Creating Robust Embeddings with LlamaIndex: Tips and Techniques

What are Embeddings?

Getting Started with LlamaIndex Embeddings

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pip install llama-index-embeddings-openai

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from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import VectorStoreIndex, Settings

# Global default settings
Settings.embed_model = OpenAIEmbedding()

documents = SimpleDirectoryReader('./data').load_data()
index = VectorStoreIndex.from_documents(documents)

Embedding Strategies: Key Considerations

1. Batch Size: The default batch size for sending requests to OpenAI’s embeddings API is 10. You might want to adjust this size based on your needs but be aware of potential rate limits. For instance:
   1 2 python embed_model = OpenAIEmbedding(embed_batch_size=42)
2. Choosing Local Models: If you're worried about costs, consider using a local embedding model. Simply install the required packages and configure LlamaIndex to use

   1 HuggingFaceEmbedding

   . Here’s a sample code snippet to set up a local model: ```python from llama_index.embeddings.huggingface import HuggingFaceEmbedding from llama_index.core import Settings
   Settings.embed_model = HuggingFaceEmbedding(model_name='BAAI/bge-small-en-v1.5') ```
3. Managing Chunk Sizes: The chunk size refers to how your documents are split into smaller segments. Smaller chunks yield more precise embeddings but might lose the broader context. On the flip side, larger chunks can dilute accuracy. As the default chunk size is 1024, consider tweaking it with options like:

   1 2 3 python Settings.chunk_size = 512 Settings.chunk_overlap = 50

   This means that every new chunk overlaps with 50 tokens from the previous chunk, preserving context better.
4. Using Hybrid Search: To improve retrieval accuracy, combining semantic search (i.e., using embeddings) with keyword search can be beneficial. LlamaIndex allows for hybrid searches employing vector databases, or you can implement local hybrid mechanisms like BM25.
5. Metadata Filtering: Embedding documents with metadata can significantly enhance retrieval accuracy. By filtering based on specific metadata fields, you can ensure that the right context flows into your queries. This can be executed as follows: ```python from llama_index.core.vector_stores import MetadataFilters, ExactMatchFilter
   filters = MetadataFilters(filters=[ExactMatchFilter(key='author', value='John Doe')]) index = VectorStoreIndex.from_documents(documents) query_engine = index.as_query_engine(filters=filters) ```

Best Practices for Creating Effective Embeddings

Experiment with Models

Leverage Model Customizations

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from typing import Any, List
from llama_index.core.embeddings import BaseEmbedding
from instructor import INSTRUCTOR

class InstructorEmbeddings(BaseEmbedding):
    def __init__(self, instructor_model_name: str = 'hkunlp/instructor-large', instruction: str = 'Represent Computer Science documentation question:', **kwargs: Any) -> None:
        self._model = INSTRUCTOR(instructor_model_name)
        self._instruction = instruction
        super().__init__(**kwargs)

Fine-Tuning Embeddings

Track and Analyze Performance

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retriever_evaluator = RetrieverEvaluator.from_metric_names(['mrr', 'hit_rate'], retriever=custom_retriever)
eval_results = await retriever_evaluator.aevaluate_dataset(qa_dataset)

Harnessing the Power of Arsturn with LlamaIndex

Conclusion