infrastructure-llm

LLM Module

Local Large Language Model integration for research assistance via Ollama.

Module Structure

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)

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)

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:

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)