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37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
from typing import List, Union, Generator
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from schemas import OpenAIChatMessage
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from llama_index.embeddings.ollama import OllamaEmbedding
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from llama_index.llms.openai import OpenAI
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from llama_index.core import Settings
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ollama_embedding = OllamaEmbedding(
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model_name="nomic-embed-text",
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base_url="http://localhost:11434",
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)
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Settings.embed_model = ollama_embedding
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Settings.llm = OpenAI(
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temperature=0, model="llama3", api_key="none", api_base="http://localhost:11434"
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)
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from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
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documents = SimpleDirectoryReader("./data").load_data()
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index = VectorStoreIndex.from_documents(documents)
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def get_response(
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user_message: str, messages: List[OpenAIChatMessage]
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) -> Union[str, Generator]:
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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 = 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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