andeplane-cognite-ai 0.5.1

A set of AI tools for working with Cognite Data Fusion in Python.

cognite-ai

A set of AI tools for working with CDF in Python.

MemoryVectorStore

Store and query vector embeddings created from CDF. This can enable a bunch of use cases where the number of vectors aren't that big.

Install the package

%pip install cognite-ai

Then you can create vectors from text (both multiple lines or a list of strings) like this

from cognite.ai import MemoryVectorStore
from cognite.client import CogniteClient

client = CogniteClient()
vector_store = MemoryVectorStore(client)

vector_store.store_text("Hi, I am a software engineer working for Cognite.")
vector_store.store_text("The moon is orbiting the earth, which is orbiting the sun.")
vector_store.store_text("Coffee can be a great way to stay awake.")

vector_store.query_text("I am tired, what can I do?")

Smart data frames

Chat with your data using LLMs. Built on top of PandasAI version 2.2.15. If you have loaded data into a Pandas dataframe, you can run

Install the package

%pip install cognite-ai

Chat with your data

from cognite.client import CogniteClient
from cognite.ai import load_pandasai

client = CogniteClient()
SmartDataframe, SmartDatalake, Agent = await load_pandasai()

workorders_df = client.raw.rows.retrieve_dataframe("tutorial_apm", "workorders", limit=-1)
workitems_df = client.raw.rows.retrieve_dataframe("tutorial_apm", "workitems", limit=-1)
workorder2items_df = client.raw.rows.retrieve_dataframe("tutorial_apm", "workorder2items", limit=-1)
workorder2assets_df = client.raw.rows.retrieve_dataframe("tutorial_apm", "workorder2assets", limit=-1)
assets_df = client.raw.rows.retrieve_dataframe("tutorial_apm", "assets", limit=-1)

smart_lake_df = SmartDatalake([workorders_df, workitems_df, assets_df, workorder2items_df, workorder2assets_df], cognite_client=client)
smart_lake_df.chat("Which workorders are the longest, and what work items do they have?")


s_workorders_df = SmartDataframe(workorders_df, cognite_client=client)
s_workorders_df.chat('Which 5 work orders are the longest?')

Configure LLM parameters

params = {
    "model": "gpt-35-turbo",
    "temperature": 0.5
}

s_workorders_df = SmartDataframe(workorders_df, cognite_client=client, params=params)

Pandas AI agent

We can also

from cognite.client import CogniteClient
from cognite.ai import load_pandasai

client = CogniteClient()
SmartDataframe, SmartDatalake, Agent = await load_pandasai()

# Create example data
sales_by_country_df = pd.DataFrame({
    "country": ["United States", "United Kingdom", "France", "Germany", "Italy", "Spain", "Canada", "Australia", "Japan", "China"],
    "revenue": [5000, 3200, 2900, 4100, 2300, 2100, 2500, 2600, 4500, 7000]
})

agent = Agent(sales_by_country_df, cognite_client=client)

print(agent.chat("Which are the top 5 countries by sales?"))