I’ve made a document summary index with embeddings, but...

ccmosguy

@kapa.ai I’ve made a document summary index with embeddings, but it is returning a ton of distractors. What can I do to mitigate the issue?

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kkapa.ai

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

kkapa.ai

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There are a few strategies you can consider to improve the quality of your document summary index and reduce the number of distractors:

Change the Retrieval Mode: As per the changelog () of LlamaIndex, the default retrieval mode is set to embedding. However, you can switch to LLM (Language Model) based retrieval if you find that the embedding-based retrieval is not performing as expected.

Fine-tune the Model: If your documents are highly specialized or dense, the embeddings might not be representative of the content. In such cases, you can consider fine-tuning your model on a similar dataset. For example, if your documents are financial reports, you might want to fine-tune your model on financial datasets such as Bloomberg GPT as suggested in this example ().

Enrich the Metadata: Providing more contextual information regarding the surrounding context that the chunk is located in can help to improve the search quality. This is also suggested in the same example ().

Use Keyword-based Approaches: If the issue persists, you might want to consider other methods of neural search that employ more keyword-based approaches, such as ColBERTv2/PLAID. This approach can help in matching on particular keywords to identify high-relevance chunks.

kkapa.ai

Remember, the effectiveness of these strategies can vary depending on the nature of your documents and the specific use case. It's often a good idea to experiment with different approaches and fine-tune the process based on the results.
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ccmosguy

@kapa.ai Is there a way to do reranker with document summary index?

kkapa.ai

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

kkapa.ai

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Yes, you can use a reranker with a document summary index in LlamaIndex. Reranking is a crucial step in advanced retrieval processes. This step involves using a separate model to re-organize nodes retrieved from the initial retrieval phase. The goal is to ensure that the most relevant nodes are prioritized and appear first.

Here are some examples of how to set up rerankers:

LLM Reranker:

Plain Text

from llama_index.managed.llm_rerank import LLMRerank
from llama_index.managed.response_synthesizer import TreeSummarizeResponseSynthesizer

reranker = LLMRerank(
    top_n=10,
    llm=gemini,
)

response_synthesizer = TreeSummarizeResponseSynthesizer(
    llm=gemini,
    max_length=512,
)

single_step_query_engine = index.as_query_engine(
    similarity_top_k=20,
    node_postprocessors=[reranker],
    response_synthesizer=response_synthesizer,
)

Source: LlamaIndex Managed GoogleDemo ()

Cohere Custom Reranker:

Plain Text

from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.postprocessor.cohere_rerank import CohereRerank

reranker = CohereRerank(top_n=10)

query_engine = RetrieverQueryEngine(retriever, node_postprocessors=[reranker])

Source: LlamaIndex Retrievers Ensemble Retrieval ()

SentenceTransformerReranker with mixedbread.ai models:

Plain Text

from llama_index.core.postprocessor import SentenceTransformerRerank

postprocessor = SentenceTransformerRerank(
    model="mixedbread-ai/mxbai-rerank-base-v1", top_n=2
)

Source: Jerryjliu Llama_index Cookbooks mixedbread Rerank Cookbook ()

kkapa.ai

Remember to replace the placeholders with your actual models and parameters. The top_n parameter in the reranker determines the number of top results to consider for reranking. You can adjust this parameter based on your specific needs.
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ccmosguy

@kapa.ai when I run the query and embedding there is always a status bar for the embedding process, how do I hide the status bar?

kkapa.ai

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

kkapa.ai

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To hide the status bar during the embedding process, you can adjust the verbosity of the logging. By default, LlamaIndex uses the Python logging module, and the verbosity can be controlled by setting the logging level.

Here's an example of how to set the logging level to WARNING, which will suppress INFO level messages such as the status bar:

Plain Text

import logging
logging.getLogger().setLevel(logging.WARNING)

This will hide the status bar and any other INFO level logs. If you want to see ERROR level logs only, you can set the level to ERROR. Please note that this will affect all logging messages, not just the status bar.

Remember to place this code before you start the embedding process to ensure the logging level is set correctly.
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ccmosguy

How do I set the logging level to warning only for llama index?

ccmosguy

@kapa.ai How do I set the logging level to warning only for llama index?

kkapa.ai

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

kkapa.ai

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To set the logging level to WARNING only for LlamaIndex, you can adjust the logging level for the specific logger associated with LlamaIndex. Here's an example of how to do it:

Plain Text

import logging
logging.getLogger('llama_index').setLevel(logging.WARNING)

This will set the logging level to WARNING for all logs generated by LlamaIndex, suppressing INFO and DEBUG level messages. This means that only WARNING, ERROR, and CRITICAL level messages will be displayed.

Remember to place this code before you start using LlamaIndex to ensure the logging level is set correctly.
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ccmosguy

How do I embed multiple queries in parallel @kapa.ai ?

kkapa.ai

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

kkapa.ai

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To embed multiple queries in parallel, you can use the parallel query pipeline setup provided by LlamaIndex. This setup allows you to execute multiple chunk sizes at once and combines the results.

Here's an example of how to set up a parallel query pipeline from the LlamaIndex documentation ():

Plain Text

from llama_index.core import SimpleDirectoryReader

# Load the data
reader = SimpleDirectoryReader(input_files=["pg_essay.txt"])
documents = reader.load_data()

# Set up the parallel query pipeline
# ... (code for setting up the pipeline goes here)

Please note that the actual code for setting up the pipeline is not provided in the knowledge sources. You would typically use a library like asyncio in Python to handle the parallel execution. The exact implementation would depend on the specifics of your application and the LlamaIndex API.

Remember to replace "pg_essay.txt" with the actual path to your document file.
----------------------
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ccmosguy

@kapa.ai my vector store seems to be out of sync with my doc store. How do I rebuild the vector store from the docstore?

kkapa.ai

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

kkapa.ai

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To rebuild your vector store from the docstore, you can use the VectorStoreIndex.from_documents() method provided by LlamaIndex. This method takes a list of documents and a storage context as input and builds a new vector store index.

Here's an example of how to do it:

Plain Text

from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.index import VectorStoreIndex

# Load the data
reader = SimpleDirectoryReader(input_files=["your_document_file"])
documents = reader.load_data()

# Create a storage context with your vector store
storage_context = StorageContext.from_defaults(vector_store=your_vector_store)

# Build the index
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

In this code, replace "your_document_file" with the actual path to your document file and your_vector_store with your actual vector store. This will create a new vector store index from the documents in your docstore.

Please note that this process can take some time if you have a large number of documents in your docstore.

Sources:

LlamaIndex Vector Store Guide ()
LlamaIndex AnalyticDBDemo ()
LlamaIndex AstraDBIndexDemo ()
LlamaIndex DuckDBDemo ()

----------------------
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ccmosguy

@kapa.ai I have already created a summary index, I just want to regenerate the vector embeddings. How did I do that?

kkapa.ai

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

kkapa.ai

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To regenerate the vector embeddings for your summary index, you can use the VectorStoreIndex.from_documents() method provided by LlamaIndex. This method takes a list of documents and a storage context as input and builds a new vector store index.

Here's an example of how to do it:

Plain Text

from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.index import VectorStoreIndex

# Load the data
reader = SimpleDirectoryReader(input_files=["your_document_file"])
documents = reader.load_data()

# Create a storage context with your vector store
storage_context = StorageContext.from_defaults(vector_store=your_vector_store)

# Build the index
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

LlamaIndex Vector Store Guide ()
LlamaIndex AnalyticDBDemo ()
LlamaIndex AstraDBIndexDemo ()
LlamaIndex DuckDBDemo ()

----------------------
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----------------------

ccmosguy

@kapa.ai how do I rebuild an index?

kkapa.ai

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

kkapa.ai

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To rebuild an index in LlamaIndex, you can use the load_index_from_storage() function. This function takes a storage context as input and loads the index from the storage.

Here's an example of how to do it:

Plain Text

from llama_index.core import StorageContext
from llama_index.index import load_index_from_storage

# Create a storage context
storage_context = StorageContext.from_defaults(persist_dir="your_persist_directory")

# Load the index from storage
index = load_index_from_storage(storage_context)

In this code, replace "your_persist_directory" with the actual directory where your index is stored. This will load the index from the storage and rebuild it.

Please note that this process can take some time if you have a large index.

Sources:

LlamaIndex DocSummary ()
LlamaIndex MultiDocTogetherHybrid ()
LlamaIndex ParamOptimizer ()
LlamaIndex QueryPipelineSQL ()
LlamaIndex QueryPipeline ()
LlamaIndex SimpleIndexDemo ()

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I’ve made a document summary index with embeddings, but it is returning a ton of distracto