Question About Chroma And Retrieval QA

[zzleaning]

I am a beginner and there are two questions that are bothering me. I built a Document QA using the components in the diagram and used a docx type file as the data source.

Whenever I want to test this QA, even if I use the same data source, it takes a long time to build a vector database.Even if I set a local storage address, it still remains the same. Is there any way to adjust components so that this vector database does not need to be repeatedly built?

In addition, it may be that there is a problem with my settings. Sometimes the answer results are obtained based on text, but sometimes they are generated independently without relying on text. The returned data source file is always the same, and the required file is not returned based on the problem. Please tell me what adjustments should be made? thank you!

[dosubot[bot]]

To address your issues with the Document QA system, you can implement a caching mechanism for the vector database to avoid rebuilding it every time and ensure consistent and accurate responses based on the text in the data source.

Avoid Rebuilding the Vector Database

You can adjust your system to cache the vector database. Here’s a general approach:

1. Check for Existing Vector Database: Before rebuilding, check if a cached version exists.
2. Load Cached Database: If a cached version exists, load it.
3. Rebuild and Cache: If no cached version exists or if the data has changed, rebuild the vector database and cache it.

Here’s a sample code snippet in Python:

import os
import pickle

class VectorDatabase:
    def __init__(self, data_source):
        self.data_source = data_source
        self.vector_db_path = 'vector_db.pkl'
        self.vector_db = self.load_vector_db()

    def load_vector_db(self):
        if os.path.exists(self.vector_db_path):
            with open(self.vector_db_path, 'rb') as f:
                return pickle.load(f)
        else:
            return self.build_vector_db()

    def build_vector_db(self):
        # Your logic to build the vector database from the data source
        vector_db = self.create_vector_db_from_data_source(self.data_source)
        with open(self.vector_db_path, 'wb') as f:
            pickle.dump(vector_db, f)
        return vector_db

    def create_vector_db_from_data_source(self, data_source):
        # Implement the logic to create the vector database from the data source
        pass

    def query(self, query_text):
        # Implement the logic to query the vector database
        pass

# Usage
data_source = "path/to/your/data/source"
vector_db = VectorDatabase(data_source)
response = vector_db.query("your query text")
print(response)

Ensure Consistent and Accurate Responses

To ensure that responses are consistently based on the text in the data source, you can use the RetrievalQAComponent from Langflow. This component allows you to build a question-answering chain against an index, which can be integrated with your vector database.

Here’s an example of how to use the RetrievalQAComponent:

from langflow.interface.custom.custom_component import CustomComponent
from langflow.field_typing import BaseLanguageModel, BaseRetriever, Text
from langchain.chains.retrieval_qa.base import RetrievalQA

class RetrievalQAComponent(CustomComponent):
    display_name = "Retrieval QA"
    description = "Chain for question-answering against an index."

    def build(
        self,
        llm: BaseLanguageModel,
        chain_type: str,
        retriever: BaseRetriever,
        input_value: str = "",
        input_key: str = "query",
        output_key: str = "result",
        return_source_documents: bool = True,
    ) -> Text:
        chain_type = chain_type.lower().replace(" ", "_")
        runnable = RetrievalQA.from_chain_type(
            llm=llm,
            chain_type=chain_type,
            retriever=retriever,
            input_key=input_key,
            output_key=output_key,
            return_source_documents=return_source_documents,
        )
        result = runnable.invoke({input_key: input_value})
        result = result.content if hasattr(result, "content") else result
        return result.get("result", "")

By implementing this caching mechanism and utilizing the RetrievalQAComponent, you can avoid rebuilding the vector database every time and ensure consistent and accurate responses based on the text in the data source ^[1].

Additional Notes

• Ensure that your FileLoaderComponent is correctly caching the loaded .docx files to avoid reprocessing ^[2].
• Verify that your TextSplitterComponent is correctly splitting the text and consider implementing a caching mechanism if necessary ^[3].

These adjustments should help improve the performance and accuracy of your Document QA system.

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