Merge pull request #158 from enoch3712/153-how-to-implement-groq-llm

Groq documentation added

docs/examples/groq-processing.md

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+# Processing Documents with Groq
+
+> ⚠️ **Warning**: Vision-based processing may not be available with Groq models. For image or document processing that requires vision capabilities, consider using other providers like Google Document AI, Azure Document Intelligence, or AWS Textract.
+
+This guide demonstrates how to process documents using Groq's powerful LLMs.
+
+## Basic Setup
+
+Here's a basic example of document extraction using Groq:
+
+```python
+from extract_thinker import Extractor
+from extract_thinker.document_loader.document_loader_pypdf import DocumentLoaderPyPdf
+from typing import List
+from pydantic import Field
+
+class InvoiceContract(Contract):
+    lines: List[LineItem] = Field("List of line items in the invoice")
+
+# Initialize extractor with PyPDF loader
+extractor = Extractor()
+extractor.load_document_loader(DocumentLoaderPyPdf())
+
+# Configure Groq
+extractor.load_llm("groq/llama-3.2-11b-vision-preview")
+
+# Process document
+result = extractor.extract("invoice.pdf", InvoiceContract)
+```
+
+## Classification Example
+
+You can also use Groq for document classification:
+
+```python
+from extract_thinker import Process
+from extract_thinker.models.classification import Classification
+
+# Setup process
+process = Process()
+process.add_classify_extractor([[extractor]])
+
+# Define classifications
+classifications = [
+    Classification(
+        name="Invoice",
+        description="This is an invoice document", 
+        contract=InvoiceContract
+    ),
+    Classification(
+        name="Driver License",
+        description="This is a driver license document",
+        contract=DriverLicense
+    )
+]
+
+# Classify document
+result = process.classify("document.pdf", classifications)
+```
+
+## Benefits
+
+1. **High Performance**
+   - Fast inference times
+   - High-quality results
+   - State-of-the-art language models