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feat: Add tool/function calling support to Ollama provider

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Fixes issue where Ollama users get 'I'm a large language model, I can't do XYZ'
responses when trying to use the AI assistant. The problem was that the
Ollama provider was not passing tool definitions to the API.

Changes:
- Add Tools field to ollamaRequest struct
- Add ollamaTool, ollamaToolFunction, ollamaToolCall structs
- Convert tools from ChatRequest to Ollama format in Chat()
- Parse tool_calls from Ollama response
- Set StopReason to 'tool_use' when model requests tool execution
- Handle tool results in multi-turn conversations

Requires Ollama v0.3.0+ and a tool-capable model (llama3.1+, mistral-nemo, etc.)

Closes: Discussion #845 comment by misterlegend
This commit is contained in:
rcourtman 2025-12-17 11:54:32 +00:00
parent 307bb9f6cc
commit bacf6b5fc9
2 changed files with 108 additions and 20 deletions
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118
internal/ai/providers/ollama.go
View file

@ -48,11 +48,36 @@ type ollamaRequest struct {
Messages []ollamaMessage `json:"messages"`
Stream bool `json:"stream"`
Options *ollamaOptions `json:"options,omitempty"`
Tools []ollamaTool `json:"tools,omitempty"` // Tool definitions for function calling
}
type ollamaMessage struct {
Role string `json:"role"`
Content string `json:"content"`
Role string `json:"role"`
Content string `json:"content"`
ToolCalls []ollamaToolCall `json:"tool_calls,omitempty"` // For assistant messages with tool calls
}
type ollamaToolCall struct {
ID string `json:"id,omitempty"` // Ollama provides an ID for tool calls
Function ollamaFunctionCall `json:"function"`
}
type ollamaFunctionCall struct {
Index int `json:"index,omitempty"` // Index in the tool call array
Name string `json:"name"`
Arguments map[string]interface{} `json:"arguments"`
}
// ollamaTool represents a tool definition for Ollama
type ollamaTool struct {
Type string `json:"type"` // "function"
Function ollamaToolFunction `json:"function"`
}
type ollamaToolFunction struct {
Name string `json:"name"`
Description string `json:"description"`
Parameters map[string]interface{} `json:"parameters"`
}
type ollamaOptions struct {
@ -62,15 +87,22 @@ type ollamaOptions struct {
// ollamaResponse is the response from the Ollama API
type ollamaResponse struct {
Model string `json:"model"`
CreatedAt string `json:"created_at"`
Message ollamaMessage `json:"message"`
Done bool `json:"done"`
DoneReason string `json:"done_reason,omitempty"`
TotalDuration int64 `json:"total_duration,omitempty"`
LoadDuration int64 `json:"load_duration,omitempty"`
PromptEvalCount int `json:"prompt_eval_count,omitempty"`
EvalCount int `json:"eval_count,omitempty"`
Model string `json:"model"`
CreatedAt string `json:"created_at"`
Message ollamaMessageResp `json:"message"`
Done bool `json:"done"`
DoneReason string `json:"done_reason,omitempty"`
TotalDuration int64 `json:"total_duration,omitempty"`
LoadDuration int64 `json:"load_duration,omitempty"`
PromptEvalCount int `json:"prompt_eval_count,omitempty"`
EvalCount int `json:"eval_count,omitempty"`
}
// ollamaMessageResp is the response message format (can include tool_calls)
type ollamaMessageResp struct {
Role string `json:"role"`
Content string `json:"content"`
ToolCalls []ollamaToolCall `json:"tool_calls,omitempty"`
}
// Chat sends a chat request to the Ollama API
@ -87,10 +119,27 @@ func (c *OllamaClient) Chat(ctx context.Context, req ChatRequest) (*ChatResponse
}
for _, m := range req.Messages {
messages = append(messages, ollamaMessage{
msg := ollamaMessage{
Role: m.Role,
Content: m.Content,
})
}
// Include tool calls for assistant messages (for multi-turn with tool use)
if len(m.ToolCalls) > 0 {
for _, tc := range m.ToolCalls {
msg.ToolCalls = append(msg.ToolCalls, ollamaToolCall{
Function: ollamaFunctionCall{
Name: tc.Name,
Arguments: tc.Input,
},
})
}
}
// Handle tool results - Ollama expects role "tool" with content
if m.ToolResult != nil {
msg.Role = "tool"
msg.Content = m.ToolResult.Content
}
messages = append(messages, msg)
}
// Use provided model or fall back to client default
@ -113,6 +162,25 @@ func (c *OllamaClient) Chat(ctx context.Context, req ChatRequest) (*ChatResponse
Stream: false, // Non-streaming for now
}
// Convert tools to Ollama format
if len(req.Tools) > 0 {
ollamaReq.Tools = make([]ollamaTool, 0, len(req.Tools))
for _, t := range req.Tools {
// Skip non-function tools (like web_search which Ollama doesn't support)
if t.Type != "" && t.Type != "function" {
continue
}
ollamaReq.Tools = append(ollamaReq.Tools, ollamaTool{
Type: "function",
Function: ollamaToolFunction{
Name: t.Name,
Description: t.Description,
Parameters: t.InputSchema,
},
})
}
}
if req.MaxTokens > 0 || req.Temperature > 0 {
ollamaReq.Options = &ollamaOptions{}
if req.MaxTokens > 0 {
@ -156,13 +224,33 @@ func (c *OllamaClient) Chat(ctx context.Context, req ChatRequest) (*ChatResponse
return nil, fmt.Errorf("failed to parse response: %w", err)
}
return &ChatResponse{
// Build response with tool calls if present
chatResp := &ChatResponse{
Content: ollamaResp.Message.Content,
Model: ollamaResp.Model,
StopReason: ollamaResp.DoneReason,
InputTokens: ollamaResp.PromptEvalCount,
OutputTokens: ollamaResp.EvalCount,
}, nil
}
// Convert Ollama tool calls to our format
if len(ollamaResp.Message.ToolCalls) > 0 {
chatResp.StopReason = "tool_use" // Signal that we need to execute tools
for _, tc := range ollamaResp.Message.ToolCalls {
// Use Ollama's ID if provided, otherwise generate one
toolCallID := tc.ID
if toolCallID == "" {
toolCallID = fmt.Sprintf("ollama_%s_%d", tc.Function.Name, time.Now().UnixNano())
}
chatResp.ToolCalls = append(chatResp.ToolCalls, ToolCall{
ID: toolCallID,
Name: tc.Function.Name,
Input: tc.Function.Arguments,
})
}
}
return chatResp, nil
}
// TestConnection validates connectivity by checking the Ollama version endpoint

10
internal/ai/providers/ollama_test.go
View file

@ -31,7 +31,7 @@ func TestOllamaClient_Chat_Success(t *testing.T) {
resp := ollamaResponse{
Model: "llama2",
CreatedAt: time.Now().Format(time.RFC3339),
Message: ollamaMessage{
Message: ollamaMessageResp{
Role: "assistant",
Content: "Hello! I'm Llama.",
},
@ -84,7 +84,7 @@ func TestOllamaClient_Chat_WithSystemPrompt(t *testing.T) {
resp := ollamaResponse{
Model: "llama2",
Message: ollamaMessage{Role: "assistant", Content: "Response"},
Message: ollamaMessageResp{Role: "assistant", Content: "Response"},
Done: true,
}
json.NewEncoder(w).Encode(resp)
@ -125,7 +125,7 @@ func TestOllamaClient_Chat_WithOptions(t *testing.T) {
resp := ollamaResponse{
Model: "llama2",
Message: ollamaMessage{Role: "assistant", Content: "Response"},
Message: ollamaMessageResp{Role: "assistant", Content: "Response"},
Done: true,
}
json.NewEncoder(w).Encode(resp)
@ -202,7 +202,7 @@ func TestOllamaClient_Chat_ModelFallback(t *testing.T) {
resp := ollamaResponse{
Model: req.Model,
Message: ollamaMessage{Role: "assistant", Content: "Response"},
Message: ollamaMessageResp{Role: "assistant", Content: "Response"},
Done: true,
}
json.NewEncoder(w).Encode(resp)
@ -238,7 +238,7 @@ func TestOllamaClient_Chat_StripModelPrefix(t *testing.T) {
resp := ollamaResponse{
Model: req.Model,
Message: ollamaMessage{Role: "assistant", Content: "Response"},
Message: ollamaMessageResp{Role: "assistant", Content: "Response"},
Done: true,
}
json.NewEncoder(w).Encode(resp)