Added support for streaming output response to HuggingFaceTextgenInference LLM class (#4633)

# Added support for streaming output response to
HuggingFaceTextgenInference LLM class

Current implementation does not support streaming output. Updated to
incorporate this feature. Tagging @agola11 for visibility.

langchain/llms/huggingface_text_gen_inference.py

@@ -1,4 +1,5 @@
 """Wrapper around Huggingface text generation inference API."""
+from functools import partial
 from typing import Any, Dict, List, Optional
 
 from pydantic import Extra, Field, root_validator
@@ -36,6 +37,7 @@ class HuggingFaceTextGenInference(LLM):
     Example:
         .. code-block:: python
 
+            # Basic Example (no streaming)
             llm = HuggingFaceTextGenInference(
                 inference_server_url = "http://localhost:8010/",
                 max_new_tokens = 512,
@@ -45,6 +47,25 @@ class HuggingFaceTextGenInference(LLM):
                 temperature = 0.01,
                 repetition_penalty = 1.03,
             )
+            print(llm("What is Deep Learning?"))
+            
+            # Streaming response example
+            from langchain.callbacks import streaming_stdout
+            
+            callbacks = [streaming_stdout.StreamingStdOutCallbackHandler()]
+            llm = HuggingFaceTextGenInference(
+                inference_server_url = "http://localhost:8010/",
+                max_new_tokens = 512,
+                top_k = 10,
+                top_p = 0.95,
+                typical_p = 0.95,
+                temperature = 0.01,
+                repetition_penalty = 1.03,
+                callbacks = callbacks,
+                stream = True
+            )
+            print(llm("What is Deep Learning?"))
+            
     """
 
     max_new_tokens: int = 512
@@ -57,6 +78,7 @@ class HuggingFaceTextGenInference(LLM):
     seed: Optional[int] = None
     inference_server_url: str = ""
     timeout: int = 120
+    stream: bool = False
     client: Any
 
     class Config:
@@ -97,22 +119,52 @@ class HuggingFaceTextGenInference(LLM):
         else:
             stop += self.stop_sequences
 
-        res = self.client.generate(
-            prompt,
-            stop_sequences=stop,
-            max_new_tokens=self.max_new_tokens,
-            top_k=self.top_k,
-            top_p=self.top_p,
-            typical_p=self.typical_p,
-            temperature=self.temperature,
-            repetition_penalty=self.repetition_penalty,
-            seed=self.seed,
-        )
-        # remove stop sequences from the end of the generated text
-        for stop_seq in stop:
-            if stop_seq in res.generated_text:
-                res.generated_text = res.generated_text[
-                    : res.generated_text.index(stop_seq)
-                ]
-
-        return res.generated_text
+        if not self.stream:
+            res = self.client.generate(
+                prompt,
+                stop_sequences=stop,
+                max_new_tokens=self.max_new_tokens,
+                top_k=self.top_k,
+                top_p=self.top_p,
+                typical_p=self.typical_p,
+                temperature=self.temperature,
+                repetition_penalty=self.repetition_penalty,
+                seed=self.seed,
+            )
+            # remove stop sequences from the end of the generated text
+            for stop_seq in stop:
+                if stop_seq in res.generated_text:
+                    res.generated_text = res.generated_text[
+                        : res.generated_text.index(stop_seq)
+                    ]
+            text = res.generated_text
+        else:
+            text_callback = None
+            if run_manager:
+                text_callback = partial(
+                    run_manager.on_llm_new_token, verbose=self.verbose
+                )
+            params = {
+                "stop_sequences": stop,
+                "max_new_tokens": self.max_new_tokens,
+                "top_k": self.top_k,
+                "top_p": self.top_p,
+                "typical_p": self.typical_p,
+                "temperature": self.temperature,
+                "repetition_penalty": self.repetition_penalty,
+                "seed": self.seed,
+            }
+            text = ""
+            for res in self.client.generate_stream(prompt, **params):
+                token = res.token
+                is_stop = False
+                for stop_seq in stop:
+                    if stop_seq in token.text:
+                        is_stop = True
+                        break
+                if is_stop:
+                    break
+                if not token.special:
+                    if text_callback:
+                        text_callback(token.text)
+        return text