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1 feat add litellm integration to support multiple providers #24

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63a7f15
feat: updated agentless
sauravpanda Aug 18, 2024
22b2350
feat: added poetry based execution
sauravpanda Aug 18, 2024
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25 changes: 11 additions & 14 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -31,12 +31,9 @@
First create the environment

```shell
git clone https://github.com/OpenAutoCoder/Agentless.git
git clone https://github.com/sauravpanda/Agentless.git
cd Agentless

conda create -n agentless python=3.11
conda activate agentless
pip install -r requirements.txt
poetry install
export PYTHONPATH=$PYTHONPATH:$(pwd)
```

Expand All @@ -45,7 +42,7 @@ export PYTHONPATH=$PYTHONPATH:$(pwd)

```shell
# for contribution, please install the pre-commit hook.
pre-commit install # this allows a more standardized code style
poetry run pre-commit install # this allows a more standardized code style
```

</div>
Expand Down Expand Up @@ -83,7 +80,7 @@ Run the following command to generate the edit locations:

```shell
mkdir results # where we will save our results
python agentless/fl/localize.py --file_level --related_level --fine_grain_line_level \
poetry run python agentless/fl/localize.py --file_level --related_level --fine_grain_line_level \
--output_folder results/location --top_n 3 \
--compress \
--context_window=10
Expand Down Expand Up @@ -118,7 +115,7 @@ We first start by localization to specific files

```shell
mkdir results # where we will save our results
python agentless/fl/localize.py --file_level --output_folder results/file_level
poetry run python agentless/fl/localize.py --file_level --output_folder results/file_level
```

This command saves the file-level localization in `results/file_level/loc_outputs.jsonl`, you can also check `results/file_level/localize.log` for detailed logs
Expand All @@ -128,7 +125,7 @@ This command saves the file-level localization in `results/file_level/loc_output
Next, we localize to related elements within each of the files we localize

```shell
python agentless/fl/localize.py --related_level \
poetry run python agentless/fl/localize.py --related_level \
--output_folder results/related_level \
--start_file results/file_level/loc_outputs.jsonl \
--top_n 3 --compress
Expand All @@ -143,7 +140,7 @@ Similar to the previous stage, this command saves the related-element localizati
Finally, we take the related elements from the previous step and localize to the edit locations we want the LLM to generate patches for

```shell
python agentless/fl/localize.py --fine_grain_line_level \
poetry run python agentless/fl/localize.py --fine_grain_line_level \
--output_folder results/edit_location \
--start_file results/related_level/loc_outputs.jsonl \
--top_n 3 --context_window=10
Expand All @@ -159,7 +156,7 @@ The final edit locations **Agentless** will perform repair on is saved in `resul
For the last localization step of localizing to edit locations, we can also perform sampling to obtain multiple sets of edit locations.

```shell
python agentless/fl/localize.py --fine_grain_line_level \
poetry run python agentless/fl/localize.py --fine_grain_line_level \
--output_folder results/edit_location_samples \
--start_file results/related_level/loc_outputs.jsonl \
--top_n 3 --context_window=10 --temperature 0.8 \
Expand All @@ -171,7 +168,7 @@ This command will sample with temperature 0.8 and generate 4 edit location sets.
Run the following command to merge:

```shell
python agentless/fl/localize.py --merge \
poetry run python agentless/fl/localize.py --merge \
--output_folder results/edit_location_samples_merged \
--start_file results/edit_location_samples/loc_outputs.jsonl \
--num_samples 4
Expand All @@ -193,7 +190,7 @@ Using the edit locations (i.e., `found_edit_locs`) from before, we now perform r
Run the following command to generate the patches:

```shell
python agentless/repair/repair.py --loc_file results/location/loc_outputs.jsonl \
poetry run python agentless/repair/repair.py --loc_file results/location/loc_outputs.jsonl \
--output_folder results/repair \
--loc_interval --top_n=3 --context_window=10 \
--max_samples 10 --cot --diff_format \
Expand All @@ -211,7 +208,7 @@ This command generates 10 samples (1 greedy and 9 via temperature sampling) as d
Finally, we perform majority voting to select the final patch to solve each issue. Run the following command:

```shell
python agentless/repair/rerank.py --patch_folder results/repair --num_samples 10 --deduplicate --plausible
poetry run python agentless/repair/rerank.py --patch_folder results/repair --num_samples 10 --deduplicate --plausible
```

In this case, we use `--num_samples 10` to pick from the 10 samples we generated previously, `--deduplicate` to apply normalization to each patch for better voting, and `--plausible` to select patches that can pass the previous regression tests (*warning: this feature is not yet implemented*)
Expand Down
2 changes: 1 addition & 1 deletion agentless/util/api_requests.py
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@@ -1,7 +1,7 @@
import time
from typing import Dict, Union

import openai
from kaizen.llms.provider import LLMProvider
import tiktoken


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