Amazon Bedrock Support Streaming (#10393)

### Description

- Add support for streaming with `Bedrock` LLM and `BedrockChat` Chat
Model.
- Bedrock as of now supports streaming for the `anthropic.claude-*` and
`amazon.titan-*` models only, hence support for those have been built.
- Also increased the default `max_token_to_sample` for Bedrock
`anthropic` model provider to `256` from `50` to keep in line with the
`Anthropic` defaults.
- Added examples for streaming responses to the bedrock example
notebooks.

**_NOTE:_**: This PR fixes the issues mentioned in #9897 and makes that
PR redundant.

docs/extras/integrations/chat/bedrock.ipynb

@@ -22,7 +22,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 1,
+            "execution_count": null,
             "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
             "metadata": {
                 "tags": []
@@ -73,13 +73,46 @@
                 "chat(messages)"
             ]
         },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "id": "a4a4f4d4",
+            "metadata": {},
+            "source": [
+                "### For BedrockChat with Streaming"
+            ]
+        },
         {
             "cell_type": "code",
             "execution_count": null,
             "id": "c253883f",
             "metadata": {},
             "outputs": [],
-            "source": []
+            "source": [
+                "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+                "\n",
+                "chat = BedrockChat(\n",
+                "    model_id=\"anthropic.claude-v2\",\n",
+                "    streaming=True,\n",
+                "    callbacks=[StreamingStdOutCallbackHandler()],\n",
+                "    model_kwargs={\"temperature\": 0.1},\n",
+                ")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "d9e52838",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "messages = [\n",
+                "    HumanMessage(\n",
+                "        content=\"Translate this sentence from English to French. I love programming.\"\n",
+                "    )\n",
+                "]\n",
+                "chat(messages)"
+            ]
         }
     ],
     "metadata": {
@@ -98,7 +131,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.11.4"
+            "version": "3.10.9"
         }
     },
     "nbformat": 4,

docs/extras/integrations/llms/bedrock.ipynb

@@ -61,6 +61,46 @@
     "\n",
     "conversation.predict(input=\"Hi there!\")"
    ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Conversation Chain With Streaming"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import Bedrock\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "\n",
+    "\n",
+    "llm = Bedrock(\n",
+    "    credentials_profile_name=\"bedrock-admin\",\n",
+    "    model_id=\"amazon.titan-tg1-large\",\n",
+    "    streaming=True,\n",
+    "    callbacks=[StreamingStdOutCallbackHandler()],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "conversation = ConversationChain(\n",
+    "    llm=llm, verbose=True, memory=ConversationBufferMemory()\n",
+    ")\n",
+    "\n",
+    "conversation.predict(input=\"Hi there!\")"
+   ]
   }
  ],
  "metadata": {

docs/package-lock.json

@@ -1,6 +0,0 @@
-{
-  "name": "docs",
-  "lockfileVersion": 3,
-  "requires": true,
-  "packages": {}
-}

libs/langchain/langchain/chat_models/bedrock.py

@@ -8,7 +8,7 @@ from langchain.chat_models.anthropic import convert_messages_to_prompt_anthropic
 from langchain.chat_models.base import BaseChatModel
 from langchain.llms.bedrock import BedrockBase
 from langchain.pydantic_v1 import Extra
-from langchain.schema.messages import AIMessage, BaseMessage
+from langchain.schema.messages import AIMessage, AIMessageChunk, BaseMessage
 from langchain.schema.output import ChatGeneration, ChatGenerationChunk, ChatResult
 
 
@@ -48,10 +48,17 @@ class BedrockChat(BaseChatModel, BedrockBase):
         run_manager: Optional[CallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> Iterator[ChatGenerationChunk]:
-        raise NotImplementedError(
+        provider = self._get_provider()
-            """Bedrock doesn't support stream requests at the moment."""
+        prompt = ChatPromptAdapter.convert_messages_to_prompt(
+            provider=provider, messages=messages
         )
 
+        for chunk in self._prepare_input_and_invoke_stream(
+            prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
+        ):
+            delta = chunk.text
+            yield ChatGenerationChunk(message=AIMessageChunk(content=delta))
+
     def _astream(
         self,
         messages: List[BaseMessage],
@@ -70,6 +77,12 @@ class BedrockChat(BaseChatModel, BedrockBase):
         run_manager: Optional[CallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> ChatResult:
+        completion = ""
+
+        if self.streaming:
+            for chunk in self._stream(messages, stop, run_manager, **kwargs):
+                completion += chunk.text
+        else:
             provider = self._get_provider()
             prompt = ChatPromptAdapter.convert_messages_to_prompt(
                 provider=provider, messages=messages

libs/langchain/langchain/llms/bedrock.py

@@ -1,11 +1,12 @@
 import json
 from abc import ABC
-from typing import Any, Dict, List, Mapping, Optional
+from typing import Any, Dict, Iterator, List, Mapping, Optional
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.pydantic_v1 import BaseModel, Extra, root_validator
+from langchain.schema.output import GenerationChunk
 
 
 class LLMInputOutputAdapter:
@@ -15,6 +16,11 @@ class LLMInputOutputAdapter:
     It also provides helper function to extract
     the generated text from the model response."""
 
+    provider_to_output_key_map = {
+        "anthropic": "completion",
+        "amazon": "outputText",
+    }
+
     @classmethod
     def prepare_input(
         cls, provider: str, prompt: str, model_kwargs: Dict[str, Any]
@@ -30,7 +36,7 @@ class LLMInputOutputAdapter:
             input_body["inputText"] = prompt
 
         if provider == "anthropic" and "max_tokens_to_sample" not in input_body:
-            input_body["max_tokens_to_sample"] = 50
+            input_body["max_tokens_to_sample"] = 256
 
         return input_body
 
@@ -47,6 +53,30 @@ class LLMInputOutputAdapter:
         else:
             return response_body.get("results")[0].get("outputText")
 
+    @classmethod
+    def prepare_output_stream(
+        cls, provider: str, response: Any, stop: Optional[List[str]] = None
+    ) -> Iterator[GenerationChunk]:
+        stream = response.get("body")
+
+        if not stream:
+            return
+
+        if provider not in cls.provider_to_output_key_map:
+            raise ValueError(
+                f"Unknown streaming response output key for provider: {provider}"
+            )
+
+        for event in stream:
+            chunk = event.get("chunk")
+            if chunk:
+                chunk_obj = json.loads(chunk.get("bytes").decode())
+
+                # chunk obj format varies with provider
+                yield GenerationChunk(
+                    text=chunk_obj[cls.provider_to_output_key_map[provider]]
+                )
+
 
 class BedrockBase(BaseModel, ABC):
     client: Any  #: :meta private:
@@ -74,6 +104,15 @@ class BedrockBase(BaseModel, ABC):
     endpoint_url: Optional[str] = None
     """Needed if you don't want to default to us-east-1 endpoint"""
 
+    streaming: bool = False
+    """Whether to stream the results."""
+
+    provider_stop_sequence_key_name_map: Mapping[str, str] = {
+        "anthropic": "stop_sequences",
+        "amazon": "stopSequences",
+        "ai21": "stop_sequences",
+    }
+
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that AWS credentials to and python package exists in environment."""
@@ -154,6 +193,49 @@ class BedrockBase(BaseModel, ABC):
 
         return text
 
+    def _prepare_input_and_invoke_stream(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[GenerationChunk]:
+        _model_kwargs = self.model_kwargs or {}
+        provider = self._get_provider()
+
+        if stop:
+            if provider not in self.provider_stop_sequence_key_name_map:
+                raise ValueError(
+                    f"Stop sequence key name for {provider} is not supported."
+                )
+
+            # stop sequence from _generate() overrides
+            # stop sequences in the class attribute
+            _model_kwargs[
+                self.provider_stop_sequence_key_name_map.get(provider),
+            ] = stop
+
+        params = {**_model_kwargs, **kwargs}
+        input_body = LLMInputOutputAdapter.prepare_input(provider, prompt, params)
+        body = json.dumps(input_body)
+
+        try:
+            response = self.client.invoke_model_with_response_stream(
+                body=body,
+                modelId=self.model_id,
+                accept="application/json",
+                contentType="application/json",
+            )
+        except Exception as e:
+            raise ValueError(f"Error raised by bedrock service: {e}")
+
+        for chunk in LLMInputOutputAdapter.prepare_output_stream(
+            provider, response, stop
+        ):
+            yield chunk
+            if run_manager is not None:
+                run_manager.on_llm_new_token(chunk.text, chunk=chunk)
+
 
 class Bedrock(LLM, BedrockBase):
     """Bedrock models.
@@ -177,7 +259,8 @@ class Bedrock(LLM, BedrockBase):
 
             llm = BedrockLLM(
                 credentials_profile_name="default", 
-                model_id="amazon.titan-tg1-large"
+                model_id="amazon.titan-tg1-large",
+                streaming=True
             )
 
     """
@@ -192,6 +275,33 @@ class Bedrock(LLM, BedrockBase):
 
         extra = Extra.forbid
 
+    def _stream(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[GenerationChunk]:
+        """Call out to Bedrock service with streaming.
+
+        Args:
+            prompt (str): The prompt to pass into the model
+            stop (Optional[List[str]], optional): Stop sequences. These will
+                override any stop sequences in the `model_kwargs` attribute.
+                Defaults to None.
+            run_manager (Optional[CallbackManagerForLLMRun], optional): Callback
+                run managers used to process the output. Defaults to None.
+
+        Returns:
+            Iterator[GenerationChunk]: Generator that yields the streamed responses.
+
+        Yields:
+            Iterator[GenerationChunk]: Responses from the model.
+        """
+        return self._prepare_input_and_invoke_stream(
+            prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
+        )
+
     def _call(
         self,
         prompt: str,
@@ -211,9 +321,15 @@ class Bedrock(LLM, BedrockBase):
         Example:
             .. code-block:: python
 
-                response = se("Tell me a joke.")
+                response = llm("Tell me a joke.")
         """
 
-        text = self._prepare_input_and_invoke(prompt=prompt, stop=stop, **kwargs)
+        if self.streaming:
+            completion = ""
+            for chunk in self._stream(
+                prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
+            ):
+                completion += chunk.text
+            return completion
 
-        return text
+        return self._prepare_input_and_invoke(prompt=prompt, stop=stop, **kwargs)