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:
The **chat** endpoint is one of two ways to generate text from an LLM with Ollama, and is introduced in version 0.1.14. At a high level, you provide the endpoint an array of objects with a role and content specified. Then with each output and prompt, you add more of those role/content objects, which builds up the history.
## 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 example:
```bash
python client.py
```
## Review the Code
You can see in the **chat** function that actually calling the endpoint is done simply with:
With the **generate** endpoint, you need to provide a `prompt`. But with **chat**, you provide `messages`. And the resulting stream of responses includes a `message` object with a `content` field.
The final JSON object doesn't provide the full content, so you will need to build the content yourself.
In the **main** function, we collect `user_input` and add it as a message to our messages and that is passed to the chat function. When the LLM is done responding the output is added as another message.
## Next Steps
In this example, all generations are kept. You might want to experiment with summarizing everything older than 10 conversations to enable longer history with less context being used.
This is a simple example using the **Generate** endpoint.
## Running the Example
1. Ensure you have the `stablelm-zephyr` model installed:
```bash
ollama pull stablelm-zephyr
```
2. Install the Python Requirements.
```bash
pip install-r requirements.txt
```
3. Run the example:
```bash
python client.py
```
## Review the Code
The **main** function simply asks for input, then passes that to the generate function. The output from generate is then passed back to generate on the next run.
The **generate** function uses `requests.post` to call `/api/generate`, passing the model, prompt, and context. The `generate` endpoint returns a stream of JSON blobs that are then iterated through, looking for the response values. That is then printed out. The final JSON object includes the full context of the conversation so far, and that is the return value from the function.
