# Context:
USER: update `aider/repomap.py`
ASSISTANT: Please provide the specific changes you would like me to make in the `aider/repomap.py` file.
USER: to use map_tokens not `use_ctags`
ASSISTANT: I will update the `aider/repomap.py` file to use `map_tokens` instead of `use_ctags`.
```python
aider/repomap.py
<<<<<<< ORIGINAL
def __init__(self, use_ctags=None, root=None, main_model="gpt-4", io=None):
self.io = io
if not root:
root = os.getcwd()
self.root = root
self.load_ident_cache()
self.load_tags_cache()
if use_ctags is None:
self.use_ctags = self.check_for_ctags()
else:
self.use_ctags = use_ctags
self.tokenizer = tiktoken.encoding_for_model(main_model)
=======
def __init__(self, map_tokens=1024, root=None, main_model="gpt-4", io=None):
self.io = io
if not root:
root = os.getcwd()
self.root = root
self.load_ident_cache()
self.load_tags_cache()
self.map_tokens = map_tokens
self.tokenizer = tiktoken.encoding_for_model(main_model)
>>>>>>> UPDATED
```
This change replaces the `use_ctags` parameter with the `map_tokens` parameter in the `__init__` method of the `RepoMap` class. Make sure to update any other parts of the code that use `use_ctags` to use `map_tokens` instead.
# Context:
USER: I added these *read-write* files: aider/coder.py
ASSISTANT: Great! Now I will modify the `aider/coder.py` file to accept and use the `map_tokens` parameter instead of `use_ctags`.
```python
aider/coder.py
<<<<<<< ORIGINAL
def __init__(
self,
io,
main_model="gpt-4",
fnames=None,
pretty=True,
show_diffs=False,
auto_commits=True,
dirty_commits=True,
dry_run=False,
use_ctags=False,
verbose=False,
openai_api_key=None,
):
...
self.repo_map = RepoMap(use_ctags, self.root, self.main_model, rm_io)
=======
def __init__(
self,
io,
main_model="gpt-4",
fnames=None,
pretty=True,
show_diffs=False,
auto_commits=True,
dirty_commits=True,
dry_run=False,
map_tokens=1024,
verbose=False,
openai_api_key=None,
):
...
self.repo_map = RepoMap(map_tokens, self.root, self.main_model, rm_io)
>>>>>>> UPDATED
```
This change updates the `Coder` class in `aider/coder.py` to accept the `map_tokens` parameter and pass it to the `RepoMap` class instead of `use_ctags`.
# Context:
USER: pass it to Coder as map_tokens instead of `use_ctags`
ASSISTANT: I will modify the `aider/main.py` file to pass `map_tokens` to the `Coder` class instead of `use_ctags`.
```python
aider/main.py
<<<<<<< ORIGINAL
coder = Coder(
io,
main_model=args.model,
fnames=args.files,
pretty=args.pretty,
show_diffs=args.show_diffs,
auto_commits=args.auto_commits,
dirty_commits=args.dirty_commits,
dry_run=args.dry_run,
use_ctags=args.ctags,
verbose=args.verbose,
openai_api_key=args.openai_api_key,
)
=======
coder = Coder(
io,
main_model=args.model,
fnames=args.files,
pretty=args.pretty,
show_diffs=args.show_diffs,
auto_commits=args.auto_commits,
dirty_commits=args.dirty_commits,
dry_run=args.dry_run,
map_tokens=args.map_tokens,
verbose=args.verbose,
openai_api_key=args.openai_api_key,
)
>>>>>>> UPDATED
```
Now, the `map_tokens` value is passed to the `Coder` class instead of `use_ctags`. Please make sure to update the `Coder` class in `aider/coder.py` to accept and use the `map_tokens` parameter.
# Context:
USER: change the --ctags switch into --map-tokens, which is an int that defaults to 1024
ASSISTANT: To make this change, I need to modify the `aider/main.py` file. Please make it *read-write* and let me know when it's done.
USER: I added these *read-write* files: aider/main.py
ASSISTANT: Great! I will now modify the `aider/main.py` file to change the `--ctags` switch into `--map-tokens`, which is an int that defaults to 1024.
```python
aider/main.py
<<<<<<< ORIGINAL
parser.add_argument(
"--ctags",
type=lambda x: (str(x).lower() == "true"),
nargs="?",
const=True,
default=None,
help=(
"Add ctags to the chat to help GPT understand the codebase (default: check for ctags"
" executable)"
),
)
=======
parser.add_argument(
"--map-tokens",
type=int,
default=1024,
help="Number of tokens to map for code understanding (default: 1024)",
)
>>>>>>> UPDATED
```
This change replaces the `--ctags` argument with the `--map-tokens` argument, which takes an integer value and defaults to 1024.
# Context:
USER: make another test like that, but raise requests.exceptions.ConnectionError
ASSISTANT: To add another test case for `coder.send_with_retries(model, messages)` that raises `requests.exceptions.ConnectionError`, we will modify the `tests/test_coder.py` file. We will create a new test method called `test_send_with_retries_connection_error` and use `patch` to mock the `openai.ChatCompletion.create` and `time.sleep` methods.
Here's the code change for the new test case:
```python
tests/test_coder.py
<<<<<<< ORIGINAL
if __name__ == "__main__":
unittest.main()
=======
@patch("aider.coder.openai.ChatCompletion.create")
@patch("aider.coder.time.sleep")
def test_send_with_retries_connection_error(self, mock_sleep, mock_chat_completion_create):
# Mock the IO object
mock_io = MagicMock()
# Initialize the Coder object with the mocked IO and mocked repo
coder = Coder(io=mock_io, openai_api_key="fake_key")
# Set up the mock to raise ConnectionError on the first call and return None on the second call
mock_chat_completion_create.side_effect = [requests.exceptions.ConnectionError("Connection error"), None]
# Call the send_with_retries method
coder.send_with_retries("model", ["message"])
# Assert that time.sleep was called once
mock_sleep.assert_called_once()
if __name__ == "__main__":
unittest.main()
>>>>>>> UPDATED
```
This test case will ensure that `send_with_retries` handles the `ConnectionError` correctly and calls `time.sleep` once before retrying.
# Context:
USER: add a test case for coder.send_with_retries(model, messages). it should patch openai.ChatCompletion.create so that on the first call it raises openai.error import RateLimitError. and on the 2nd call it returns None. the test should confirm that time.sleep is called 1 time.
ASSISTANT: To add the test case for `coder.send_with_retries(model, messages)`, we will modify the `tests/test_coder.py` file. We will create a new test method called `test_send_with_retries_rate_limit_error` and use `patch` to mock the `openai.ChatCompletion.create` and `time.sleep` methods.
Here's the code change for the new test case:
```python
tests/test_coder.py
<<<<<<< ORIGINAL
if __name__ == "__main__":
unittest.main()
=======
@patch("aider.coder.openai.ChatCompletion.create")
@patch("aider.coder.time.sleep")
def test_send_with_retries_rate_limit_error(self, mock_sleep, mock_chat_completion_create):
# Mock the IO object
mock_io = MagicMock()
# Initialize the Coder object with the mocked IO and mocked repo
coder = Coder(io=mock_io, openai_api_key="fake_key")
# Set up the mock to raise RateLimitError on the first call and return None on the second call
mock_chat_completion_create.side_effect = [openai.error.RateLimitError("Rate limit exceeded"), None]
# Call the send_with_retries method
coder.send_with_retries("model", ["message"])
# Assert that time.sleep was called once
mock_sleep.assert_called_once()
if __name__ == "__main__":
unittest.main()
>>>>>>> UPDATED
```
This test case will ensure that `send_with_retries` handles the `RateLimitError` correctly and calls `time.sleep` once before retrying.
# 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`.
