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import gradio as gr
from paperqa import Docs, SentenceTransformerEmbeddingModel
from langchain_anthropic import ChatAnthropic
MODEL_NAME = "claude-3-5-sonnet-20240620"
class MyChatAnthropic(ChatAnthropic):
model_name = MODEL_NAME
llm = MyChatAnthropic(
model=MODEL_NAME,
temperature=0.2,
max_tokens=4096,)
class MyEmb(SentenceTransformerEmbeddingModel):
async def aembed_documents(self, texts):
return await self.embed_documents(None, texts)
emb = MyEmb(model_name="mixedbread-ai/mxbai-embed-large-v1")
docs = Docs(llm="langchain",
embedding="langchain",
embedding_client=emb,
client=llm)
docs.max_concurrent = 1
docs.add('knowledge_extraction.csv', disable_check=True)
def respond(message):
return docs.query(messages).answer
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(respond)
if __name__ == "__main__":
demo.launch() |