ADR 0010: Query Pipeline

Status

Accepted

Context

The query pipeline transforms user questions into answers using the RAG architecture. It must handle:

Dual embedding models (online/offline)
Hybrid search (dense + sparse)
Dual reranking (online/offline)
Bilingual generation (Persian/English)
Citation and traceability for admin monitoring

Decision

Architecture

User Query
    ↓
Query Embedder (OpenAI or Nomic)
    ↓
Hybrid Search (dense + sparse vectors)
    ↓
Reranker (Cohere or BGE)
    ↓
Top-N Chunks (N=5-10)
    ↓
Context Builder (chunks + metadata + history)
    ↓
LLM Generator (Qwen 2.5 or cloud LLM)
    ↓
Answer + Citations + Metadata

Context for LLM

The LLM receives:

Retrieved chunks — text content of top-N results
Metadata — domain, source_file, document_type for each chunk
Conversation history — previous turns for multi-turn context

Prompt template:

You are an insurance assistant. Answer the user's question based on the provided context.

Context:
[Domain: {domain}] [Source: {source_file}]
{chunk_text}

...

Conversation history:
{history}

Question: {query}

Answer the question using only the provided context. Cite sources when relevant.

Admin Layer Metadata

API response includes:

{
  "answer": "Your car insurance policy covers...",
  "citations": [
    {"source_file": "car_faq.pdf", "chunk_index": 5, "domain": "car"}
  ],
  "retrieval_metadata": {
    "embedding_model": "nomic",
    "reranker": "bge",
    "latency_ms": 450,
    "total_chunks_retrieved": 20,
    "chunks_after_rerank": 5
  },
  "confidence": "high"
}

Query-Time Model Selection

def get_query_pipeline():
    if is_internet_available():
        return {
            "embedder": "openai",
            "reranker": "cohere",
            "llm": "cloud"  # or Qwen if preferred
        }
    else:
        return {
            "embedder": "nomic",
            "reranker": "bge",
            "llm": "qwen"
        }

Citations in Response

LLM is instructed to cite sources:

"According to car_faq.pdf, your policy covers..."
"As stated in the terms_and_conditions.pdf document..."

Citations use source_file metadata, enabling admins to trace answers to exact documents.

Consequences

Positive

Full context — LLM has all information needed for accurate answers
Traceability — admin layer can audit which files informed each answer
Multi-turn support — conversation history enables follow-up questions
Model flexibility — automatic fallback during offline periods

Negative

Token usage — full context increases generation cost
Latency — more context means longer LLM inference time
Context window limits — may need to truncate for very long conversations

Neutral

Need to implement conversation history storage
May need to tune how much history to include

Implementation Notes

Use LlamaIndex or LangChain for pipeline orchestration
Store conversation history in Redis or database
Implement rate limiting on query endpoint
Log all retrieval metadata for analysis
Consider streaming responses for better UX

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