semvec/pypi-packages huggingface dataset:
accuralai-ollama 0.2.1
Ollama backend adapter for the AccuralAI LLM orchestration pipeline.
License: Apache-2.0
Requires Python: >=3.10

# accuralai-ollama

`accuralai-ollama` provides an `ollama` backend implementation for `accuralai-core`, enabling local model inference through the Ollama service. Once installed, you can configure the core orchestrator to route requests to Ollama by setting the backend plugin to `ollama`.

## Installation

```bash
pip install accuralai-core accuralai-ollama
```

## Configuration

Add an Ollama backend block to your AccuralAI configuration (e.g., `~/.accuralai/core.toml`):

```toml
[backends.ollama]
plugin = "ollama"
[backends.ollama.options]
model = "gemma3:4b"
host = "http://127.0.0.1:11434"
timeout_s = 60
keep_alive = "5m"
```

Then run:

```bash
accuralai-core generate --prompt "Explain quantum entanglement" --route ollama
```

The backend will call Ollama’s `/api/generate` endpoint, return the completion, and populate usage metadata based on the server’s response.
accuralai-core 0.2.1
Async orchestration nucleus for the AccuralAI local LLM ecosystem.
License: Apache-2.0
Requires Python: >=3.10

# accuralai-core

`accuralai-core` is the orchestration nucleus for the AccuralAI open-source ecosystem. It provides an async pipeline that coordinates canonicalization, caching, routing, backend invocation, validation, and post-processing for local LLM text generation workflows.

This package exposes both a Python API and CLI surface while remaining transport-agnostic. Concrete functionality (canonicalizers, caches, routers, backends, validators) is supplied by sibling packages or third-party plugins via entry-point discovery.

## Quick start

Install with the canonicalizer plugin for a fully working local pipeline:

```bash
pip install accuralai-core accuralai-canonicalize
```

Generate completions via the CLI:

```bash
accuralai-core generate \
  --prompt " Summarize   this text.  " \
  --system-prompt "You are a precise assistant." \
  --tag demo --metadata topic=news --param temperature=0.3
```

The `accuralai-canonicalize` plugin normalizes the prompt, merges metadata defaults, and creates deterministic cache keys before the request is routed to the configured backend.

Add `accuralai-cache` to enable TTL-aware in-memory caching:

```bash
pip install accuralai-cache
```

With caching enabled, repeat invocations are served instantly until the configured TTL expires.

See `plan/accuralai-core-spec.md` for the full architectural specification guiding implementation.

## Interactive CLI

Launch the Codex-style REPL by invoking `accuralai` (or `accuralai-cli`) with no subcommand:

```bash
accuralai --config ~/.accuralai/core.toml
```

The shell remains active until `/exit`, and supports `/` commands for adjusting settings on the fly (`/help`, `/backend`, `/model`, `/meta`, `/history`, `/save`, `/tool ...`, etc.). Plain text input triggers pipeline runs using the current session defaults, and multi-line prompts are accepted by entering `"""` on an empty line. Tools can be listed via `/tool list`, executed manually with `/tool run ...`, and made available to the model for function calling via `/tool enable <name>`.
accuralai-validator 0.2.1
Validator plugins for the AccuralAI orchestration pipeline.
License: Apache-2.0
Requires Python: >=3.10

# accuralai-validator

`accuralai-validator` delivers reusable validator plugins for the AccuralAI pipeline. Validators apply safety policies, length constraints, and heuristic checks while emitting structured `validator_events` for downstream observability.

## Included Validators

- **noop** – explicit pass-through validator.
- **regex** – deny/allow lists backed by compiled regular expressions.
- **length** – prompt/completion length guards with token/character modes.
- **toxicity** – pluggable moderation adapter that integrates external scoring providers.
- **prompt_injection** – heuristic jailbreak detector using keyword and ratio checks.
- **json_schema** – validates responses against JSON structure or schemas declared inline or per-request.

## Development

```bash
python -m venv .venv && source .venv/bin/activate
pip install -e packages/accuralai-core[dev]
pip install -e packages/accuralai-validator[dev]
pytest packages/accuralai-validator/tests -q
```

Validators register entry points under `accuralai_core.validators` so `accuralai-core` can compose them into validation chains.
accuralai-google 0.2.1
Google GenAI backend adapter for the AccuralAI orchestration pipeline.
License: Apache-2.0
Requires Python: >=3.10

# AccuralAI Google Backend

This package provides the Google GenAI backend adapter for the AccuralAI orchestration core. It wraps the official `google-genai` client so the orchestrator can target Gemini models with the same pipeline used for other local or remote backends.

## Features

- Async-compatible adapter that internally uses `google-genai`'s synchronous client.
- Configurable model, generation parameters, safety settings, and client options.
- Pulls API keys from configuration or the `GOOGLE_GENAI_API_KEY` environment variable.
- Converts Google usage metadata into AccuralAI's standard `Usage` payload.

## Installation

```bash
pip install accuralai-google
```

## Configuration

From `accuralai-core`, reference the backend ID you register under `backends`:

```toml
[backends.google]
plugin = "google"
[backends.google.options]
model = "gemini-1.5-pro"
generation_config = { temperature = 0.7 }
```

Provide an API key either in the options (`api_key = "...")` or via `GOOGLE_GENAI_API_KEY`.
accuralai-rag 0.2.1
High-performance retrieval augmented generation components for the AccuralAI ecosystem.
License: Apache-2.0
Requires Python: >=3.10

# accuralai-rag

High-performance Retrieval Augmented Generation (RAG) utilities built for the AccuralAI orchestration ecosystem. The package provides:

- Multi-vector embedding utilities with optional GPU + hybrid sparse support.
- Intelligent chunking and metadata extraction helpers tuned for large documents.
- Hybrid dense/sparse retrieval with reciprocal rank fusion and reranking hooks.
- Query optimizers (HyDE, decomposition, semantic augmentation) plus advanced context builders.
- UltraFastRAG pipeline that combines the components with aggressive caching and streaming-friendly hooks.

All heavy dependencies are optional. Install extras such as `torch`, `transformers`, `faiss`, or `redis` based on your production topology:

```bash
pip install -e packages/accuralai-rag[torch,transformers,faiss,redis]
```

See `accuralai_rag/pipeline.py` for the orchestrated entry point and `tests/` for lightweight examples.