Source code for langchain_community.chat_models.llama_edge

import json
import logging
import re
from typing import Any, Dict, Iterator, List, Mapping, Optional, Type

import requests
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.chat_models import (
from langchain_core.messages import (
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, 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 "")
        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}
        raise TypeError(f"Got unknown type {message}")

    return message_dict

def _convert_delta_to_message_chunk(
    _dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
) -> BaseMessageChunk:
    role = _dict.get("role")
    content = _dict.get("content") or ""

    if role == "user" or default_class == HumanMessageChunk:
        return HumanMessageChunk(content=content)
    elif role == "assistant" or default_class == AIMessageChunk:
        return AIMessageChunk(content=content)
    elif role or default_class == ChatMessageChunk:
        return ChatMessageChunk(content=content, role=role)  # type: ignore[arg-type]
        return default_class(content=content)  # type: ignore[call-arg]

[docs]class LlamaEdgeChatService(BaseChatModel): """Chat with LLMs via `llama-api-server` For the information about `llama-api-server`, visit """ 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`.""" streaming: bool = False """Whether to stream the results or not.""" 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: if self.streaming: stream_iter = self._stream( messages=messages, stop=stop, run_manager=run_manager, **kwargs ) return generate_from_stream(stream_iter) 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 _stream( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[ChatGenerationChunk]: res = self._chat(messages, **kwargs) default_chunk_class = AIMessageChunk substring = '"object":"chat.completion.chunk"}' for line in res.iter_lines(): chunks = [] if line: json_string = line.decode("utf-8") # Find all positions of the substring positions = [m.start() for m in re.finditer(substring, json_string)] positions = [-1 * len(substring)] + positions for i in range(len(positions) - 1): chunk = json.loads( json_string[ positions[i] + len(substring) : positions[i + 1] + len(substring) ] ) chunks.append(chunk) for chunk in chunks: if not isinstance(chunk, dict): chunk = chunk.dict() if len(chunk["choices"]) == 0: continue choice = chunk["choices"][0] chunk = _convert_delta_to_message_chunk( choice["delta"], default_chunk_class ) if ( choice.get("finish_reason") is not None and choice.get("finish_reason") == "stop" ): break finish_reason = choice.get("finish_reason") generation_info = ( dict(finish_reason=finish_reason) if finish_reason is not None else None ) default_chunk_class = chunk.__class__ cg_chunk = ChatGenerationChunk( message=chunk, generation_info=generation_info ) if run_manager: run_manager.on_llm_new_token(cg_chunk.text, chunk=cg_chunk) yield cg_chunk 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" if self.streaming: payload = { "model": self.model, "messages": [_convert_message_to_dict(m) for m in messages], "stream": self.streaming, } else: payload = { "model": self.model, "messages": [_convert_message_to_dict(m) for m in messages], } res = 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"