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rony-llm-agent/pkg/llm
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Victor Vargas 2f6f5fab1c feat(llm): add multimodal ContentPart/Parts + per-provider serialization 
Message gains an optional Parts []ContentPart alongside the existing
plain-text Content, so a turn can carry text plus image/video
attachments. Content stays the single source of truth for every
existing text-only caller (sidebar.go, memory_tools.go, etc. are
untouched); Parts only matters to a provider client when non-empty.

openai and llamacpp (both OpenAI-compatible) serialize Parts into the
standard text/image_url content-array shape; llamacpp additionally
passes video through as a best-effort video_url part, since llama.cpp
itself has no video support but the whole point of this client is the
user's own OpenAI-compatible server sitting in front of a
video-capable model — the server decides whether it understands it,
not this client. anthropic converts image parts to its base64 image
content block, and rejects a video part outright with a clear error:
the Messages API has no video block type at all, so sending one would
just produce a confusing 400 instead.

ProviderCapabilities gains SupportsVideo, true only for llamacpp.
2026-07-16 22:23:54 -07:00
..
mock style: gofmt 2026-07-12 16:14:15 -07:00
providers feat(llm): add multimodal ContentPart/Parts + per-provider serialization 2026-07-16 22:23:54 -07:00
README.es.md docs(i18n): translate all docs to English (with .es.md as Spanish alternative) 2026-06-30 13:40:37 -07:00
README.md docs(i18n): translate all docs to English (with .es.md as Spanish alternative) 2026-06-30 13:40:37 -07:00
types.go feat(llm): add multimodal ContentPart/Parts + per-provider serialization 2026-07-16 22:23:54 -07:00
types_test.go style: gofmt 2026-07-12 16:14:15 -07:00

README.md

pkg/llm

Multi-provider abstraction for language models.

Responsibility

Define a common interface (LLMClient) and adapters for the main providers.

Public API

type LLMClient interface {
    Generate(ctx context.Context, req CompletionRequest) (CompletionResponse, error)
    Stream(ctx context.Context, req CompletionRequest) iter.Seq2[StreamChunk, error]
    Name() string
    Capabilities() ProviderCapabilities
}

type CompletionRequest struct {
    Messages    []Message
    Tools       []tools.Tool
    ToolChoice  ToolChoice
    Model       string
    Temperature *float32
    MaxTokens   *int
}

type CompletionResponse struct {
    Content    string
    ToolCalls  []tools.Call
    Usage      TokenUsage
    StopReason string
}

type ProviderCapabilities struct {
    SupportsTools    bool
    SupportsVision   bool
    MaxContextWindow int
}

Included providers

Provider Package Tool support
OpenAI providers/openai ✅
Anthropic providers/anthropic ✅
Ollama providers/ollama ✅ (models that support it)
llama.cpp providers/llamacpp ✅ (with grammar)

Usage

import "github.com/VictorVargas/rony-llm-agent/pkg/llm/providers/anthropic"

client, err := anthropic.New(anthropic.Config{
    APIKey: os.Getenv("ANTHROPIC_API_KEY"),
    Model:  "claude-sonnet-4.5",
})

resp, err := client.Generate(ctx, llm.CompletionRequest{
    Messages: []llm.Message{
        {Role: llm.RoleUser, Content: "Hello"},
    },
})

Streaming

for chunk, err := range client.Stream(ctx, req) {
    if err != nil { return err }
    fmt.Print(chunk.Delta)
}

Mock for tests

import "github.com/VictorVargas/rony-llm-agent/pkg/llm/mock"

mockClient := mock.New(mock.Responses{
    {Match: "hello", Response: "Hi! How are you?"},
    {Match: "*",    Response: "default"},
})

See also