# SEO Site Analysis
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### detect_duplicate_content
``` python
def detect_duplicate_content(
session:Session, # Active database session
website_id:int, # Website to search within
content:str, # Normalized page content
file_path:str, # Current article path (to exclude self)
similarity_threshold:float=0.8, # Minimum similarity to flag
)->dict:
```
*Find articles within a website with content similar to the given
content.*
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### analyze_content_groups
``` python
def analyze_content_groups(
session:Session, # Active database session
website_id:int, # Website to search within
similarity_threshold:float=0.8, # Minimum similarity to group
)->dict:
```
*Group similar articles together across a website.*
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### analyze_keyword_cannibalization
``` python
def analyze_keyword_cannibalization(
session:Session, # Active database session
website_id:int, # Website to search within
keyword:str, # Focus keyword to check
)->dict:
```
*Find articles within a website competing for the same focus keyword.*
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### analyze_content_groups_fast
``` python
def analyze_content_groups_fast(
session:Session, website_id:int, similarity_threshold:float=0.8, num_hashes:int=128
)->dict:
```
*Group similar articles using MinHash for fast approximate similarity.*
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### minhash_similarity
``` python
def minhash_similarity(
sig1:list, sig2:list
)->float:
```
*Estimate Jaccard similarity from two MinHash signatures.*
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### minhash_signature
``` python
def minhash_signature(
shingles:set, num_hashes:int=128
)->list:
```
*Compute MinHash signature for a set of shingles.*
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### get_shingles
``` python
def get_shingles(
text:str, n:int=3
)->set:
```
*Generate n-word shingles from text.*