# Manifest
How to make prompt programming with FMs a little easier.

# Install
Download the code:
```bash
git clone git@github.com:HazyResearch/manifest.git
cd manifest
```

Install:
```bash
pip install -e .
```

Dev Install:
```bash
make dev
```

# Getting Started
Running is simple to get started. If using OpenAI, set `export OPENAI_API_KEY=<OPENAIKEY>` then run

```python
from manifest import Manifest

# Start a manifest session
manifest = Manifest(
    client_name = "openai",
)
manifest.run("Why is the grass green?")
```

# Manifest Components
Manifest is meant to be a very light weight package to help with prompt iteration. When a user starts a Manifest session, we start to record user query history for that session. This is saved locally and is user specific. We also optionally cache all model results globally so that queries can be shared across users.

Three key design decisions of Manifest are

* Prompt are functional -- they can take an input example and dynamically change
* All models are behind API calls (e.g., OpenAI)
* Model inputs/outputs are locally cached woth the optional ability to globally cache model results

## Prompts
A Manifest prompt is a function that accepts a single input to generate a string prompt to send to a model.

```python
from manifest import Prompt
prompt = Prompt(lambda x: "Hello, my name is {x}")
print(prompt("Laurel"))
>>> "Hello, my name is Laurel"
```

We also let you use static strings
```python
prompt = Prompt("Hello, my name is static")
print(prompt())
>>> "Hello, my name is static"
```

## Sessions

Each Manifest run is a session that connects to a model endpoint and a local SQLite DB to store user query history.
```python

# Start a manifest session
manifest = Manifest(
    client_name = "openai",
    session_id = "grass_color",
)
```
will start a Manifest session with the session name `grass_color`. This can be helpful for a user to logically keep track of sessions and resume them if desired. If no id is provided, we generate a random id for the user.

After a few queries, the user can explore their history
```python
manifest.get_last_queries(4)
```
will retrieve the last 4 model queries and responses.

We further support having queries and results stored in a global cache (without any unique session information) that can be shared across users. We treat inputs and outputs as key value pairs and support SQLite or Redis backends. To start a session with additional global caching using SQLite, run

```python
manifest = Manifest(
    client_name = "openai",
    session_id = "grass_color",
    cache_name = "sqlite",
    cache_connection = "mycache.sqlite",
)
```
The cache will be saved in mycache.sqlite.

We also support Redis backend.
```python
manifest = Manifest(
    client_name = "openai",
    cache_name = "redis",
    cache_connection = "localhost:6379"
)
```
As a hint, if you want to get Redis running, see the `docker run` command below under development.

We will explain [below](#huggingface-models) how to use Manifest for a locally hosted HuggingFace model.

## Running Queries

Once you have a session open, you can write and develop prompts.

```python
prompt = Prompt(lambda x: "Hello, my name is {x}")
result = manifest.run(prompt, "Laurel")
```

You can also run over multiple examples.
```python
results = manifest.batch_run(prompt, ["Laurel", "Avanika"])
```

If something doesn't go right, you can also ask to get a raw manifest Response.
```python
result_object = manifest.batch_run(prompt, ["Laurel", "Avanika"], return_response=True)
print(result_object.get_request())
print(result_object.is_cached())
print(result_object.get_json_response())
```

By default, we do not truncate results based on a stop token. You can change this by either passing a new stop token to a Manifest session or to a `run` or `batch_run`. If you set the stop token to `""`, we will not truncate the model output.
```python
result = manifest.run(prompt, "Laurel", stop_token="and")
```

If you want to change default parameters to a model, we pass those as `kwargs` to the client.
```python
result = manifest.run(prompt, "Laurel", max_tokens=50)
```

## Huggingface Models
To use a HuggingFace generative model, in `manifest/api` we have a Falsk application that hosts the models for you.

In a separate terminal or Tmux/Screen session, run
```python
python3 manifest/api/app.py --model_type huggingface --model_name EleutherAI/gpt-j-6B --device 0
```
You will see the Flask session start and output a URL `http://127.0.0.1:5000`. Pass this in to Manifest. If you want to use a different port, set the `FLASK_PORT` environment variable.

```python
manifest = Manifest(
    client_name = "huggingface",
    client_connection = "http://127.0.0.1:5000",
)
```

If you have a custom model you trained, pass the model path to `--model_name`.

# Development
Before submitting a PR, run
```bash
export REDIS_PORT="6380"  # or whatever PORT local redis is running for those tests
cd <REDIS_PATH>
docker run -d -p 127.0.0.1:${REDIS_PORT}:6379 -v `pwd`:`pwd` -w `pwd` --name manifest_redis_test redis
make test
```

To use our development Redis database, email [Laurel](lorr1@cs.stanford.edu). If you have access to our GCP account, in a separate terminal, run
```bash
gcloud compute ssh "manifest-connect" --zone "europe-west4-a" --project "hai-gcp-head-models" -- -N -L 6379:10.152.93.107:6379
```

Then if you issue
```bash
redis-cli ping
```
You should see a `PONG` response from our database.