Weights in RAG with FAISS

2025-02-27 09:21 UTC gpt-4o Open in ChatGPT ↗

Yes, you can add weights to a Retrieval-Augmented Generation (RAG) system when using FAISS by adjusting how retrieved documents are scored or ranked. Here are a few ways to achieve this:

1. Adjusting Similarity Scores

When retrieving documents from FAISS, instead of using the raw similarity scores (e.g., cosine similarity or L2 distance), you can multiply the scores by a weight based on document importance.

Example:

adjusted_score = original_score * weight

The weight can be predefined for each document based on metadata (e.g., priority, recency, or reliability).

2. Using Hybrid Search (Sparse + Dense)

Combine FAISS (dense vector retrieval) with BM25 or keyword-based retrieval.
Assign different weights to results from each method before ranking:
```
final_score = α * dense_score + β * sparse_score
```
- Here, α and β are tunable parameters to give more weight to either method.

3. Re-Ranking with LLM or Heuristics

Use Cross-Encoder (re-ranker) models like ColBERT or BERT-based rerankers to adjust rankings post-retrieval.
Alternatively, assign a weight to each document and manually re-sort results before passing them to the LLM.

4. Index-Time Weighting

Modify your FAISS embeddings during insertion:
- Scale more important embeddings by a factor before storing:
```
weighted_embedding = importance_factor * original_embedding
```
- This helps important elements appear closer in the embedding space.

5. Metadata Filtering and Boosting

Store metadata like “priority” or “trustworthiness” separately.
Filter or boost based on this metadata during retrieval.

Would you like help implementing any of these approaches in Python? 🚀

If you want to weight how much retrieved items influence the LLM response, there are several strategies you can use. The idea is to control how much importance each retrieved document has in the final answer. Here’s how you can do it:

1. Weighted Context Injection

Before passing retrieved documents into the LLM, you can modify their presence or formatting based on importance:

A. Repetition-Based Weighting

If an item is more important, include it multiple times in the context to reinforce it.

context = "\n\n".join([doc.text * int(doc.weight * 10) for doc in retrieved_docs])

More important documents appear multiple times, making them more likely to influence the response.

B. Formatting-Based Weighting

Highlight more important items explicitly:
- Prefix key information with “IMPORTANT:”
- Use bold or uppercase text
- Place important content at the start of the context
```
context = "\n".join([
    f"IMPORTANT: {doc.text}" if doc.weight > 0.8 else doc.text
    for doc in retrieved_docs
])
```
- LLMs pay more attention to the beginning of the context, so putting high-weight items first increases their influence.

2. Token Allocation for Context

Dynamically allocate tokens based on weight.
If your LLM has a context limit, allow high-weight documents to take up more tokens.

Example:

max_tokens = 1024
total_weight = sum(doc.weight for doc in retrieved_docs)
allocated_context = [
    (doc.text[:int(doc.weight / total_weight * max_tokens)])
    for doc in retrieved_docs
]
context = "\n\n".join(allocated_context)

This ensures that high-weight documents get a larger share of the context window.

3. Weight-Aware Prompt Engineering

Instead of modifying documents, adjust how the prompt guides the LLM:

Use the following sources to answer the question.
- Important sources: {high_weight_docs}
- Other sources: {low_weight_docs}

Prioritize important sources when responding.

This method tells the LLM to focus more on high-weight documents.

4. Fine-Tuning or LoRA for Weighting

If using an open-weight LLM (e.g., Llama, Mistral), fine-tune it to recognize metadata like “importance score” and adjust its response accordingly.

Example fine-tuning data:

Context: Important: X. Less important: Y.
User: What is the best approach?
LLM: X is the best approach because it is more reliable.

A lightweight LoRA adapter can also help if full fine-tuning is impractical.

5. Retrieval-Augmented Re-Ranking

Instead of including everything in the prompt, choose how much to pass based on weight.

Only pass the top N highest-weighted documents if context size is limited.
Use a separate re-ranker (e.g., ColBERT, GPT-4-turbo) to decide which retrieved results to prioritize.