mirror of
https://github.com/open-webui/pipelines
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65 lines
1.7 KiB
Python
65 lines
1.7 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.ollama import Ollama
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from llama_index.core import VectorStoreIndex, Settings
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from llama_index.readers.github import GithubRepositoryReader, GithubClient
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Settings.embed_model = 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.llm = Ollama(model="llama3")
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import os
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github_token = os.environ.get("GITHUB_TOKEN")
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owner = "open-webui"
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repo = "open-webui"
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branch = "main"
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github_client = GithubClient(github_token=github_token, verbose=True)
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documents = GithubRepositoryReader(
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github_client=github_client,
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owner=owner,
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repo=repo,
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use_parser=False,
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verbose=False,
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filter_directories=(
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["docs"],
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GithubRepositoryReader.FilterType.INCLUDE,
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),
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filter_file_extensions=(
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[
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".png",
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".jpg",
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".jpeg",
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".gif",
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".svg",
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".ico",
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"json",
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".ipynb",
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],
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GithubRepositoryReader.FilterType.EXCLUDE,
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),
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).load_data(branch=branch)
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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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