mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-05 07:08:03 +00:00
bf5193bb99
Upgrade to using a literal for specifying the extra which is the
recommended approach in pydantic 2.
This works correctly also in pydantic v1.
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel, extra="forbid"):
x: int
Foo(x=5, y=1)
```
And
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel):
x: int
class Config:
extra = "forbid"
Foo(x=5, y=1)
```
## Enum -> literal using grit pattern:
```
engine marzano(0.1)
language python
or {
`extra=Extra.allow` => `extra="allow"`,
`extra=Extra.forbid` => `extra="forbid"`,
`extra=Extra.ignore` => `extra="ignore"`
}
```
Resorted attributes in config and removed doc-string in case we will
need to deal with going back and forth between pydantic v1 and v2 during
the 0.3 release. (This will reduce merge conflicts.)
## Sort attributes in Config:
```
engine marzano(0.1)
language python
function sort($values) js {
return $values.text.split(',').sort().join("\n");
}
class_definition($name, $body) as $C where {
$name <: `Config`,
$body <: block($statements),
$values = [],
$statements <: some bubble($values) assignment() as $A where {
$values += $A
},
$body => sort($values),
}
```
595 lines
20 KiB
Python
595 lines
20 KiB
Python
import base64
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import hashlib
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import hmac
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import json
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import logging
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import queue
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import threading
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from datetime import datetime
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from queue import Queue
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from time import mktime
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from typing import Any, Dict, Generator, Iterator, List, Mapping, Optional, Type
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from urllib.parse import urlencode, urlparse, urlunparse
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from wsgiref.handlers import format_date_time
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from langchain_core.callbacks import (
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CallbackManagerForLLMRun,
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)
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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 (
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ChatGeneration,
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ChatGenerationChunk,
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ChatResult,
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)
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from langchain_core.pydantic_v1 import Field, root_validator
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from langchain_core.utils import (
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get_from_dict_or_env,
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get_pydantic_field_names,
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)
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logger = logging.getLogger(__name__)
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SPARK_API_URL = "wss://spark-api.xf-yun.com/v3.5/chat"
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SPARK_LLM_DOMAIN = "generalv3.5"
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def _convert_message_to_dict(message: BaseMessage) -> dict:
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if isinstance(message, ChatMessage):
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message_dict = {"role": "user", "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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elif isinstance(message, SystemMessage):
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message_dict = {"role": "system", "content": message.content}
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else:
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raise ValueError(f"Got unknown type {message}")
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return message_dict
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def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
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msg_role = _dict["role"]
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msg_content = _dict["content"]
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if msg_role == "user":
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return HumanMessage(content=msg_content)
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elif msg_role == "assistant":
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content = msg_content or ""
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return AIMessage(content=content)
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elif msg_role == "system":
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return SystemMessage(content=msg_content)
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else:
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return ChatMessage(content=msg_content, role=msg_role)
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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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msg_role = _dict["role"]
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msg_content = _dict.get("content", "")
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if msg_role == "user" or default_class == HumanMessageChunk:
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return HumanMessageChunk(content=msg_content)
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elif msg_role == "assistant" or default_class == AIMessageChunk:
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return AIMessageChunk(content=msg_content)
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elif msg_role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=msg_content, role=msg_role)
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else:
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return default_class(content=msg_content) # type: ignore[call-arg]
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class ChatSparkLLM(BaseChatModel):
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"""IFlyTek Spark chat model integration.
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Setup:
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To use, you should have the environment variable``IFLYTEK_SPARK_API_KEY``,
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``IFLYTEK_SPARK_API_SECRET`` and ``IFLYTEK_SPARK_APP_ID``.
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Key init args — completion params:
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model: Optional[str]
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Name of IFLYTEK SPARK model to use.
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temperature: Optional[float]
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Sampling temperature.
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top_k: Optional[float]
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What search sampling control to use.
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streaming: Optional[bool]
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Whether to stream the results or not.
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Key init args — client params:
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api_key: Optional[str]
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IFLYTEK SPARK API KEY. If not passed in will be read from env var IFLYTEK_SPARK_API_KEY.
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api_secret: Optional[str]
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IFLYTEK SPARK API SECRET. If not passed in will be read from env var IFLYTEK_SPARK_API_SECRET.
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api_url: Optional[str]
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Base URL for API requests.
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timeout: Optional[int]
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Timeout for requests.
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See full list of supported init args and their descriptions in the params section.
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Instantiate:
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.. code-block:: python
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from langchain_community.chat_models import ChatSparkLLM
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chat = MiniMaxChat(
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api_key=api_key,
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api_secret=ak,
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model='Spark4.0 Ultra',
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# temperature=...,
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# other params...
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)
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Invoke:
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.. code-block:: python
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messages = [
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("system", "你是一名专业的翻译家，可以将用户的中文翻译为英文。"),
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("human", "我喜欢编程。"),
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]
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chat.invoke(messages)
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.. code-block:: python
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AIMessage(
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content='I like programming.',
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response_metadata={
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'token_usage': {
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'question_tokens': 3,
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'prompt_tokens': 16,
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'completion_tokens': 4,
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'total_tokens': 20
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}
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},
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id='run-af8b3531-7bf7-47f0-bfe8-9262cb2a9d47-0'
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)
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Stream:
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.. code-block:: python
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for chunk in chat.stream(messages):
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print(chunk)
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.. code-block:: python
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content='I' id='run-fdbb57c2-2d32-4516-b894-6c5a67605d83'
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content=' like programming' id='run-fdbb57c2-2d32-4516-b894-6c5a67605d83'
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content='.' id='run-fdbb57c2-2d32-4516-b894-6c5a67605d83'
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.. code-block:: python
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stream = chat.stream(messages)
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full = next(stream)
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for chunk in stream:
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full += chunk
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full
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.. code-block:: python
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AIMessageChunk(
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content='I like programming.',
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id='run-aca2fa82-c2e4-4835-b7e2-865ddd3c46cb'
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)
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Response metadata
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.. code-block:: python
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ai_msg = chat.invoke(messages)
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ai_msg.response_metadata
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.. code-block:: python
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{
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'token_usage': {
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'question_tokens': 3,
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'prompt_tokens': 16,
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'completion_tokens': 4,
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'total_tokens': 20
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}
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}
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""" # noqa: E501
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@classmethod
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def is_lc_serializable(cls) -> bool:
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"""Return whether this model can be serialized by Langchain."""
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return False
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {
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"spark_app_id": "IFLYTEK_SPARK_APP_ID",
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"spark_api_key": "IFLYTEK_SPARK_API_KEY",
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"spark_api_secret": "IFLYTEK_SPARK_API_SECRET",
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"spark_api_url": "IFLYTEK_SPARK_API_URL",
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"spark_llm_domain": "IFLYTEK_SPARK_LLM_DOMAIN",
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}
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client: Any = None #: :meta private:
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spark_app_id: Optional[str] = Field(default=None, alias="app_id")
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"""Automatically inferred from env var `IFLYTEK_SPARK_APP_ID`
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if not provided."""
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spark_api_key: Optional[str] = Field(default=None, alias="api_key")
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"""Automatically inferred from env var `IFLYTEK_SPARK_API_KEY`
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if not provided."""
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spark_api_secret: Optional[str] = Field(default=None, alias="api_secret")
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"""Automatically inferred from env var `IFLYTEK_SPARK_API_SECRET`
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if not provided."""
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spark_api_url: Optional[str] = Field(default=None, alias="api_url")
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"""Base URL path for API requests, leave blank if not using a proxy or service
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emulator."""
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spark_llm_domain: Optional[str] = Field(default=None, alias="model")
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"""Model name to use."""
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spark_user_id: str = "lc_user"
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streaming: bool = False
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"""Whether to stream the results or not."""
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request_timeout: int = Field(30, alias="timeout")
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"""request timeout for chat http requests"""
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temperature: float = Field(default=0.5)
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"""What sampling temperature to use."""
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top_k: int = 4
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"""What search sampling control to use."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for API call not explicitly specified."""
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class Config:
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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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@root_validator(pre=True)
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def validate_environment(cls, values: Dict) -> Dict:
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values["spark_app_id"] = get_from_dict_or_env(
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values,
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["spark_app_id", "app_id"],
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"IFLYTEK_SPARK_APP_ID",
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)
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values["spark_api_key"] = get_from_dict_or_env(
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values,
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["spark_api_key", "api_key"],
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"IFLYTEK_SPARK_API_KEY",
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)
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values["spark_api_secret"] = get_from_dict_or_env(
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values,
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["spark_api_secret", "api_secret"],
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"IFLYTEK_SPARK_API_SECRET",
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)
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values["spark_api_url"] = get_from_dict_or_env(
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values,
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"spark_api_url",
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"IFLYTEK_SPARK_API_URL",
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SPARK_API_URL,
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)
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values["spark_llm_domain"] = get_from_dict_or_env(
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values,
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"spark_llm_domain",
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"IFLYTEK_SPARK_LLM_DOMAIN",
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SPARK_LLM_DOMAIN,
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)
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# put extra params into model_kwargs
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default_values = {
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name: field.default
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for name, field in cls.__fields__.items()
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if field.default is not None
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}
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values["model_kwargs"]["temperature"] = default_values.get("temperature")
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values["model_kwargs"]["top_k"] = default_values.get("top_k")
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values["client"] = _SparkLLMClient(
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app_id=values["spark_app_id"],
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api_key=values["spark_api_key"],
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api_secret=values["spark_api_secret"],
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api_url=values["spark_api_url"],
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spark_domain=values["spark_llm_domain"],
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model_kwargs=values["model_kwargs"],
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)
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return values
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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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default_chunk_class = AIMessageChunk
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self.client.arun(
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[_convert_message_to_dict(m) for m in messages],
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self.spark_user_id,
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self.model_kwargs,
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streaming=True,
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)
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for content in self.client.subscribe(timeout=self.request_timeout):
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if "data" not in content:
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continue
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delta = content["data"]
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chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
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cg_chunk = ChatGenerationChunk(message=chunk)
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if run_manager:
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run_manager.on_llm_new_token(str(chunk.content), chunk=cg_chunk)
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yield cg_chunk
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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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stream: Optional[bool] = None,
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**kwargs: Any,
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) -> ChatResult:
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if stream or 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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self.client.arun(
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[_convert_message_to_dict(m) for m in messages],
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self.spark_user_id,
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self.model_kwargs,
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False,
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)
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completion = {}
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llm_output = {}
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for content in self.client.subscribe(timeout=self.request_timeout):
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if "usage" in content:
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llm_output["token_usage"] = content["usage"]
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if "data" not in content:
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continue
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completion = content["data"]
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message = _convert_dict_to_message(completion)
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generations = [ChatGeneration(message=message)]
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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 "spark-llm-chat"
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|
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class _SparkLLMClient:
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"""
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Use websocket-client to call the SparkLLM interface provided by Xfyun,
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which is the iFlyTek's open platform for AI capabilities
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"""
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def __init__(
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self,
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app_id: str,
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api_key: str,
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api_secret: str,
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api_url: Optional[str] = None,
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spark_domain: Optional[str] = None,
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model_kwargs: Optional[dict] = None,
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):
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try:
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import websocket
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self.websocket_client = websocket
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except ImportError:
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raise ImportError(
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"Could not import websocket client python package. "
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"Please install it with `pip install websocket-client`."
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)
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self.api_url = SPARK_API_URL if not api_url else api_url
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self.app_id = app_id
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self.model_kwargs = model_kwargs
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self.spark_domain = spark_domain or SPARK_LLM_DOMAIN
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self.queue: Queue[Dict] = Queue()
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self.blocking_message = {"content": "", "role": "assistant"}
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self.api_key = api_key
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self.api_secret = api_secret
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|
|
@staticmethod
|
|
def _create_url(api_url: str, api_key: str, api_secret: str) -> str:
|
|
"""
|
|
Generate a request url with an api key and an api secret.
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"""
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# generate timestamp by RFC1123
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date = format_date_time(mktime(datetime.now().timetuple()))
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|
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# urlparse
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parsed_url = urlparse(api_url)
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host = parsed_url.netloc
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path = parsed_url.path
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signature_origin = f"host: {host}\ndate: {date}\nGET {path} HTTP/1.1"
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|
|
# encrypt using hmac-sha256
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|
signature_sha = hmac.new(
|
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api_secret.encode("utf-8"),
|
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signature_origin.encode("utf-8"),
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|
digestmod=hashlib.sha256,
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|
).digest()
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signature_sha_base64 = base64.b64encode(signature_sha).decode(encoding="utf-8")
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authorization_origin = f'api_key="{api_key}", algorithm="hmac-sha256", \
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headers="host date request-line", signature="{signature_sha_base64}"'
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|
authorization = base64.b64encode(authorization_origin.encode("utf-8")).decode(
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encoding="utf-8"
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)
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# generate url
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params_dict = {"authorization": authorization, "date": date, "host": host}
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encoded_params = urlencode(params_dict)
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url = urlunparse(
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(
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parsed_url.scheme,
|
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parsed_url.netloc,
|
|
parsed_url.path,
|
|
parsed_url.params,
|
|
encoded_params,
|
|
parsed_url.fragment,
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)
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)
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|
return url
|
|
|
|
def run(
|
|
self,
|
|
messages: List[Dict],
|
|
user_id: str,
|
|
model_kwargs: Optional[dict] = None,
|
|
streaming: bool = False,
|
|
) -> None:
|
|
self.websocket_client.enableTrace(False)
|
|
ws = self.websocket_client.WebSocketApp(
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|
_SparkLLMClient._create_url(
|
|
self.api_url,
|
|
self.api_key,
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|
self.api_secret,
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),
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on_message=self.on_message,
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|
on_error=self.on_error,
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|
on_close=self.on_close,
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|
on_open=self.on_open,
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|
)
|
|
ws.messages = messages # type: ignore[attr-defined]
|
|
ws.user_id = user_id # type: ignore[attr-defined]
|
|
ws.model_kwargs = self.model_kwargs if model_kwargs is None else model_kwargs # type: ignore[attr-defined]
|
|
ws.streaming = streaming # type: ignore[attr-defined]
|
|
ws.run_forever()
|
|
|
|
def arun(
|
|
self,
|
|
messages: List[Dict],
|
|
user_id: str,
|
|
model_kwargs: Optional[dict] = None,
|
|
streaming: bool = False,
|
|
) -> threading.Thread:
|
|
ws_thread = threading.Thread(
|
|
target=self.run,
|
|
args=(
|
|
messages,
|
|
user_id,
|
|
model_kwargs,
|
|
streaming,
|
|
),
|
|
)
|
|
ws_thread.start()
|
|
return ws_thread
|
|
|
|
def on_error(self, ws: Any, error: Optional[Any]) -> None:
|
|
self.queue.put({"error": error})
|
|
ws.close()
|
|
|
|
def on_close(self, ws: Any, close_status_code: int, close_reason: str) -> None:
|
|
logger.debug(
|
|
{
|
|
"log": {
|
|
"close_status_code": close_status_code,
|
|
"close_reason": close_reason,
|
|
}
|
|
}
|
|
)
|
|
self.queue.put({"done": True})
|
|
|
|
def on_open(self, ws: Any) -> None:
|
|
self.blocking_message = {"content": "", "role": "assistant"}
|
|
data = json.dumps(
|
|
self.gen_params(
|
|
messages=ws.messages, user_id=ws.user_id, model_kwargs=ws.model_kwargs
|
|
)
|
|
)
|
|
ws.send(data)
|
|
|
|
def on_message(self, ws: Any, message: str) -> None:
|
|
data = json.loads(message)
|
|
code = data["header"]["code"]
|
|
if code != 0:
|
|
self.queue.put(
|
|
{"error": f"Code: {code}, Error: {data['header']['message']}"}
|
|
)
|
|
ws.close()
|
|
else:
|
|
choices = data["payload"]["choices"]
|
|
status = choices["status"]
|
|
content = choices["text"][0]["content"]
|
|
if ws.streaming:
|
|
self.queue.put({"data": choices["text"][0]})
|
|
else:
|
|
self.blocking_message["content"] += content
|
|
if status == 2:
|
|
if not ws.streaming:
|
|
self.queue.put({"data": self.blocking_message})
|
|
usage_data = (
|
|
data.get("payload", {}).get("usage", {}).get("text", {})
|
|
if data
|
|
else {}
|
|
)
|
|
self.queue.put({"usage": usage_data})
|
|
ws.close()
|
|
|
|
def gen_params(
|
|
self, messages: list, user_id: str, model_kwargs: Optional[dict] = None
|
|
) -> dict:
|
|
data: Dict = {
|
|
"header": {"app_id": self.app_id, "uid": user_id},
|
|
"parameter": {"chat": {"domain": self.spark_domain}},
|
|
"payload": {"message": {"text": messages}},
|
|
}
|
|
|
|
if model_kwargs:
|
|
data["parameter"]["chat"].update(model_kwargs)
|
|
logger.debug(f"Spark Request Parameters: {data}")
|
|
return data
|
|
|
|
def subscribe(self, timeout: Optional[int] = 30) -> Generator[Dict, None, None]:
|
|
while True:
|
|
try:
|
|
content = self.queue.get(timeout=timeout)
|
|
except queue.Empty as _:
|
|
raise TimeoutError(
|
|
f"SparkLLMClient wait LLM api response timeout {timeout} seconds"
|
|
)
|
|
if "error" in content:
|
|
raise ConnectionError(content["error"])
|
|
if "usage" in content:
|
|
yield content
|
|
continue
|
|
if "done" in content:
|
|
break
|
|
if "data" not in content:
|
|
break
|
|
yield content
|