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.
213 lines
8.5 KiB
C++
213 lines
8.5 KiB
C++
#pragma once
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#include "json.hpp"
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#include "streaming_chat.hpp"
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#include "parsers/kimi_k2_parser.hpp"
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#include "parsers/qwen3_parser.hpp"
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#include "qwen3_tools.hpp"
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#include "deepseek_r1_tools.hpp"
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#include "../../common/chat.h"
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#include "../../common/chat-parser.h"
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#include <string>
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#include <regex>
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using json = nlohmann::ordered_json;
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// Function calling interface for Kimi-K2 format
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static json parse_kimi_k2_tool_calls(const std::string& text) {
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return kimi_k2::parse_tool_calls(text);
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}
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// Function calling interface for Qwen3 format
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static json parse_qwen3_tool_calls(const std::string& text) {
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return qwen3::parse_tool_calls(text);
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}
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static std::string clean_function_calls_from_content(const std::string& content) {
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return kimi_k2::clean_content(content);
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}
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// New llama.cpp-style content extraction with streaming support
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static std::string extract_content_from_mixed_input(const std::string& content, bool is_partial, const std::string& model_name = "") {
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if (is_qwen3_model(model_name)) {
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return qwen3::extract_content_during_parsing(content, is_partial);
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} else if (is_deepseek_r1_model(model_name)) {
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// DeepSeek R1 content extraction - remove <think> tags and tool calls
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std::string result = content;
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// Remove <think>...</think> tags
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size_t think_start = 0;
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while ((think_start = result.find("<think>", think_start)) != std::string::npos) {
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size_t think_end = result.find("</think>", think_start);
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if (think_end != std::string::npos) {
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result.erase(think_start, think_end + 8 - think_start);
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} else {
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break;
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}
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}
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// Remove DeepSeek R1 tool call syntax
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size_t tool_start = 0;
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while ((tool_start = result.find("<|tool▁calls▁begin|>", tool_start)) != std::string::npos) {
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size_t tool_end = result.find("<|tool▁calls▁end|>", tool_start);
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if (tool_end != std::string::npos) {
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result.erase(tool_start, tool_end + strlen("<|tool▁calls▁end|>") - tool_start);
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} else {
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break;
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}
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}
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return result;
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} else {
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return kimi_k2::extract_content_during_parsing(content, is_partial);
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}
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}
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// Incremental parsing for streaming tool calls with model detection
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static ik_chat_msg parse_chat_message_incremental(const std::string& content, bool is_partial = false, const std::string& model_name = "") {
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ik_chat_msg msg;
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msg.role = "assistant";
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try {
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json tool_calls_json;
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bool has_function_syntax = false;
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// Route parsing based on model type
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if (is_qwen3_model(model_name)) {
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// Use Qwen3 XML parser
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tool_calls_json = parse_qwen3_tool_calls(content);
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// Check for partial content during streaming
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if (is_partial && qwen3::is_partial_content_advanced(content)) {
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throw std::runtime_error("partial structured content detected");
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}
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// Check for malformed XML tool call syntax
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has_function_syntax = content.find("<tool_call>") != std::string::npos;
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} else if (is_deepseek_r1_model(model_name)) {
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// Use common chat parser for DeepSeek R1
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try {
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common_chat_syntax syntax;
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syntax.format = COMMON_CHAT_FORMAT_DEEPSEEK_R1;
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syntax.enable_tool_calls = true;
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common_chat_msg_parser parser(content, is_partial, syntax);
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parser.parse();
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auto result = parser.result();
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// Convert tool calls to JSON format expected by the system
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tool_calls_json = json::array();
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for (const auto& tool_call : result.tool_calls) {
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json tc;
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tc["id"] = tool_call.id.empty() ? ("call_" + std::to_string(rand())) : tool_call.id;
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tc["type"] = "function";
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tc["function"]["name"] = tool_call.name;
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tc["function"]["arguments"] = tool_call.arguments;
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tool_calls_json.push_back(tc);
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}
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// Check for malformed DeepSeek R1 tool call syntax
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has_function_syntax = content.find("<|tool▁calls▁begin|>") != std::string::npos;
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} catch (const common_chat_msg_partial_exception&) {
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if (is_partial) {
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throw std::runtime_error("partial structured content detected");
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}
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// If not partial, treat as regular content
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tool_calls_json = json::array();
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has_function_syntax = false;
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}
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} else {
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// Default to Kimi-K2 parser
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tool_calls_json = parse_kimi_k2_tool_calls(content);
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// Check for partial content during streaming
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if (is_partial && kimi_k2::is_partial_content_advanced(content)) {
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throw std::runtime_error("partial structured content detected");
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}
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// Check for malformed function call syntax
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has_function_syntax = content.find("functions.") != std::string::npos;
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}
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bool parsing_succeeded = !tool_calls_json.empty();
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if (has_function_syntax && !parsing_succeeded) {
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throw std::runtime_error("malformed function call syntax detected");
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}
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// Process successful parsing results
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if (!tool_calls_json.empty()) {
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for (const auto& tc_json : tool_calls_json) {
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try {
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ik_chat_tool_call tc;
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tc.id = tc_json.value("id", "");
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if (!tc_json.contains("function") || !tc_json["function"].is_object() || !tc_json["function"].contains("name")) {
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continue;
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}
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tc.name = tc_json["function"]["name"];
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if (tc.name.empty()) {
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continue;
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}
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if (tc_json["function"].contains("arguments")) {
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tc.arguments = tc_json["function"]["arguments"];
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} else {
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tc.arguments = "{}";
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}
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// Validate arguments (only if not partial)
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if (!is_partial && !tc.arguments.empty()) {
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try {
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auto parsed = json::parse(tc.arguments);
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(void)parsed;
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} catch (const std::exception&) {
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continue;
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}
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}
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msg.tool_calls.push_back(tc);
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} catch (const std::exception&) {
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continue;
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}
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}
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// Use model-specific content extraction
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if (is_qwen3_model(model_name)) {
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msg.content = qwen3::extract_content_during_parsing(content, is_partial);
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} else {
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msg.content = kimi_k2::extract_content_during_parsing(content, is_partial);
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}
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} else {
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// No tool calls found, extract content
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if (is_qwen3_model(model_name)) {
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msg.content = qwen3::extract_content_during_parsing(content, is_partial);
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} else {
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msg.content = kimi_k2::extract_content_during_parsing(content, is_partial);
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}
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}
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} catch (const std::exception& e) {
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if (!is_partial) {
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// Original llama.cpp fallback pattern - use public API
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common_chat_syntax syntax;
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syntax.format = COMMON_CHAT_FORMAT_CONTENT_ONLY; // Use content-only format
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// Use the public API that handles fallback internally
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common_chat_msg fallback_result = common_chat_parse(content, is_partial, syntax);
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// Convert to ik_chat_msg
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msg.tool_calls.clear();
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msg.content = fallback_result.content;
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}
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// If is_partial=true, keep empty result (no content chunks during streaming)
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}
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return msg;
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}
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static std::string generate_tool_call_id() {
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static int counter = 0;
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return "call_" + std::to_string(++counter);
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} |