> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pandas-ai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Train PandasAI

You can train PandasAI to understand your data better and to improve its performance.

## Training with local Vector stores

If you want to train the model with a local vector store, you can use the local `ChromaDB`, `Qdrant` or `Pinecone` vector stores. Here's how to do it:
An enterprise license is required for using the vector stores locally, ([check it out](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE)).
If you plan to use it in production, [contact us](https://pandas-ai.com).

```python theme={null}
from pandasai import Agent
from pandasai.ee.vectorstores import ChromaDB
from pandasai.ee.vectorstores import Qdrant
from pandasai.ee.vectorstores import Pinecone
from pandasai.ee.vector_stores import LanceDB

# Instantiate the vector store
vector_store = ChromaDB()
# or with Qdrant
# vector_store = Qdrant()
# or with LanceDB
vector_store = LanceDB()
# or with Pinecone
# vector_store = Pinecone(
#     api_key="*****",
#     embedding_function=embedding_function,
#     dimensions=384, # dimension of your embedding model
# )

# Instantiate the agent with the custom vector store
agent = Agent("data.csv", vectorstore=vector_store)

# Train the model
query = "What is the total sales for the current fiscal year?"
response = """
import pandas as pd

df = dfs[0]

# Calculate the total sales for the current fiscal year
total_sales = df[df['date'] >= pd.to_datetime('today').replace(month=4, day=1)]['sales'].sum()
result = { "type": "number", "value": total_sales }
"""
agent.train(queries=[query], codes=[response])

response = agent.chat("What is the total sales for the last fiscal year?")
print(response)
# The model will use the information provided in the training to generate a response
```