Add async support for HuggingFaceTextGenInference (#6507)

Adding support for async calls in `HuggingFaceTextGenInference`


Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>

langchain/llms/huggingface_text_gen_inference.py

@@ -4,7 +4,10 @@ from typing import Any, Dict, List, Optional
 
 from pydantic import Extra, Field, root_validator
 
-from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
 from langchain.llms.base import LLM
 
 
@@ -26,10 +29,13 @@ class HuggingFaceTextGenInference(LLM):
     - seed: The seed to use when generating text.
     - inference_server_url: The URL of the inference server to use.
     - timeout: The timeout value in seconds to use while connecting to inference server.
+    - server_kwargs: The keyword arguments to pass to the inference server.
     - client: The client object used to communicate with the inference server.
+    - async_client: The async client object used to communicate with the server.
 
     Methods:
     - _call: Generates text based on a given prompt and stop sequences.
+    - _acall: Async generates text based on a given prompt and stop sequences.
     - _llm_type: Returns the type of LLM.
     """
 
@@ -78,8 +84,10 @@ class HuggingFaceTextGenInference(LLM):
     seed: Optional[int] = None
     inference_server_url: str = ""
     timeout: int = 120
+    server_kwargs: Dict[str, Any] = Field(default_factory=dict)
     stream: bool = False
     client: Any
+    async_client: Any
 
     class Config:
         """Configuration for this pydantic object."""
@@ -94,7 +102,14 @@ class HuggingFaceTextGenInference(LLM):
             import text_generation
 
             values["client"] = text_generation.Client(
-                values["inference_server_url"], timeout=values["timeout"]
+                values["inference_server_url"],
+                timeout=values["timeout"],
+                **values["server_kwargs"],
+            )
+            values["async_client"] = text_generation.AsyncClient(
+                values["inference_server_url"],
+                timeout=values["timeout"],
+                **values["server_kwargs"],
             )
         except ImportError:
             raise ImportError(
@@ -171,3 +186,69 @@ class HuggingFaceTextGenInference(LLM):
                         text_callback(token.text)
                     text += token.text
         return text
+
+    async def _acall(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        if stop is None:
+            stop = self.stop_sequences
+        else:
+            stop += self.stop_sequences
+
+        if not self.stream:
+            res = await self.async_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,
+                **kwargs,
+            )
+            # 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: str = 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,
+                },
+                **kwargs,
+            }
+            text = ""
+            async for res in self.async_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:
+                        await text_callback(token.text)
+        return text