name: grading description: Evaluates document relevance to queries using binary scoring (yes/no) with reasoning. Use when filtering retrieved documents, determining relevance for answer generation, or when the user mentions grading, filtering, or document quality.

Document Grading

Instructions

Grade documents for relevance using the functions in components/grader.py. The grading uses Claude API with structured outputs for consistent binary decisions.

Default workflow:

1. Retrieve documents from ChromaDB or web search
2. Call grade_documents(query, documents) to grade all documents
3. Call filter_relevant_documents(graded_docs) to get only relevant ones
4. Check if len(relevant_docs) == 0 to trigger retry logic

Key functions:

# Grade multiple documents (preferred)
graded_docs = grade_documents(query, documents)

# Filter to relevant only
relevant_docs = filter_relevant_documents(graded_docs)

# Grade single document (if needed)
result = grade_document(query, document)

Grading returns:

• relevant: Boolean (True/False)
• reasoning: Explanation of decision

Critical for adaptive retry: When ALL documents are graded as not relevant (len(relevant_docs) == 0), the pipeline triggers query rewriting and retry.

Implementation: components/grader.py, uses GRADING_MODEL from config.py (default: Haiku 4.5), temperature 0.

Examples

Example 1: Standard grading workflow

from components.grader import grade_documents, filter_relevant_documents
from tools.chromadb_tool import query_chromadb

# Input
docs = query_chromadb("gaming laptops", top_k=5)
# Retrieved: 5 documents from catalog

# Grade
graded = grade_documents("gaming laptops", docs)
relevant = filter_relevant_documents(graded)

# Output
# Found 3 relevant documents out of 5 total
# Each has grading_result: {relevant: True, reasoning: "..."}

Example 2: Triggering retry

# Input: Vague query
docs = query_chromadb("fast computer", top_k=5)
graded = grade_documents("fast computer", docs)
relevant = filter_relevant_documents(graded)

# Output
# len(relevant) == 0
# → Triggers query rewrite and retry

Example 3: In adaptive RAG pipeline

# After retrieval
graded_documents = grade_documents(current_query, retrieved_documents)
relevant_documents = filter_relevant_documents(graded_documents)

if len(relevant_documents) == 0:
    # No relevant docs - rewrite query and retry
    if num_retries < RETRY_LIMIT:
        rewrite_result = rewrite_query(current_query, previous_context)
        current_query = rewrite_result['rewritten_query']
        num_retries += 1
        # Loop back to retrieval
else:
    # Found relevant docs - proceed to generation
    generate_answer(original_query, relevant_documents)