You are an experienced Devops engineer focused on docker. When given specifications for a particular need or application you know the best way to host that within a docker container. For instance if someone tells you they want an nginx server to host files located at /web you will answer as follows
---start
FROM nginx:alpine
COPY /myweb /usr/share/nginx/html
EXPOSE 80
---end
Notice that the answer you should give is just the contents of the dockerfile with no explanation and there are three dashes and the word start at the beginning and 3 dashes and the word end. The full output can be piped into a file and run as is. Here is another example. The user will ask to launch a Postgres server with a password of abc123. And the response should be
---start
FROM postgres:latest
ENV POSTGRES_PASSWORD=abc123
EXPOSE 5432
---end
Again it's just the contents of the dockerfile and nothing else.
DockerIt is a tool to help you build and run your application in a Docker container. It consists of a model that defines the system prompt and model weights to use, along with a python script to then build the container and run the image automatically.
## Running the Example
1. Ensure you have the `mattw/dockerit` model installed:
```bash
ollama pull mattw/dockerit
```
2. Make sure Docker is running on your machine.
3. Install the Python Requirements.
```bash
pip install-r requirements.txt
```
4. Run the example:
```bash
python dockerit.py "simple postgres server with admin password set to 123"
```
5. Enter the name you would like to use for your container image.
## Caveats
This is a simple example. It's assuming the Dockerfile content generated is going to work. In many cases, even with simple web servers, it fails when trying to copy files that don't exist. It's simply an example of what you could possibly do.
prompt=f"generate one realistically believable sample data set of a persons first name, last name, address in the US, and phone number. \nUse the following template: {json.dumps(template)}."
prompt=f"generate one realistically believable sample data set of a persons first name, last name, address in {country}, and phone number. Do not use common names. Respond using JSON. Key names should have no backslashes, values should use plain ascii with no special characters."
There are two python scripts in this example. `randomaddresses.py` generates random addresses from different countries. `predefinedschema.py` sets a template for the model to fill in.
## Running the Example
1. Ensure you have the `llama3` model installed:
```bash
ollama pull llama3
```
2. Install the Python Requirements.
```bash
pip install-r requirements.txt
```
3. Run the Random Addresses example:
```bash
python randomaddresses.py
```
4. Run the Predefined Schema example:
```bash
python predefinedschema.py
```
## Review the Code
Both programs are basically the same, with a different prompt for each, demonstrating two different ideas. The key part of getting JSON out of a model is to state in the prompt or system prompt that it should respond using JSON, and specifying the `format` as `json` in the data body.
```python
prompt=f"generate one realistically believable sample data set of a persons first name, last name, address in {country}, and phone number. Do not use common names. Respond using JSON. Key names should with no backslashes, values should use plain ascii with no special characters."
When running `randomaddresses.py` you will see that the schema changes and adapts to the chosen country.
In `predefinedschema.py`, a template has been specified in the prompt as well. It's been defined as JSON and then dumped into the prompt string to make it easier to work with.
Both examples turn streaming off so that we end up with the completed JSON all at once. We need to convert the `response.text` to JSON so that when we output it as a string we can set the indent spacing to make the output easy to read.
You are a log file analyzer. You will receive a set of lines from a log file for some software application, find the errors and other interesting aspects of the logs, and explain them so a new user can understand what they mean. If there are any steps they can do to resolve them, list the steps in your answer.
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:
```plaintext
SYSTEM """
You are a log file analyzer. You will receive a set of lines from a log file for some software application, find the errors and other interesting aspects of the logs, and explain them so a new user can understand what they mean. If there are any steps they can do to resolve them, list the steps in your answer.
"""
```
This model is available at https://ollama.com/mattw/loganalyzer. You can customize it and add to your own namespace using the command `ollama create <namespace/modelname> -f <path-to-modelfile>` then `ollama push <namespace/modelname>`.
Then loganalysis.py scans all the lines in the given log file and searches for the word 'error'. When the word is found, the 10 lines before and after are set as the prompt for a call to the Generate API.
```python
data={
"prompt":"\n".join(error_logs),
"model":"mattw/loganalyzer"
}
```
Finally, the streamed output is parsed and the response field in the output is printed to the line.
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.
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.
1. You choose a topic area (e.g., "news", "NVidia", "music", etc.).
2. Gets the most recent articles on that topic from various sources.
3. Uses Ollama to summarize each article.
4. Creates chunks of sentences from each article.
5. Uses Sentence Transformers to generate embeddings for each of those chunks.
6. You enter a question regarding the summaries shown.
7. Uses Sentence Transformers to generate an embedding for that question.
8. Uses the embedded question to find the most similar chunks.
9. Feeds all that to Ollama to generate a good answer to your question based on these news articles.
This example lets you pick from a few different topic areas, then summarize the most recent x articles for that topic. It then creates chunks of sentences from each article and then generates embeddings for each of those chunks.
## Running the Example
1. Ensure you have the `mistral-openorca` model installed:
