michaelfeil
commited on
Commit
•
11ac6f7
1
Parent(s):
02e3a03
add mongooseminer
Browse files- Dockerfile +8 -0
- main.py +160 -0
- search.py +65 -0
Dockerfile
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from python:3.10-slim
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RUN pip install groq gradio infinity_emb[all] usearch
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WORKDIR /app
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COPY . .
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CMD python main.py
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main.py
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import gradio as gr
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import os
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import json
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from groq import Groq
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from search import answer_query
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try:
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from dotenv import load_dotenv
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load_dotenv(dotenv_path="./.env")
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except:
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pass
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client = Groq(
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api_key=os.environ.get("GROQ_API_KEY"),
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)
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_related_functions",
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"description": "Get docstrings for internal functions for any library on PyPi.",
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"parameters": {
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"type": "object",
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"properties": {
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"user_query": {
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"type": "string",
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"description": "A query to retrieve docstrings and find useful information.",
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}
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},
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"required": ["user_query"],
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},
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},
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}
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]
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def user(user_message, history):
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return "", history + [[user_message, None]]
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def get_related_functions(user_query: str) -> dict:
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docstring_top10 = answer_query(user_query)
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print("added torch mul")
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return docstring_top10[0]
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def generate_rag(history):
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messages = [
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{
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"role": "system",
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"content": "You are a function calling LLM that uses the data extracted from the get_related_functions function to answer questions around writing Python code. Use the extraced docstrings to write better code."
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},
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{
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"role": "user",
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"content": history[-1][0],
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}
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]
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history[-1][1] = ""
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tool_call_count = 0
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max_tool_calls = 3
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while tool_call_count <= max_tool_calls:
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response = client.chat.completions.create(
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model="llama3-70b-8192",
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messages=messages,
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tools=tools if tool_call_count < 3 else None,
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tool_choice="auto",
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max_tokens=4096
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)
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tool_call_count += 1
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response_message = response.choices[0].message
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tool_calls = response_message.tool_calls
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if tool_calls:
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available_functions = {
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"get_related_functions": get_related_functions,
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}
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messages.append(response_message)
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for tool_call in tool_calls:
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function_name = tool_call.function.name
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function_to_call = available_functions[function_name]
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function_args = json.loads(tool_call.function.arguments)
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function_response = function_to_call(
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user_query=function_args.get("user_query")
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)
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messages.append(
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{
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"tool_call_id": tool_call.id,
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"role": "tool",
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"name": function_name,
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"content": function_response,
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}
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)
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else:
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break
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history[-1][1] += response_message.content
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return history
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def generate_llama3(history):
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history[-1][1] = ""
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stream = client.chat.completions.create(
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messages=[
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# Set an optional system message. This sets the behavior of the
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# assistant and can be used to provide specific instructions for
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# how it should behave throughout the conversation.
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{
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"role": "system",
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"content": "you are a helpful assistant."
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},
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# Set a user message for the assistant to respond to.
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{
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"role": "user",
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"content": history[-1][0],
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}
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],
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stream=True,
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model="llama3-8b-8192",
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max_tokens=1024,
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temperature=0
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)
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for chunk in stream:
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if chunk.choices[0].delta.content != None:
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history[-1][1] += chunk.choices[0].delta.content
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yield history
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else:
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return
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with gr.Blocks() as demo:
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with gr.Row():
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with gr.Column():
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gr.Markdown("# Mongoose Miner Search Demo")
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gr.Markdown(
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"Augmenting LLM code generation with function-level search across all of PyPi.")
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with gr.Row():
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chatbot = gr.Chatbot(height="35rem", label="Llama3 unaugmented")
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chatbot2 = gr.Chatbot(
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height="35rem", label="Llama3 with MongooseMiner Search")
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msg = gr.Textbox()
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clear = gr.Button("Clear")
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msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
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generate_llama3, chatbot, chatbot
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)
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msg.submit(user, [msg, chatbot2], [msg, chatbot2], queue=False).then(
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generate_rag, chatbot2, chatbot2
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)
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clear.click(lambda: None, None, chatbot, queue=False)
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clear.click(lambda: None, None, chatbot2, queue=False)
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demo.queue()
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demo.launch()
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search.py
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from infinity_emb import AsyncEmbeddingEngine, EngineArgs
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import numpy as np
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from usearch.index import Index, Matches
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import asyncio
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import pandas as pd
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engine = AsyncEmbeddingEngine.from_args(
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EngineArgs(
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model_name_or_path="michaelfeil/jina-embeddings-v2-base-code",
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batch_size=8,
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)
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)
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async def embed_texts(texts: list[str]) -> np.ndarray:
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async with engine:
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embeddings = (await engine.embed(texts))[0]
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return np.array(embeddings)
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def embed_texts_sync(texts: list[str]) -> np.ndarray:
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loop = asyncio.new_event_loop()
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return loop.run_until_complete(embed_texts(texts))
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index = None
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docs_index = None
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def build_index(demo_mode=True):
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global index, docs_index
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index = Index(
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ndim=embed_texts_sync(["Hi"]).shape[
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-1
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], # Define the number of dimensions in input vectors
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metric="cos", # Choose 'l2sq', 'haversine' or other metric, default = 'ip'
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dtype="f16", # Quantize to 'f16' or 'i8' if needed, default = 'f32'
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connectivity=16, # How frequent should the connections in the graph be, optional
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expansion_add=128, # Control the recall of indexing, optional
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expansion_search=64, # Control the quality of search, optional
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)
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if demo_mode:
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docs_index = [
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"torch.add(*demo)",
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"torch.mul(*demo)",
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"torch.div(*demo)",
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"torch.sub(*demo)",
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]
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embeddings = embed_texts_sync(docs_index)
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index.add(np.arange(len(docs_index)), embeddings)
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return
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# TODO: Michael, load parquet with embeddings
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if index is None:
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build_index()
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def answer_query(query: str) -> list[str]:
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embedding = embed_texts_sync([query])
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matches = index.search(embedding, 10)
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texts = [docs_index[match.key] for match in matches]
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return texts
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if __name__ == "__main__":
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print(answer_query("torch.mul(*demo2)"))
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