43 lines
No EOL
1.5 KiB
Python
43 lines
No EOL
1.5 KiB
Python
# Import the required libraries
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import tempfile
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from embedchain import App
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from embedchain.loaders.github import GithubLoader
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import streamlit as st
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loader = GithubLoader(
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config={
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"token":"Your GitHub Token",
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}
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)
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# Define the embedchain_bot function
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def embedchain_bot(db_path):
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return App.from_config(
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config={
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"llm": {"provider": "ollama", "config": {"model": "llama3:instruct", "max_tokens": 250, "temperature": 0.5, "stream": True, "base_url": 'http://localhost:11434'}},
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"vectordb": {"provider": "chroma", "config": {"dir": db_path}},
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"embedder": {"provider": "ollama", "config": {"model": "llama3:instruct", "base_url": 'http://localhost:11434'}},
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}
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)
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# Create Streamlit app
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st.title("Chat with GitHub Repository 💬")
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st.caption("This app allows you to chat with a GitHub Repo using Llama-3 running with Ollama")
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# Initialize the Embedchain App
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db_path = tempfile.mkdtemp()
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app = embedchain_bot(db_path)
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# Get the GitHub repo from the user
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git_repo = st.text_input("Enter the GitHub Repo", type="default")
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if git_repo:
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# Add the repo to the knowledge base
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app.add("repo:" + git_repo + " " + "type:repo", data_type="github", loader=loader)
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st.success(f"Added {git_repo} to knowledge base!")
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# Ask a question about the Github Repo
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prompt = st.text_input("Ask any question about the GitHub Repo")
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# Chat with the GitHub Repo
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if prompt:
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answer = app.chat(prompt)
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st.write(answer) |