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* fix(openresponses): populate Content and accept bare {role,content} items (#10039)
Fixes mudler/LocalAI#10039 — `/v1/responses` silently returned empty
output on any model whose YAML doesn't include a Go-side
`template.chat_message` block.
Three cooperating bugs:
* `convertORInputToMessages` populated only `StringContent` for string
input and for the `input.Instructions` system message, leaving the
`Content` (any) field nil.
* `TemplateMessages` gated all fallback content-rendering branches on
`Content != nil && StringContent != ""` — but every branch in that
function consumes `StringContent`, not `Content`. The `&&` silently
dropped messages that had StringContent set and Content nil, producing
an empty prompt that the 5× empty-retry guard then turned into a
200 OK with `output: []`.
* The array-input branch of `convertORInputToMessages` dispatched on
`itemMap["type"]` with no default, dropping bare `{role, content}`
items emitted by the OpenAI Python SDK helper
`client.responses.create(input=[{...}])`.
Fix:
* Set both `Content` and `StringContent` in the two openresponses
message-construction sites that only set one.
* Treat a bare `{role, content}` item (no `type`) as
`type: "message"` for OpenAI-SDK compatibility.
* Gate `TemplateMessages` fallback rendering on `StringContent != ""`,
which is what every downstream branch in that function actually
reads.
Regression test added to `evaluator_test.go` covering the fallback
path (no `ChatMessage` template) with a StringContent-only message,
both with and without a role mapping.
* test(openresponses): guard Content population and ToProto path (#10039)
Add regression tests for the two seams the original fix touched but
left uncovered:
* convertORInputToMessages must populate both Content and StringContent
for plain string input and for bare {role, content} array items (the
OpenAI SDK shape that omits the type discriminator). Both are
functional reds against the pre-fix code.
* Messages.ToProto reads Content, not StringContent — this is the path
UseTokenizerTemplate backends (imported GGUFs) take. The cases pin
that contract so a future regression on the producer side is caught.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
373 lines
10 KiB
Go
373 lines
10 KiB
Go
package schema_test
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import (
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"encoding/json"
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. "github.com/mudler/LocalAI/core/schema"
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. "github.com/onsi/ginkgo/v2"
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. "github.com/onsi/gomega"
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)
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var _ = Describe("LLM tests", func() {
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Context("ToProtoMessages conversion", func() {
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It("should convert basic message with string content", func() {
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messages := Messages{
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{
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Role: "user",
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Content: "Hello, world!",
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},
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}
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protoMessages := messages.ToProto()
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Expect(protoMessages).To(HaveLen(1))
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Expect(protoMessages[0].Role).To(Equal("user"))
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Expect(protoMessages[0].Content).To(Equal("Hello, world!"))
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Expect(protoMessages[0].Name).To(BeEmpty())
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Expect(protoMessages[0].ToolCalls).To(BeEmpty())
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})
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It("should convert message with nil content to empty string", func() {
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messages := Messages{
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{
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Role: "assistant",
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Content: nil,
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},
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}
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protoMessages := messages.ToProto()
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Expect(protoMessages).To(HaveLen(1))
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Expect(protoMessages[0].Role).To(Equal("assistant"))
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Expect(protoMessages[0].Content).To(Equal(""))
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})
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It("should convert message with array content (multimodal)", func() {
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messages := Messages{
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{
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Role: "user",
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Content: []any{
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map[string]any{
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"type": "text",
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"text": "Hello",
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},
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map[string]any{
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"type": "text",
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"text": " World",
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},
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},
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},
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}
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protoMessages := messages.ToProto()
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Expect(protoMessages).To(HaveLen(1))
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Expect(protoMessages[0].Role).To(Equal("user"))
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Expect(protoMessages[0].Content).To(Equal("Hello World"))
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})
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It("should convert message with tool_calls", func() {
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messages := Messages{
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{
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Role: "assistant",
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Content: "I'll call a function",
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ToolCalls: []ToolCall{
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{
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Index: 0,
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ID: "call_123",
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Type: "function",
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FunctionCall: FunctionCall{
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Name: "get_weather",
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Arguments: `{"location": "San Francisco"}`,
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},
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},
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},
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},
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}
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protoMessages := messages.ToProto()
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Expect(protoMessages).To(HaveLen(1))
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Expect(protoMessages[0].Role).To(Equal("assistant"))
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Expect(protoMessages[0].Content).To(Equal("I'll call a function"))
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Expect(protoMessages[0].ToolCalls).NotTo(BeEmpty())
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// Verify tool_calls JSON is valid
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var toolCalls []ToolCall
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err := json.Unmarshal([]byte(protoMessages[0].ToolCalls), &toolCalls)
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Expect(err).NotTo(HaveOccurred())
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Expect(toolCalls).To(HaveLen(1))
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Expect(toolCalls[0].ID).To(Equal("call_123"))
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Expect(toolCalls[0].FunctionCall.Name).To(Equal("get_weather"))
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})
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It("should convert message with name field", func() {
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messages := Messages{
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{
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Role: "tool",
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Content: "Function result",
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Name: "get_weather",
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},
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}
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protoMessages := messages.ToProto()
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Expect(protoMessages).To(HaveLen(1))
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Expect(protoMessages[0].Role).To(Equal("tool"))
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Expect(protoMessages[0].Content).To(Equal("Function result"))
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Expect(protoMessages[0].Name).To(Equal("get_weather"))
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})
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It("should convert message with tool_calls and nil content", func() {
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messages := Messages{
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{
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Role: "assistant",
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Content: nil,
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ToolCalls: []ToolCall{
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{
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Index: 0,
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ID: "call_456",
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Type: "function",
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FunctionCall: FunctionCall{
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Name: "search",
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Arguments: `{"query": "test"}`,
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},
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},
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},
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},
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}
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protoMessages := messages.ToProto()
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Expect(protoMessages).To(HaveLen(1))
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Expect(protoMessages[0].Role).To(Equal("assistant"))
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Expect(protoMessages[0].Content).To(Equal(""))
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Expect(protoMessages[0].ToolCalls).NotTo(BeEmpty())
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var toolCalls []ToolCall
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err := json.Unmarshal([]byte(protoMessages[0].ToolCalls), &toolCalls)
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Expect(err).NotTo(HaveOccurred())
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Expect(toolCalls).To(HaveLen(1))
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Expect(toolCalls[0].FunctionCall.Name).To(Equal("search"))
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})
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It("should convert multiple messages", func() {
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messages := Messages{
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{
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Role: "user",
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Content: "Hello",
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},
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{
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Role: "assistant",
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Content: "Hi there!",
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},
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{
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Role: "user",
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Content: "How are you?",
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},
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}
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protoMessages := messages.ToProto()
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Expect(protoMessages).To(HaveLen(3))
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Expect(protoMessages[0].Role).To(Equal("user"))
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Expect(protoMessages[0].Content).To(Equal("Hello"))
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Expect(protoMessages[1].Role).To(Equal("assistant"))
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Expect(protoMessages[1].Content).To(Equal("Hi there!"))
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Expect(protoMessages[2].Role).To(Equal("user"))
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Expect(protoMessages[2].Content).To(Equal("How are you?"))
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})
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It("should handle empty messages slice", func() {
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messages := Messages{}
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protoMessages := messages.ToProto()
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Expect(protoMessages).To(HaveLen(0))
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})
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It("should handle message with all optional fields", func() {
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messages := Messages{
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{
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Role: "assistant",
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Content: "I'll help you",
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Name: "test_tool",
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ToolCalls: []ToolCall{
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{
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Index: 0,
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ID: "call_789",
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Type: "function",
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FunctionCall: FunctionCall{
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Name: "test_function",
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Arguments: `{"param": "value"}`,
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},
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},
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},
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},
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}
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protoMessages := messages.ToProto()
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Expect(protoMessages).To(HaveLen(1))
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Expect(protoMessages[0].Role).To(Equal("assistant"))
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Expect(protoMessages[0].Content).To(Equal("I'll help you"))
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Expect(protoMessages[0].Name).To(Equal("test_tool"))
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Expect(protoMessages[0].ToolCalls).NotTo(BeEmpty())
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var toolCalls []ToolCall
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err := json.Unmarshal([]byte(protoMessages[0].ToolCalls), &toolCalls)
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Expect(err).NotTo(HaveOccurred())
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Expect(toolCalls).To(HaveLen(1))
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})
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It("should handle message with empty string content", func() {
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messages := Messages{
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{
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Role: "user",
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Content: "",
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},
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}
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protoMessages := messages.ToProto()
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Expect(protoMessages).To(HaveLen(1))
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Expect(protoMessages[0].Role).To(Equal("user"))
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Expect(protoMessages[0].Content).To(Equal(""))
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})
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It("should serialize ToolCallID and Reasoning fields", func() {
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reasoning := "thinking..."
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messages := Messages{
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{
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Role: "tool",
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Content: "result",
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ToolCallID: "call_123",
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Reasoning: &reasoning,
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},
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}
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protoMessages := messages.ToProto()
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Expect(protoMessages).To(HaveLen(1))
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Expect(protoMessages[0].ToolCallId).To(Equal("call_123"))
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Expect(protoMessages[0].ReasoningContent).To(Equal("thinking..."))
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})
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It("should not leak unset LocalAI-only or cross-endpoint request fields into JSON", func() {
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// OpenAIRequest is a union over chat / completion /
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// embedding / image / whisper. Strict upstream providers
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// (OpenAI, Anthropic) 400 on unknown parameters when
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// cloud-proxy passthrough re-marshals a chat request and
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// whisper's `file`, image's `step`, embedding's `input`,
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// etc. tag along as empty zero values.
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req := OpenAIRequest{}
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req.Model = "gpt-4"
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data, err := json.Marshal(req)
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Expect(err).NotTo(HaveOccurred())
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body := string(data)
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// Anchor with the trailing `:` so e.g. `"stream"` doesn't
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// false-match `"stream_options"` if a future test setup
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// populates the latter.
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for _, key := range []string{
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// LocalAI-only fields
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`"backend":`, `"grammar":`, `"grammar_json_functions":`,
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`"model_base_name":`, `"reasoning_effort":`,
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// Cross-endpoint fields that don't belong on chat
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`"file":`, `"size":`, `"prompt":`, `"instruction":`,
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`"input":`, `"stop":`, `"messages":`, `"functions":`,
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`"function_call":`, `"stream":`, `"quality":`, `"step":`,
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`"metadata":`,
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} {
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Expect(body).NotTo(ContainSubstring(key), "unset field "+key+" must not appear in marshalled JSON")
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}
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})
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It("should not leak internal String* staging fields into JSON", func() {
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// Regression: the request middleware copies decoded
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// Content into StringContent/StringImages/etc. for
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// templating. When cloud-proxy passthrough re-marshals
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// the request, strict providers (Anthropic) 400 with
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// "messages.0.string_content: Extra inputs are not
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// permitted" if these leak.
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msg := Message{
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Role: "user",
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Content: "Hello",
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StringContent: "Hello",
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StringImages: []string{"base64-blob"},
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StringVideos: []string{"base64-blob"},
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StringAudios: []string{"base64-blob"},
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}
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data, err := json.Marshal(msg)
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Expect(err).NotTo(HaveOccurred())
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Expect(string(data)).NotTo(ContainSubstring("string_content"))
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Expect(string(data)).NotTo(ContainSubstring("string_images"))
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Expect(string(data)).NotTo(ContainSubstring("string_videos"))
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Expect(string(data)).NotTo(ContainSubstring("string_audios"))
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Expect(string(data)).To(ContainSubstring(`"content":"Hello"`))
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})
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It("should handle message with array content containing non-text parts", func() {
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messages := Messages{
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{
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Role: "user",
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Content: []any{
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map[string]any{
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"type": "text",
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"text": "Hello",
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},
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map[string]any{
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"type": "image",
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"url": "https://example.com/image.jpg",
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},
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},
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},
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}
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protoMessages := messages.ToProto()
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Expect(protoMessages).To(HaveLen(1))
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Expect(protoMessages[0].Role).To(Equal("user"))
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// Should only extract text parts
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Expect(protoMessages[0].Content).To(Equal("Hello"))
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})
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// Regression for mudler/LocalAI#10039: ToProto is the path taken by
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// UseTokenizerTemplate backends (e.g. imported GGUFs, where the backend
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// applies the GGUF's jinja template to the raw messages). It reads
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// Content, not StringContent — so a message that only populated
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// StringContent (the shape /v1/responses produced before the fix)
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// reached the backend with empty content. These two cases pin that
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// contract: Content is authoritative, and producers must set it.
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It("emits empty content when only StringContent is set (Content nil)", func() {
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messages := Messages{
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{
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Role: "user",
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StringContent: "Hello",
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},
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}
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protoMessages := messages.ToProto()
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Expect(protoMessages).To(HaveLen(1))
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Expect(protoMessages[0].Content).To(BeEmpty())
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})
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It("carries Content through to proto regardless of StringContent", func() {
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messages := Messages{
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{
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Role: "user",
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Content: "Hello",
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StringContent: "Hello",
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},
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}
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protoMessages := messages.ToProto()
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Expect(protoMessages).To(HaveLen(1))
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Expect(protoMessages[0].Content).To(Equal("Hello"))
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})
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})
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})
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