Recurrent Language Model with LangGraph

Overview

This project provides a modular API interface for interacting with a Recurrent Language Model (RLM) powered by LangGraph, combining stateful sequential modeling with graph-based workflow orchestration.

The system allows:

• Text input ingestion and preprocessing
• Model inference using a recurrent architecture
• Structured response delivery via REST API
• Integration with vector stores and embedding pipelines

It is designed as a robust backend for AI-driven applications, supporting scalable, context-aware, and multi-step text generation pipelines.

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What is a Recurrent Language Model (RLM)?

A Recurrent Language Model (RLM) is a neural network that processes sequences of tokens one at a time, maintaining an internal hidden state that captures prior context. Unlike feedforward models, RLMs are capable of remembering information from previous steps, enabling coherent, context-aware text generation.

Key features:

• Sequential Processing: Updates hidden states at each token, maintaining context
• Context Awareness: Predicts next tokens based on the full prior sequence
• Generative Capabilities: Produces text continuations, summaries, or multi-turn dialogue
• Applications: Chatbots, summarization, question answering, AI assistants, code generation

In this project, the RLM is orchestrated using LangGraph, allowing developers to define workflow graphs connecting preprocessing, embedding, retrieval, inference, and output formatting nodes.

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Key Components

1. Text Input Ingestion & Preprocessing

   • Accepts single or batch text prompts
   • Cleans input, removes unwanted characters, and tokenizes text
   • Optionally uses metadata for context-aware generation

2. API-Based Model Inference

   • Connects to LangGraph RLM for sequential generation
   • Configurable parameters: max_tokens, temperature, top-k, top-p
   • Supports multi-turn reasoning, chained tasks, and sequential query handling

3. Structured Response Generation

   • Converts raw model output into structured JSON
   • Includes metadata: request ID, timestamps, inference parameters
   • Outputs ready for downstream API consumers

4. Modular Architecture

   • Clear separation of API, model, and workflow logic
   • Extensible pipelines for new endpoints, models, or workflows
   • Reusable components across different NLP or AI tasks

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Design Priorities

• Clean API structure: Easy to maintain, test, and extend
• Scalable endpoints: Handle concurrent requests efficiently
• Modular architecture: Add new LangGraph workflows without rewriting code
• Future-ready: Compatible with RAG pipelines, vector databases, and AI-driven applications
• Developer-friendly: Detailed logging, error handling, and structured JSON responses

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System Capabilities

API Request Handling

• Accepts structured text input via REST endpoints
• Validates requests with Pydantic schemas
• Supports single or batch prompts
• Optional metadata fields for context-aware generation
• Handles errors gracefully with standardized responses

Model Inference

• Integrates with LangGraph RLM
• Generates structured outputs from sequential inputs
• Configurable inference parameters:
  • temperature – randomness in generation
  • max_tokens – length of output
  • top-k / top-p – sampling strategy for token selection
• Supports:
  • Sequential text generation
  • Multi-turn conversation
  • Chained reasoning tasks

Response Structuring

• Converts raw model output into structured JSON
• Includes metadata:
  • Request timestamp
  • Request ID
  • Inference parameters
• Prepares output for downstream pipeline consumption

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API Pipeline Structure

The system uses a modular pipeline design with clear separation of responsibilities:

1. Input Handling Layer – Validation, cleaning, tokenization
2. Processing Layer – Embedding, RLM inference, optional retrieval
3. Output Layer – Converts model outputs to structured JSON with metadata

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High-Level Workflow

flowchart TD
    %% Document Ingestion & Storage
    subgraph Document Services
        A[Parse Text & Chunk] --> B[Generate Embeddings]
        B --> F[Store in Vector DB]
    end

    %% Query & Retrieval
    subgraph Query Workflow
        C[API Receives Query] --> D[Retrieve Relevant Chunks]
        D --> E[Rerank Chunks]
        E --> G[LangGraph Execution]
        G --> H[Generate Structured Output]
        H --> I[Return JSON Response]
    end

    %% Connections
    F --> D
    C --> D

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Recurrent Language Model with LangGraph

API Services & Core Language Modeling Pipeline

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Workflow Steps

1. Parse & Chunk – Break input text into meaningful chunks
2. Embed & Store – Convert chunks into embeddings in a vector database
3. Retrieve Chunks – Fetch relevant data based on query
4. Rerank – Sort retrieved results by relevance
5. LangGraph Execution – Orchestrate model inference and downstream logic
6. Structured Output – Format output with metadata for API consumers

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LangGraph Integration

LangGraph is a workflow orchestration framework for large language models. It enables:

• Graph-based node execution – Each node handles a step: parsing, embedding, inference, etc.
• Stateful context flow – Hidden states and outputs flow between nodes
• Conditional and looping edges – Enables complex workflows like RAG or multi-step reasoning
• Reusable pipelines – Easily extendable for additional models or AI tasks

By combining RLM with LangGraph, this system supports context-aware sequential generation, multi-turn queries, and structured responses.

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Module-Level Breakdown

app/core

• Core RLM integration and inference logic
• Classes for sequential generation, multi-turn memory, and inference parameters

app/graph

• LangGraph workflow definitions
• Nodes, edges, and state flow between preprocessing, embedding, and inference

app/pipelines

• Modular pipelines for text ingestion, embedding, retrieval, and output generation
• Reusable components for API endpoints

app/schemas

• Pydantic schemas for request validation and response structuring
• Ensures consistent API payloads

app/services

• Helper functions for vector store interactions, embeddings, and retrieval

app/main.py

• FastAPI entrypoint
• Initializes endpoints, pipelines, and LangGraph execution environment

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Technology Stack

Component	Technology
Language	Python
API Framework	FastAPI
Model Integration	LangGraph RLM
Validation	Pydantic / Pydantic-Settings
Server	Uvicorn
Data Formats	JSON
Architecture Style	Modular API Pipeline
Vector Stores	FAISS / Chroma / Pinecone (optional)

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Intended Use Cases

• API-based text generation and NLP
• Backend service for RAG and AI workflows
• Structured response generation for downstream AI applications
• Foundation for experimentation with graph-based sequential language models

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Example Response

{
  "request_id": "12345",
  "timestamp": "2026-03-08T23:50:00Z",
  "output": "A Recurrent Language Model processes text sequentially, maintaining context from previous tokens...",
  "inference_params": {
    "max_tokens": 150,
    "temperature": 0.7
  }
}

Advanced Use Cases

• RAG Workflows – Combine RLM generation with retrieval-augmented knowledge
• Multi-turn Dialogue Systems – Maintain conversational state across API calls
• Structured Document Processing – Parse large documents and generate summaries with metadata
• Pipeline Experimentation – Swap in new models, embeddings, or workflow nodes easily

License

This project is licensed under the MIT License.