When a user hits a ModuleNotFound Error, we should send a different kind of prompt and context. Sometimes the user makes a typo, and sometimes they don't have the package installed. genai should be able to figure out between the two.
Missing Package
Typo
Context
Send the user's code
Send the output of pip freeze (assuming this isn't too big) as a role: system message
Bonus Context / Optimization
As a bonus, if the `pip freeze` output is too big maybe we can use something like levenshtein distance to pull the closest string matches.
```python
packages = !pip list --format=freeze
packages = [pkg.split('==')[0] for pkg in packages]
import Levenshtein
def find_closest_strings(target, string_list, n=20):
"""
Finds the n closest strings in string_list to the target string.
"""
# Compute the Levenshtein distance between the target string and each string in the list
similarity_scores = [(string, Levenshtein.distance(target, string)) for string in string_list]
# Sort the list based on the similarity scores
similarity_scores.sort(key=lambda x: x[1])
# Return the n closest strings
return [x[0] for x in similarity_scores[:n]]
find_closest_strings("pndas", packages)
```
Outputs
```python
['pandas',
'conda',
'dask',
'anyio',
'appdirs',
'attrs',
'dx',
'fqdn',
'fs',
'genai',
'geopandas',
'idna',
'jedi',
'Jinja2',
'openai',
'parso',
'partd',
'patsy',
'pip',
'py']
```
When a user hits a
ModuleNotFoundError, we should send a different kind of prompt and context. Sometimes the user makes a typo, and sometimes they don't have the package installed.genaishould be able to figure out between the two.Missing Package
Typo
Context
pip freeze(assuming this isn't too big) as arole: systemmessageBonus Context / Optimization
As a bonus, if the `pip freeze` output is too big maybe we can use something like levenshtein distance to pull the closest string matches. ```python packages = !pip list --format=freeze packages = [pkg.split('==')[0] for pkg in packages] import Levenshtein def find_closest_strings(target, string_list, n=20): """ Finds the n closest strings in string_list to the target string. """ # Compute the Levenshtein distance between the target string and each string in the list similarity_scores = [(string, Levenshtein.distance(target, string)) for string in string_list] # Sort the list based on the similarity scores similarity_scores.sort(key=lambda x: x[1]) # Return the n closest strings return [x[0] for x in similarity_scores[:n]] find_closest_strings("pndas", packages) ``` Outputs ```python ['pandas', 'conda', 'dask', 'anyio', 'appdirs', 'attrs', 'dx', 'fqdn', 'fs', 'genai', 'geopandas', 'idna', 'jedi', 'Jinja2', 'openai', 'parso', 'partd', 'patsy', 'pip', 'py'] ```Prompt suggestion
!pipor%pipif an install is needed