Dynamic Parsing¶

Dynamic parsing enables flexible handling of structured outputs from LLM responses. This feature is particularly useful when reasoning models are used (e.g. DeepSeek R1).

Overview¶

The dynamic parsing feature can be enabled using the set_dynamic() method on your LLM instance. When enabled, the LLM will:

Attempt to parse and validate JSON responses
Include structured thinking process in the output
Handle complex response models dynamically

Usage¶

Here's how to enable dynamic parsing:¶

from extract_thinker import LLM

# Initialize LLM
llm = LLM("ollama/deepseek-r1:1.5b")

# Enable dynamic parsing
llm.set_dynamic(True)

Uses this template structure:¶

Please provide your thinking process within <think> tags, followed by your JSON output.

JSON structure:
{your_structure}

OUTPUT example:
<think>
Your step-by-step reasoning and analysis goes here...
</think>

##JSON OUTPUT
{
    ...
}

Example: Invoice Extraction¶

Here's a complete example of using dynamic parsing for invoice extraction:

from extract_thinker import LLM, Extractor
from extract_thinker.document_loader import DocumentLoaderPyPdf
from pydantic import BaseModel
from typing import List, Optional

# Define your invoice model
class InvoiceData(BaseModel):
    invoice_number: str
    date: str
    total_amount: float
    vendor_name: str
    line_items: List[dict]
    payment_terms: Optional[str]

# Initialize LLM with dynamic parsing
llm = LLM("ollama/deepseek-r1:1.5b")
llm.set_dynamic(True)  # Enable dynamic JSON parsing

# Setup document loader and extractor
document_loader = DocumentLoaderPyPdf()
extractor = Extractor(document_loader=document_loader, llm=llm)

# Extract information from invoice
result = extractor.extract("path/to/invoice.pdf", response_model=InvoiceData)