i have some doubts

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Please stand by @bidda7287 as I search the knowledge base. I normally take about 20 seconds or so. :wave:

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Of course! I'm here to help. Could you please specify what your doubts are about? If they're related to LlamaIndex, I'll do my best to provide you with the information you need.
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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bbidda7287

@kapa.ai rag over dynamic external data : my rag app is running meanwhile updating the vector db , it fill answer from new data? i'm using VectorStoreIndex and it's presisted

bbidda7287

@kapa.ai rag over dynamic external data : my rag app is running meanwhile updating the vector db , it fill answer from new data? i'm using VectorStoreIndex and it's presisted

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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Yes, your RAG application can answer from new data even while the vector database is being updated. This is possible because of the dynamic nature of the VectorStoreIndex in LlamaIndex.

When you load new data into the vector store, it becomes immediately available for querying. This means that if your RAG application is running and you update the vector store with new data, the RAG application will be able to use this new data for answering queries.

Here's a simple example of how you might load new data into the vector store:

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from llama_index.core import SimpleDirectoryReader, VectorStoreIndex

# Assuming you have new data in a directory called "new_data"
loader = SimpleDirectoryReader(input_dir="new_data")
new_documents = loader.load_data()

# Assuming "index" is your existing VectorStoreIndex
index = VectorStoreIndex.from_documents(new_documents)

# Now your RAG application can use the new data
rag = index.as_query_engine(llm=mistral_llm)

In this example, new_documents would be the new data that you want to add to the vector store. After you call from_documents(new_documents), the new data is loaded into the vector store and is immediately available for querying.

Please note that this is a simplified example and the actual code you would need to use might be slightly different depending on your specific use case and setup.

For more details, you can refer to the LlamaIndex documentation () and the GitHub example ().
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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bbidda7287

@kapa.ai i mean , my main file is running seperately that have query engine , i have another script file that will update the vector index , so my query engine will answer?

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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Yes, your query engine should be able to answer queries with the updated data, as long as the updated VectorStoreIndex is persisted and the query engine is able to access the updated index.

However, it's important to note that the query engine might not immediately reflect the changes in the VectorStoreIndex. This is because the query engine might have loaded the index into memory at the time of its creation, and it might not automatically reload the index when it's updated.

Here's a simplified example of how you might update the VectorStoreIndex and then use it in a query engine:

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# Updating the VectorStoreIndex in one script
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex

# Assuming you have new data in a directory called "new_data"
loader = SimpleDirectoryReader(input_dir="new_data")
new_documents = loader.load_data()

# Assuming "index" is your existing VectorStoreIndex
index.update(new_documents)

# Persist the updated index
index.save("path_to_your_index")

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# Using the updated VectorStoreIndex in another script
from llama_index.core import VectorStoreIndex

# Load the updated index
index = VectorStoreIndex.load("path_to_your_index")

# Create a query engine with the updated index
query_engine = index.as_query_engine()

# Now your query engine can use the updated data
response = query_engine.query("Your question here")

In this example, the first script updates the VectorStoreIndex with new data and then saves the updated index. The second script then loads the updated index and uses it to create a new query engine.

Please note that this is a simplified example and the actual code you would need to use might be slightly different depending on your specific use case and setup.

kkapa.ai

For more details, you can refer to the LlamaIndex documentation () and the GitHub example ().
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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bbidda7287

@kapa.ai any solution for this "However, it's important to note that the query engine might not immediately reflect the changes in the VectorStoreIndex. This is because the query engine might have loaded the index into memory at the time of its creation, and it might not automatically reload the index when it's updated.
"

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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To ensure that your query engine reflects the changes in the VectorStoreIndex, you would need to reload the index in the script where the query engine is running whenever an update occurs. This can be achieved by periodically checking for updates in the VectorStoreIndex and reloading the index if an update is detected.

Here's a simplified example of how you might do this:

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from llama_index.core import VectorStoreIndex
import time

# Load the index and create the query engine
index = VectorStoreIndex.load("path_to_your_index")
query_engine = index.as_query_engine()

while True:
    # Check for updates in the VectorStoreIndex
    updated_index = VectorStoreIndex.load("path_to_your_index")
    
    if updated_index != index:
        # If an update is detected, reload the index and recreate the query engine
        index = updated_index
        query_engine = index.as_query_engine()
    
    # Now your query engine can use the updated data
    response = query_engine.query("Your question here")
    
    # Wait for a while before checking for updates again
    time.sleep(60)

In this example, the script periodically checks for updates in the VectorStoreIndex by reloading the index. If an update is detected, it reloads the index and recreates the query engine. This ensures that the query engine always reflects the latest changes in the VectorStoreIndex.

Please note that this is a simplified example and the actual code you would need to use might be slightly different depending on your specific use case and setup. Also, keep in mind that constantly reloading the index could have performance implications, so you might want to adjust the frequency of checking for updates based on your specific needs.

kkapa.ai

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i have some doubts