mirror of
https://github.com/open-webui/pipelines
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40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
from typing import List, Union, Generator, Iterator
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from schemas import OpenAIChatMessage
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class Pipeline:
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def __init__(self):
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self.documents = None
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self.index = None
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async def on_startup(self):
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import os
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# Set the OpenAI API key
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os.environ["OPENAI_API_KEY"] = "your-api-key-here"
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from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
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self.documents = SimpleDirectoryReader("./data").load_data()
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self.index = VectorStoreIndex.from_documents(self.documents)
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# This function is called when the server is started.
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pass
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async def on_shutdown(self):
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# This function is called when the server is stopped.
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pass
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def pipe(
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self, user_message: str, model_id: str, messages: List[dict], body: dict
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) -> Union[str, Generator, Iterator]:
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# This is where you can add your custom RAG pipeline.
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# Typically, you would retrieve relevant information from your knowledge base and synthesize it to generate a response.
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print(messages)
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print(user_message)
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query_engine = self.index.as_query_engine(streaming=True)
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response = query_engine.query(user_message)
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return response.response_gen
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