Query Translation Patterns

1. Query Translation

Effective Retrieval-Augmented Generation begins by shaping your user’s raw input into a form that maximizes retrieval quality. Here are core techniques:

1. Query Expansion
   By automatically injecting synonyms, hypernyms, or semantic anchors, you broaden the search net. For example, turning “car performance” into “automobile performance, speed, fuel efficiency” can surface more relevant docs. Expansion can come from thesauri, word embeddings (finding nearest neighbors), or external knowledge graphs.

2. Prompt‑Based Reformulation
   Leverage a small LLM prompt to rewrite or clarify the user’s question.

    textCopyEdit“Original user query: ‘improve engine.’  
    Reformulate for search: ‘What are best practices to improve car engine performance?’”
   

   This often boosts retrieval precision because the reformulated query is closer to the language found in your document corpus.

3. Pseudo‑Query Generation
   Flip the process: generate “questions” from each document chunk (via an LLM), then index these pseudo‑queries alongside the chunks. At runtime, match user queries against this dual index to improve recall on niche topics.

4. User‑Context Personalization
   Prepend or append session‑level context—user profile, past queries, or domain—so the retrieval stage is hyperspecific. E.g.,

    textCopyEdit“(User is a mechanical engineer) How to troubleshoot overheating engine?”
   

   This helps the retriever surface documents written at the right technical depth.

2. Document Embedding Strategies

Dense embeddings (e.g. OpenAI’s text‑embedding‑ada) capture semantics in a fixed‑size vector; sparse embeddings (BM25-style) emphasize term matching. Multi‑vector chunking lets you represent long docs via multiple embeddings, with a lightweight aggregator to score them.

3. Retrieval Techniques

Hybrid search blends BM25 for high‑precision term matches with vector search for semantic recall. ANN libraries like FAISS or HNSW provide sub‑millisecond lookup, vital at scale.

4. Re‑Ranking & Fusion Methods

Once you pull the top N candidates, a cross‑encoder model (e.g. BERT) can re‑rank them by scoring [query; document] pairs. Fusion‑in‑decoder then concatenates the top passages into one prompt, allowing the generator to synthesize across sources.

5. Context Window Management

For LLMs with limited context length, split long documents into overlapping chunks or use a hierarchical attention model that first narrows down to key sections, then attends deeply on those.

6. Evaluation & Feedback Loops

Measure retrieval with precision/recall@k. For generation, track BLEU/ROUGE but also run human A/B testing. Capture user ratings or correction logs to retrain your retriever and generator in a closed loop.

Parallel Query (Fan‑out) Retrieval

In Retrieval‑Augmented Generation pipelines, parallel query or fan‑out retrieval refers to sending multiple, often diverse, queries simultaneously to one or more retrieval backends. By “fanning out” your original user query into several sub‑queries or variant forms, you increase the chances of capturing different facets of a topic and surface a richer set of candidate documents for downstream generation.

Core Patterns of Fan‑out Retrieval

1. Query Variants

   • Synonym‑expanded: Insert synonyms or related terms.

   • Question vs. Statement: Phrase one query as a question (“How to optimize…?”) and another as a declarative phrase (“optimization techniques for…”).

   • Entity anchoring: Focus one sub‑query on a specific entity (“TensorFlow tuning”) and another on a broader category (“deep learning model optimization”).

2. Multi‑Index Fan‑out

   • Vector store (semantic): Sends the original or reformulated query to FAISS/Weaviate to retrieve semantically similar chunks.

   • Sparse store (lexical): Hits a BM25 backend (e.g., Elasticsearch) to catch exact or near‑exact term matches overlooked by dense vectors.

   • Metadata‑filter: Filters by date, author, or domain-specific tags—useful when recency or source credibility matters.

3. Parallelism & Asynchrony

   • Use asynchronous HTTP or gRPC calls to each backend so that retrievals happen in parallel, minimizing end‑to‑end latency.

   • Collect all results once every call completes (or after a timeout) and merge them according to a chosen strategy (union, weighted interleaving, etc.).

Fan‑out in Code (Python‑style Pseudocode)

import asyncio

async def fetch_vector_hits(query):
    # Call vector store client
    return await vector_client.search(query, top_k=10)

async def fetch_bm25_hits(query):
    # Call BM25 backend (e.g., Elasticsearch)
    return await bm25_client.search(query, top_k=10)

async def fanout_retrieve(user_query):
    # Generate variants
    variants = [
        user_query,
        synonym_expand(user_query),
        to_question_form(user_query),
    ]

    # Launch all retrievals in parallel
    tasks = []
    for q in variants:
        tasks.append(fetch_vector_hits(q))
        tasks.append(fetch_bm25_hits(q))
    results = await asyncio.gather(*tasks, return_exceptions=False)

    # Flatten and merge
    all_hits = [hit for sublist in results for hit in sublist]
    merged = merge_and_dedup(all_hits)
    return merged

When to Use Fan‑out Retrieval

• Complex, Multi‑faceted Queries: Topics with technical jargon, ambiguous phrasing, or multiple subtopics.

• Large, Heterogeneous Corpora: When your data spans scientific papers, blog posts, FAQs, and dynamic logs, each benefitting from different retrieval strategies.

• Low‑confidence Original Query: In chatbots or assistant systems, where user queries can be brief, under‑specified, or error‑prone.

Reciprocal Rank Fusion (RRF)

Reciprocal Rank Fusion is a simple yet surprisingly effective method for merging multiple ranked retrieval lists into a single, robust ranking. Unlike score-based fusion that requires normalizing disparate scoring schemes, RRF leverages the positions (ranks) of documents in each list to compute a combined score.

How It Works

1. Input:
   You have m retrieval outputs (e.g., from different query variants or different retrieval backends). Each output is an ordered list of documents, where document d appears at rank r_i(d) in list i (1‐indexed).

2. Reciprocal Score Calculation:
   For each document d, compute its RRF score as:

   RRF(d)  =  ∑i=1m1k+ri(d) \mathrm{RRF}(d) \;=\; \sum_{i=1}^{m} \frac{1}{k + r_i(d)}RRF(d)=i=1∑m​k+ri​(d)1​

   • r_i(d) = the rank of d in list i (if d isn’t in the top‐K, you can treat r_i(d)=∞ or simply omit that term).

   • k = a constant (typically between 50 and 100) that dampens the influence of top positions.

3. Merged Ranking:
   Sort all documents in descending order of their RRF score. Documents that appear near the top in even one list will get a significant boost, while those consistently high across lists will dominate.

Why RRF?

• Robust to Score Scales: No need to normalize cosine similarities, BM25 scores, etc.

• Emphasizes Consensus: Documents found early in multiple lists accrue a higher combined score.

• Simplicity & Efficiency: Linear‐time merging with a single pass over each ranked list.

Pseudocode Example

def reciprocal_rank_fusion(ranked_lists, k=60):
    """
    ranked_lists: List of lists, where each inner list is an ordered sequence of doc_ids.
    k: Rank damping constant.
    """
    from collections import defaultdict
    rrf_scores = defaultdict(float)

    for lst in ranked_lists:
        for rank, doc_id in enumerate(lst, start=1):
            rrf_scores[doc_id] += 1.0 / (k + rank)

    # Produce a list of (doc_id, score) sorted by descending score
    fused = sorted(rrf_scores.items(), key=lambda x: x[1], reverse=True)
    return [doc_id for doc_id, score in fused]

When to Use

• Combining Heterogeneous Retrievals: Fuse outputs from semantic (vector) and lexical (BM25) retrievers.

• Ensembling Query Variants: Merge results from multiple query reformulations (e.g., fan‑out retrieval).

• Resource‑constrained Settings: Low overhead compared to learning‐to‐rank or heavy score normalizations.

Query Decomposition

When user questions are complex or multi‑faceted, feeding them wholesale into your retriever often dilutes the focus and returns noisy or partial matches. Query Decomposition breaks a single, compound query into several smaller, targeted sub‑queries—each designed to retrieve evidence for one aspect of the original question. The retrieved passages can then be combined or synthesized by the generator to produce a comprehensive answer.

Why Decompose Queries?

• Precision over Recall: Narrower sub‑queries hit highly relevant sections rather than scattering attention across an entire long query.

• Modular Retrieval: You can apply specialized retrieval strategies per sub‑query (e.g., date filters for “when” components, glossary lookups for “definition” parts).

• Improved Context Assembly: Enables the generator to piece together answers from distinct, clearly retrieved snippets.

Best Practices

• Leverage LLMs for Decomposition: Prompt a smaller model to identify sub‑questions from user input automatically.

• Maintain Query Context: Tag each snippet with its originating sub‑query so the generator knows which aspect it addresses.

• Adaptive Splitting: Monitor downstream generation quality; if answers miss an angle, adjust your decomposition heuristics or prompts.

Step‑Back Prompting

Step‑back prompting is a reflective technique in which the model is guided to pause and reconsider its own outputs or reasoning steps before proceeding. By inserting a “step‑back” instruction—such as “Before answering, list any assumptions you’re making” or “Now review the key points you’ve covered and check for gaps”—you encourage the LLM to self‑audit, surface hidden premises, and catch potential errors or omissions. This meta‑cognitive nudge often leads to more accurate, coherent responses, especially in complex tasks where blind forward generation can propagate mistakes.

Hypothetical Document Embeddings

Hypothetical document embeddings augment your index by generating vector representations for “virtual” or anticipated content that isn’t explicitly in your corpus. Using an LLM, you can synthesize likely questions, summaries, or alternate phrasings for each document chunk—then embed those hypotheticals alongside the original text. At query time, user inputs may match these synthetic embeddings even if the exact wording never appears in the source, boosting recall on corner‑case topics or infrequent terminology. This approach effectively broadens your semantic net without bloating the actual document store.