git:20260612.8db3bb9 to git:20260820.0db2afc

---

name: infrastructure-llm

description: Skill for the LLM infrastructure module providing local Large Language Model integration via Ollama. Covers client initialization, prompt templates, output validation, manuscript review generation, conversation context, and CLI usage. Use when querying LLMs, generating manuscript reviews, validating LLM outputs, or managing Ollama models.

---

# LLM Module

Local Large Language Model integration for research assistance via Ollama.

## Module Structure

```mermaid

flowchart LR

LLM[llm/]

LLM --> CORE[core<br/>client · config · context]

LLM --> TPL[templates<br/>prompt templates for research]

LLM --> VAL[validation<br/>output quality validation]

LLM --> RV[review<br/>manuscript review generation]

LLM --> PR[prompts<br/>fragment composition system]

LLM --> UT[utils<br/>Ollama server management]

LLM --> CLI[cli<br/>command-line interface]

classDef d fill:#0f172a,stroke:#0f172a,color:#fff

classDef pkg fill:#1e3a8a,stroke:#0f172a,color:#fff

class LLM d

class CORE,TPL,VAL,RV,PR,UT,CLI pkg

```

## LLM Client (`core/client.py`)

```python

from infrastructure.llm import LLMClient, OllamaClientConfig, GenerationOptions

# Initialize with defaults

client = LLMClient()

# Custom configuration

config = OllamaClientConfig(default_model="gemma3:4b", temperature=0.7)

client = LLMClient(config)

# Generate a response

response = client.query("Summarize this paper...", options=GenerationOptions(

max_tokens=2000,

temperature=0.3,

))

```

## Conversation Context (`core/context.py`)

```python

from infrastructure.llm.core import ConversationContext, Message

context = ConversationContext()

context.add_message(role="user", content="What is active inference?")

context.add_message(role="assistant", content="Active inference is...")

```

## Prompt Templates (`templates/`)

Pre-built research task templates:

```python

from infrastructure.llm import get_template

from infrastructure.llm.templates import (

ResearchTemplate, PaperSummarization,

ManuscriptExecutiveSummary, ManuscriptQualityReview,

ManuscriptMethodologyReview, ManuscriptImprovementSuggestions,

ManuscriptTranslationAbstract,

)

# Get a template by name

template = get_template("paper_summarization")

# Use specific template classes

summary_template = ManuscriptExecutiveSummary()

prompt = summary_template.render(text=text)

```

## Output Validation (`validation/`)

Validation was decomposed into module-level functions in v0.6.0 — the

previous `OutputValidator` class is gone; call the individual checks

directly.

```python

from infrastructure.llm import is_off_topic

from infrastructure.llm.validation import (

detect_repetition,

check_format_compliance, validate_section_completeness,

calculate_unique_content_ratio, deduplicate_sections,

)

# Individual checks

if is_off_topic(response_text):

logger.warning("Response appears off-topic")

if detect_repetition(response_text):

logger.warning("Response contains repeated content")

ratio = calculate_unique_content_ratio(response_text)

```

## Manuscript Review Generation (`review/`)

```python

from infrastructure.llm.review import (

create_review_client, select_and_start_ollama_model, warmup_model,

extract_manuscript_text, generate_review_with_metrics,

generate_llm_executive_summary, generate_improvement_suggestions,

generate_translation, save_review_outputs,

)

from infrastructure.llm.review.generator import (

generate_quality_review, generate_methodology_review,

)

# Full review workflow

client = create_review_client()

warmup_model(client)

text = extract_manuscript_text(manuscript_dir)

executive = generate_llm_executive_summary(client, text)

quality = generate_quality_review(client, text)

methodology = generate_methodology_review(client, text)

suggestions = generate_improvement_suggestions(client, text)

save_review_outputs(output_dir, executive=executive, quality=quality,

methodology=methodology, suggestions=suggestions)

```

## Ollama Utilities (`utils/`)

```python

from infrastructure.llm.utils import (

is_ollama_running, start_ollama_server, ensure_ollama_ready,

get_model_names, select_best_model,

select_small_fast_model, preload_model, check_model_loaded,

)

# Check and start Ollama

if not is_ollama_running():

start_ollama_server()

ensure_ollama_ready()

models = get_model_names()

best = select_best_model()

```

## Prompt Composition (`prompts/`)

```python

from infrastructure.llm.prompts import PromptFragmentLoader, PromptComposer

loader = PromptFragmentLoader()

composer = PromptComposer(loader)

prompt = composer.compose_template(

"manuscript_reviews.json#manuscript_executive_summary",

text=manuscript_text,

)

```

## CLI Usage

```bash

# Query the LLM

# Check Ollama status

# List available models

# List available research templates

# Apply a research template (reads input from --input or stdin)

```