[Question]: Is it possible automatically update documents after data addition or data update using lllamaindex on real time application?

[iriye]

Question Validation

• I have searched both the documentation and discord for an answer.

Question

Is it possible automatically update documents after data addition or data update using lllamaindex on real time application?
"WillLlamaindex be able to perform the necessary updates simultaneously when there are changes in documents or new documents added in real-time application usage with a lot of data?"

[dosubot[bot]]

Answer generated by a 🤖

Answer

Yes, LlamaIndex can automatically update documents in real-time as data is added or updated in a high-volume application. This is achieved through the insert, update_ref_doc, and refresh_ref_docs methods.

The insert method allows you to add a new document to the index. It first parses the document into nodes, then adds these nodes to the document store and the index structure.

def insert(self, document: Document, **insert_kwargs: Any) -> None:
    """Insert a document."""
    with self._service_context.callback_manager.as_trace("insert"):
        nodes = self.service_context.node_parser.get_nodes_from_documents(
            [document]
        )
        self.insert_nodes(nodes, **insert_kwargs)
        self.docstore.set_document_hash(document.get_doc_id(), document.hash)

The update_ref_doc method allows you to update an existing document in the index. It does this by first deleting the document and its corresponding nodes from the index, and then re-inserting the updated document.

def update_ref_doc(self, document: Document, **update_kwargs: Any) -> None:
    """Update a document and it's corresponding nodes.

    This is equivalent to deleting the document and then inserting it again.

    Args:
        document (Union[BaseDocument, BaseIndex]): document to update
        insert_kwargs (Dict): kwargs to pass to insert
        delete_kwargs (Dict): kwargs to pass to delete

    """
    with self._service_context.callback_manager.as_trace("update"):
        self.delete_ref_doc(
            document.get_doc_id(), **update_kwargs.pop("delete_kwargs", {})
        )
        self.insert(document, **update_kwargs.pop("insert_kwargs", {}))

The refresh_ref_docs method allows you to refresh the index with documents that have changed. It checks if the hash of the document in the document store is different from the hash of the provided document. If they are different, it updates the document in the index. If the document does not exist in the document store, it inserts the document into the index.

def refresh_ref_docs(
    self, documents: Sequence[Document], **update_kwargs: Any
) -> List[bool]:
    """Refresh an index with documents that have changed.

    This allows users to save LLM and Embedding model calls, while only
    updating documents that have any changes in text or metadata. It
    will also insert any documents that previously were not stored.
    """
    with self._service_context.callback_manager.as_trace("refresh"):
        refreshed_documents = [False] * len(documents)
        for i, document in enumerate(documents):
            existing_doc_hash = self._docstore.get_document_hash(
                document.get_doc_id()
            )
            if existing_doc_hash != document.hash:
                self.update_ref_doc(
                    document, **update_kwargs.pop("update_kwargs", {})
                )
                refreshed_documents[i] = True
            elif existing_doc_hash is None:
                self.insert(document, **update_kwargs.pop("insert_kwargs", {}))
                refreshed_documents[i] = True

        return refreshed_documents

You can find more details in the source code of LlamaIndex, specifically in the llama_index/indices/base.py file.

Sources

• llama_index/indices/base.py
• llama_index/indices/tree/inserter.py

---

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[iriye]

"The answer I was looking for wasn't exactly this; actually, if the documents in the folder where the vector index created while running the question-answer application with the LLM model change or new ones are added, can the data within the query be updated automatically?"

İn this example more helpfull about my aim representation:
documents = SimpleDirectoryReader('./source_documents').load_data()
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embeddings)
index = GPTVectorStoreIndex.from_documents(documents, service_context=service_context, use_async=True, show_progress=True)
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=False)
res = qa(query)

İn this script after create index and running qa, files in './source_documents' path changed , automatically update index and , how is it possible?

[dosubot[bot]]

Answer generated by a 🤖

Answer

Hello,

Yes, you can update the data within the query automatically when new documents are added or existing ones change in the './source_documents' folder. This can be achieved by checking if the index exists, if it doesn't, create a new one. If it does, load the existing index and add new documents to it. This can be done by parsing the new documents into nodes and adding these nodes to the existing index using the index.insert_nodes(new_nodes) method.

Here is the code snippet:

# Check if index exists
persist_directory = 'index'
index_files = ['vector_store.json', 'docstore.json', 'index_store.json']
index_exists = all(os.path.exists(os.path.join(persist_directory, file)) for file in index_files)

if not index_exists:
    # Index doesn't exist, so create it
    print("Loading documents...")
    # Load documents from your custom folder
    documents = SimpleDirectoryReader(data_directory).load_data()

    # Create index from documents
    print("Creating index...")
    index = GPTVectorStoreIndex.from_documents(documents, service_context=service_context)

else:
    # Index exists, so load it
    storage_context = StorageContext.from_defaults(persist_dir=persist_directory)
    index = load_index_from_storage(storage_context)

    # Load new documents
    print("Loading new documents...")
    new_documents = SimpleDirectoryReader(data_directory).load_data()

    # Parse documents into nodes
    print("Parsing new documents into nodes...")
    parser = SimpleNodeParser()
    new_nodes = parser.get_nodes_from_documents(new_documents)

    # Add nodes to the existing index
    print("Adding new nodes to the existing index...")
    index.insert_nodes(new_nodes)

The SimpleDirectoryReader class in the LlamaIndex codebase is a reader that reads files of different formats from a directory. It automatically selects the best file reader given file extensions. The load_data() method of the SimpleDirectoryReader class is used to load data from the input directory. It returns a list of documents.

The GPTVectorStoreIndex.from_documents() method is used to create an index from the documents. Unfortunately, I couldn't find detailed information about this method in the repository.

If the index is not updating, you may need to refresh it as suggested in the document management section of the GPT Index documentation: https://gpt-index.readthedocs.io/en/latest/how_to/index/document_management.html

I hope this helps! If you have any other questions, feel free to ask.

Best,
Dosu

Sources

• llama_index/readers/file/base.py
• tests/readers/test_file.py

---

This response is meant to be useful, save you time, and share context. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

[iriye]

This answer is not sufficient; let me give you examples

index = VectorStoreIndex.from_documents(documents=documents, service_context=service_context)
query_engine = index.as_query_engine(
service_context=service_context
)

in the code line, when 'index' is used as_query_engine, and if the 'documents' change while the query_engine is running, how would the relevant query be constructed with the new VectorStoreIndex during runtime in a real-time application?

[dosubot[bot]]

🤖

Hello,

To update the VectorStoreIndex in real-time when there are changes or additions to the documents in your LLM application using LlamaIndex, you can use the add method of the DynamoDBVectorStore class. This method allows you to add new documents to the index during runtime. Here's an example of how you can use it:

from llama_index.vector_stores.dynamodb import DynamoDBVectorStore
from llama_index.vector_stores.types import NodeWithEmbedding
from llama_index.schema import TextNode

# Create a DynamoDBVectorStore
vector_store = DynamoDBVectorStore.from_table_name(
    table_name="my_table",
    namespace="my_namespace"
)

# Create a list of NodeWithEmbedding objects
embedding_results = [
    NodeWithEmbedding(
        id="doc1",
        ref_doc_id="ref1",
        node=TextNode(text="This is a document."),
        embedding=[0.1, 0.2, 0.3, 0.4]
    ),
    # Add more NodeWithEmbedding objects as needed
]

# Add the documents to the index
vector_store.add(embedding_results)

To construct a relevant query in real-time, you can use the query method of the DynamoDBVectorStore class. This method allows you to query the index with the updated documents. Here's an example of how you can use it:

from llama_index.vector_stores.types import VectorStoreQuery

# Create a VectorStoreQuery object
query = VectorStoreQuery(
    query_embedding=[0.1, 0.2, 0.3, 0.4],  # The embedding to query
    similarity_top_k=10  # The number of most similar documents to return
)

# Query the index
result = vector_store.query(query)

# The result is a VectorStoreQueryResult object
# You can access the ids, nodes, and similarities of the returned documents like this:
ids = result.ids
similarities = result.similarities

Please note that you need to replace "my_table" and "my_namespace" with the name of your DynamoDB table and the namespace you're using, respectively. Also, replace the embedding and query_embedding with the actual embeddings of your documents.

You can find more information about these methods in the LlamaIndex repository:

• DynamoDBVectorStore.add()
• DynamoDBVectorStore.query()

I hope this helps! If you have any other questions, feel free to ask.

Sources

• llama_index/vector_stores/redis.py
• llama_index/vector_stores/mongodb.py
• llama_index/vector_stores/dynamodb.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[iriye]

While uber_engine working as_query_engine, if the files contained within the data folder updated or new file added, how would the queries made to "uber_engine" be updated in a real-time application When runtime?
Example code script, please create code scipt using my shared code example

from llamaindex import SimpleDirectoryReader, GPTVectoreStoreIndex
uber_docs = SimpleDirectoryReader('data').load_data()
uber_index = GPTVectorStoreIndex.from_documents(uber_docs)
uber_engine = uber_index.as_query_engine()
uber_engine.query('Compare revenue grow of Uber from 202 to 2021?')

[iriye]

While uber_engine working as_query_engine, if the files contained within the data folder updated or new file added, how would the queries made to "uber_engine" be updated in a real-time application When runtime?
Example code script, please create code scipt using my shared code example

from llamaindex import SimpleDirectoryReader, GPTVectoreStoreIndex
uber_docs = SimpleDirectoryReader('data').load_data()
uber_index = GPTVectorStoreIndex.from_documents(uber_docs)
uber_engine = uber_index.as_query_engine()
uber_engine.query('Compare revenue grow of Uber from 202 to 2021?')

[dosubot[bot]]

🤖

Hello,

To achieve real-time updates to your "uber_engine" when there are changes or additions to the files in your data folder, you can use the watchdog library in Python. This library allows you to monitor file system events. You can set up an observer to watch your data folder and reload the data whenever a change is detected.

Here's a basic example of how you can use watchdog to monitor your data folder:

from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler
from llamaindex import SimpleDirectoryReader, GPTVectorStoreIndex

class FileChangeHandler(FileSystemEventHandler):
    def on_modified(self, event):
        # Check if the modified file is in the data folder
        if 'data' in event.src_path:
            # Create an instance of SimpleDirectoryReader
            reader = SimpleDirectoryReader('data')
            # Load the data
            uber_docs = reader.load_data()
            # Create an instance of GPTVectorStoreIndex
            uber_index = GPTVectorStoreIndex.from_documents(uber_docs)
            # Create a query engine
            uber_engine = uber_index.as_query_engine()

# Initialize the event handler and observer
event_handler = FileChangeHandler()
observer = Observer()

# Set the observer to monitor the data folder
observer.schedule(event_handler, path='data', recursive=True)

# Start the observer
observer.start()

In this example, SimpleDirectoryReader is a class from the LlamaIndex that reads files of different formats from a directory. You need to replace 'data' with the actual path to your data folder.

Please note that this is a basic example and you might need to adjust it to fit your specific needs. For example, you might want to add error handling or specify what types of files to watch for.

Also, it's important to note that the SimpleDirectoryReader class in LlamaIndex does not automatically handle changes in the data directory. It does not have a mechanism to watch for changes in the directory and update the loaded data accordingly. The load_data method needs to be called again to reload the data if there are any changes in the directory after the SimpleDirectoryReader object is created.

I hope this helps! Let me know if you have any other questions.

Sources

• llama_index/tools/tool_spec/load_and_search/base.py
• llama_index/readers/file/base.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.