mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-01-26 17:20:01 +00:00
* Implement function calling / tools for ik_llama.cpp for Kimi K2
* Implement basic tool choice
* Backport llama.cpp tool calls support
* Enhance function calls with improved chat parser and string utilities
- Add new chat.h/chat.cpp and chat-parser.h/chat-parser.cpp for better chat handling
- Improve function calls parsing with fallback to llama.cpp builder pattern
- Add string utility functions (starts_with, ends_with, find_partial_stop)
- Update README with function calls testing instructions
- Enhance Kimi K2 parser and function calls documentation
- Add comprehensive test suite for function calls
- Update CMakeLists.txt and Makefile for new components
* Enhance function calling with unified streaming and parser improvements
- Fix streaming content cleanup to prevent function syntax in output
- Unify content extraction patterns with llama.cpp approach
- Improve Kimi K2 parser robustness and partial content handling
- Add comprehensive test coverage for function call scenarios
- Optimize chat message parsing and diff computation
* Replace hardcoded values in kimi_k2_parser.hpp with named constants
- Add compile-time constants for all token format markers
- Add compile-time constants for XML format markers
- Add compile-time constants for simple format patterns
- Replace all hardcoded string literals with named constants
- Use compile-time length calculation to avoid manual counting
- Improve maintainability and reduce magic numbers throughout parser
* Fix duplicate common_chat_parse definition
- Remove duplicate implementation from chat-parser.cpp
- Keep single implementation in chat.cpp following llama.cpp patterns
- Resolves linker error: multiple definition of common_chat_parse
* Fix JSON assertion failure in function call parsing
- Add proper validation that 'function' field is an object before accessing nested keys
- Handle missing 'arguments' field gracefully with default "{}"
- Prevents crash when parsing malformed tool call JSON structures
* Add comprehensive Qwen3 XML tool calling support with unit tests
- Implement Qwen3 XML parser with <tool_call>{"name": "func", "arguments": {...}}</tool_call> format
- Add model detection and routing for Qwen3 vs Kimi-K2 formats
- Create 8 comprehensive unit tests covering parsing, streaming, error handling
- Fix token format cleaning bug in kimi_k2_parser.hpp processing order
- Remove progressive parsing code and related utilities
- Add tool injection support for Qwen3 format in server utils
* Add DeepSeek R1 function calling support with comprehensive unit tests
- Implement complete DeepSeek R1 tool call parsing in common_chat_parser.cpp
- Add DeepSeek R1 model detection and tool injection in deepseek_r1_tools.hpp
- Update function_calls.hpp with DeepSeek R1 integration and content extraction
- Update documentation to reflect support for Kimi-K2, Qwen3, and DeepSeek R1 models
- Add comprehensive unit tests for DeepSeek R1 reasoning, tool calls, and integration
- Port exact implementation patterns from original llama.cpp for compatibility
Key features:
- Native DeepSeek R1 format: <|tool▁calls▁begin|>function<|tool▁sep|>name```json{}```<|tool▁call▁end|><|tool▁calls▁end|>
- Reasoning content extraction from <think>...</think> tags
- Multiple tool calls support with separate call blocks
- Model detection for deepseek-r1, deepseek_r1 naming patterns
- Integration with incremental parsing and streaming support
* Add partial parsing support for JSON and regex
- json-partial.h/cpp: JSON partial parsing functionality
- regex-partial.h/cpp: Regex partial parsing functionality
* Add format_chat integration tests for Qwen3 tool injection
- Add test_qwen3_format_chat_integration() to validate tool injection pipeline
- Test tool injection conditions and system message enhancement
- Verify JSON formatting and anti-preamble instructions
- Add comprehensive test documentation
Tests confirm tool injection works correctly - conversational preamble
issue is not in ik_llama.cpp but likely in UI configuration.
* Fix Qwen3 tool call parsing - pass model name to parser
Server was not passing model name to parse_chat_message_incremental(),
causing Qwen3 to fall back to Kimi-K2 parser and return tool calls
as content instead of proper tool_calls array.
* Fix non-streaming path to use model-specific parsing
Non-streaming responses were hardcoded to use Kimi-K2 format,
causing Qwen3 XML tool calls to be returned as content instead
of proper tool_calls array. Now uses same model detection as
streaming path for consistency.
217 lines
7.2 KiB
C++
217 lines
7.2 KiB
C++
#pragma once
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#include "../../common/common.h"
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#include "json.hpp"
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#include <string>
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#include <vector>
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#include <functional>
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using json = nlohmann::ordered_json;
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//
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// Streaming chat data structures ported from original llama.cpp
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// Enables differential streaming of tool calls during generation
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//
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// Tool call structure for streaming
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struct ik_chat_tool_call {
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std::string name;
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std::string arguments;
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std::string id;
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bool operator==(const ik_chat_tool_call & other) const {
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return name == other.name && arguments == other.arguments && id == other.id;
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}
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bool operator!=(const ik_chat_tool_call & other) const {
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return !(*this == other);
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}
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};
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// Chat message structure with tool call support
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struct ik_chat_msg {
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std::string role;
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std::string content;
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std::vector<ik_chat_tool_call> tool_calls = {};
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// Check if message is empty
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bool empty() const {
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return content.empty() && tool_calls.empty();
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}
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// Ensure all tool calls have IDs set
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void ensure_tool_call_ids_set(std::vector<std::string> & ids_cache, const std::function<std::string()> & gen_tool_call_id) {
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for (auto i = 0u; i < tool_calls.size(); i++) {
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if (ids_cache.size() <= i) {
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auto id = tool_calls[i].id;
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if (id.empty()) {
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id = gen_tool_call_id();
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}
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ids_cache.push_back(id);
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}
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tool_calls[i].id = ids_cache[i];
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}
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}
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bool operator==(const ik_chat_msg & other) const {
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return role == other.role
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&& content == other.content
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&& tool_calls == other.tool_calls;
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}
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bool operator!=(const ik_chat_msg & other) const {
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return !(*this == other);
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}
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};
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// Differential update structure for streaming
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struct ik_chat_msg_diff {
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std::string content_delta;
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size_t tool_call_index = std::string::npos;
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ik_chat_tool_call tool_call_delta;
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// Compute differences between two messages for streaming
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static std::vector<ik_chat_msg_diff> compute_diffs(const ik_chat_msg & previous_msg, const ik_chat_msg & new_msg);
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bool operator==(const ik_chat_msg_diff & other) const {
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return content_delta == other.content_delta
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&& tool_call_index == other.tool_call_index
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&& tool_call_delta == other.tool_call_delta;
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}
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};
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// Helper functions for string diffing
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static std::string string_diff(const std::string & last, const std::string & current) {
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if (last.empty()) {
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return current;
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}
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if (!string_starts_with(current, last)) {
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if (string_starts_with(last, current)) {
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// This happens if the last generation ended on a partial stop word (not erased),
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// and the current ended on a stop word (erased).
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return "";
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}
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// For robustness, return the full current string if diff fails
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return current;
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}
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return current.substr(last.size());
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}
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// Implementation of compute_diffs function
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inline std::vector<ik_chat_msg_diff> ik_chat_msg_diff::compute_diffs(const ik_chat_msg & previous_msg, const ik_chat_msg & new_msg) {
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std::vector<ik_chat_msg_diff> diffs;
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// Compute content diff
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if (previous_msg.content != new_msg.content) {
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auto & diff = diffs.emplace_back();
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diff.content_delta = string_diff(previous_msg.content, new_msg.content);
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}
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// Validate tool call consistency
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if (new_msg.tool_calls.size() < previous_msg.tool_calls.size()) {
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// For robustness, handle this case by treating as content change
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// Rather than throwing an exception
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return diffs;
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}
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// Compute diff for existing tool calls (arguments may be extended)
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if (!previous_msg.tool_calls.empty() && !new_msg.tool_calls.empty()) {
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auto idx = previous_msg.tool_calls.size() - 1;
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// Safety check: ensure index is valid for new message
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if (idx < new_msg.tool_calls.size()) {
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const auto & prev_call = previous_msg.tool_calls[idx];
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const auto & new_call = new_msg.tool_calls[idx];
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// Check if this is the same tool call being extended
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if (prev_call.name == new_call.name || new_call.name.empty()) {
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try {
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auto args_diff = string_diff(prev_call.arguments, new_call.arguments);
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if (!args_diff.empty() || prev_call.id != new_call.id) {
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auto & diff = diffs.emplace_back();
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diff.tool_call_index = idx;
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if (prev_call.id != new_call.id) {
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diff.tool_call_delta.id = new_call.id;
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diff.tool_call_delta.name = new_call.name;
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}
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diff.tool_call_delta.arguments = args_diff;
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}
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} catch (const std::exception&) {
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// Skip if string diff fails
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}
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}
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}
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}
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// Add new tool calls
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for (size_t idx = previous_msg.tool_calls.size(); idx < new_msg.tool_calls.size(); ++idx) {
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auto & diff = diffs.emplace_back();
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diff.tool_call_index = idx;
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diff.tool_call_delta = new_msg.tool_calls[idx];
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}
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return diffs;
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}
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// Convert diff to OpenAI streaming format
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static json chat_msg_diff_to_oai_streaming(const ik_chat_msg_diff & diff) {
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json delta = json::object();
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if (!diff.content_delta.empty()) {
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delta["content"] = diff.content_delta;
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}
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if (diff.tool_call_index != std::string::npos) {
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json tool_call;
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tool_call["index"] = diff.tool_call_index;
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if (!diff.tool_call_delta.id.empty()) {
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tool_call["id"] = diff.tool_call_delta.id;
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tool_call["type"] = "function";
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}
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json function = json::object();
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if (!diff.tool_call_delta.name.empty()) {
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function["name"] = diff.tool_call_delta.name;
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}
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function["arguments"] = diff.tool_call_delta.arguments;
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tool_call["function"] = function;
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delta["tool_calls"] = json::array({tool_call});
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}
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return delta;
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}
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// Generate streaming chunks from diffs
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static std::vector<json> generate_streaming_chunks(const std::vector<ik_chat_msg_diff> & diffs, const std::string & completion_id, const std::string & model_name) {
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std::vector<json> chunks;
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std::time_t t = std::time(0);
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for (const auto & diff : diffs) {
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try {
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json delta = chat_msg_diff_to_oai_streaming(diff);
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if (!delta.empty()) {
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json chunk = {
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{"choices", json::array({json{
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{"finish_reason", nullptr},
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{"index", 0},
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{"delta", delta}
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}})},
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{"created", t},
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{"id", completion_id},
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{"model", model_name},
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{"object", "chat.completion.chunk"}
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};
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chunks.push_back(chunk);
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}
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} catch (const std::exception&) {
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// Skip malformed diffs but continue processing
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continue;
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}
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}
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return chunks;
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} |