41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
import os
|
|
from typing import Optional
|
|
|
|
from langchain.llms import Replicate
|
|
|
|
from embedchain.config import AppConfig, ChatConfig
|
|
from embedchain.embedchain import EmbedChain
|
|
|
|
|
|
class Llama2App(EmbedChain):
|
|
"""
|
|
The EmbedChain Llama2App class.
|
|
Has two functions: add and query.
|
|
|
|
adds(data_type, url): adds the data from the given URL to the vector db.
|
|
query(query): finds answer to the given query using vector database and LLM.
|
|
"""
|
|
|
|
def __init__(self, config: AppConfig = None, system_prompt: Optional[str] = None):
|
|
"""
|
|
:param config: AppConfig instance to load as configuration. Optional.
|
|
:param system_prompt: System prompt string. Optional.
|
|
"""
|
|
if "REPLICATE_API_TOKEN" not in os.environ:
|
|
raise ValueError("Please set the REPLICATE_API_TOKEN environment variable.")
|
|
|
|
if config is None:
|
|
config = AppConfig()
|
|
|
|
super().__init__(config, system_prompt)
|
|
|
|
def get_llm_model_answer(self, prompt, config: ChatConfig = None):
|
|
# TODO: Move the model and other inputs into config
|
|
if self.system_prompt or config.system_prompt:
|
|
raise ValueError("Llama2App does not support `system_prompt`")
|
|
llm = Replicate(
|
|
model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",
|
|
input={"temperature": 0.75, "max_length": 500, "top_p": 1},
|
|
)
|
|
return llm(prompt)
|