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from typing import Callable, TypedDict
from langchain.prompts import (
ChatPromptTemplate,
FewShotChatMessagePromptTemplate,
)
from langchain.schema import AIMessage
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.language_models.llms import BaseLLM
class Example(TypedDict):
input: str
output: str
async def call_llm(
system: str,
model: BaseChatModel | BaseLLM,
message: str,
examples: list[Example] = [],
callback: Callable[[str], None] | None = None
):
example_prompt = ChatPromptTemplate.from_messages(
[
HumanMessage(content="{input}"),
AIMessage(content="{output}"),
]
)
few_shot_prompt = FewShotChatMessagePromptTemplate(
example_prompt=example_prompt,
examples=examples, # type: ignore
input_variables=[],
)
few_shot_prompt.format()
final_prompt = ChatPromptTemplate.from_messages(
[
SystemMessage(content=system),
few_shot_prompt,
HumanMessage(content=message),
]
)
chain = final_prompt | model
response = ""
async for chunk in chain.astream({}):
# await self.handle_intervention() # wait for intervention and handle it, if paused
if isinstance(chunk, str):
content = chunk
elif hasattr(chunk, "content"):
content = str(chunk.content)
else:
content = str(chunk)
if callback:
callback(content)
response += content
return response
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