Large language models have fundamentally altered how engineers approach information retrieval. Where traditional systems relied on lexical overlap and inverted indexes, modern pipelines use dense embeddings, generative reranking, and agentic search loops to return answers rather than documents. This shift creates new opportunities for accuracy and user experience, but it also introduces architectural complexity around latency, cost predictability, and context management. For teams building these systems, the choice of inference backend determines whether the economics of semantic search scale sustainably.
From Keywords to Embeddings
Dense retrieval with embedding models is now the standard first stage in semantic search. Instead of matching query terms to document terms, you encode both sides into a shared vector space and perform approximate nearest neighbor search.
Oxlo.ai hosts production embedding models including BGE-Large and E5-Large, accessible through a fully OpenAI-compatible embeddings endpoint. Because the platform uses request-based pricing, the cost of embedding large document batches is predictable. You pay per request, not per input token, which removes the pricing surprises that come with ingesting high-volume corpora on token-based providers.
import openai
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)
# Embed a batch of documents
docs = [
"Oxlo.ai offers flat per-request pricing for LLM inference.",
"Dense retrieval uses vector representations of text."
]
response = client.embeddings.create(
model="bge-large",
input=docs
)
vectors = [item.embedding for item in response.data]
Retrieval-Augmented Generation
Retrieval-augmented generation, or RAG, pairs a retriever with a generator. The retriever fetches relevant passages, and the generator synthesizes them into a coherent answer. The quality of the final output depends heavily on the reasoning capability of the generator and the amount of context it can process at once.
Oxlo.ai provides models that cover both requirements. For reasoning, DeepSeek R1 671B MoE and Kimi K2.6 offer advanced chain-of-thought capabilities. For context capacity, DeepSeek V4 Flash supports a 1 million token context window, and Kimi K2.6 handles 131K tokens. These long-context models let you feed more retrieved passages into a single request, reducing the engineering overhead of aggressive chunking and hierarchical retrieval.
# RAG generation with a long-context model on Oxlo.ai
context = "\n\n".join(retrieved_passages)
completion = client.chat.completions.create(
model="kimi-k2-6",
messages=[
{"role": "system", "content": "Answer using only the provided context."},
{"role": "user", "content": f"Context:\n{context}\n\nQuestion: {query}"}
],
stream=True
)
for chunk in completion:
print(chunk.choices[0].delta.content or "", end="")
The Context Window Advantage
Long-context models are changing the economics of RAG. When a model accepts 131K or 1M tokens, you can often skip complex re-ranking pipelines and instead present dozens of candidate documents directly to the model for in-context selection. This simplifies architecture and reduces latency from multiple retrieval rounds.
The benefit is especially pronounced on Oxlo.ai because of its request-based pricing. With token-based providers, long-context workloads become expensive as input tokens accumulate. On Oxlo.ai, one flat cost per API request means that stuffing a 100K context window costs the same as a 1K context window. For information retrieval pipelines that need to evaluate large document sets, request-based pricing can be 10-100x cheaper than token-based for long-context workloads. You can see the details at https://oxlo.ai/pricing.
Agentic Retrieval Patterns
Static RAG is often insufficient for complex research tasks. Agentic retrieval uses tool-calling loops where the model decides to search, read, summarize, and search again until it satisfies the query. This requires reliable function calling and strong reasoning.
Oxlo.ai supports function calling and tool use across its chat models, including Qwen 3 32B and GLM 5, which are designed for multilingual reasoning and long-horizon agentic tasks. You can define search tools, let the model invoke them iteratively, and stream the results back without cold starts.
tools = [
{
"type": "function",
"function": {
"name": "search_corpus",
"description": "Search internal documents",
"parameters": {
"type": "object",
"properties": {
"query": {"type": "string"},
"top_k": {"type": "integer", "default": 5}
},
"required": ["query"]
}
}
}
]
response = client.chat.completions.create(
model="qwen-3-32b",
messages=[{"role": "user", "content": "Find revenue data from Q3 2024."}],
tools=tools,
tool_choice="auto"
)
# The model may return a tool call to refine retrieval
if response.choices[0].message.tool_calls:
# Execute search and return results for another turn
pass
Challenges and Practical Limitations
Despite the promise, LLM-based retrieval faces real engineering constraints. Hallucination remains a risk when generators synthesize retrieved content, so grounding and citation requirements are still necessary. Latency in multi-step agentic flows can degrade user experience, especially if each reasoning step triggers a separate API call. Evaluation is also harder than with traditional IR, because relevance now depends on both recall and the fluency of the generated answer.
Cost volatility is another operational concern. Token-based billing makes it difficult to budget for workloads with variable input lengths, such as user-uploaded documents or agentic loops that append context on each turn. Oxlo.ai addresses this with flat per-request pricing, giving teams a predictable unit cost that does not scale with prompt size. This stability is useful when prototyping agentic systems where context length fluctuates between requests.
Putting It Together
Modern information retrieval is becoming a stack of embeddings, long-context generators, and agentic orchestration. The right infrastructure should provide fast access to strong embedding models, reasoning-capable LLMs with large context windows, and predictable costs that do not punish long inputs.
Oxlo.ai offers all of these through a single OpenAI-compatible API. With embedding endpoints for BGE-Large and E5-Large, generation models ranging from Qwen 3 32B to DeepSeek V4 Flash, and flat per-request pricing, it is built for the retrieval workloads that teams are deploying today. If you are moving from keyword search to semantic RAG or agentic research loops, you can start integrating at https://api.oxlo.ai/v1 and review pricing at https://oxlo.ai/pricing.
Top comments (0)