Yes — and this is becoming a core part of modern RAG + LLM systems.

There are libraries and techniques specifically designed to help you:

✅ Structure data
✅ Parse context
✅ Validate outputs
✅ Ensure LLM understands rich structured input

Let’s go through the RELEVANT ones.

🧠 1️⃣ Pydantic + Prompt Templates

Not exactly a parser library, but:

👉 You define structured models
👉 Then serialize them to JSON for the prompt
👉 And optionally validate LLM output

Example:

from pydantic import BaseModel

class Product(BaseModel):
    title: str
    price: float
    rating: float
    reviews: str

Then embed this in prompt.

LLM sees:

{
  "products": [...],
  "question": "..."
}

This greatly improves interpretation.

🧠 2️⃣ LangChain Structured Output Parsers

LangChain has built-in structured parsers like:

✔ OutputFixingParser
✔ PydanticOutputParser
✔ ResponseSchema

They help LLM output structured JSON that you can parse back into objects.

Example:

from langchain.output_parsers import PydanticOutputParser

🧠 3️⃣ LlamaIndex (GPT Index)

LlamaIndex features:

✅ Document schema definitions
✅ Structured ingestion
✅ JSON-style responses

It automatically handles:

product -> metadata -> vector index

Then produces structured output.

Example output:

{
  "product": "HP 15s",
  "price": 48000,
  "rating": 4.3,
  "best_under_budget": true
}

🧠 4️⃣ Zeno (by Mistral/Anthropic)

Zeno provides:

🔹 Structured reinforcement
🔹 Control flow
🔹 Output schemas
🔹 Program-like reasoning

It works best for:

👨‍💻 Programmers
🧠 Systems that enforce rules

🧠 5️⃣ Data-First Prompting Libraries

Examples:

🟡 EXtreme

👉 Write SQL-like queries to LLM
👉 Get structured responses

🟡 TextQL

👉 Treat context as table
👉 Query it with structured output

🧠 6️⃣ Embedding + Schema Extraction Libraries

Examples:

📍 Haystack
📍 Semantic Kernel
📍 AutoGen

These support:

✔ JSON schema enforcement
✔ Entity extraction
✔ Slot filling

🚀 Recommended for Your Ecommerce Use Case

Best Fit

🧠 Example: Using Pydantic Parser

from pydantic import BaseModel
from langchain.output_parsers import PydanticOutputParser

class ProductResponse(BaseModel):
    recommended: str
    price: float
    reasons: str

parser = PydanticOutputParser(pydantic_model=ProductResponse)

Prompt:

You are ecommerce assistant.

Context:
{structured_data}

Return JSON matching ProductResponse.

LLM returns

{"recommended":"HP 15s","price":48000,"reasons":"Better battery..."}

That is now machine-readable JSON 🔥

🧠 Example: How You’d Build It

Step 1 — Retrieve

docs = retriever.invoke(query)

Step 2 — Convert to Structured JSON

products = [
    {
      "title": doc.metadata["product_title"],
      "price": float(doc.metadata["price"]),
      "rating": float(doc.metadata["rating"]),
      "reviews": doc.page_content
    }
    for doc in docs
]

Step 3 — Pass into Prompt

Context: {products_json}

Instructions:
Use this JSON to answer the question:
{question}
Return JSON with recommendation.

Step 4 — Parse Output

parsed = parser.parse(response_text)

Now parsed is a real object.

🔥 Why This Is Better

✔ LLM reason on structured data
✔ Less hallucination
✔ Schema enforcement
✔ Output consistency
✔ Easily machine-readable

This is enterprise-grade RAG.

🧠 So Bottom Line

Yes — libraries exist that let you store, provide, parse, and enforce structured context for LLMs:

✔ LangChain Output Parsers
✔ LlamaIndex Structured nodes
✔ Pydantic models
✔ TextQL / EXtreme
✔ Zeno
✔ Haystack + Schema adapters