# Context:
USER: is there a standard way to version these cache files?
ASSISTANT: Yes, a standard way to version cache files is to include a version number in the cache file name or within the cache data. This allows you to easily identify and manage different versions of the cache, and update the cache handling logic when needed.
Here's how you can implement cache versioning in your `RepoMap` class:
1. Add a constant for the cache version at the beginning of the class.
2. Modify the cache directory names to include the version number.
3. When loading the cache, check if the version matches the current version. If not, clear the cache and start fresh.
Here are the changes you need to make in `aider/repomap.py`:
```python
aider/repomap.py
<<<<<<< ORIGINAL
class RepoMap:
ctags_cmd = ["ctags", "--fields=+S", "--extras=-F", "--output-format=json"]
IDENT_CACHE_DIR = ".aider.ident.cache"
TAGS_CACHE_DIR = ".aider.tags.cache"
=======
class RepoMap:
CACHE_VERSION = 1
ctags_cmd = ["ctags", "--fields=+S", "--extras=-F", "--output-format=json"]
IDENT_CACHE_DIR = f".aider.ident.cache.v{CACHE_VERSION}"
TAGS_CACHE_DIR = f".aider.tags.cache.v{CACHE_VERSION}"
>>>>>>> UPDATED
```
Now, whenever you need to change the cache format or handling logic, you can simply increment the `CACHE_VERSION` constant, and the cache will be automatically invalidated and recreated with the new version.
# Context:
USER: File "/Users/gauthier/Projects/aider/aider/repomap.py", line 107, in choose_files_listing
files_listing = self.get_ranked_tags_map(chat_files, other_files)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/gauthier/Projects/aider/aider/repomap.py", line 318, in get_ranked_tags_map
tree = to_tree(ranked_tags[:middle])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/gauthier/Projects/aider/aider/repomap.py", line 26, in to_tree
last = [None] * len(tags[0])
~~~~^^^
ASSISTANT: I see the code snippets from the `aider/repomap.py` file. Please let me know what changes you would like to make, and make sure to provide me with the *read-write* access to the file.
USER: I added these *read-write* files: aider/repomap.py
ASSISTANT: Great! Now, please let me know what changes you would like to make in the `aider/repomap.py` file.
USER: File "/Users/gauthier/Projects/aider/aider/repomap.py", line 318, in get_ranked_tags_map
tree = to_tree(ranked_tags[:middle])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/gauthier/Projects/aider/aider/repomap.py", line 26, in to_tree
last = [None] * len(tags[0])
~~~~^^^
IndexError: list index out of range
ASSISTANT: It seems like you are encountering an IndexError due to an empty `tags` list. To fix this issue, you can add a check to ensure that the `tags` list is not empty before accessing its elements. Here's the change you need to make in the `aider/repomap.py` file:
```python
aider/repomap.py
<<<<<<< ORIGINAL
def to_tree(tags):
tags = sorted(tags)
output = ""
last = [None] * len(tags[0])
=======
def to_tree(tags):
if not tags:
return ""
tags = sorted(tags)
output = ""
last = [None] * len(tags[0])
>>>>>>> UPDATED
```
This change adds a check for an empty `tags` list and returns an empty string if the list is empty, preventing the IndexError.
# 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: 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.
