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Update readmes, requirements, packagejsons, etc for all examples (#1452)

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Most of the examples needed updates of Readmes to show how to run them. Some of the requirements.txt files had extra content that wasn't needed, or missing altogether. Apparently some folks like to run npm start
to run typescript, so a script was added to all typescript examples which
hadn't been done before.

Basically just a lot of cleanup.

Signed-off-by: Matt Williams <m@technovangelist.com>
This commit is contained in:
Matt Williams
2023-12-22 09:10:41 -08:00
committed by GitHub
parent 291700c92d
commit 5a85070c22
26 changed files with 346 additions and 101 deletions
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examples/python-loganalysis/readme.md
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@@ -2,12 +2,34 @@
![loganalyzer 2023-11-10 08_53_29](https://github.com/jmorganca/ollama/assets/633681/ad30f1fc-321f-4953-8914-e30e24db9921)
This example shows one possible way to create a log file analyzer. To use it, run:
This example shows one possible way to create a log file analyzer. It uses the model **mattw/loganalyzer** which is based on **codebooga**, a 34b parameter model.
To use it, run:
`python loganalysis.py <logfile>`
You can try this with the `logtest.logfile` file included in this directory.
## Running the Example
1. Ensure you have the `mattw/loganalyzer` model installed:
```bash
ollama pull mattw/loganalyzer
```
2. Install the Python Requirements.
```bash
pip install -r requirements.txt
```
3. Run the example:
```bash
python loganalysis.py logtest.logfile
```
## Review the code
The first part of this example is a Modelfile that takes `codebooga` and applies a new System Prompt:
@@ -45,4 +67,4 @@ for line in response.iter_lines():
There is a lot more that can be done here. This is a simple way to detect errors, looking for the word error. Perhaps it would be interesting to find anomalous activity in the logs. It could be interesting to create embeddings for each line and compare them, looking for similar lines. Or look into applying Levenshtein Distance algorithms to find similar lines to help identify the anomalous lines.
Also try different models and different prompts to analyze the data. You could consider adding retrieval augmented generation (RAG) to this to help understand newer log formats.
Try different models and different prompts to analyze the data. You could consider adding retrieval augmented generation (RAG) to this to help understand newer log formats.