->to_json());
} else {
res->data = format_oai_sse(first_result->to_json()); // to be sent immediately
}
res->status = 200;
res->content_type = "text/event-stream";
res->next = [res_this = res.get(), oaicompat, &should_stop](std::string & output) -> bool {
res->next = [res_this = res.get(), res_type, &should_stop](std::string & output) -> bool {
if (should_stop()) {
SRV_DBG("%s", "stopping streaming due to should_stop condition\n");
return false; // should_stop condition met
@ -3372,7 +3407,10 @@ private:
// check if there is more data
if (!rd.has_next()) {
if (oaicompat != OAICOMPAT_TYPE_NONE) {
if (res_type == TASK_RESPONSE_TYPE_ANTHROPIC) {
// Anthropic doesn't send [DONE], message_stop was already sent
output = "";
} else if (res_type != TASK_RESPONSE_TYPE_NONE) {
output = "data: [DONE]\n\n";
} else {
output = "";
@ -3391,7 +3429,14 @@ private:
// send the results
json res_json = result->to_json();
if (result->is_error()) {
output = format_sse(json {{ "error", res_json }});
if (res_type == TASK_RESPONSE_TYPE_ANTHROPIC) {
output = format_anthropic_sse({
{"event", "error"},
{"data", res_json},
});
} else {
output = format_oai_sse(json {{ "error", res_json }});
}
SRV_DBG("%s", "error received during streaming, terminating stream\n");
return false; // terminate on error
} else {
@ -3399,7 +3444,11 @@ private:
dynamic_cast<server_task_result_cmpl_partial*>(result.get()) != nullptr
|| dynamic_cast<server_task_result_cmpl_final*>(result.get()) != nullptr
);
output = format_sse(res_json);
if (res_type == TASK_RESPONSE_TYPE_ANTHROPIC) {
output = format_anthropic_sse(res_json);
} else {
output = format_oai_sse(res_json);
}
}
// has next data, continue
@ -3507,14 +3556,14 @@ private:
return res;
}
std::unique_ptr<server_res_generator> handle_embeddings_impl(const server_http_req & req, oaicompat_type oaicompat) {
std::unique_ptr<server_res_generator> handle_embeddings_impl(const server_http_req & req, task_response_type res_type) {
auto res = std::make_unique<server_res_generator>(ctx_server);
if (!ctx_server.params_base.embedding) {
res->error(format_error_response("This server does not support embeddings. Start it with `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
return res;
}
if (oaicompat != OAICOMPAT_TYPE_NONE && llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) {
if (res_type != TASK_RESPONSE_TYPE_NONE && llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) {
res->error(format_error_response("Pooling type 'none' is not OAI compatible. Please use a different pooling type", ERROR_TYPE_INVALID_REQUEST));
return res;
}
@ -3526,7 +3575,7 @@ private:
if (body.count("input") != 0) {
prompt = body.at("input");
} else if (body.contains("content")) {
oaicompat = OAICOMPAT_TYPE_NONE; // "content" field is not OAI compatible
res_type = TASK_RESPONSE_TYPE_NONE; // "content" field is not OAI compatible
prompt = body.at("content");
} else {
res->error(format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST));
@ -3574,7 +3623,7 @@ private:
task.tokens = std::move(tokenized_prompts[i]);
// OAI-compat
task.params.oaicompat = oaicompat;
task.params.res_type = res_type;
task.params.embd_normalize = embd_normalize;
tasks.push_back(std::move(task));
@ -3599,7 +3648,7 @@ private:
}
// write JSON response
json root = oaicompat == OAICOMPAT_TYPE_EMBEDDING
json root = res_type == TASK_RESPONSE_TYPE_OAI_EMBD
? format_embeddings_response_oaicompat(body, responses, use_base64)
: json(responses);
res->ok(root);
@ -3712,6 +3761,8 @@ int main(int argc, char ** argv) {
ctx_http.post("/chat/completions", ex_wrapper(routes.post_chat_completions));
ctx_http.post("/v1/chat/completions", ex_wrapper(routes.post_chat_completions));
ctx_http.post("/api/chat", ex_wrapper(routes.post_chat_completions)); // ollama specific endpoint
ctx_http.post("/v1/messages", ex_wrapper(routes.post_anthropic_messages)); // anthropic messages API
ctx_http.post("/v1/messages/count_tokens", ex_wrapper(routes.post_anthropic_count_tokens)); // anthropic token counting
ctx_http.post("/infill", ex_wrapper(routes.post_infill));
ctx_http.post("/embedding", ex_wrapper(routes.post_embeddings)); // legacy
ctx_http.post("/embeddings", ex_wrapper(routes.post_embeddings));

6
tools/server/tests/conftest.py
View File

@ -13,3 +13,9 @@ def stop_server_after_each_test():
) # copy the set to prevent 'Set changed size during iteration'
for server in instances:
server.stop()
@pytest.fixture(scope="module", autouse=True)
def do_something():
# this will be run once per test session, before any tests
ServerPreset.load_all()

6
tools/server/tests/unit/test_basic.py
View File

@ -5,12 +5,6 @@ from utils import *
server = ServerPreset.tinyllama2()
@pytest.fixture(scope="session", autouse=True)
def do_something():
# this will be run once per test session, before any tests
ServerPreset.load_all()
@pytest.fixture(autouse=True)
def create_server():
global server

807
tools/server/tests/unit/test_compat_anthropic.py Normal file
View File

@ -0,0 +1,807 @@
#!/usr/bin/env python3
import pytest
import base64
import requests
from utils import *
server: ServerProcess
def get_test_image_base64() -> str:
"""Get a test image in base64 format"""
# Use the same test image as test_vision_api.py
IMG_URL = "https://huggingface.co/ggml-org/tinygemma3-GGUF/resolve/main/test/11_truck.png"
response = requests.get(IMG_URL)
response.raise_for_status()
return base64.b64encode(response.content).decode("utf-8")
@pytest.fixture(autouse=True)
def create_server():
global server
server = ServerPreset.tinyllama2()
server.model_alias = "tinyllama-2-anthropic"
server.server_port = 8082
server.n_slots = 1
server.n_ctx = 8192
server.n_batch = 2048
@pytest.fixture
def vision_server():
"""Separate fixture for vision tests that require multimodal support"""
global server
server = ServerPreset.tinygemma3()
server.offline = False # Allow downloading the model
server.model_alias = "tinygemma3-anthropic"
server.server_port = 8083 # Different port to avoid conflicts
server.n_slots = 1
return server
# Basic message tests
def test_anthropic_messages_basic():
"""Test basic Anthropic messages endpoint"""
server.start()
res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 50,
"messages": [
{"role": "user", "content": "Say hello"}
]
})
assert res.status_code == 200, f"Expected 200, got {res.status_code}"
assert res.body["type"] == "message", f"Expected type 'message', got {res.body.get('type')}"
assert res.body["role"] == "assistant", f"Expected role 'assistant', got {res.body.get('role')}"
assert "content" in res.body, "Missing 'content' field"
assert isinstance(res.body["content"], list), "Content should be an array"
assert len(res.body["content"]) > 0, "Content array should not be empty"
assert res.body["content"][0]["type"] == "text", "First content block should be text"
assert "text" in res.body["content"][0], "Text content block missing 'text' field"
assert res.body["stop_reason"] in ["end_turn", "max_tokens"], f"Invalid stop_reason: {res.body.get('stop_reason')}"
assert "usage" in res.body, "Missing 'usage' field"
assert "input_tokens" in res.body["usage"], "Missing usage.input_tokens"
assert "output_tokens" in res.body["usage"], "Missing usage.output_tokens"
assert isinstance(res.body["usage"]["input_tokens"], int), "input_tokens should be integer"
assert isinstance(res.body["usage"]["output_tokens"], int), "output_tokens should be integer"
assert res.body["usage"]["output_tokens"] > 0, "Should have generated some tokens"
# Anthropic API should NOT include timings
assert "timings" not in res.body, "Anthropic API should not include timings field"
def test_anthropic_messages_with_system():
"""Test messages with system prompt"""
server.start()
res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 50,
"system": "You are a helpful assistant.",
"messages": [
{"role": "user", "content": "Hello"}
]
})
assert res.status_code == 200
assert res.body["type"] == "message"
assert len(res.body["content"]) > 0
def test_anthropic_messages_multipart_content():
"""Test messages with multipart content blocks"""
server.start()
res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 50,
"messages": [
{
"role": "user",
"content": [
{"type": "text", "text": "What is"},
{"type": "text", "text": " the answer?"}
]
}
]
})
assert res.status_code == 200
assert res.body["type"] == "message"
def test_anthropic_messages_conversation():
"""Test multi-turn conversation"""
server.start()
res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 50,
"messages": [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi there!"},
{"role": "user", "content": "How are you?"}
]
})
assert res.status_code == 200
assert res.body["type"] == "message"
# Streaming tests
def test_anthropic_messages_streaming():
"""Test streaming messages"""
server.start()
res = server.make_stream_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 30,
"messages": [
{"role": "user", "content": "Say hello"}
],
"stream": True
})
events = []
for data in res:
# Each event should have type and other fields
assert "type" in data, f"Missing 'type' in event: {data}"
events.append(data)
# Verify event sequence
event_types = [e["type"] for e in events]
assert "message_start" in event_types, "Missing message_start event"
assert "content_block_start" in event_types, "Missing content_block_start event"
assert "content_block_delta" in event_types, "Missing content_block_delta event"
assert "content_block_stop" in event_types, "Missing content_block_stop event"
assert "message_delta" in event_types, "Missing message_delta event"
assert "message_stop" in event_types, "Missing message_stop event"
# Check message_start structure
message_start = next(e for e in events if e["type"] == "message_start")
assert "message" in message_start, "message_start missing 'message' field"
assert message_start["message"]["type"] == "message"
assert message_start["message"]["role"] == "assistant"
assert message_start["message"]["content"] == []
assert "usage" in message_start["message"]
assert message_start["message"]["usage"]["input_tokens"] > 0
# Check content_block_start
block_start = next(e for e in events if e["type"] == "content_block_start")
assert "index" in block_start, "content_block_start missing 'index'"
assert block_start["index"] == 0, "First content block should be at index 0"
assert "content_block" in block_start
assert block_start["content_block"]["type"] == "text"
# Check content_block_delta
deltas = [e for e in events if e["type"] == "content_block_delta"]
assert len(deltas) > 0, "Should have at least one content_block_delta"
for delta in deltas:
assert "index" in delta
assert "delta" in delta
assert delta["delta"]["type"] == "text_delta"
assert "text" in delta["delta"]
# Check content_block_stop
block_stop = next(e for e in events if e["type"] == "content_block_stop")
assert "index" in block_stop
assert block_stop["index"] == 0
# Check message_delta
message_delta = next(e for e in events if e["type"] == "message_delta")
assert "delta" in message_delta
assert "stop_reason" in message_delta["delta"]
assert message_delta["delta"]["stop_reason"] in ["end_turn", "max_tokens"]
assert "usage" in message_delta
assert message_delta["usage"]["output_tokens"] > 0
# Check message_stop
message_stop = next(e for e in events if e["type"] == "message_stop")
# message_stop should NOT have timings for Anthropic API
assert "timings" not in message_stop, "Anthropic streaming should not include timings"
# Token counting tests
def test_anthropic_count_tokens():
"""Test token counting endpoint"""
server.start()
res = server.make_request("POST", "/v1/messages/count_tokens", data={
"model": "test",
"messages": [
{"role": "user", "content": "Hello world"}
]
})
assert res.status_code == 200
assert "input_tokens" in res.body
assert isinstance(res.body["input_tokens"], int)
assert res.body["input_tokens"] > 0
# Should only have input_tokens, no other fields
assert "output_tokens" not in res.body
def test_anthropic_count_tokens_with_system():
"""Test token counting with system prompt"""
server.start()
res = server.make_request("POST", "/v1/messages/count_tokens", data={
"model": "test",
"system": "You are a helpful assistant.",
"messages": [
{"role": "user", "content": "Hello"}
]
})
assert res.status_code == 200
assert res.body["input_tokens"] > 0
def test_anthropic_count_tokens_no_max_tokens():
"""Test that count_tokens doesn't require max_tokens"""
server.start()
# max_tokens is NOT required for count_tokens
res = server.make_request("POST", "/v1/messages/count_tokens", data={
"model": "test",
"messages": [
{"role": "user", "content": "Hello"}
]
})
assert res.status_code == 200
assert "input_tokens" in res.body
# Tool use tests
def test_anthropic_tool_use_basic():
"""Test basic tool use"""
server.jinja = True
server.start()
res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 200,
"tools": [{
"name": "get_weather",
"description": "Get the current weather in a location",
"input_schema": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City name"
}
},
"required": ["location"]
}
}],
"messages": [
{"role": "user", "content": "What's the weather in Paris?"}
]
})
assert res.status_code == 200
assert res.body["type"] == "message"
assert len(res.body["content"]) > 0
# Check if model used the tool (it might not always, depending on the model)
content_types = [block.get("type") for block in res.body["content"]]
if "tool_use" in content_types:
# Model used the tool
assert res.body["stop_reason"] == "tool_use"
# Find the tool_use block
tool_block = next(b for b in res.body["content"] if b.get("type") == "tool_use")
assert "id" in tool_block
assert "name" in tool_block
assert tool_block["name"] == "get_weather"
assert "input" in tool_block
assert isinstance(tool_block["input"], dict)
def test_anthropic_tool_result():
"""Test sending tool results back
This test verifies that tool_result blocks are properly converted to
role="tool" messages internally. Without proper conversion, this would
fail with a 500 error: "unsupported content[].type" because tool_result
blocks would remain in the user message content array.
"""
server.jinja = True
server.start()
res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 100,
"messages": [
{"role": "user", "content": "What's the weather?"},
{
"role": "assistant",
"content": [
{
"type": "tool_use",
"id": "test123",
"name": "get_weather",
"input": {"location": "Paris"}
}
]
},
{
"role": "user",
"content": [
{
"type": "tool_result",
"tool_use_id": "test123",
"content": "The weather is sunny, 25°C"
}
]
}
]
})
# This would be 500 with the old bug where tool_result blocks weren't converted
assert res.status_code == 200
assert res.body["type"] == "message"
# Model should respond to the tool result
assert len(res.body["content"]) > 0
assert res.body["content"][0]["type"] == "text"
def test_anthropic_tool_result_with_text():
"""Test tool result mixed with text content
This tests the edge case where a user message contains both text and
tool_result blocks. The server must properly split these into separate
messages: a user message with text, followed by tool messages.
Without proper handling, this would fail with 500: "unsupported content[].type"
"""
server.jinja = True
server.start()
res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 100,
"messages": [
{"role": "user", "content": "What's the weather?"},
{
"role": "assistant",
"content": [
{
"type": "tool_use",
"id": "tool_1",
"name": "get_weather",
"input": {"location": "Paris"}
}
]
},
{
"role": "user",
"content": [
{"type": "text", "text": "Here are the results:"},
{
"type": "tool_result",
"tool_use_id": "tool_1",
"content": "Sunny, 25°C"
}
]
}
]
})
assert res.status_code == 200
assert res.body["type"] == "message"
assert len(res.body["content"]) > 0
def test_anthropic_tool_result_error():
"""Test tool result with error flag"""
server.jinja = True
server.start()
res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 100,
"messages": [
{"role": "user", "content": "Get the weather"},
{
"role": "assistant",
"content": [
{
"type": "tool_use",
"id": "test123",
"name": "get_weather",
"input": {"location": "InvalidCity"}
}
]
},
{
"role": "user",
"content": [
{
"type": "tool_result",
"tool_use_id": "test123",
"is_error": True,
"content": "City not found"
}
]
}
]
})
assert res.status_code == 200
assert res.body["type"] == "message"
def test_anthropic_tool_streaming():
"""Test streaming with tool use"""
server.jinja = True
server.start()
res = server.make_stream_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 200,
"stream": True,
"tools": [{
"name": "calculator",
"description": "Calculate math",
"input_schema": {
"type": "object",
"properties": {
"expression": {"type": "string"}
},
"required": ["expression"]
}
}],
"messages": [
{"role": "user", "content": "Calculate 2+2"}
]
})
events = []
for data in res:
events.append(data)
event_types = [e["type"] for e in events]
# Should have basic events
assert "message_start" in event_types
assert "message_stop" in event_types
# If tool was used, check for proper tool streaming
if any(e.get("type") == "content_block_start" and
e.get("content_block", {}).get("type") == "tool_use"
for e in events):
# Find tool use block start
tool_starts = [e for e in events if
e.get("type") == "content_block_start" and
e.get("content_block", {}).get("type") == "tool_use"]
assert len(tool_starts) > 0, "Should have tool_use content_block_start"
# Check index is correct (should be 0 if no text, 1 if there's text)
tool_start = tool_starts[0]
assert "index" in tool_start
assert tool_start["content_block"]["type"] == "tool_use"
assert "name" in tool_start["content_block"]
# Vision/multimodal tests
def test_anthropic_vision_format_accepted():
"""Test that Anthropic vision format is accepted (format validation only)"""
server.start()
# Small 1x1 red PNG image in base64
red_pixel_png = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8DwHwAFBQIAX8jx0gAAAABJRU5ErkJggg=="
res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 10,
"messages": [
{
"role": "user",
"content": [
{
"type": "image",
"source": {
"type": "base64",
"media_type": "image/png",
"data": red_pixel_png
}
},
{
"type": "text",
"text": "What is this?"
}
]
}
]
})
# Server accepts the format but tinyllama doesn't support images
# So it should return 500 with clear error message about missing mmproj
assert res.status_code == 500
assert "image input is not supported" in res.body.get("error", {}).get("message", "").lower()
def test_anthropic_vision_base64_with_multimodal_model(vision_server):
"""Test vision with base64 image using Anthropic format with multimodal model"""
global server
server = vision_server
server.start()
# Get test image in base64 format
image_base64 = get_test_image_base64()
res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 10,
"messages": [
{
"role": "user",
"content": [
{
"type": "image",
"source": {
"type": "base64",
"media_type": "image/png",
"data": image_base64
}
},
{
"type": "text",
"text": "What is this:\n"
}
]
}
]
})
assert res.status_code == 200, f"Expected 200, got {res.status_code}: {res.body}"
assert res.body["type"] == "message"
assert len(res.body["content"]) > 0
assert res.body["content"][0]["type"] == "text"
# The model should generate some response about the image
assert len(res.body["content"][0]["text"]) > 0
# Parameter tests
def test_anthropic_stop_sequences():
"""Test stop_sequences parameter"""
server.start()
res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 100,
"stop_sequences": ["\n", "END"],
"messages": [
{"role": "user", "content": "Count to 10"}
]
})
assert res.status_code == 200
assert res.body["type"] == "message"
def test_anthropic_temperature():
"""Test temperature parameter"""
server.start()
res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 50,
"temperature": 0.5,
"messages": [
{"role": "user", "content": "Hello"}
]
})
assert res.status_code == 200
assert res.body["type"] == "message"
def test_anthropic_top_p():
"""Test top_p parameter"""
server.start()
res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 50,
"top_p": 0.9,
"messages": [
{"role": "user", "content": "Hello"}
]
})
assert res.status_code == 200
assert res.body["type"] == "message"
def test_anthropic_top_k():
"""Test top_k parameter (llama.cpp specific)"""
server.start()
res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 50,
"top_k": 40,
"messages": [
{"role": "user", "content": "Hello"}
]
})
assert res.status_code == 200
assert res.body["type"] == "message"
# Error handling tests
def test_anthropic_missing_messages():
"""Test error when messages are missing"""
server.start()
res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 50
# missing "messages" field
})
# Should return an error (400 or 500)
assert res.status_code >= 400
def test_anthropic_empty_messages():
"""Test permissive handling of empty messages array"""
server.start()
res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 50,
"messages": []
})
# Server is permissive and accepts empty messages (provides defaults)
# This matches the permissive validation design choice
assert res.status_code == 200
assert res.body["type"] == "message"
# Content block index tests
def test_anthropic_streaming_content_block_indices():
"""Test that content block indices are correct in streaming"""
server.jinja = True
server.start()
# Request that might produce both text and tool use
res = server.make_stream_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 200,
"stream": True,
"tools": [{
"name": "test_tool",
"description": "A test tool",
"input_schema": {
"type": "object",
"properties": {
"param": {"type": "string"}
},
"required": ["param"]
}
}],
"messages": [
{"role": "user", "content": "Use the test tool"}
]
})
events = []
for data in res:
events.append(data)
# Check content_block_start events have sequential indices
block_starts = [e for e in events if e.get("type") == "content_block_start"]
if len(block_starts) > 1:
# If there are multiple blocks, indices should be sequential
indices = [e["index"] for e in block_starts]
expected_indices = list(range(len(block_starts)))
assert indices == expected_indices, f"Expected indices {expected_indices}, got {indices}"
# Check content_block_stop events match the starts
block_stops = [e for e in events if e.get("type") == "content_block_stop"]
start_indices = set(e["index"] for e in block_starts)
stop_indices = set(e["index"] for e in block_stops)
assert start_indices == stop_indices, "content_block_stop indices should match content_block_start indices"
# Extended features tests
def test_anthropic_thinking():
"""Test extended thinking parameter"""
server.jinja = True
server.start()
res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 100,
"thinking": {
"type": "enabled",
"budget_tokens": 50
},
"messages": [
{"role": "user", "content": "What is 2+2?"}
]
})
assert res.status_code == 200
assert res.body["type"] == "message"
def test_anthropic_metadata():
"""Test metadata parameter"""
server.start()
res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 50,
"metadata": {
"user_id": "test_user_123"
},
"messages": [
{"role": "user", "content": "Hello"}
]
})
assert res.status_code == 200
assert res.body["type"] == "message"
# Compatibility tests
def test_anthropic_vs_openai_different_response_format():
"""Verify Anthropic format is different from OpenAI format"""
server.start()
# Make OpenAI request
openai_res = server.make_request("POST", "/v1/chat/completions", data={
"model": "test",
"max_tokens": 50,
"messages": [
{"role": "user", "content": "Hello"}
]
})
# Make Anthropic request
anthropic_res = server.make_request("POST", "/v1/messages", data={
"model": "test",
"max_tokens": 50,
"messages": [
{"role": "user", "content": "Hello"}
]
})
assert openai_res.status_code == 200
assert anthropic_res.status_code == 200
# OpenAI has "object", Anthropic has "type"
assert "object" in openai_res.body
assert "type" in anthropic_res.body
assert openai_res.body["object"] == "chat.completion"
assert anthropic_res.body["type"] == "message"
# OpenAI has "choices", Anthropic has "content"
assert "choices" in openai_res.body
assert "content" in anthropic_res.body
# Different usage field names
assert "prompt_tokens" in openai_res.body["usage"]
assert "input_tokens" in anthropic_res.body["usage"]
assert "completion_tokens" in openai_res.body["usage"]
assert "output_tokens" in anthropic_res.body["usage"]

13
tools/server/tests/unit/test_security.py
View File

@ -49,6 +49,19 @@ def test_correct_api_key():
assert "content" in res.body
def test_correct_api_key_anthropic_header():
global server
server.start()
res = server.make_request("POST", "/completions", data={
"prompt": "I believe the meaning of life is",
}, headers={
"X-Api-Key": TEST_API_KEY,
})
assert res.status_code == 200
assert "error" not in res.body
assert "content" in res.body
def test_openai_library_correct_api_key():
global server
server.start()