nirvana-ai 1.0.1

An LLM-powered semantic data analytics programming framework.

Nirvana

An LLM-powered semantic data analytics programming framework.

Tutorial

Installation

pip install nirvana-ai

To install the latest version from main:

pip install git+https://github.com/JunHao-Zhu/nirvana.git

LLM backbone configuration

Before using semantic operators, first configure the llm settings used in the system. Taking OpenAI as an example,

>>> import nirvana as nv
>>> nv.configure_llm_backbone(
...     model_name = "gpt-4.1", 
...     api_key = "<Your API Key>",
...     base_url = None
... )

Operators

Operator map: Perform a projecton on the target data based on a predicate (the code refers to ops/map.py (execution) and prompt_templates/map_prompter.py (prompts))

>>> import pandas as pd
>>> import nirvana as nv

>>> df = pd.DataFrame(
... {
...     "title": ["The Godfather", "The Dark Knight"], 
...     "overview": [
...         "An organized crime dynasty's aging patriarch transfers control of his clandestine empire to his reluctant son.", 
...         "When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice."
...     ]
... })
>>> nv.ops.map(df, "According to the movie overview, extract the genre of each movie.", input_column="overview", output_columns = ["genre"], strategy="plain")
MapOpOutputs(
    field_name = ["genre"],
    output = {"genre": ["crime, drama", "action, thriller, superhero"]}
)

Operator filter: Evaluate a condition on the target data (returning either True or False) (the code refers to ops/filter.py (execution) and prompt_templates/filter_prompter.py (prompts))

>>> nv.ops.filter(df, "Whether the movie is released after 2000?", input_column="title", strategy="plain")
FilterOpOutputs(
    output = [False, True]
)

Operator reduce: Aggregate a set of data based on the user instruction (the code refers to ops/reduce.py (execution) and prompt_templates/reduce_prompter.py (prompts))

>>> nv.ops.reduce(df, "Based on the overviews of the given movies, provide several common points of these movies. The common points should be concise.", input_column="overview")
ReduceOpOutputs(
    output = "1. Both movies involve a transfer or test of leadership and capability. 2. The protagonists face significant psychological challenges. 3. The stories revolve around crime and justice. 4. The main characters are reluctant or tested in their roles. 5. Both narratives feature a clash between order and chaos."
)

The current version of the reduce operator is simple. In the next step, we will implement it using several optimizations, like summarize and aggregate and incremental aggregation.

Operator rank: Rank a set of data based on the user instruction by quicksort (under intensive testing)

>>> nv.ops.rank(df, "rank the movies by their relevance to DC Comics.", input_column="title")
RankOpOutputs(
    output = [2, 1]
)

Operator join: Join values of two columns against a specific user's instruction (under intensive testing). The current implementation has inherent quadratic complexity which we aim to avoid.

# left_data: clinical_note
# | name | age | gender | symptom |
# | Alice | 12 | F | headache |
# | Bob | 20 | M | have a cough |

# right_data: drug
# | name | medical_use |
# | Salbutamol | treat bronchospasm, as well as chronic obstructive pulmonary disease |
# | ibuprofen | treat mild to moderate pain, painful menstruation, osteoarthritis, dental pain, headaches, and pain from kidney stones|
>>> nv.ops.join(left_data=clinical_note, right_data=drug, user_instruction="Does the drug cure the possible disease according to the symptoms?", left_on="symptom", right_on="medical_use", how="inner")
RankOpOutputs(
    joined_pairs = [("headache", "ibuprofen"), ("have a cough", "Salbutamol")],
    left_join_keys = [1, 2],
    right_join_keys = [2, 1]
)

For now, it supports inner join, left join, and right join by setting parameter how to inner, left, and right.

Operator discover: Discover relevant data from a data lake based on a query. This operation is applied to a data lake with an interface of DataLake class above it (see datalake/datalake.py)

Data lineage

Data lineage (a directed acyclic graph) enables lazy exeuction and logical and physical plan optimizations. Data lineage is created along with dataframe.

>>> logo_imgs = nv.ImageArray([  # Image data
...     "https://spark.apache.org/images/spark-logo.png",
...     "https://upload.wikimedia.org/wikipedia/commons/thumb/c/c6/PyTorch_logo_black.svg/488px-PyTorch_logo_black.svg.png",
...     "https://upload.wikimedia.org/wikipedia/commons/thumb/6/63/Databricks_Logo.png/960px-Databricks_Logo.png"
... ])
>>> df = pd.DataFrame({
...     "names": ["Spark", "Pytorch", "Databricks"],
...     "logos": logo_imgs
... })
>>> df = nv.DataFrame(df)
>>> df.semantic_filter("Whether the image is a software logo?", input_schema="logos", strategy="plain_llm")
>>> config = nv.optim.OptimizeConfig(do_logical_optimization=True, do_physical_optimization=True)
>>> df.optimize_and_execute(optim_config=config)

For example, with semantic_filter, it creates an op node in the data lineage of dataframe df, and optimize_and_execute() optimizes the logical plan and physical plan, then executes the query processing.

The core abstractions of data lineage is LineageNode (found in lineage/abstractions.py), serving the operator optimization and execution. The usages of LineageNode are found in lineage/mixin.py.

Plan optimization

Considering the following semantic data analytics query,

>>> df = nv.DataFrame(pd.read_csv("/testdata/movie_data.csv"))
>>> df.semantic_map(user_instruction="According to the movie overview, extract the genre of each movie.", input_column="Overview", output_column="Genre")
>>> df.semantic_filter(user_instruction="The rating is higher than 7.", input_column="IMDB_Rating")
>>> df.semantic_filter(user_instruction="The rating is lower than 8.", input_column="IMDB_Rating")
>>> df.semantic_reduce(user_instruction="Count the number of crime movies.", input_column="Genre")

Nirvana does logical optimization and physical optimization separately. You can turn on/off each optimization in OptimizeConfig and execute the query processing with the configure.

>>> config = nv.optim.OptimizeConfig(do_logical_optimization=True, do_physical_optimization=False)
>>> output, cost, runtime = df.optimize_and_execute(optim_config=config)

Logical plan optimization

Rule-based logical plan optimization is adopted in Nirvana. Now we support 5 transformation rules (found in optim/rules.py):

• Non-llm replacement: Replaces NL instructions over non-image/video/audio data with an equivalent compute function (powered by LLMs).
• Map pullup: Pulls up maps to the top of the query plan.
• Filter pullup: Identifies cases where a filter can be applied on columns in other tables.
• Filter pushdown: Pushes filters down into the query plan and duplicates filters over equivalency sets.
• Non-llm pushdown: Pushes operators using non-llm functions/tools down into the query plan.

The rules are applied in a sequential order as they are listed here. The knob to turn on/off each rule is defined in OptimizeConfig, and by default all the rules will be used. If you want to turn off the LLM-powered semantic transformation rule Non-llm replacement, for example, you can do this:

>>> config = nv.optim.OptimizeConfig(do_logical_optimization=True, non_llm_replace=False)

Physical plan optimization

In this version, physical plan optimization assigns the most cost-effective model to each operator in a query plan (found in optim/physical.py). To use it, you need to set do_physical_optimization to True and set hyperparameters like sample_size and improve_margin in OptimizeConfig.

>>> config = nv.optim.OptimizeConfig(do_logical_optimization=True, do_physical_optimization=True, sample_size=5, improve_margin=0.2)