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Replace environment variables with configuration file (OpenHands#339)
* Replace environment variables with configuration file * Add config.toml to .gitignore * Remove unused os imports * Update README.md * Update README.md * Update README.md * Fix merge conflict * Fallback to environment variables * Use template file for config.toml * Update config.toml.template * Update config.toml.template --------- Co-authored-by: Robert Brennan <[email protected]>
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‎.gitignore‎

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@@ -192,3 +192,6 @@ yarn-error.log*
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.envrc
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/workspace
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/debug
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# configuration
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config.toml

‎README.md‎

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@@ -26,51 +26,58 @@ First, make sure Docker is running:
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```bash
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docker ps # this should exit successfully
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```
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Then pull our latest image [here](https://github.com/opendevin/OpenDevin/pkgs/container/sandbox)
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```bash
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docker pull ghcr.io/opendevin/sandbox:v0.1
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```
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Then copy `config.toml.template` to `config.toml`. Add an API key to `config.toml`.
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(See below for how to use different models.)
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```toml
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OPENAI_API_KEY="..."
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WORKSPACE_DIR="..."
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```
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Next, start the backend.
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We manage python packages and the virtual environment with `pipenv`.
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Make sure python >= 3.10.
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Make sure you have python >= 3.10.
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```bash
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python -m pip install pipenv
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pipenv install -v
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pipenv shell
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export OPENAI_API_KEY="..."
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export WORKSPACE_DIR="/path/to/your/project"
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python -m pip install -r requirements.txt
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uvicorn opendevin.server.listen:app --port 3000
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```
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Then in a second terminal:
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Then, in a second terminal, start the frontend:
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```bash
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cd frontend
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npm install
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npm start
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```
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The virtual environment is now activated and you should see `(OpenDevin)` in front of your cmdline prompt.
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### Picking a Model
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We use LiteLLM, so you can run OpenDevin with any foundation model, including OpenAI, Claude, and Gemini.
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LiteLLM has a [full list of providers](https://docs.litellm.ai/docs/providers).
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To change the model, set the `LLM_MODEL` and `LLM_API_KEY` environment variables.
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To change the model, set the `LLM_MODEL` and `LLM_API_KEY` in `config.toml`.
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For example, to run Claude:
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```bash
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export LLM_API_KEY="your-api-key"
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export LLM_MODEL="claude-3-opus-20240229"
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```toml
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LLM_API_KEY="your-api-key"
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LLM_MODEL="claude-3-opus-20240229"
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```
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You can also set the base URL for local/custom models:
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```bash
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export LLM_BASE_URL="https://localhost:3000"
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```toml
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LLM_BASE_URL="https://localhost:3000"
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```
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And you can customize which embeddings are used for the vector database storage:
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```bash
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export LLM_EMBEDDING_MODEL="llama2" # can be "llama2", "openai", "azureopenai", or "local"
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```toml
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LLM_EMBEDDING_MODEL="llama2" # can be "llama2", "openai", "azureopenai", or "local"
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```
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### Running the app

‎agenthub/langchains_agent/utils/memory.py‎

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@@ -1,37 +1,36 @@
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import os
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import chromadb
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from llama_index.core import Document
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from llama_index.core.retrievers import VectorIndexRetriever
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from llama_index.core import VectorStoreIndex
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from llama_index.vector_stores.chroma import ChromaVectorStore
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from opendevin import config
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from . import json
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embedding_strategy = os.getenv("LLM_EMBEDDING_MODEL", "local")
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embedding_strategy = config.get_or_default("LLM_EMBEDDING_MODEL", "local")
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# TODO: More embeddings: https://docs.llamaindex.ai/en/stable/examples/embeddings/OpenAI/
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# There's probably a more programmatic way to do this.
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if embedding_strategy == "llama2":
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from llama_index.embeddings.ollama import OllamaEmbedding
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embed_model = OllamaEmbedding(
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model_name="llama2",
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base_url=os.getenv("LLM_BASE_URL", "http://localhost:8000"),
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base_url=config.get_or_default("LLM_BASE_URL", "http://localhost:8000"),
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ollama_additional_kwargs={"mirostat": 0},
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)
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elif embedding_strategy == "openai":
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from llama_index.embeddings.openai import OpenAIEmbedding
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embed_model = OpenAIEmbedding(
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base_url=os.getenv("LLM_BASE_URL"),
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base_url=config.get_or_error("LLM_BASE_URL"),
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)
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elif embedding_strategy == "azureopenai":
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from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding # Need to instruct to set these env variables in documentation
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embed_model = AzureOpenAIEmbedding(
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model="text-embedding-ada-002",
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deployment_name=os.getenv("LLM_DEPLOYMENT_NAME"),
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api_key=os.getenv("LLM_API_KEY"),
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azure_endpoint=os.getenv("LLM_BASE_URL"),
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api_version=os.getenv("LLM_API_VERSION"),
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deployment_name=config.get_or_error("LLM_DEPLOYMENT_NAME"),
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api_key=config.get_or_error("LLM_API_KEY"),
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azure_endpoint=config.get_or_error("LLM_BASE_URL"),
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api_version=config.get_or_error("LLM_API_VERSION"),
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)
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else:
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding

‎agenthub/langchains_agent/utils/prompts.py‎

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import os
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from typing import List, Dict, Type
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from langchain_core.pydantic_v1 import BaseModel
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from langchain.prompts import PromptTemplate
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from langchain_core.output_parsers import JsonOutputParser
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if os.getenv("DEBUG"):
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from opendevin import config
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if config.get_or_default("DEBUG", False):
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from langchain.globals import set_debug
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set_debug(True)
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‎config.toml.template‎

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# This is a template. Run `cp config.toml.template config.toml` to use it.
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OPENAI_API_KEY="<YOUR OPENAI API KEY>"
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WORKSPACE_DIR="./workspace"

‎evaluation/regression/run_tests.py‎

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import os
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import sys
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import pytest
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from opendevin import config
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if __name__ == '__main__':
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args = ['-v', 'evaluation/regression/cases']
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for arg in sys.argv[1:]:
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if arg.startswith('--OPENAI_API_KEY='):
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os.environ['OPENAI_API_KEY'] = arg.split('=')[1]
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config.config['OPENAI_API_KEY'] = arg.split('=')[1]
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elif arg.startswith('--model='):
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args.append(f'-o model={arg.split('=')[1]}')
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pytest.main(args)

‎opendevin/config.py‎

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import os
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import tomllib
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from dotenv import load_dotenv
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load_dotenv()
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with open("config.toml", "rb") as f:
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config = tomllib.load(f)
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def _get(key: str, default):
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value = config.get(key, default)
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if not value:
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value = os.environ.get(key, default)
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return value
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def get_or_error(key: str):
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"""
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Get a key from the config, or raise an error if it doesn't exist.
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"""
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value = get_or_none(key)
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if not value:
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raise KeyError(f"Please set '{key}' in `config.toml` or `.env`.")
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return value
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def get_or_default(key: str, default):
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"""
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Get a key from the config, or return a default value if it doesn't exist.
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"""
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return _get(key, default)
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def get_or_none(key: str):
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"""
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Get a key from the config, or return None if it doesn't exist.
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"""
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return _get(key, None)

‎opendevin/llm/llm.py‎

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from litellm import completion as litellm_completion
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from functools import partial
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DEFAULT_MODEL = os.getenv("LLM_MODEL", "gpt-4-0125-preview")
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DEFAULT_API_KEY = os.getenv("LLM_API_KEY")
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DEFAULT_BASE_URL = os.getenv("LLM_BASE_URL")
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PROMPT_DEBUG_DIR = os.getenv("PROMPT_DEBUG_DIR", "")
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from opendevin import config
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DEFAULT_MODEL = config.get_or_default("LLM_MODEL", "gpt-4-0125-preview")
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DEFAULT_API_KEY = config.get_or_none("LLM_API_KEY")
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DEFAULT_BASE_URL = config.get_or_none("LLM_BASE_URL")
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PROMPT_DEBUG_DIR = config.get_or_default("PROMPT_DEBUG_DIR", "")
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class LLM:
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def __init__(self, model=DEFAULT_MODEL, api_key=DEFAULT_API_KEY, base_url=DEFAULT_BASE_URL, debug_dir=PROMPT_DEBUG_DIR):

‎opendevin/main.py‎

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import os
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import asyncio
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import argparse
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from typing import Type
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import agenthub # noqa F401 (we import this to get the agents registered)
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from opendevin import config
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from opendevin.agent import Agent
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from opendevin.controller import AgentController
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from opendevin.llm.llm import LLM
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parser.add_argument(
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"-m",
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"--model-name",
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default=os.getenv("LLM_MODEL") or "gpt-4-0125-preview",
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default=config.get_or_default("LLM_MODEL", "gpt-4-0125-preview"),
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type=str,
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help="The (litellm) model name to use",
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)

‎opendevin/sandbox/sandbox.py‎

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import docker
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import concurrent.futures
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from opendevin import config
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InputType = namedtuple("InputType", ["content"])
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OutputType = namedtuple("OutputType", ["content"])
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DIRECTORY_REWRITE = os.getenv(
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DIRECTORY_REWRITE = config.get_or_default(
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"DIRECTORY_REWRITE", ""
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) # helpful for docker-in-docker scenarios
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CONTAINER_IMAGE = os.getenv("SANDBOX_CONTAINER_IMAGE", "ghcr.io/opendevin/sandbox:v0.1")
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CONTAINER_IMAGE = config.get_or_default("SANDBOX_CONTAINER_IMAGE", "ghcr.io/opendevin/sandbox:v0.1")
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# FIXME: On some containers, the devin user doesn't have enough permission, e.g. to install packages
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# How do we make this more flexible?
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RUN_AS_DEVIN = os.getenv("RUN_AS_DEVIN", "true").lower() != "false"
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RUN_AS_DEVIN = config.get_or_default("RUN_AS_DEVIN", "true").lower() != "false"
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USER_ID = 1000
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if os.getenv("SANDBOX_USER_ID") is not None:
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USER_ID = int(os.getenv("SANDBOX_USER_ID", ""))
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if config.get_or_none("SANDBOX_USER_ID") is not None:
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USER_ID = int(config.get_or_default("SANDBOX_USER_ID", ""))
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elif hasattr(os, "getuid"):
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USER_ID = os.getuid()
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