feat: new integration wasm_chat (#14787)

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Adds `WasmChat` integration. `WasmChat` runs GGUF models locally or via
chat service in lightweight and secure WebAssembly containers. In this
PR, `WasmChatService` is introduced as the first step of the
integration. `WasmChatService` is driven by
[llama-api-server](https://github.com/second-state/llama-utils) and
[WasmEdge Runtime](https://wasmedge.org/).

---------

Signed-off-by: Xin Liu <sam@secondstate.io>

docs/docs/integrations/chat/wasm_chat.ipynb

@@ -0,0 +1,85 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Wasm Chat\n",
+    "\n",
+    "`Wasm-chat` allows you to chat with LLMs of [GGUF](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/README.md) format both locally and via chat service.\n",
+    "\n",
+    "- `WasmChatService` provides developers an OpenAI API compatible service to chat with LLMs via HTTP requests.\n",
+    "\n",
+    "- `WasmChatLocal` enables developers to chat with LLMs locally (coming soon).\n",
+    "\n",
+    "Both `WasmChatService` and `WasmChatLocal` run on the infrastructure driven by [WasmEdge Runtime](https://wasmedge.org/), which provides a lightweight and portable WebAssembly container environment for LLM inference tasks.\n",
+    "\n",
+    "## Chat via API Service\n",
+    "\n",
+    "`WasmChatService` provides chat services by the `llama-api-server`. Following the steps in [llama-api-server quick-start](https://github.com/second-state/llama-utils/tree/main/api-server#readme), you can host your own API service so that you can chat with any models you like on any device you have anywhere as long as the internet is available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.chat_models.wasm_chat import WasmChatService\n",
+    "from langchain_core.messages import AIMessage, HumanMessage, SystemMessage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Bot] Paris\n"
+     ]
+    }
+   ],
+   "source": [
+    "# service url\n",
+    "service_url = \"https://b008-54-186-154-209.ngrok-free.app\"\n",
+    "\n",
+    "# create wasm-chat service instance\n",
+    "chat = WasmChatService(service_url=service_url)\n",
+    "\n",
+    "# create message sequence\n",
+    "system_message = SystemMessage(content=\"You are an AI assistant\")\n",
+    "user_message = HumanMessage(content=\"What is the capital of France?\")\n",
+    "messages = [system_message, user_message]\n",
+    "\n",
+    "# chat with wasm-chat service\n",
+    "response = chat(messages)\n",
+    "\n",
+    "print(f\"[Bot] {response.content}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

libs/community/langchain_community/chat_models/__init__.py

@@ -49,10 +49,12 @@ from langchain_community.chat_models.promptlayer_openai import PromptLayerChatOp
 from langchain_community.chat_models.tongyi import ChatTongyi
 from langchain_community.chat_models.vertexai import ChatVertexAI
 from langchain_community.chat_models.volcengine_maas import VolcEngineMaasChat
+from langchain_community.chat_models.wasm_chat import WasmChatService
 from langchain_community.chat_models.yandex import ChatYandexGPT
 from langchain_community.chat_models.zhipuai import ChatZhipuAI
 
 __all__ = [
+    "WasmChatService",
     "ChatOpenAI",
     "BedrockChat",
     "AzureChatOpenAI",

libs/community/langchain_community/chat_models/wasm_chat.py

@@ -0,0 +1,144 @@
+import json
+import logging
+from typing import Any, Dict, List, Mapping, Optional
+
+import requests
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.messages import (
+    AIMessage,
+    BaseMessage,
+    ChatMessage,
+    HumanMessage,
+    SystemMessage,
+)
+from langchain_core.outputs import ChatGeneration, ChatResult
+from langchain_core.pydantic_v1 import root_validator
+from langchain_core.utils import get_pydantic_field_names
+
+logger = logging.getLogger(__name__)
+
+
+def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
+    role = _dict["role"]
+    if role == "user":
+        return HumanMessage(content=_dict["content"])
+    elif role == "assistant":
+        return AIMessage(content=_dict.get("content", "") or "")
+    else:
+        return ChatMessage(content=_dict["content"], role=role)
+
+
+def _convert_message_to_dict(message: BaseMessage) -> dict:
+    message_dict: Dict[str, Any]
+    if isinstance(message, ChatMessage):
+        message_dict = {"role": message.role, "content": message.content}
+    elif isinstance(message, SystemMessage):
+        message_dict = {"role": "system", "content": message.content}
+    elif isinstance(message, HumanMessage):
+        message_dict = {"role": "user", "content": message.content}
+    elif isinstance(message, AIMessage):
+        message_dict = {"role": "assistant", "content": message.content}
+    else:
+        raise TypeError(f"Got unknown type {message}")
+
+    return message_dict
+
+
+class WasmChatService(BaseChatModel):
+    """Chat with LLMs via `llama-api-server`
+
+    For the information about `llama-api-server`, visit https://github.com/second-state/llama-utils
+    """
+
+    request_timeout: int = 60
+    """request timeout for chat http requests"""
+    service_url: Optional[str] = None
+    """URL of WasmChat service"""
+    model: str = "NA"
+    """model name, default is `NA`."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        allow_population_by_field_name = True
+
+    @root_validator(pre=True)
+    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        """Build extra kwargs from additional params that were passed in."""
+        all_required_field_names = get_pydantic_field_names(cls)
+        extra = values.get("model_kwargs", {})
+        for field_name in list(values):
+            if field_name in extra:
+                raise ValueError(f"Found {field_name} supplied twice.")
+            if field_name not in all_required_field_names:
+                logger.warning(
+                    f"""WARNING! {field_name} is not default parameter.
+                    {field_name} was transferred to model_kwargs.
+                    Please confirm that {field_name} is what you intended."""
+                )
+                extra[field_name] = values.pop(field_name)
+
+        invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
+        if invalid_model_kwargs:
+            raise ValueError(
+                f"Parameters {invalid_model_kwargs} should be specified explicitly. "
+                f"Instead they were passed in as part of `model_kwargs` parameter."
+            )
+
+        values["model_kwargs"] = extra
+        return values
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        res = self._chat(messages, **kwargs)
+
+        if res.status_code != 200:
+            raise ValueError(f"Error code: {res.status_code}, reason: {res.reason}")
+
+        response = res.json()
+
+        return self._create_chat_result(response)
+
+    def _chat(self, messages: List[BaseMessage], **kwargs: Any) -> requests.Response:
+        if self.service_url is None:
+            res = requests.models.Response()
+            res.status_code = 503
+            res.reason = "The IP address or port of the chat service is incorrect."
+            return res
+
+        service_url = f"{self.service_url}/v1/chat/completions"
+
+        payload = {
+            "model": self.model,
+            "messages": [_convert_message_to_dict(m) for m in messages],
+        }
+
+        res = requests.post(
+            url=service_url,
+            timeout=self.request_timeout,
+            headers={
+                "accept": "application/json",
+                "Content-Type": "application/json",
+            },
+            data=json.dumps(payload),
+        )
+
+        return res
+
+    def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
+        message = _convert_dict_to_message(response["choices"][0].get("message"))
+        generations = [ChatGeneration(message=message)]
+
+        token_usage = response["usage"]
+        llm_output = {"token_usage": token_usage, "model": self.model}
+        return ChatResult(generations=generations, llm_output=llm_output)
+
+    @property
+    def _llm_type(self) -> str:
+        return "wasm-chat"

libs/community/tests/integration_tests/chat_models/test_wasm_chat.py

@@ -0,0 +1,28 @@
+import pytest
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
+
+from langchain_community.chat_models.wasm_chat import WasmChatService
+
+
+@pytest.mark.enable_socket
+def test_chat_wasm_service() -> None:
+    """This test requires the port 8080 is not occupied."""
+
+    # service url
+    service_url = "https://b008-54-186-154-209.ngrok-free.app"
+
+    # create wasm-chat service instance
+    chat = WasmChatService(service_url=service_url)
+
+    # create message sequence
+    system_message = SystemMessage(content="You are an AI assistant")
+    user_message = HumanMessage(content="What is the capital of France?")
+    messages = [system_message, user_message]
+
+    # chat with wasm-chat service
+    response = chat(messages)
+
+    # check response
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, str)
+    assert "Paris" in response.content

libs/community/tests/unit_tests/chat_models/test_imports.py

@@ -33,6 +33,7 @@ EXPECTED_ALL = [
     "ChatHunyuan",
     "GigaChat",
     "VolcEngineMaasChat",
+    "WasmChatService",
     "GPTRouter",
     "ChatZhipuAI",
 ]

libs/community/tests/unit_tests/chat_models/test_wasmchat.py

@@ -0,0 +1,78 @@
+import pytest
+from langchain_core.messages import (
+    AIMessage,
+    ChatMessage,
+    FunctionMessage,
+    HumanMessage,
+    SystemMessage,
+)
+
+from langchain_community.chat_models.wasm_chat import (
+    WasmChatService,
+    _convert_dict_to_message,
+    _convert_message_to_dict,
+)
+
+
+def test__convert_message_to_dict_human() -> None:
+    message = HumanMessage(content="foo")
+    result = _convert_message_to_dict(message)
+    expected_output = {"role": "user", "content": "foo"}
+    assert result == expected_output
+
+
+def test__convert_message_to_dict_ai() -> None:
+    message = AIMessage(content="foo")
+    result = _convert_message_to_dict(message)
+    expected_output = {"role": "assistant", "content": "foo"}
+    assert result == expected_output
+
+
+def test__convert_message_to_dict_system() -> None:
+    message = SystemMessage(content="foo")
+    result = _convert_message_to_dict(message)
+    expected_output = {"role": "system", "content": "foo"}
+    assert result == expected_output
+
+
+def test__convert_message_to_dict_function() -> None:
+    message = FunctionMessage(name="foo", content="bar")
+    with pytest.raises(TypeError) as e:
+        _convert_message_to_dict(message)
+    assert "Got unknown type" in str(e)
+
+
+def test__convert_dict_to_message_human() -> None:
+    message_dict = {"role": "user", "content": "foo"}
+    result = _convert_dict_to_message(message_dict)
+    expected_output = HumanMessage(content="foo")
+    assert result == expected_output
+
+
+def test__convert_dict_to_message_ai() -> None:
+    message_dict = {"role": "assistant", "content": "foo"}
+    result = _convert_dict_to_message(message_dict)
+    expected_output = AIMessage(content="foo")
+    assert result == expected_output
+
+
+def test__convert_dict_to_message_other_role() -> None:
+    message_dict = {"role": "system", "content": "foo"}
+    result = _convert_dict_to_message(message_dict)
+    expected_output = ChatMessage(role="system", content="foo")
+    assert result == expected_output
+
+
+def test_wasm_chat_without_service_url() -> None:
+    chat = WasmChatService()
+
+    # create message sequence
+    system_message = SystemMessage(content="You are an AI assistant")
+    user_message = HumanMessage(content="What is the capital of France?")
+    messages = [system_message, user_message]
+
+    with pytest.raises(ValueError) as e:
+        chat(messages)
+
+    assert "Error code: 503" in str(e)
+    assert "reason: The IP address or port of the chat service is incorrect." in str(e)