Log Detective

A Python tool to analyze logs using a Language Model (LLM) and Drain template miner.

Installation

Fedora 40+

dnf install logdetective

From Pypi repository

The logdetective project is published on the the the the the Pypi repository. The pip tool can be used for installation.

First, ensure that the necessary dependencies for the llama-cpp-python project are installed. For Fedora, install gcc-c++:

# for Fedora it will be:
dnf install gcc-c++

Then, install the logdetective project using pip:

# then install logdetective project
pip install logdetective

Local repository install

pip install .

Usage

To analyze a log file, run the script with the following command line arguments:

• url (required): The URL of the log file to be analyzed.
• --model (optional, default: "Mistral-7B-Instruct-v0.2-GGUF"): The path or URL of the language model for analysis. As we are using LLama.cpp we want this to be in the gguf format. You can include the download link to the model here. If the model is already on your machine it will skip the download.
• --summarizer (optional, default: "drain"): Choose between LLM and Drain template miner as the log summarizer. You can also provide the path to an existing language model file instead of using a URL.
• --n_lines (optional, default: 8): The number of lines per chunk for LLM analysis. This only makes sense when you are summarizing with LLM.
• --n_clusters (optional, default 8): Number of clusters for Drain to organize log chunks into. This only makes sense when you are summarizing with Drain

Example usage:

logdetective https://example.com/logs.txt

Or if the log file is stored locally:

logdetective ./data/logs.txt

Example you want to use a different model:

logdetective https://example.com/logs.txt --model https://huggingface.co/QuantFactory/Meta-Llama-3-8B-Instruct-GGUF/resolve/main/Meta-Llama-3-8B-Instruct.Q5_K_S.gguf?download=true
logdetective https://example.com/logs.txt --model QuantFactory/Meta-Llama-3-8B-Instruct-GGUF

Note that streaming with some models (notably Meta-Llama-3 is broken) is broken and can be workarounded by no-stream option:

logdetective https://example.com/logs.txt --model QuantFactory/Meta-Llama-3-8B-Instruct-GGUF --no-stream

Real Example

Let's have a look at a real world example. Log Detective can work with any logs though we optimize it for build logs.

We're going to analyze a failed build of a python-based library that happened in Fedora Koji buildsystem:

$ logdetective https://kojipkgs.fedoraproject.org//work/tasks/8157/117788157/build.log
Explanation:
[Child return code was: 0] : The rpm build process executed successfully without any errors until the 'check' phase.

[wamp/test/test_wamp_component_aio.py::test_asyncio_component] : Pytest found
two tests marked with '@pytest.mark.asyncio' but they are not async functions.
This warning can be ignored unless the tests are intended to be run
asynchronously.

[wamp/test/test_wamp_component_aio.py::test_asyncio_component_404] : Another
Pytest warning for the same issue as test_asyncio_component.

[-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html] :
This line is not related to the error, but it is a reminder to refer to Pytest
documentation for handling warnings.

[=========================== short test summary info
============================] : This section shows the summary of tests that
were executed. It shows the number of tests passed, failed, skipped,
deselected, and warnings.

[FAILED wamp/test/test_wamp_cryptosign.py::TestSigVectors::test_vectors] : A
failed test is reported with the name of the test file, the name of the test
method, and the name of the test case that failed. In this case,
TestSigVectors::test_vectors failed.

[FAILED
websocket/test/test_websocket_protocol.py::WebSocketClientProtocolTests::test_auto_ping]
: Another failed test is reported with the same format as the previous test. In
this case, it is WebSocketClientProtocolTests::test_auto_ping that failed.

[FAILED websocket/test/test_websocket_protocol.py::WebSocketServerProtocolTests::test_interpolate_server_status_template]
: A third failed test is reported with the same format as the previous tests.
In this case, it is
WebSocketServerProtocolTests::test_interpolate_server_status_template that
failed.

[FAILED websocket/test/test_websocket_protocol.py::WebSocketServerProtocolTests::test_sendClose_reason_with_no_code]
: Another failed test is reported. This time it is
WebSocketServerProtocolTests::test_sendClose_reason_with_no_code.

[FAILED websocket/test/test_websocket_protocol.py::WebSocketServerProtocolTests::test_sendClose_str_reason]
: Another failed test is reported with the same test file and test method name,
but a different test case name: test_sendClose_str_reason.

[==== 13 failed, 195 passed, 64 skipped, 13 deselected, 2 warnings in 6.55s
=====] : This is the summary of all tests that were executed, including the
number of tests that passed, failed, were skipped, deselected, or produced
warnings. In this case, there were 13 failed tests among a total of 211 tests.

[error: Bad exit status from /var/tmp/rpm-tmp.8C0L25 (%check)] : An error
message is reported indicating that the 'check' phase of the rpm build process
failed with a bad exit status.

It looks like a wall of text. Similar to any log. The main difference is that here we have the most significant lines of a logfile wrapped in [ ] : and followed by textual explanation of the log text done by mistral 7b.

Contributing

Contributions are welcome! Please submit a pull request if you have any improvements or new features to add. Make sure your changes pass all existing tests before submitting.

To develop logdetective, you should fork this repository, clone your fork, and install dependencies using pip:

git clone https://github.com/yourusername/logdetective.git
cd logdetective
pip install .

Make changes to the code as needed and run pre-commit.

Tests

The tox is used to manage tests. Please install tox package into your distribution and run:

tox

This will create a virtual environment with dependencies and run all the tests. For more information follow the tox help.

To run only a specific test execute this:

tox run -e style # to run flake8

or

tox run -e lint # to run pylint

Visual Studio Code testing with podman/docker-compose

• In Containerfile, add debugpy as a dependency

-RUN pip3 install llama_cpp_python==0.2.85 sse-starlette starlette-context \
+RUN pip3 install llama_cpp_python==0.2.85 sse-starlette starlette-context debugpy\

• Rebuild server image with new dependencies

make rebuild-server

• Forward debugging port in docker-compose.yaml for server service.

     ports:
       - "${LOGDETECTIVE_SERVER_PORT:-8080}:${LOGDETECTIVE_SERVER_PORT:-8080}"
+      - "${VSCODE_DEBUG_PORT:-5678}:${VSCODE_DEBUG_PORT:-5678}"

• Add debugpy code in a logdetective file where you want to stop at first.

+import debugpy
+debugpy.listen(("0.0.0.0", 5678))
+debugpy.wait_for_client()

• Prepare .vscode/lunch.json configuration for Visual Studio Code (at least the following configuration is needed)

{
  "version": "0.2.0",
  "configurations": [
    {
      "name": "Python Debugger: Remote Attach",
      "type": "debugpy",
      "request": "attach",
      "connect": {
        "host": "localhost",
        "port": 5678
      },
      "pathMappings": [
        {
          "localRoot": "${workspaceFolder}",
          "remoteRoot": "/src"
        }
      ]
    }
  ]
}

• Run the server

podman-compose up server

• Run Visual Stdio Code debug configuration named Python Debug: Remote Attach

Server

FastApi based server is implemented in logdetective/server.py. In order to run it in a development mode, simply start llama-cpp-python server with your chosen model as described in llama-cpp-python docs.

Afterwards, start the logdetective server with fastapi dev logdetective/server.py --port 8080. Requests can then be made with post requests, for example:

curl --header "Content-Type: application/json" --request POST --data '{"url":"<YOUR_URL_HERE>"}' http://localhost:8080/analyze

For more accurate responses, you can use /analyze/staged endpoint. This will submit snippets to model for individual analysis first. Afterwards the model outputs are used to construct final prompt. This will take substantially longer, compared to plain /analyze

curl --header "Content-Type: application/json" --request POST --data '{"url":"<YOUR_URL_HERE>"}' http://localhost:8080/analyze/staged

We also have a Containerfile and composefile to run the logdetective server and llama server in containers.

Before doing podman-compose up, make sure to set MODELS_PATH environment variable and point to a directory with your local model files:

$ export MODELS_PATH=/path/to/models/
$ ll $MODELS_PATH
-rw-r--r--. 1 tt tt 3.9G apr 10 17:18  mistral-7b-instruct-v0.2.Q4_K_S.gguf

If the variable is not set, ./models is mounted inside by default.

Model can be downloaded from our Hugging Space by:

$ curl -L -o models/mistral-7b-instruct-v0.2.Q4_K_S.gguf https://huggingface.co/fedora-copr/Mistral-7B-Instruct-v0.2-GGUF/resolve/main/ggml-model-Q4_K_S.gguf

Generate a new database revision with alembic

Modify the database models (`logdetective/server/database/model.py).

Generate a new database revision with the command:

Warning: this command will start up a new server and shut it down when the operation completes.

CHANGE="A change comment" make alembic-generate-revision

Our production instance

Our FastAPI server and model inference server run through podman-compose on an Amazon AWS intance. The VM is provisioned by an ansible playbook.

You can control the server through:

cd /root/logdetective
podman-compose -f docker-compose-prod.yaml ...

The /root directory contains valuable data. If moving to a new instance, please backup the whole directory and transfer it to the new instance.

Fore some reason, we need to manually run this command after every reboot:

nvidia-ctk cdi generate --output=/etc/cdi/nvidia.yaml

License

This project is licensed under the Apache-2.0 License - see the LICENSE file for details.