add doc for CustomStreamer with buffer

src/README.md

@@ -196,6 +196,88 @@ int main(int argc, char* argv[]) {
 }
 ```
 
+```py
+import openvino_genai as ov_genai
+
+class TextStreamerIterator(ov_genai.StreamerBase):
+    def __init__(self, tokenizer, stop_tokens=None):
+        super().__init__()
+        self.tokenizer = tokenizer
+
+        # TODO don't know from where to import Queue
+        self.text_queue = []  # self.text_queue = Queue() # TODO: replace list with Queue()
+        self.stop_signal = None
+        self.tokens_cache = []
+        self.print_len = 0
+        self.stop_tokens = stop_tokens if stop_tokens else []
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        value = self.text_queue.pop()  # value = self.text_queue.get() # TODO: replace list with Queue()
+        if value is self.stop_signal:
+            raise StopIteration()
+        else:
+            return value
+    
+    def put_word(self, word: str):
+        # remove labels/special symbols
+        word = re.sub("<.*>", "", word)
+        self.text_queue.append(word) # self.text_queue.put(text)  # TODO: replace list with Queue()
+
+    def put(self, token_id):
+        self.tokens_cache.append(token_id)
+        text = self.tokenizer.decode(self.tokens_cache)
+
+        if not text and '\n' == text[-1] and len(text) > self.print_len:
+            # Flush the cache after the new line symbol
+            word = text[self.print_len:]            
+            self.tokens_cache = []
+            self.print_len = 0
+            self.put_word(word)
+
+        if len(text) >= 3 and text[-3:] == "�":
+            # Don't print incomplete text
+            return self.put_word(word)
+        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.put_word(word)
+
+        if token_id in self.stop_tokens:
+            # 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.put_word(word)
+            self.tokens_cache = []
+            self.print_len = 0
+
+        self.text_queue.append(self.stop_signal)  # self.text_queue.put(self.stop_signal) # TODO: replace list with Queue()
+
+tokenizer = ov_genai.Tokenizer(model_path)
+text_streamer = TextStreamerIterator(tokenizer)
+
+prompt = 'The Sun is yellow because'
+pipe.generate(prompt, streamer=text_streamer, max_new_tokens=10)
+
+import openvino_genai as ov_genai
+pipe = ov_genai.LLMPipeline(model_path, "CPU")
+
+pipe.generate("The Sun is yellow because", max_new_tokens=100, streamer=text_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: