spyder-ai-assistant 0.4.0

AI chat and code completion plugin for Spyder IDE using local Ollama models

spyder-ai-assistant

A local-first AI assistant for Spyder IDE. Chat with a model about your code, get Copilot-style inline completions, inspect live variables and tracebacks, and browse your conversation history — all running on your own GPU through Ollama, with optional support for OpenAI-compatible endpoints.

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Quick start

1. Install Ollama and pull a model

curl -fsSL https://ollama.com/install.sh | sh
ollama pull qwen2.5:7b          # chat model (~5 GB VRAM)

Pick a model that fits your GPU. See model recommendations below for more options.

2. Install the plugin

pip install spyder-ai-assistant

Install into the same Python environment where Spyder lives (e.g. your conda env).

3. Restart Spyder and open the chat

The plugin registers automatically. After restart:

• Open the chat panel: View > Panes > AI Chat
• Pick your model from the toolbar dropdown
• Start typing — inline completions appear automatically as ghost text

To manually trigger an inline suggestion: press Ctrl+Shift+Space.

That's it. Everything runs locally and works offline.

Optional: to use cloud or self-hosted chat endpoints, open AI Chat > More > Provider Profiles... and add one or more OpenAI-compatible profiles.

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Features

Inline code completions

Copilot-style ghost text that appears as you type, powered by Ollama's Fill-in-Middle (FIM) API.

Shortcut	Action
Ctrl+Shift+Space	Manually trigger a suggestion
Tab	Accept the full suggestion
Alt+Right	Accept next word
Alt+Shift+Right	Accept next line
Escape	Dismiss

The provider is tuned beyond a basic API call: it caches recent prompts, trims suffix overlap so brackets aren't duplicated, filters repetitive output, pulls relevant snippets from other open files for richer context, suppresses Spyder's native popup when a ghost suggestion is active, and cycles through alternative candidates locally without extra model round-trips. The status bar shows the active completion model and its state (generating, offline, or ready).

Chat panel

A dockable pane for talking to a model about your code. Open it from View > Panes > AI Chat.

• Multi-tab sessions — each conversation lives in its own tab
• Streaming responses — tokens arrive in real time
• Syntax-highlighted code blocks — with copy, insert-at-cursor, and replace-selection actions
• Thinking/reasoning display — models that emit <think> blocks (QwQ, DeepSeek-R1, etc.) show their reasoning in a dimmed section
• Per-tab chat modes — switch between Coding, Debugging, Review, Data Analysis, Explanation, or Documentation presets
• Per-tab inference settings — override temperature and max tokens for individual tabs
• Mid-conversation model switching — change models from the toolbar without losing context
• Stop and regenerate — cancel a response mid-stream, or rerun the last turn
• Delete individual exchanges — remove any saved turn from the conversation
• Export to Markdown — save any session with full metadata

Kernel integration and runtime inspection

The chat panel has read-only access to your active Spyder IPython session. It can inspect:

• Tracebacks and errors — the latest exception from your kernel
• Console output — recent visible output from your IPython session
• Live variables — the current variable list and targeted inspection of specific variables by name
• Structured runtime values — arrays, images, DataFrames, Series, and bounded nested-container previews
• Kernel state — shown in the chat toolbar so you always know what's running
• Multiple consoles — the toolbar can follow the active console or pin runtime inspection to a different open console

This is on-demand, not automatic — ordinary questions stay file-focused and lean. The AI only pulls runtime state when the question actually depends on it, and it never executes code on your behalf.

Quick-action buttons for common debugging workflows:

Control	What it does
Debug	Opens runtime-aware actions such as Explain Error, Fix Traceback, Use Variables, and Use Console
Regenerate	Reruns the last turn on the active tab

When more than one IPython console is open, the runtime target selector in the chat toolbar lets you choose Follow Active Console or pin the debugging context to a specific console. The runtime tooltip shows which console is currently active and which one is actually being inspected.

Editor integration

The AI automatically sees your current file, cursor position, selection, other open tabs, and your project's file tree. Right-click any selection for AI actions:

Action	What it does
Ask AI	Opens chat with your selection as context
Explain	Explains the selected code
Fix	Finds and fixes bugs in the selection
Add Docstring	Generates a docstring for the selected function or class

Code blocks in chat responses now expose Copy and Apply.... Apply... opens a preview dialog that lets you choose insert-vs-replace, inspect the diff, and confirm or cancel before the editor changes. The final mutation is grouped into a single undo step.

Session history and persistence

Chat sessions save automatically. When a Spyder project is open, conversations persist in .spyproject/ai-assistant/chat-sessions.json and restore when the project reopens. Without a project, sessions fall back to a global store.

The Sessions button keeps session actions in one place. Its history browser lets you search, filter, sort, reopen, duplicate, or delete saved sessions. Per-tab chat modes and inference overrides persist with each session.

Multi-provider support

By default, everything runs through Ollama. For chat, you can also manage multiple named OpenAI-compatible profiles from AI Chat > More > Provider Profiles....

• each profile has its own label, endpoint, API key, and enabled state
• the shared model selector groups entries by provider/profile and keeps endpoint details in the tooltip
• the status label reports provider issues without hiding working models
• removing a stale profile falls back cleanly to another available model

Inline completions stay Ollama-backed for low latency.

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Model recommendations

Chat models

Pick one that fits your GPU:

VRAM	Model	Command
8 GB	Qwen 2.5 7B	ollama pull qwen2.5:7b
12 GB	Qwen 2.5 14B	ollama pull qwen2.5:14b
16 GB+	Qwen 3.5 27B	ollama pull huihui_ai/qwen3.5-abliterated:27b

Completion models (optional)

A smaller, faster model for inline suggestions. Recommended but not required — the chat model handles completions if no separate model is configured.

VRAM	Model	Command
8 GB	Qwen 2.5 Coder 3B	ollama pull qwen2.5-coder:3b
12 GB+	Qwen3 Coder 30B (3B active)	ollama pull qooba/qwen3-coder-30b-a3b-instruct:q3_k_m

GPU and memory

Ollama uses your GPU automatically if CUDA or ROCm drivers are installed. Rough VRAM requirements for Q4_K_M quantized models:

Model size	VRAM needed	Typical use
3B	~2.5 GB	Completions
7B	~5 GB	Basic chat
14B	~9 GB	Good chat
27B	~15 GB	Excellent chat

Running chat and completions simultaneously keeps both models in VRAM. A 7B chat + 3B completion model needs about 7.5 GB total. Without a GPU, Ollama falls back to CPU (slower but functional).

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Configuration

All settings live in Preferences:

• Preferences > AI Chat — default chat provider, Ollama server URL, model names, temperature, max tokens, keyboard shortcuts, system prompt, and action prompt templates (with {filename} and {code} placeholders)
• Preferences > Completion and linting > AI Chat — completion toggle, model, temperature, max tokens, debounce delay

OpenAI-compatible chat endpoints are managed directly from the chat pane through More > Provider Profiles.... Existing single-endpoint settings are imported automatically the first time you open that dialog.

Per-tab chat modes and inference overrides are set directly in the chat pane and persist with the session.

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Troubleshooting

"No models found" in the dropdown — Run curl http://localhost:11434/api/tags to check Ollama, or pull a model with ollama pull qwen2.5:7b. For OpenAI-compatible profiles, open More > Provider Profiles... and confirm the endpoint responds on /v1/models.

Completions aren't appearing — Enable them in Preferences > Completion and linting > AI Chat. The status bar should show AI: model-name. If it says AI: offline, Ollama isn't reachable.

Chat panel doesn't show up — Check View > Panes for "AI Chat". If missing, the plugin may be in a different Python env than Spyder. Verify: python -c "import spyder_ai_assistant; print('OK')".

Slow responses — Try a smaller model. Check nvidia-smi for GPU usage. First requests are always slower while the model loads into VRAM.

Too much VRAM — Run ollama ps to see loaded models and ollama stop <model> to unload.

Runtime inspection returns generic answers — Use a stronger instruction-following model. The runtime bridge requires the model to emit structured inspection requests. Qwen-based models handle this reliably.

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Roadmap

Active development. Rough priority order:

• Session management — pinning, labeling, bulk operations
• More providers — adapters beyond OpenAI-compatible, profile import/export, provider health checks
• Smart setup — one-click Ollama install, guided model downloads, hardware-aware recommendations
• Smarter completions — scope-aware truncation, rename-aware suggestions, multi-site edits
• Polish the apply workflow further — richer inline diff rendering, smarter multi-block edit handling
• Agent workflows — multi-step task execution with approval gates, git-aware context

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Contributing

See CONTRIBUTING.md for development setup, architecture overview, validation workflow, and release process.

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License

Creative Commons Attribution-NonCommercial 4.0 International. Free to use, share, and adapt for non-commercial purposes with attribution. See LICENSE.