# Context:
USER: is there another persistent dictionary besides shelve?
ASSISTANT: Yes, there are several alternatives to `shelve` for creating persistent dictionaries in Python. Some popular options include:
1. `pickle`: A built-in Python module for object serialization and deserialization. You can use it to store dictionaries in binary format and load them back into memory when needed.
2. `json`: Another built-in Python module for working with JSON data. You can use it to store dictionaries in a human-readable format and load them back into memory when needed.
3. `sqlite3`: A built-in Python module for working with SQLite databases. You can use it to create a simple key-value store with SQL queries.
4. `TinyDB`: A lightweight, document-oriented database for Python
^C KeyboardInterrupt
USER: i want something which presents as a dict though
ASSISTANT: In that case, you can use the `sqlite3` module to create a custom class that behaves like a dictionary. Here's an example of how you can create a persistent dictionary using SQLite:
```python
import sqlite3
class
^C KeyboardInterrupt
USER: i don't want to build something. i want an existing package.
ASSISTANT: I apologize for the confusion. In that case, you can use the `diskcache` package, which provides a dictionary-like interface for disk-based storage. It is easy to use and has good performance.
To install `diskcache`, you can use pip:
```
pip install diskcache
```
Here's an example of how to use `diskcache`:
```python
from diskcache import Cache
cache = Cache('path/to/cache/directory')
# Set a key-value pair
cache['key'] = 'value'
# Get the value for a key
value = cache['key']
# Check if a key is in the cache
if 'key' in cache:
print('Key exists')
# Delete a key-value pair
del cache['key']
# Close the cache when done
cache.close()
```
This will create a persistent dictionary-like object using the `diskcache` package. The data will be stored on disk in the specified directory, and you can use the standard dictionary methods to interact with it.
USER: great. use that.
ASSISTANT: To use `diskcache` in the `aider/repomap.py` file, you'll need to replace the `shelve` usage with `diskcache`. Here are the changes you need to make:
1. Install `diskcache` using pip:
```
pip install diskcache
```
2. Modify the `aider/repomap.py` file:
```python
aider/repomap.py
<<<<<<< ORIGINAL
import colorsys
import json
import os
import random
import subprocess
import sys
import tempfile
from collections import Counter, defaultdict
# import shelve
=======
import colorsys
import json
import os
import random
import subprocess
import sys
import tempfile
from collections import Counter, defaultdict
from diskcache import Cache
>>>>>>> UPDATED
```
```python
aider/repomap.py
<<<<<<< ORIGINAL
IDENT_CACHE_FILE = ".aider.ident.cache"
TAGS_CACHE_FILE = ".aider.tags.cache"
=======
IDENT_CACHE_DIR = ".aider.ident.cache"
TAGS_CACHE_DIR = ".aider.tags.cache"
>>>>>>> UPDATED
```
```python
aider/repomap.py
<<<<<<< ORIGINAL
def load_tags_cache(self):
self.TAGS_CACHE = dict() # shelve.open(self.TAGS_CACHE_FILE)
def save_tags_cache(self):
# self.TAGS_CACHE.sync()
pass
def load_ident_cache(self):
self.IDENT_CACHE = dict() # shelve.open(self.IDENT_CACHE_FILE)
def save_ident_cache(self):
# self.IDENT_CACHE.sync()
pass
=======
def load_tags_cache(self):
self.TAGS_CACHE = Cache(self.TAGS_CACHE_DIR)
def save_tags_cache(self):
self.TAGS_CACHE.close()
def load_ident_cache(self):
self.IDENT_CACHE = Cache(self.IDENT_CACHE_DIR)
def save_ident_cache(self):
self.IDENT_CACHE.close()
>>>>>>> UPDATED
```
These changes will replace the usage of `shelve` with `diskcache` for the `IDENT_CACHE` and `TAGS_CACHE` dictionaries. Make sure to install the `diskcache` package before running the updated code.
# Context:
USER: make `get_ranked_tags_map` use binary search
ASSISTANT: To modify the `get_ranked_tags_map` function to use binary search, follow these steps:
1. Replace the for loop with a binary search implementation.
2. Update the loop to calculate the middle index and generate the tree with the middle index.
3. Check if the number of tokens is less than `max_map_tokens`. If it is, update the lower bound. Otherwise, update the upper bound.
4. Continue the binary search until the lower bound is greater than or equal to the upper bound.
Here's the updated `get_ranked_tags_map` function:
```python
aider/repomap.py
<<<<<<< ORIGINAL
def get_ranked_tags_map(self, fnames):
# 1/4 of gpt-4's context window
max_map_tokens = 2048
ranked_tags = self.get_ranked_tags(fnames)
num_tags = len(ranked_tags)
for i in range(num_tags, 0, -1):
tree = to_tree(ranked_tags[:i])
num_tokens = self.token_count(tree)
dump(i, num_tokens)
if num_tokens < max_map_tokens:
return tree
=======
def get_ranked_tags_map(self, fnames):
# 1/4 of gpt-4's context window
max_map_tokens = 2048
ranked_tags = self.get_ranked_tags(fnames)
num_tags = len(ranked_tags)
lower_bound = 0
upper_bound = num_tags
best_tree = None
while lower_bound <= upper_bound:
middle = (lower_bound + upper_bound) // 2
tree = to_tree(ranked_tags[:middle])
num_tokens = self.token_count(tree)
if num_tokens < max_map_tokens:
best_tree = tree
lower_bound = middle + 1
else:
upper_bound = middle - 1
return best_tree
>>>>>>> UPDATED
```
# Context:
USER: do the todo
ASSISTANT: I see a "todo" comment in the `aider/repomap.py` file. To complete the task, we need to calculate the sum of the weights of every inbound edge to each node in the graph. Here's how to do it:
```python
aider/repomap.py
<<<<<<< ORIGINAL
for node in G.nodes:
# todo: sum up the weight of every inbound edge to node
pass
=======
inbound_weights = {}
for node in G.nodes:
inbound_weights[node] = sum(data["weight"] for _, _, data in G.in_edges(node, data=True))
>>>>>>> UPDATED
```
This code calculates the sum of the weights of every inbound edge to each node and stores the result in the `inbound_weights` dictionary.
# Context:
USER: how can i move the patch to setUp so that it is active for all tests?
ASSISTANT: To move the patch to the `setUp` method and make it active for all tests, you can use the `patch` as a context manager in the `setUp` method and store the mock object as an instance variable. Then, in the `tearDown` method, you can stop the patch. Here's how to do it:
```python
tests/test_main.py
<<<<<<< ORIGINAL
def setUp(self):
os.environ["OPENAI_API_KEY"] = "deadbeef"
self.original_cwd = os.getcwd()
self.tempdir = tempfile.mkdtemp()
os.chdir(self.tempdir)
def tearDown(self):
os.chdir(self.original_cwd)
shutil.rmtree(self.tempdir)
=======
def setUp(self):
os.environ["OPENAI_API_KEY"] = "deadbeef"
self.original_cwd = os.getcwd()
self.tempdir = tempfile.mkdtemp()
os.chdir(self.tempdir)
self.patcher = patch("aider.main.Coder.check_model_availability")
self.mock_check = self.patcher.start()
self.mock_check.return_value = True
def tearDown(self):
os.chdir(self.original_cwd)
shutil.rmtree(self.tempdir)
self.patcher.stop()
>>>>>>> UPDATED
```
Now the patch is active for all tests, and you can remove the `with` statement from the `test_main_with_empty_dir_no_files_on_command` method:
```python
tests/test_main.py
<<<<<<< ORIGINAL
def test_main_with_empty_dir_no_files_on_command(self):
with patch("aider.main.Coder.check_model_availability") as mock_check:
mock_check.return_value = True
pipe_input = create_input(StringIO(""))
main([], input=pipe_input, output=DummyOutput())
pipe_input.close()
=======
def test_main_with_empty_dir_no_files_on_command(self):
pipe_input = create_input(StringIO(""))
main([], input=pipe_input, output=DummyOutput())
pipe_input.close()
>>>>>>> UPDATED
```
# Context:
USER: the patch in `setUp` is not correcting forcing a return of true
ASSISTANT: To ensure that the patch in `setUp` correctly forces a return of `True`, you need to modify the `setUp` method in the `TestMain` class in the `tests/test_main.py` file. Here's the change:
```python
tests/test_main.py
<<<<<<< ORIGINAL
def setUp(self):
os.environ["OPENAI_API_KEY"] = "deadbeef"
self.original_cwd = os.getcwd()
self.tempdir = tempfile.mkdtemp()
os.chdir(self.tempdir)
with patch("aider.main.Coder.check_model_availability") as self.mock_check:
self.mock_check.return_value = True
super().setUp()
=======
def setUp(self):
os.environ["OPENAI_API_KEY"] = "deadbeef"
self.original_cwd = os.getcwd()
self.tempdir = tempfile.mkdtemp()
os.chdir(self.tempdir)
self.mock_check = MagicMock(return_value=True)
with patch("aider.main.Coder.check_model_availability", new=self.mock_check):
super().setUp()
>>>>>>> UPDATED
```
This change ensures that the `check_model_availability` method is patched with a `MagicMock` object that always returns `True`.
# Context:
USER: the patch in `setUp` is not correcting forcing a return of true
ASSISTANT: To fix the patch in `setUp`, you need to start the patch and set the return value to `True`. Here's the change you need to make in the `tests/test_main.py` file:
```python
tests/test_main.py
<<<<<<< ORIGINAL
self.mock_check = patch("aider.main.Coder.check_model_availability")
self.mock_check.return_value = True
=======
with patch("aider.main.Coder.check_model_availability") as self.mock_check:
self.mock_check.return_value = True
super().setUp()
>>>>>>> UPDATED
```
This change will ensure that the `check_model_availability` method returns `True` during the test setup.
# Context:
USER: E AttributeError: <class 'aider.coder.Coder'> does not have the attribute 'check_for_model_availability'
ASSISTANT: It seems like you are trying to access the `check_for_model_availability` method of the `Coder` class, but it is not defined as an instance method. To fix this issue, you need to move the `check_model_availability` method inside the `Coder` class and update the tests accordingly.
Here are the changes you need to make:
1. Move the `
^C KeyboardInterrupt
USER: fix the `setUp`
ASSISTANT: To fix the `setUp` method in the `TestMain` class, you need to update the way you are mocking the `check_for_model_availability` method. Here are the changes you need to make:
1. Modify the `setUp` method in the `TestMain` class to correctly mock the `check_for_model_availability` method of the `Coder` class.
```python
tests/test_main.py
<<<<<<< ORIGINAL
with patch("aider.coder.Coder.check_for_model_availability") as mock_check:
mock_check.return_value = True
self.mock_check = mock_check
=======
with patch("aider.coder.Coder.check_model_availability") as mock_check:
mock_check.return_value = True
self.mock_check = mock_check
>>>>>>> UPDATED
```
This change will ensure that the `check_for_model_availability` method is correctly mocked during the tests.
# Context:
USER: in setup, patch coder.check_for_model_availability to return true
ASSISTANT: To patch the `check_for_model_availability` method in the `setUp` method of the `TestMain` class, you can use the `unittest.mock.patch` context manager. Here's the change you need to make in the `tests/test_main.py` file:
```python
tests/test_main.py
<<<<<<< ORIGINAL
def setUp(self):
os.environ["OPENAI_API_KEY"] = "deadbeef"
self.original_cwd = os.getcwd()
self.tempdir = tempfile.mkdtemp()
os.chdir(self.tempdir)
=======
from unittest.mock import MagicMock
def setUp(self):
os.environ["OPENAI_API_KEY"] = "deadbeef"
self.original_cwd = os.getcwd()
self.tempdir = tempfile.mkdtemp()
os.chdir(self.tempdir)
with patch("aider.coder.Coder.check_for_model_availability") as mock_check:
mock_check.return_value = True
self.mock_check = mock_check
>>>>>>> UPDATED
```
This will patch the `check_for_model_availability` method to always return `True` during the `setUp` method execution.
# Context:
USER: in init, call check the list of available openai models and make sure the `main_model` is in there. if not, print an err msg
ASSISTANT:
^C KeyboardInterrupt
USER: in init, call check the list of available openai models and make sure the `main_model` is in there. if not, print an err msg
ASSISTANT: To check the list of available OpenAI models and make sure the `main_model` is in there, you need to modify the `__init__` method in the `Coder` class. Here's how you can do it:
1. Add a new method `check_model_availability` to the `Coder` class that takes the `main_model` as an argument and checks if it's available in the list of OpenAI models.
2. Call this method in the `__init__` method of the `Coder` class.
Here are the changes you need to make:
```python
aider/coder.py
<<<<<<< ORIGINAL
class Coder:
abs_fnames = None
repo = None
last_aider_commit_hash = None
last_asked_for_commit_time = 0
def __init__(
=======
class Coder:
abs_fnames = None
repo = None
last_aider_commit_hash = None
last_asked_for_commit_time = 0
def check_model_availability(self, main_model):
available_models = openai.Model.list()
model_ids = [model.id for model in available_models['data']]
if main_model not in model_ids:
raise ValueError(f"Model {main_model} is not available. Please choose from the available models: {model_ids}")
def __init__(
>>>>>>> UPDATED
```
```python
aider/coder.py
<<<<<<< ORIGINAL
self.main_model = main_model
if main_model == "gpt-3.5-turbo":
self.io.tool_error(
f"aider doesn't work well with {main_model}, use gpt-4 for best results."
)
self.set_repo(fnames)
=======
self.check_model_availability(main_model)
self.main_model = main_model
if main_model == "gpt-3.5-turbo":
self.io.tool_error(
f"aider doesn't work well with {main_model}, use gpt-4 for best results."
)
self.set_repo(fnames)
>>>>>>> UPDATED
```
These changes will add a new method `check_model_availability` to the `Coder` class and call it in the `__init__` method to ensure the `main_model` is available in the list of OpenAI models. If not, it will raise a `ValueError` with an appropriate error message.
