fluidityai 0.1.53

## Fluidity

### Introduction Fluidity is a set of classes written in Python to enable you to build AI workflows simply and intuitively. Basically, you create a workflow and add tasks to it. Each task points to its next task - alternatively, branch objects can be added to the workflow to programmatically decide where to go next by setting the "next_task" pointer. This way potentially complex workflow graphs, which can include cycles, can be constructed easily.

### Task types A task can be either a code task (i.e. Python instructions executed locally) or an LLM task, meaning (natural language) instructions are sent to a large language model. The output type of each task can be specified as string (the default), num (an integer or decimal number), json (a JSON string), list, or dict (i.e. a dictionary). To refer to a task's output value(s) in LLM instructions, use the "{}" notation: {TaskName} for string, num or json types and an entire list or dictionary or {TaskName:n} and {TaskName:KeyName} for a specific list or dictionary element.

In Python code (i.e. for code tasks) use task_outputs["TaskName"], task_outputs["TaskName"][n] and task_outputs["TaskName"]["KeyName"] respectively.

For example:

w = Workflow("Monarchs")
t1 = Task(name="King", instructions="Please give me the name of a famous king", output_type="string")
t2 = Task(name="Queen", instructions="Please give me the name of a famous queen", output_type="string")
t3 = Task(name="DOB", instructions="Please tell me the dates of birth of {King} and {Queen} in the format dd-Mmm-yyyy", output_type="dict")
t4 = Task(name="Older", instructions="Please tell me which of these two monarchs was older, given their dates of birth: {DOB}")
t1.setNextTask("Queen")
t2.setNextTask("DOB")
t3.setNextTask("Older")
w.addTasks(t1, t2, t3, t4)
w.run("King")

After the above routine runs, {Older} or task_outputs["Older"] will be the name of the older monarch for subsequent LLM and code tasks respectively. You can run and test the actual code in fluidity_tests.py.

Classes

This section consists of a brief introduction to each of the classes in the Fluidity package. Note that the package includes many useful subclasses of the Task class for e.g. file loading, user input/form processing and RAG.

LLM, LLMCache, llmCache global instance Use these to set up your LLM connections and specify a default. Any task can override the default LLM with its own instance. See fluidity_examples.py lines 11-13.

Utils and WebUtils Helper functions for text processing and network operations.

Task This class is where the work is done: add instances of it to your Workflow (see below) object to send a request to an LLM or execute Python code locally. The constructor takes these arguments:
name [required]: the name of the task. It's also the label for the task's output elsewhere in the workflow.
instructions [required]: Natural language intructions for the LLM, Python code or 'file:<Python code filepath>'. If Python code, it is executed dynamically. It is required to set the result (i.e. the task's output value) and next_task variables in the code. next_task can be set to 'STOP' to terminate the workflow run.
role [optional]: How should the LLM behave? e.g. 'You are an AI legal assistant specialising in family law enquiries'.
output_type [optional - 'string' (default), 'num', 'json', 'list' or 'dict']
llm_or_code [optional - 'llm' (default) or 'code']: Describes instructions
llm [optional]: overrides the default LLM connection
The setNextTask("<task_name>") function allows you to build your graph.

TaskGroup Executes tasks in parallel. Add tasks to a task group instance, then add the task group to a workflow. See lines 18-72 in fluidity_examples.py

Branch Set name and instructions (Python code) in the constructor, then add to a workflow like a task. It is required to set the next_task variable in the code - set it to 'STOP' to terminate the workflow run.

Workflow Add tasks and branches to a workflow object. Either setFirstTask("<first_task_name>") and run(), or run with run("<first_task_name>").

UserInputTask Subclass of Task. Makes repeated command line or web chat requests until user enters the information required in a recognisable format. See fluidity_examples.py lines 74-164. Uses an LLM call to extract the required field from natural language.

PasswordInputTask Subclass of Task. Asks the user to input 3 randomly chosen characters from the password. Provide Python code instructions to implement obtaining the password, possibly from a database. See fluidity_examples.py lines 74-164.

FileLoaderClass Loads and caches CSV files as DataFrames; any other file types are read into a string, e.g. PDF, .txt files. Construct with name and files, a filename => filepath dictionary. See fluidity_examples.py lines 74-164.

RAGRetrievalTask Chunks all pre loaded files, creates vector embeddings and stores them in an in-memory vector database, then creates a context from the top_pct (10% by default) of chunks by relevance to the (pre loaded) query. Construct with name, name of the file loader and query, i.e. if query='My Question' then it reads task_outputs['My Question']. See fluidity_examples.py lines 198-241.

RAGCache Not a task, but a useful global cache class for e.g. chat apps in which all threads query the same files.

ChatbotTask Makes repeated cmd line or web chat requests for submission to the LLM. See fluidity_examples.py lines 74-164. The ChatbotTask constructor takes the following arguments:
name [required]: the task name
response [optional]: the name of the task answering the question
prompt [required]: the initial message or prompt to the user
toxicMsg [required]: the reponse given to offensive or potentially harmful input, e.g. hacking attempts
greeting [optional - False (default) or True]: greet user with Good morning/afternoon/evening?
initialPromptOnly [optional - False (default) or True]: is prompt displayed only the first time?

FormTask Requests the LLM to extract specific information fields from natural language text. See fluidity_examples.py lines 243-326 for usage.

FormFillTask Fills form fields by processing the response from the LLM. See fluidity_examples.py lines 243-326 and the task code file taxi.py for usage.