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feat: Implement stream interface (#15875)
<!-- Thank you for contributing to LangChain! Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified. Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes if applicable, - **Dependencies:** any dependencies required for this change, - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/ If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> Major changes: - Rename `wasm_chat.py` to `llama_edge.py` - Rename the `WasmChatService` class to `ChatService` - Implement the `stream` interface for `ChatService` - Add `test_chat_wasm_service_streaming` in the integration test - Update `llama_edge.ipynb` --------- Signed-off-by: Xin Liu <sam@secondstate.io>
This commit is contained in:
Xin Liu
GitHub
241
libs/community/langchain_community/chat_models/llama_edge.py
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241
libs/community/langchain_community/chat_models/llama_edge.py
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import json
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import logging
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import re
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from typing import Any, Dict, Iterator, List, Mapping, Optional, Type
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import requests
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models.chat_models import (
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BaseChatModel,
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generate_from_stream,
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)
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from langchain_core.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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BaseMessageChunk,
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ChatMessage,
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ChatMessageChunk,
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HumanMessage,
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HumanMessageChunk,
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SystemMessage,
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)
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.pydantic_v1 import root_validator
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from langchain_core.utils import get_pydantic_field_names
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logger = logging.getLogger(__name__)
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def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
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role = _dict["role"]
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if role == "user":
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return HumanMessage(content=_dict["content"])
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elif role == "assistant":
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return AIMessage(content=_dict.get("content", "") or "")
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else:
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return ChatMessage(content=_dict["content"], role=role)
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def _convert_message_to_dict(message: BaseMessage) -> dict:
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message_dict: Dict[str, Any]
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if isinstance(message, ChatMessage):
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message_dict = {"role": message.role, "content": message.content}
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elif isinstance(message, SystemMessage):
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message_dict = {"role": "system", "content": message.content}
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elif isinstance(message, HumanMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, AIMessage):
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message_dict = {"role": "assistant", "content": message.content}
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else:
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raise TypeError(f"Got unknown type {message}")
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return message_dict
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def _convert_delta_to_message_chunk(
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_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
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) -> BaseMessageChunk:
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role = _dict.get("role")
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content = _dict.get("content") or ""
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if role == "user" or default_class == HumanMessageChunk:
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return HumanMessageChunk(content=content)
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elif role == "assistant" or default_class == AIMessageChunk:
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return AIMessageChunk(content=content)
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elif role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=content, role=role)
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else:
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return default_class(content=content)
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class LlamaEdgeChatService(BaseChatModel):
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"""Chat with LLMs via `llama-api-server`
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For the information about `llama-api-server`, visit https://github.com/second-state/LlamaEdge
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"""
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request_timeout: int = 60
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"""request timeout for chat http requests"""
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service_url: Optional[str] = None
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"""URL of WasmChat service"""
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model: str = "NA"
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"""model name, default is `NA`."""
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streaming: bool = False
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"""Whether to stream the results or not."""
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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@root_validator(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = get_pydantic_field_names(cls)
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extra = values.get("model_kwargs", {})
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for field_name in list(values):
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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if field_name not in all_required_field_names:
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logger.warning(
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f"""WARNING! {field_name} is not default parameter.
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{field_name} was transferred to model_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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extra[field_name] = values.pop(field_name)
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invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
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if invalid_model_kwargs:
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raise ValueError(
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f"Parameters {invalid_model_kwargs} should be specified explicitly. "
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f"Instead they were passed in as part of `model_kwargs` parameter."
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)
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values["model_kwargs"] = extra
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return values
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def _generate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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if self.streaming:
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stream_iter = self._stream(
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messages=messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return generate_from_stream(stream_iter)
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res = self._chat(messages, **kwargs)
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if res.status_code != 200:
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raise ValueError(f"Error code: {res.status_code}, reason: {res.reason}")
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response = res.json()
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return self._create_chat_result(response)
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def _stream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[ChatGenerationChunk]:
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res = self._chat(messages, **kwargs)
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default_chunk_class = AIMessageChunk
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substring = '"object":"chat.completion.chunk"}'
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for line in res.iter_lines():
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chunks = []
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if line:
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json_string = line.decode("utf-8")
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# Find all positions of the substring
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positions = [m.start() for m in re.finditer(substring, json_string)]
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positions = [-1 * len(substring)] + positions
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for i in range(len(positions) - 1):
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chunk = json.loads(
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json_string[
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positions[i] + len(substring) : positions[i + 1]
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+ len(substring)
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]
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)
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chunks.append(chunk)
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for chunk in chunks:
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if not isinstance(chunk, dict):
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chunk = chunk.dict()
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if len(chunk["choices"]) == 0:
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continue
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choice = chunk["choices"][0]
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chunk = _convert_delta_to_message_chunk(
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choice["delta"], default_chunk_class
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)
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if (
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choice.get("finish_reason") is not None
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and choice.get("finish_reason") == "stop"
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):
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break
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finish_reason = choice.get("finish_reason")
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generation_info = (
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dict(finish_reason=finish_reason)
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if finish_reason is not None
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else None
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)
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default_chunk_class = chunk.__class__
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chunk = ChatGenerationChunk(
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message=chunk, generation_info=generation_info
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)
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yield chunk
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if run_manager:
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run_manager.on_llm_new_token(chunk.text, chunk=chunk)
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def _chat(self, messages: List[BaseMessage], **kwargs: Any) -> requests.Response:
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if self.service_url is None:
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res = requests.models.Response()
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res.status_code = 503
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res.reason = "The IP address or port of the chat service is incorrect."
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return res
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service_url = f"{self.service_url}/v1/chat/completions"
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if self.streaming:
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payload = {
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"model": self.model,
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"messages": [_convert_message_to_dict(m) for m in messages],
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"stream": self.streaming,
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}
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else:
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payload = {
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"model": self.model,
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"messages": [_convert_message_to_dict(m) for m in messages],
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}
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res = requests.post(
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url=service_url,
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timeout=self.request_timeout,
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headers={
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"accept": "application/json",
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"Content-Type": "application/json",
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},
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data=json.dumps(payload),
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)
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return res
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def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
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message = _convert_dict_to_message(response["choices"][0].get("message"))
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generations = [ChatGeneration(message=message)]
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token_usage = response["usage"]
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llm_output = {"token_usage": token_usage, "model": self.model}
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return ChatResult(generations=generations, llm_output=llm_output)
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@property
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def _llm_type(self) -> str:
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return "wasm-chat"
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