add doc for Python CustomStreamer with buffer

src/README.md

@@ -210,6 +210,70 @@ int main(int argc, char* argv[]) {
 }
 ```
 
+This Python example demonstrates custom detokenization with buferisation. The streamer receives 
+integer tokens corresponding to each word or subword, one by one. If tokens are decoded individually, 
+subwords will not be concatenated correctly, and the resulting text will lack appropriate spaces. 
+To address this, we accumulate tokens in a tokens_cache buffer and decode multiple tokens together, 
+returning the text only when a complete decoded chunk is ready.
+
+```py
+import openvino_genai as ov_genai
+
+class TextPrintStreamer(ov_genai.StreamerBase):
+    def __init__(self, tokenizer):
+        super().__init__()
+        self.tokenizer = tokenizer
+        self.tokens_cache = []
+        self.print_len = 0
+    
+    def get_stop_flag(self):
+        return False
+    
+    def process_word(self, word: str):
+        print(word, end='', flush=True)
+
+    def put(self, token_id):
+        self.tokens_cache.append(token_id)
+        text = self.tokenizer.decode(self.tokens_cache)
+
+        word = ''
+        if len(text) > self.print_len and '\n' == text[-1]:
+            # Flush the cache after the new line symbol.
+            word = text[self.print_len:]            
+            self.tokens_cache = []
+            self.print_len = 0
+        elif len(text) >= 3 and text[-3:] == "�":
+            # Don't print incomplete text.
+            pass
+        elif len(text) > self.print_len:
+            # It is possible to have a shorter text after adding new token.
+            # Print to output only if text lengh is increaesed.
+            word = text[self.print_len:]
+            self.print_len = len(text)
+        self.process_word(word)        
+        
+        if self.get_stop_flag():
+            # When generation is stopped from streamer then end is not called, need to call it here manually.
+            self.end()
+            return True  # True means stop  generation
+        else:
+            return False  # False means continue generation
+
+    def end(self):
+        # Flush residual tokens from the buffer.
+        text = self.tokenizer.decode(self.tokens_cache)
+        if len(text) > self.print_len:
+            word = text[self.print_len:]
+            self.process_word(word)
+            self.tokens_cache = []
+            self.print_len = 0
+
+pipe = ov_genai.LLMPipeline(model_path, "CPU")
+text_print_streamer = TextPrintStreamer(pipe.get_tokenizer())
+
+pipe.generate("The Sun is yellow because", max_new_tokens=15, streamer=text_print_streamer)
+```
+
 ### Performance Metrics
 
 `openvino_genai.PerfMetrics` (referred as `PerfMetrics` for simplicity) is a structure that holds performance metrics for each generate call. `PerfMetrics` holds fields with mean and standard deviations for the following metrics: