what to do after embedding ?

[neonarc4]

zzz

[dosubot[bot]]

After you've obtained the embeddings for passages and a query using the ollama_embedding module, the next step involves using these embeddings to find the most relevant passages for your query. This can be achieved by leveraging a similarity search or retrieval mechanism. Here's a concise guide on what to do next:

1. Initialize a Retrieval System: Use a retrieval system like SummaryIndexEmbeddingRetriever (if available in your framework or library) that can take the embeddings and perform a similarity search. This system should be initialized with your embeddings model and configured to retrieve a specific number of top similar passages.

2. Perform Retrieval: Use the retrieval system to compare the query embedding against the passage embeddings. This involves computing the similarity scores between the query embedding and each passage embedding, then ranking the passages based on these scores.

3. Process Results: The retrieval system will return a list of passages (or their identifiers) ranked by their relevance to the query. You can then use this list for further processing, analysis, or directly in your application, depending on your specific needs.

This process allows you to effectively use the embeddings you've generated to find the most relevant information in response to a query, which is a common task in information retrieval, question answering, and similar applications.

Details

Sources

• llama-index-core/llama_index/core/indices/list/retrievers.py
• llama-index-core/llama_index/core/indices/document_summary/retrievers.py
• llama-index-legacy/llama_index/legacy/indices/document_summary/retrievers.py
• llama-index-legacy/llama_index/legacy/vector_stores/singlestoredb.py
• llama-index-integrations/embeddings/llama-index-embeddings-llm-rails/llama_index/embeddings/llm_rails/base.py
• llama-index-integrations/embeddings/llama-index-embeddings-cloudflare-workersai/llama_index/embeddings/cloudflare_workersai/base.py

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