Implementing Hybrid Search with LlamaIndex: Techniques & Tips

What is Hybrid Search?

Getting Started with LlamaIndex

1. Install LlamaIndex: You’ll need to install LlamaIndex if you haven’t done it already:
   1 2 bash pip install llama-index
2. Import Libraries: In your Python script or notebook, import the necessary libraries:
   1 2 3 4 python from llama_index.core import VectorStoreIndex, SimpleDirectoryReader from llama_index.vector_stores.pinecone import PineconeVectorStore from llama_index.core import StorageContext
3. Load Your Data: Organize your data into a directory and load it for processing:
   1 2 python documents = SimpleDirectoryReader('./data').load_data()
4. Set Up a Storage Context: Create a storage context that specifies how data will be stored.
   1 2 python storage_context = StorageContext.from_defaults()

Key Techniques for Implementing Hybrid Search

1. Defining the Hybrid Search Mechanism

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from llama_index.core.retrievers import VectorIndexRetriever, KeywordTableSimpleRetriever
from llama_index.core import QueryBundle, BaseRetriever
from typing import List

class CustomRetriever(BaseRetriever):
    def __init__(self, vector_retriever, keyword_retriever, mode='AND'):
        self.vector_retriever = vector_retriever
        self.keyword_retriever = keyword_retriever
        self.mode = mode

    def _retrieve(self, query_bundle: QueryBundle) -> List:
        vector_nodes = self.vector_retriever.retrieve(query_bundle)
        keyword_nodes = self.keyword_retriever.retrieve(query_bundle)
        combined_nodes = self.combine_nodes(vector_nodes, keyword_nodes)
        return combined_nodes

    def combine_nodes(self, vector_nodes, keyword_nodes):
        # Logic to combine nodes based on 'AND'/'OR' logic
        ...

2. Leveraging Vector Stores

1. Create a Pinecone Index:
   1 2 3 python import pinecone pinecone.create_index(name="my-index", dimension=1536)
2. Upsert the Data: You can push your data into the index, forming embeddings that can be referenced during queries.
   1 2 python pinecone_index.upsert(vectors)
3. Query the Vector Store: When a query comes in, convert it to a vector and use it to fetch results from the Pinecone index:
   1 2 python results = pinecone_index.query(vector)

3. Use Metadata Filters for Increased Precision

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from llama_index.core.vector_stores import MetadataFilters, ExactMatchFilter

filters = MetadataFilters(filters=[ExactMatchFilter(key="author", value="LlamaIndex")])

query_engine = index.as_query_engine(filters=filters)

4. Optimize Your Chunk Sizes

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Settings.chunk_size = 256
Settings.chunk_overlap = 20

5. Utilize Rerankers Effectively

Practical Tips for Successful Implementation

• Leverage the Community: Join forums & discussions in platforms like Discord & GitHub where many share insights & solutions on common issues.
• Analyze Usage Metrics: Make sure to utilize LlamaIndex’s analytic features to assess how your application is being used. Knowing your audience can help refine system performance & relevance.
• Iterate & Adjust Frequently: In the world of AI, never stop refining your algorithms & approaches. Gather feedback, utilize A/B tests, & adjust parameters like alpha values based on receiver data.

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Conclusion