### Install Markovify via pip

Source: https://github.com/jsvine/markovify/blob/master/README.md

The standard command to install the Markovify library using the Python package manager.

```bash
pip install markovify
```

--------------------------------

### Generating Sentences with Start Word and State Size in markovify

Source: https://context7.com/jsvine/markovify/llms.txt

Demonstrates how to generate sentences using a starting word and control the state size for more context. The `strict` parameter determines if the starting word must appear at the beginning of a sentence in the corpus.

```python
import markovify

text = "your corpus text here"
text_model = markovify.Text(text)

# Non-strict mode: starting words can appear anywhere in corpus
sentence = text_model.make_sentence_with_start("dog", strict=False)
print(sentence)

# With state_size=3, you can use up to 3 starting words
text_model_ss3 = markovify.Text(text, state_size=3)
sentence = text_model_ss3.make_sentence_with_start("I was walking", tries=50)
```

--------------------------------

### Generating Sentences with Specific Starting Words

Source: https://context7.com/jsvine/markovify/llms.txt

Shows how to generate sentences that begin with a user-specified word or phrase, with an option for strict matching against the original corpus.

```APIDOC
## Generating Sentences with Specific Starting Words

### Description
The `make_sentence_with_start()` method generates sentences that begin with a specified word or phrase. The `strict` parameter controls whether the starting words must appear at sentence beginnings in the original corpus.

### Method
`text_model.make_sentence_with_start(beginning, strict=True, tries=100, max_overlap_ratio=0.7, max_overlap_total=15)`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **beginning** (str) - Required - The word or phrase the sentence must start with.
- **strict** (bool) - Optional - If True, the beginning must appear at the start of a sentence in the corpus (default: True).
- **tries** (int) - Optional - Number of attempts to generate a sentence (default: 100).
- **max_overlap_ratio** (float) - Optional - Maximum ratio of overlapping words allowed (default: 0.7).
- **max_overlap_total** (int) - Optional - Maximum number of consecutive overlapping words allowed (default: 15).

### Request Example
```python
import markovify

with open("sherlock.txt") as f:
    text_model = markovify.Text(f.read(), state_size=2)

# Generate sentence starting with specific words (strict mode)
# Words must begin a sentence in the original corpus
sentence = text_model.make_sentence_with_start("Sherlock Holmes")
print(sentence)
# Output: "Sherlock Holmes was not a man who was easily surprised."

# Start with a single word
sentence = text_model.make_sentence_with_start("The")
print(sentence)

# Generate sentence starting with specific words (non-strict mode)
sentence_non_strict = text_model.make_sentence_with_start("The game", strict=False)
print(sentence_non_strict)
```

### Response
#### Success Response (200)
- **sentence** (str or None) - A generated sentence starting with the specified beginning, or None if generation failed.
```

--------------------------------

### Custom Text Parsing with NLTK and spaCy

Source: https://context7.com/jsvine/markovify/llms.txt

Shows how to extend the markovify.Text class to create custom parsers. This example uses NLTK for part-of-speech tagging and spaCy for faster tagging, demonstrating custom word splitting and joining.

```python
import markovify
import re
import nltk

class POSText(markovify.Text):
    def word_split(self, sentence):
        words = re.split(self.word_split_pattern, sentence)
        tagged = nltk.pos_tag(words)
        return ["::".join(tag) for tag in tagged]

    def word_join(self, words):
        return " ".join(word.split("::")[0] for word in words)

with open("corpus.txt") as f:
    pos_model = POSText(f.read())

sentence = pos_model.make_sentence()
print(sentence)

import spacy
lp = spacy.load("en_core_web_sm")

class SpacyText(markovify.Text):
    def word_split(self, sentence):
        return ["::".join((word.orth_, word.pos_)) for word in nlp(sentence)]

    def word_join(self, words):
        return " ".join(word.split("::")[0] for word in words)
```

--------------------------------

### Generate Sentences with Specific Starting Words

Source: https://context7.com/jsvine/markovify/llms.txt

Generates sentences that begin with a specific word or phrase. The strict parameter ensures the starting phrase aligns with original corpus sentence structures.

```python
import markovify

with open("sherlock.txt") as f:
    text_model = markovify.Text(f.read(), state_size=2)

sentence = text_model.make_sentence_with_start("Sherlock Holmes")
sentence = text_model.make_sentence_with_start("The")
```

--------------------------------

### Build Markov Models with markovify.Text

Source: https://context7.com/jsvine/markovify/llms.txt

Demonstrates how to initialize a Markov model from a text corpus. Includes configuration options for state size, memory management, and input validation.

```python
import markovify

with open("corpus.txt") as f:
    text = f.read()

text_model = markovify.Text(text)
text_model_coherent = markovify.Text(text, state_size=3)
text_model_lean = markovify.Text(text, retain_original=False)
text_model_messy = markovify.Text(text, well_formed=False)
text_model_custom = markovify.Text(text, well_formed=True, reject_reg=r"#")
```

--------------------------------

### Generate Sentences with Markovify

Source: https://github.com/jsvine/markovify/blob/master/README.md

Demonstrates how to load text, build a Markov model, and generate both standard and length-constrained sentences.

```python
import markovify

with open("/path/to/my/corpus.txt") as f:
    text = f.read()

text_model = markovify.Text(text)

for i in range(5):
    print(text_model.make_sentence())

for i in range(3):
    print(text_model.make_short_sentence(280))
```

--------------------------------

### Configure Markov Model State Size

Source: https://github.com/jsvine/markovify/blob/master/README.md

Shows how to instantiate a Markov model with a custom state size, which determines how many words the probability of the next word depends on.

```python
text_model = markovify.Text(text, state_size=3)
```

--------------------------------

### Building a Text Model with markovify.Text

Source: https://context7.com/jsvine/markovify/llms.txt

Demonstrates how to create Markovify text models from a corpus, with options for state size, memory efficiency, and handling of malformed text.

```APIDOC
## Building a Text Model with markovify.Text

### Description
The `Text` class is the primary interface for building Markov models from text corpora. It accepts a string of text, parses it into sentences and words, and builds an underlying Markov chain. The `state_size` parameter controls how many words the model considers when predicting the next word—higher values produce more coherent but less varied output.

### Method
`markovify.Text(text, state_size=2, tries=100, retain_original=True, well_formed=True, reject_reg=None, chain=None)`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **text** (str) - Required - The input text corpus.
- **state_size** (int) - Optional - Controls the number of words considered for prediction (default: 2).
- **tries** (int) - Optional - Number of attempts to generate a sentence (default: 100).
- **retain_original** (bool) - Optional - Whether to retain the original text for overlap checking (default: True).
- **well_formed** (bool) - Optional - Whether to parse text assuming well-formed sentences (default: True).
- **reject_reg** (str) - Optional - Regular expression to reject sentences that match.
- **chain** (markovify.Chain) - Optional - A pre-built Markov chain to use.

### Request Example
```python
import markovify

# Load your text corpus
with open("corpus.txt") as f:
    text = f.read()

# Build a basic model (default state_size=2)
text_model = markovify.Text(text)

# Build a model with higher state size for more coherent output
text_model_coherent = markovify.Text(text, state_size=3)

# Build a memory-efficient model without retaining original text
# (disables overlap checking)
text_model_lean = markovify.Text(text, retain_original=False)

# Build a model that allows "messy" text with quotes/parentheses
text_model_messy = markovify.Text(text, well_formed=False)

# Build with custom rejection pattern
text_model_custom = markovify.Text(text, well_formed=True, reject_reg=r"#")
```

### Response
#### Success Response (200)
- **text_model** (markovify.Text) - An instance of the Markovify Text class, trained on the provided corpus.
```

--------------------------------

### Markovify Text Generation and Chain Operations

Source: https://context7.com/jsvine/markovify/llms.txt

Demonstrates basic text generation using markovify chains, including walking the chain, using generators, exporting/importing chains, accessing the internal model, and compiling for performance.

```APIDOC
## Markovify Text Generation and Chain Operations

### Description
Basic usage of the markovify library for generating text sequences and manipulating Markov chains.

### Method
N/A (Illustrative code)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
N/A

## Code Examples:

# Generate a sequence
```python
sequence = chain.walk()
print(sequence)
# Output: ['A', 'B', 'E', 'F']
```

# Use generator for lazy evaluation
```python
for item in chain.gen():
    print(item, end=" -> ")
# Output: A -> B -> C -> D ->
```

# Export/import chain
```python
chain_json = chain.to_json()
restored_chain = markovify.Chain.from_json(chain_json)
```

# Access the internal model
```python
print(chain.model)
# Output: {('A', 'B'): {'C': 1, 'E': 1}, ('B', 'C'): {'D': 2}, ...}
```

# Compile for performance
```python
compiled_chain = chain.compile()
```
```

--------------------------------

### Using the Chain Class Directly in markovify

Source: https://context7.com/jsvine/markovify/llms.txt

Demonstrates the direct use of the `markovify.Chain` class for building Markov chains from arbitrary sequences, not just text. This allows for applications beyond natural language processing.

```python
import markovify

# Build a chain from sequences (not text-specific)
corpus = [
    ["A", "B", "C", "D"],
    ["A", "B", "E", "F"],
    ["A", "C", "D", "E"],
    ["B", "C", "D", "F"],
]

chain = markovify.Chain(corpus, state_size=2)
```

--------------------------------

### Generate Sentences with make_sentence()

Source: https://context7.com/jsvine/markovify/llms.txt

Shows how to generate random sentences from a trained model. Covers parameters for controlling generation attempts, overlap sensitivity, and word counts.

```python
import markovify

with open("corpus.txt") as f:
    text_model = markovify.Text(f.read())

sentence = text_model.make_sentence()
sentence = text_model.make_sentence(tries=100)
sentence = text_model.make_sentence(test_output=False)
sentence = text_model.make_sentence(max_overlap_ratio=0.5, max_overlap_total=10)
sentence = text_model.make_sentence(min_words=5, max_words=20)
for i in range(5):
    print(text_model.make_sentence())
```

--------------------------------

### Combine Multiple Markov Models

Source: https://github.com/jsvine/markovify/blob/master/README.md

Demonstrates how to merge multiple Markov models into a single combined model with weighted probabilities for each source.

```python
model_a = markovify.Text(text_a)
model_b = markovify.Text(text_b)

model_combo = markovify.combine([ model_a, model_b ], [ 1.5, 1 ])
```

--------------------------------

### Intelligent Sentence Splitting Utility

Source: https://context7.com/jsvine/markovify/llms.txt

Demonstrates the use of the `split_into_sentences` utility function from the markovify library. It intelligently splits text into sentences, correctly handling abbreviations and other edge cases.

```python
from markovify import split_into_sentences

text = """Dr. Watson was amazed. Mr. Holmes had solved the case in record time!
The U.S. government was grateful. \"Excellent work,\" said Gen. Smith."""

sentences = split_into_sentences(text)
for i, sentence in enumerate(sentences, 1):
    print(f"{i}. {sentence}")
```

--------------------------------

### Generating Sentences with make_sentence()

Source: https://context7.com/jsvine/markovify/llms.txt

Explains how to generate random sentences from a trained Markovify model, with options to control generation attempts, overlap testing, and word count.

```APIDOC
## Generating Sentences with make_sentence()

### Description
The `make_sentence()` method generates random sentences from the trained model. It attempts multiple times (default 10) to produce a sentence that doesn't overlap too much with the original corpus. Returns the sentence string if successful, or `None` if it fails to generate a valid sentence.

### Method
`text_model.make_sentence(tries=100, בדיקה_output=True, max_overlap_ratio=0.7, max_overlap_total=15, min_words=None, max_words=None)`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **tries** (int) - Optional - Number of attempts to generate a sentence (default: 100).
- **test_output** (bool) - Optional - Whether to test for overlap with the original corpus (default: True).
- **max_overlap_ratio** (float) - Optional - Maximum ratio of overlapping words allowed between generated and original sentences (default: 0.7).
- **max_overlap_total** (int) - Optional - Maximum number of consecutive overlapping words allowed (default: 15).
- **min_words** (int) - Optional - Minimum number of words the generated sentence must have.
- **max_words** (int) - Optional - Maximum number of words the generated sentence can have.

### Request Example
```python
import markovify

with open("corpus.txt") as f:
    text_model = markovify.Text(f.read())

# Generate a basic sentence
sentence = text_model.make_sentence()
print(sentence)
# Output: "The quick brown fox decided to take a different path through the forest."

# Generate with more attempts for difficult corpora
sentence = text_model.make_sentence(tries=100)

# Disable overlap testing for faster generation (may produce verbatim text)
sentence = text_model.make_sentence(test_output=False)

# Control overlap sensitivity
sentence = text_model.make_sentence(
    max_overlap_ratio=0.5,  # Max 50% of words can match original
    max_overlap_total=10    # Max 10 consecutive words from original
)

# Constrain word count
sentence = text_model.make_sentence(min_words=5, max_words=20)

# Generate multiple sentences
for i in range(5):
    print(text_model.make_sentence())
```

### Response
#### Success Response (200)
- **sentence** (str or None) - A generated sentence string, or None if generation failed.
```

--------------------------------

### Markov Chain Generation and Model Access

Source: https://context7.com/jsvine/markovify/llms.txt

Demonstrates generating text sequences, using generators for lazy evaluation, exporting and importing Markov chain models to JSON, and accessing the internal model structure.

```python
sequence = chain.walk()
print(sequence)

for item in chain.gen():
    print(item, end=" -> ")

chain_json = chain.to_json()
restored_chain = markovify.Chain.from_json(chain_json)

print(chain.model)
```

--------------------------------

### Sentence Splitting Utility

Source: https://context7.com/jsvine/markovify/llms.txt

Demonstrates the use of the `split_into_sentences()` utility function for intelligent sentence boundary detection in markovify.

```APIDOC
## Sentence Splitting Utility

### Description
Utilizes the `split_into_sentences()` function for accurate sentence segmentation, handling common edge cases.

### Method
N/A (Illustrative code)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
N/A

## Code Example:
```python
from markovify import split_into_sentences

text = """Dr. Watson was amazed. Mr. Holmes had solved the case in record time!
The U.S. government was grateful. \"Excellent work,\" said Gen. Smith."""

sentences = split_into_sentences(text)
for i, sentence in enumerate(sentences, 1):
    print(f"{i}. {sentence}")

# Output:
# 1. Dr. Watson was amazed.
# 2. Mr. Holmes had solved the case in record time!
# 3. The U.S. government was grateful.
# 4. "Excellent work," said Gen. Smith.
```
```

--------------------------------

### Extend Markovify Text Class

Source: https://github.com/jsvine/markovify/blob/master/README.md

Demonstrates how to override word_split and word_join methods to integrate NLTK or spaCy for part-of-speech tagging, allowing for more structurally accurate sentence generation.

```python
import markovify
import nltk
import re

class POSifiedText(markovify.Text):
    def word_split(self, sentence):
        words = re.split(self.word_split_pattern, sentence)
        words = [ "::".join(tag) for tag in nltk.pos_tag(words) ]
        return words

    def word_join(self, words):
        sentence = " ".join(word.split("::")[0] for word in words)
        return sentence
```

```python
import markovify
import re
import spacy

nlp = spacy.load("en_core_web_sm")

class POSifiedText(markovify.Text):
    def word_split(self, sentence):
        return ["::".join((word.orth_, word.pos_)) for word in nlp(sentence)]

    def word_join(self, words):
        sentence = " ".join(word.split("::")[0] for word in words)
        return sentence
```

--------------------------------

### Tokenize Sentences with markovify.split_into_sentences

Source: https://context7.com/jsvine/markovify/llms.txt

Uses the built-in utility to split raw text into individual sentences, correctly handling common abbreviations and punctuation.

```python
import markovify

text = "Dr. Smith went to Washington D.C. on Jan. 5th. He met with Sen. Johnson."
sentences = markovify.split_into_sentences(text)
for i, s in enumerate(sentences):
    print(f"{i+1}. {s}")
```

--------------------------------

### Compiling a Markovify Model

Source: https://github.com/jsvine/markovify/blob/master/README.md

This section explains how to compile a generated Markovify Text model to improve generation speed and reduce size. It also covers in-place compilation and notes on combining compiled models.

```APIDOC
## Compiling a Model

Once a model has been generated, it may also be compiled for improved text generation speed and reduced size.

### Method
```python
text_model = markovify.Text(text)
text_model = text_model.compile()
```

Models may also be compiled in-place:

### Method
```python
text_model = markovify.Text(text)
text_model.compile(inplace = True)
```

Currently, compiled models may not be combined with other models using `markovify.combine(...)`. If you wish to combine models, do that first and then compile the result.
```

--------------------------------

### Using NewlineText for Line-Delimited Corpora in markovify

Source: https://context7.com/jsvine/markovify/llms.txt

Illustrates the use of `NewlineText` for generating sentences from corpora where lines are the logical units, such as poetry or lyrics. It shows how to instantiate `NewlineText` from a string or a file and generate new lines.

```python
import markovify

# Poetry or lyrics where each line is separate
lyrics = """
I walk the lonely road
The only one that I have ever known
Don't know where it goes
But it's home to me and I walk alone
"""

# Use NewlineText instead of Text
model = markovify.NewlineText(lyrics)
line = model.make_sentence()
print(line)

# Works with files too
with open("haiku_collection.txt") as f:
    haiku_model = markovify.NewlineText(f.read())

# Generate new haiku-style lines
for _ in range(3):
    print(haiku_model.make_sentence())
```

--------------------------------

### Extending markovify.Text with NLTK or spaCy

Source: https://github.com/jsvine/markovify/blob/master/README.md

Demonstrates how to extend the `markovify.Text` class by overriding methods like `word_split` and `word_join` to incorporate part-of-speech tagging using NLTK or spaCy for more structured text generation.

```APIDOC
## Extending `markovify.Text`

The `markovify.Text` class is highly extensible; most methods can be overridden. For example, the following `POSifiedText` class uses NLTK's part-of-speech tagger to generate a Markov model that obeys sentence structure better than a naive model. (It works; however, be warned: `pos_tag` is very slow.)

### Method
```python
import markovify
import nltk
import re

class POSifiedText(markovify.Text):
    def word_split(self, sentence):
        words = re.split(self.word_split_pattern, sentence)
        words = [ "::".join(tag) for tag in nltk.pos_tag(words) ]
        return words

    def word_join(self, words):
        sentence = " ".join(word.split("::")[0] for word in words)
        return sentence
```

Or, you can use [spaCy](https://spacy.io/) which is [way faster](https://spacy.io/docs/api/#benchmarks):

### Method
```python
import markovify
import re
import spacy

nlp = spacy.load("en_core_web_sm")

class POSifiedText(markovify.Text):
    def word_split(self, sentence):
        return ["::".join((word.orth_, word.pos_)) for word in nlp(sentence)]

    def word_join(self, words):
        sentence = " ".join(word.split("::")[0] for word in words)
        return sentence
```

The most useful `markovify.Text` models you can override are:

- `sentence_split`
- `sentence_join`
- `word_split`
- `word_join`
- `test_sentence_input`
- `test_sentence_output`

For details on what they do, see [the (annotated) source code](markovify/text.py).
```

--------------------------------

### Extending markovify.Text with Custom Parsing

Source: https://context7.com/jsvine/markovify/llms.txt

Shows how to subclass the `markovify.Text` class to implement custom word splitting and joining logic, using NLTK and spaCy for part-of-speech tagging, and custom sentence splitting.

```APIDOC
## Extending markovify.Text with Custom Parsing

### Description
Demonstrates subclassing `markovify.Text` to customize word splitting and joining, enabling advanced text processing like part-of-speech tagging.

### Method
N/A (Illustrative code)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
N/A

## Code Examples:

### Custom parser using NLTK part-of-speech tagging
```python
import markovify
import re
import nltk

class POSText(markovify.Text):
    def word_split(self, sentence):
        words = re.split(self.word_split_pattern, sentence)
        # Tag each word with its part of speech
        tagged = nltk.pos_tag(words)
        return ["::".join(tag) for tag in tagged]

    def word_join(self, words):
        # Remove POS tags when joining
        return " ".join(word.split("::")[0] for word in words)

# Use the custom class
with open("corpus.txt") as f:
    pos_model = POSText(f.read())

sentence = pos_model.make_sentence()
print(sentence)
```

### Alternative: using spaCy for faster POS tagging
```python
import spacy
nlp = spacy.load("en_core_web_sm")

class SpacyText(markovify.Text):
    def word_split(self, sentence):
        return ["::".join((word.orth_, word.pos_)) for word in nlp(sentence)]

    def word_join(self, words):
        return " ".join(word.split("::")[0] for word in words)
```

### Custom sentence splitter
```python
class CustomText(markovify.Text):
    def sentence_split(self, text):
        # Split on double newlines instead of periods
        return re.split(r"\n\n+", text)
```
```

--------------------------------

### Text Model Generation

Source: https://github.com/jsvine/markovify/blob/master/README.md

Initialize a Markov model from a string of text and generate random sentences.

```APIDOC
## POST /markovify/text

### Description
Builds a Markov model from provided text and generates random sentences based on the model.

### Method
POST

### Endpoint
/markovify/text

### Parameters
#### Request Body
- **text** (string) - Required - The raw corpus text to build the model.
- **state_size** (integer) - Optional - The number of words the probability of a next word depends on (default: 2).

### Request Example
{
  "text": "Sample corpus text for model generation.",
  "state_size": 2
}

### Response
#### Success Response (200)
- **sentence** (string) - A randomly generated sentence from the model.

#### Response Example
{
  "sentence": "Sample generated sentence."
}
```

--------------------------------

### Exporting and Importing Markovify Models

Source: https://github.com/jsvine/markovify/blob/master/README.md

Instructions on how to export a trained Markovify Text model to a JSON string using `to_json()` and how to load it back using `from_json()`, preserving the model for later use.

```APIDOC
## Exporting

It can take a while to generate a Markov model from a large corpus. Sometimes you'll want to generate once and reuse it later. To export a generated `markovify.Text` model, use `my_text_model.to_json()`. For example:

### Method
```python
corpus = open("sherlock.txt").read()

text_model = markovify.Text(corpus, state_size=3)
model_json = text_model.to_json()
# In theory, here you'd save the JSON to disk, and then read it back later.

reconstituted_model = markovify.Text.from_json(model_json)
reconstituted_model.make_short_sentence(280)

>>> 'It cost me something in foolscap, and I had no idea that he was a man of evil reputation among women.'
```

You can also export the underlying Markov chain on its own — i.e., excluding the original corpus and the `state_size` metadata — via `my_text_model.chain.to_json()`.
```

--------------------------------

### Working with Messy Texts

Source: https://github.com/jsvine/markovify/blob/master/README.md

Details on how to handle less structured input texts using the `well_formed` and `reject_reg` parameters in the `markovify.Text` constructor.

```APIDOC
## Working with Messy Texts

Starting with `v0.7.2`, `markovify.Text` accepts two additional parameters: `well_formed` and `reject_reg`.

- Setting `well_formed = False` skips the step in which input sentences are rejected if they contain one of the 'bad characters' (i.e. `()[]'"`)

- Setting `reject_reg` to a regular expression of your choice allows you change the input-sentence rejection pattern. This only applies if `well_formed` is True, and if the expression is non-empty.
```

--------------------------------

### Generating Short Sentences with make_short_sentence()

Source: https://context7.com/jsvine/markovify/llms.txt

Details how to generate sentences constrained by character length, suitable for platforms like Twitter or SMS.

```APIDOC
## Generating Short Sentences with make_short_sentence()

### Description
The `make_short_sentence()` method generates sentences constrained by character length, useful for Twitter posts, SMS, or other length-limited contexts. It repeatedly calls `make_sentence()` until it finds one within the specified character limits.

### Method
`text_model.make_short_sentence(max_chars, min_chars=None, tries=100, max_overlap_ratio=0.7, max_overlap_total=15)`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **max_chars** (int) - Required - The maximum number of characters the sentence can have.
- **min_chars** (int) - Optional - The minimum number of characters the sentence must have.
- **tries** (int) - Optional - Number of attempts to generate a sentence (default: 100).
- **max_overlap_ratio** (float) - Optional - Maximum ratio of overlapping words allowed (default: 0.7).
- **max_overlap_total** (int) - Optional - Maximum number of consecutive overlapping words allowed (default: 15).

### Request Example
```python
import markovify

with open("corpus.txt") as f:
    text_model = markovify.Text(f.read())

# Generate a tweet-length sentence (max 280 characters)
tweet = text_model.make_short_sentence(280)
print(f"({len(tweet)} chars) {tweet}")
# Output: "(142 chars) The detective examined the evidence carefully before making his conclusion."

# Generate with minimum length requirement
sentence = text_model.make_short_sentence(200, min_chars=100)

# Combine with other parameters
sentence = text_model.make_short_sentence(
    max_chars=140,
    min_chars=50,
    tries=100,
    max_overlap_ratio=0.5
)
```

### Response
#### Success Response (200)
- **sentence** (str or None) - A generated sentence string within the character limits, or None if generation failed.
```

--------------------------------

### Extend markovify.Text for Custom Tokenization

Source: https://context7.com/jsvine/markovify/llms.txt

Shows how to subclass the Text class to implement custom word splitting, joining, or sentence segmentation, useful for POS tagging or specialized language handling.

```python
import markovify
import re

class POSText(markovify.Text):
    def word_split(self, sentence):
        words = re.split(self.word_split_pattern, sentence)
        return ["::".join(tag) for tag in nltk.pos_tag(words)]

    def word_join(self, words):
        return " ".join(word.split("::")[0] for word in words)
```

--------------------------------

### Combining Multiple Markovify Models with Weights

Source: https://context7.com/jsvine/markovify/llms.txt

Explains how to merge multiple `markovify.Text` models into a single model using `markovify.combine()`. This function allows for weighted combinations to blend different writing styles or topics. All models must share the same `state_size` and type.

```python
import markovify

# Assume shakespeare.txt, scifi.txt, news.txt exist
with open("shakespeare.txt") as f:
    model_shakespeare = markovify.Text(f.read())

with open("scifi.txt") as f:
    model_scifi = markovify.Text(f.read())

with open("news.txt") as f:
    model_news = markovify.Text(f.read())

# Combine with equal weights (default)
combined = markovify.combine([model_shakespeare, model_scifi])
print(combined.make_sentence())

# Combine with custom weights (more Shakespeare influence)
combined = markovify.combine(
    [model_shakespeare, model_scifi],
    [2.0, 1.0]  # Shakespeare has 2x weight
)

# Combine three models
combined = markovify.combine(
    [model_shakespeare, model_scifi, model_news],
    [1.0, 1.5, 0.5]
)
print(combined.make_sentence())

# Can also combine raw chains
chain_combo = markovify.combine([model_shakespeare.chain, model_scifi.chain])
```

--------------------------------

### Compile Markovify Models

Source: https://github.com/jsvine/markovify/blob/master/README.md

Compiles a Markovify model to improve text generation speed and reduce memory footprint. Compilation can be performed on a new object or in-place.

```python
text_model = markovify.Text(text)
text_model = text_model.compile()

# Or in-place
text_model.compile(inplace = True)
```

--------------------------------

### Generate Length-Constrained Sentences with make_short_sentence()

Source: https://context7.com/jsvine/markovify/llms.txt

Generates sentences constrained by character length, ideal for social media or messaging applications. Supports minimum and maximum character limits.

```python
import markovify

with open("corpus.txt") as f:
    text_model = markovify.Text(f.read())

tweet = text_model.make_short_sentence(280)
sentence = text_model.make_short_sentence(200, min_chars=100)
sentence = text_model.make_short_sentence(max_chars=140, min_chars=50, tries=100, max_overlap_ratio=0.5)
```

--------------------------------

### Custom Sentence Splitting

Source: https://context7.com/jsvine/markovify/llms.txt

Illustrates how to customize sentence splitting in markovify by subclassing the Text class and overriding the sentence_split method to use a different delimiter, such as double newlines.

```python
import markovify

class CustomText(markovify.Text):
    def sentence_split(self, text):
        return re.split(r"\n\n+", text)
```

--------------------------------

### Export and Reconstitute Models

Source: https://github.com/jsvine/markovify/blob/master/README.md

Exports a trained model to a JSON format for persistence and reloads it later to continue generating text without retraining.

```python
corpus = open("sherlock.txt").read()
text_model = markovify.Text(corpus, state_size=3)
model_json = text_model.to_json()

reconstituted_model = markovify.Text.from_json(model_json)
reconstituted_model.make_short_sentence(280)
```

--------------------------------

### Model Combination

Source: https://github.com/jsvine/markovify/blob/master/README.md

Combine multiple Markov models with weighted emphasis.

```APIDOC
## POST /markovify/combine

### Description
Combines two or more Markov models into a single model with optional relative weights.

### Method
POST

### Endpoint
/markovify/combine

### Parameters
#### Request Body
- **models** (array) - Required - A list of model objects to combine.
- **weights** (array) - Optional - A list of floats indicating relative emphasis for each model.

### Request Example
{
  "models": ["model_a", "model_b"],
  "weights": [1.5, 1.0]
}

### Response
#### Success Response (200)
- **model** (object) - The combined Markov model.

#### Response Example
{
  "model": "combined_model_data"
}
```

--------------------------------

### Processing Large Corpora Efficiently

Source: https://context7.com/jsvine/markovify/llms.txt

Provides methods for processing large text corpora efficiently in markovify, including disabling original text retention, processing files incrementally, and handling massive single files line by line.

```APIDOC
## Processing Large Corpora Efficiently

### Description
Techniques for handling large text corpora with markovify to optimize memory usage and processing time.

### Method
N/A (Illustrative code)

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
N/A

## Code Examples:

### Method 1: Don't retain original (disables overlap checking)
```python
import markovify
import os

with open("huge_corpus.txt") as f:
    text_model = markovify.Text(f, retain_original=False)

print(text_model.make_sentence())
```

### Method 2: Process files incrementally and combine
```python
combined_model = None

for dirpath, _, filenames in os.walk("corpus_directory"):
    for filename in filenames:
        filepath = os.path.join(dirpath, filename)
        with open(filepath) as f:
            model = markovify.Text(f, retain_original=False)
            if combined_model:
                combined_model = markovify.combine([combined_model, model])
            else:
                combined_model = model

print(combined_model.make_sentence())
```

### Method 3: Process line by line for huge single files
```python
with open("massive_file.txt") as f:
    lines = []
    for line in f:
        lines.append(line.strip())
        if len(lines) >= 10000:  # Process in batches
            batch_model = markovify.NewlineText("\n".join(lines), retain_original=False)
            if combined_model:
                combined_model = markovify.combine([combined_model, batch_model])
            else:
                combined_model = batch_model
            lines = []

# Compile final model for fast generation
combined_model.compile(inplace=True)
```
```

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