mirror of
https://github.com/open-webui/pipelines
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95 lines
2.9 KiB
Python
95 lines
2.9 KiB
Python
"""
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title: Llama Index Ollama Github Pipeline
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author: open-webui
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date: 2024-05-30
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version: 1.0
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license: MIT
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description: A pipeline for retrieving relevant information from a knowledge base using the Llama Index library with Ollama embeddings from a GitHub repository.
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requirements: llama-index, llama-index-llms-ollama, llama-index-embeddings-ollama, llama-index-readers-github
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"""
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from typing import List, Union, Generator, Iterator
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from schemas import OpenAIChatMessage
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import os
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import asyncio
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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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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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global index, documents
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github_token = os.environ.get("GITHUB_TOKEN")
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owner = "open-webui"
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repo = "plugin-server"
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branch = "main"
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github_client = GithubClient(github_token=github_token, verbose=True)
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reader = 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_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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)
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loop = asyncio.new_event_loop()
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reader._loop = loop
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try:
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# Load data from the branch
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self.documents = await asyncio.to_thread(reader.load_data, branch=branch)
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self.index = VectorStoreIndex.from_documents(self.documents)
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finally:
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loop.close()
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print(self.documents)
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print(self.index)
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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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