Building a Language Generation Model using LLM and Other AI Models

#aiinfrastructure #oxlo #ai

Building a production-grade language generation system today requires more than a single large language model. A complete stack combines reasoning LLMs, embedding models for retrieval, vision encoders for document understanding, and specialized code or audio models, all orchestrated behind a unified inference layer. The choice of backend determines not only latency and throughput, but also how cost scales as you add context, tools, and multimodal inputs.

Architecture of a Modern Language Generation System

At minimum, a language generation pipeline needs a core LLM for reasoning and text completion. In practice, production systems layer additional models around that core. Retrieval-augmented generation depends on embedding models to encode knowledge bases. Multimodal applications need vision models to parse diagrams or screenshots. Code agents benefit from dedicated coding models, while voice interfaces require audio transcription and speech synthesis.

Oxlo.ai offers 45+ open-source and proprietary models across seven categories, including LLMs, code models, vision models, image generation, audio, embeddings, and object detection. This breadth lets you keep the entire pipeline on one platform with a single API key and consistent latency semantics.

Selecting an Inference Backend

Token-based pricing dominates the inference market. Providers such as Together AI, Fireworks AI, OpenRouter, Replicate, and Anyscale charge by input and output tokens, which means long system prompts, few-shot examples, and large retrieved contexts directly increase your bill. For agentic workflows that loop over lengthy tool schemas and conversation history, costs compound quickly.

Oxlo.ai uses request-based pricing: one flat cost per API request regardless of prompt length. For long-context and agentic workloads, this can be significantly cheaper than token-based alternatives because cost does not scale with input length. The platform is fully OpenAI SDK compatible and exposes a single base URL, https://api.oxlo.ai/v1, with no cold starts on popular models.

SDK Setup and Authentication

Because Oxlo.ai is a drop-in replacement for the OpenAI SDK, you can prototype locally and promote the same code to production without vendor lock-in. Install the official OpenAI Python package and point the client at Oxlo.ai.

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key=os.environ["OXLO_API_KEY"]
)

Implementing Core Text Generation

For general-purpose generation, Oxlo.ai hosts models such as Llama 3.3 70B and Qwen 3 32B. For deep reasoning or complex coding, DeepSeek R1 671B MoE and DeepSeek V4 Flash provide strong performance. The following example streams a chat completion.

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[
        {"role": "system", "content": "You are a precise technical assistant."},
        {"role": "user", "content": "Generate a Python function that validates an email address."}
    ],
    stream=True
)

for chunk in response:
    print(chunk.choices[0].delta.content or "", end="")

Streaming responses, JSON mode, and multi-turn conversations are all supported through the standard chat/completions endpoint.

Retrieval and Embedding Workflows

To ground generation in private data, you need embeddings. Oxlo.ai provides BGE-Large and E5-Large through the embeddings endpoint. You can chunk documents, encode them, store vectors in any vector database, then retrieve relevant context at query time.

embed_response = client.embeddings.create(
    model="bge-large",
    input=[
        "Oxlo.ai offers flat per-request pricing for open-source LLMs.",
        "Embeddings are useful for retrieval-augmented generation."
    ]
)

vectors = [item.embedding for item in embed_response.data]

Because Oxlo.ai bills by request, embedding a long document split across multiple chunks still incurs predictable costs per call, which simplifies budgeting for RAG pipelines.

Multimodal Extensions

Language generation is no longer limited to text. Vision models let you describe images or extract structured data from diagrams. Oxlo.ai hosts vision-capable models such as Gemma 3 27B and Kimi VL A3B, accessible through the same chat/completions endpoint.

response = client.chat.completions.create(

    model="gemma-3-27b-it",

    messages=[{

        "role": "user",

        "content": [

            {"type": "text", "text": "Summarize the architecture in this diagram."},

            {"type": "image_url", "image_url":

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