Deleting the Trained data from the Pandas Agent

[Subhashchandra-P]

System Info

OS Version : windows 11 Python Version : 3.11.9 Pandas ai version : 2.2.14

🐛 Describe the bug

Thanks for the Great work! As a part of training the agent (QA Training) ,started training on some questions...Now, I want to remove the trained data from the agent..how can I do that? @dosubot

[dosubot[bot]]

Hey @Subhashchandra-P! 👋 I'm here to help you with any issues or questions you have. Just ask away, and I'll do my best to assist you! Dosu

To delete trained data from the PandasAI agent, you can use the methods provided for managing the vector store. Specifically, you can use the delete_question_and_answers and delete_docs methods to remove data by vector ID. These methods are part of the vector store functionality in PandasAI. Here's an example of how you might use these methods:

# Assuming you have access to the vector store instance
vector_store.delete_question_and_answers(ids=["your_vector_id"])
vector_store.delete_docs(ids=["your_vector_id"])

These methods will delete the specified data from the vector store, effectively removing the trained data from the agent ^[1][2].

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[Subhashchandra-P]

I have used the default vectorstore (i.e. Bamboo vector store), in that case , how can we delete the QA pairs from it? @dosu

[dosubot[bot]]

To delete QA pairs from the default Bamboo vector store in PandasAI, you can use the delete_question_and_answers method of the Milvus class. This method requires a list of question-answer IDs that you want to delete. Here's an example of how to use it:

# Assuming `milvus_instance` is an instance of the Milvus class
ids_to_delete = ['id1', 'id2', 'id3']  # Replace with the actual IDs you want to delete
milvus_instance.delete_question_and_answers(ids=ids_to_delete)

This will remove the specified question-answer pairs from the QA collection ^[1].

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[Subhashchandra-P]

Ok, can you give me the code to use Pinecone vector store for storing the trained data. using the Open AI embeddings. @dosu

[dosubot[bot]]

To use the Pinecone vector store for storing trained data with OpenAI embeddings, you can follow these steps:

1. Initialize the Pinecone Vector Store: Create an instance of the Pinecone class by providing the necessary parameters such as api_key, index, and an embedding_function that generates embeddings using OpenAI.

   from your_embedding_module import your_openai_embedding_function
   
   pinecone_store = Pinecone(
       api_key="your_pinecone_api_key",
       embedding_function=your_openai_embedding_function
   )

2. Add Documents: Use the add_docs method to add your documents to the Pinecone vector store. This method will use the provided embedding_function to generate embeddings for the documents and store them in the vector store.

   documents = ["Document 1 text", "Document 2 text"]
   pinecone_store.add_docs(docs=documents)

3. Add Question and Answer Pairs: If you have question and answer pairs, use the add_question_answer method to store them. This method also uses the embedding_function to generate embeddings for the combined question and answer text.

   questions = ["What is AI?", "Explain machine learning."]
   answers = ["AI is the simulation of human intelligence in machines.", "Machine learning is a subset of AI."]
   pinecone_store.add_question_answer(queries=questions, codes=answers)

Make sure to replace your_openai_embedding_function with the actual function that generates embeddings using OpenAI's API ^[1].

To continue talking to Dosu, mention @dosu.

[Subhashchandra-P]

If we re initiate the agent, does the trained data on it gets deleted? Explain the ways to delete the trained data and bring the agent to initial state. @dosu

[dosubot[bot]]

Reinitializing the PandasAI agent does not automatically delete the trained data. The trained data is persisted, so you only need to train the model once unless you explicitly delete it ^[1].

To delete the trained data and bring the agent to its initial state, you can use the following methods:

1. Delete Trained Data: If you're using the Bamboo vector store, there isn't specific information available on methods for deleting data. However, for other vector stores like Pinecone or Milvus, you can use methods like delete_question_and_answers to remove specific data by their IDs.

2. Reset Agent State: Use the start_new_conversation method to reset the agent's state. This method clears the memory and resets the conversation ID, effectively bringing the agent to its initial state without affecting the trained data in the vector store.

agent.start_new_conversation()

This method will clear the previous conversation context but won't delete the trained data from the vector store ^[2].

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[Subhashchandra-P]

what is the API key of the default Bamboo Vector Store? Reset Agent State: Use the start_new_conversation method to reset the agent's state. This method clears the memory and resets the conversation ID, effectively bringing the agent to its initial state without affecting the trained data in the vector store. I want to reset along with erasing the training data...does initialising the agent to new variable will delete it? @dosu

[dosubot[bot]]

The default Bamboo vector store does not have a predefined API key in the codebase, or at least it is not explicitly mentioned in the available information.

Reinitializing the agent by creating a new instance does not delete the trained data stored in the vector store. The data persists unless you explicitly delete it using methods like delete_question_and_answers or delete_docs if they are implemented for your vector store. To reset the agent and erase the training data, you need to manually delete the data from the vector store using these methods.

To continue talking to Dosu, mention @dosu.

[Subhashchandra-P]

How can I retrieve the Bamboo Vector Store Id with only the API? @dosu

[dosubot[bot]]

There isn't specific information available on how to retrieve the Bamboo Vector Store ID using only the API. The documentation or codebase does not provide details on methods or endpoints for retrieving vector store IDs from the Bamboo vector store. If you have access to the API, you might need to explore the available endpoints or consult additional resources to find a way to retrieve the vector store ID.

To continue talking to Dosu, mention @dosu.

[Subhashchandra-P]

can you give me the code to use Pinecone vector store for storing the trained data. using the Open AI embeddings. and store it with the index name as well as the namespace name. @dosu

[dosubot[bot]]

To use the Pinecone vector store for storing trained data with OpenAI embeddings, specifying both the index name and the namespace name, follow these steps:

1. Initialize the Pinecone Vector Store: Create an instance of the Pinecone class with the necessary parameters, including api_key, index, and an embedding_function that generates embeddings using OpenAI.

   from your_embedding_module import your_openai_embedding_function
   
   pinecone_store = Pinecone(
       api_key="your_pinecone_api_key",
       index="your_index_name",
       embedding_function=your_openai_embedding_function
   )

2. Add Data to the Vector Store: Use the add_question_answer or add_docs method to add your data, specifying the namespace parameter.

   # For question and answer pairs
   questions = ["What is AI?", "Explain machine learning."]
   answers = ["AI is the simulation of human intelligence.", "Machine learning is a subset of AI."]
   pinecone_store.add_question_answer(
       queries=questions,
       codes=answers,
       namespace="your_namespace_name"
   )
   
   # For documents
   documents = ["Document 1 content", "Document 2 content"]
   pinecone_store.add_docs(
       docs=documents,
       namespace="your_namespace_name"
   )

Replace "your_pinecone_api_key", "your_index_name", "your_namespace_name", and your_openai_embedding_function with your actual API key, desired index name, namespace name, and the function that generates embeddings, respectively ^[1].

To continue talking to Dosu, mention @dosu.

[Subhashchandra-P]

From which module, do we need to import the Pinecone? It is giving me the ModuleNotFoundError: No module named 'chromadb' @dosu

[dosubot[bot]]

To use Pinecone, you should import the pinecone-client module. To resolve the "ModuleNotFoundError: No module named 'chromadb'" error, you need to install the chromadb package. You can do this by running the following command:

pip install chromadb

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[Subhashchandra-P]

So, after installing ChromaDB and qdrant_client, I am still getting the error { "name": "ImportError", "message": "cannot import name 'Pinecone' from 'pandasai.ee.vectorstores' (C:\Users\subhashchandra.p\AppData\Roaming\Python\Python311\site-packages\pandasai\ee\vectorstores\init.py)", "stack": "--------------------------------------------------------------------------- ImportError Traceback (most recent call last) Cell In[7], line 1 ----> 1 from pandasai.ee.vectorstores import Pinecone

ImportError: cannot import name 'Pinecone' from 'pandasai.ee.vectorstores' (C:\Users\subhashchandra.p\AppData\Roaming\Python\Python311\site-packages\pandasai\ee\vectorstores\init.py)" } @dosu

[dosubot[bot]]

The error you're encountering suggests that the Pinecone class is not being correctly imported from the pandasai.ee.vectorstores module. This could be due to a few reasons:

1. Check Installation: Ensure that you have the correct version of the pandas-ai package installed that includes the Pinecone implementation. You might need to update the package to the latest version.

2. Verify Module Structure: Double-check the __init__.py file in the pandasai/ee/vectorstores directory to ensure that the Pinecone class is properly imported there. The Pinecone class should be defined in pandasai/ee/vectorstores/pinecone.py ^[1].

3. Direct Import: Try importing the Pinecone class directly from its file path:

   from pandasai.ee.vectorstores.pinecone import Pinecone

If these steps do not resolve the issue, there might be a problem with the package installation or the specific version you are using.

To continue talking to Dosu, mention @dosu.