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Contextual recall

ContextualRecallMetric is a DeepEval RAG metric that scores whether the retrieved chunks contain everything the expected answer says: the share of the expected answer's sentences that the judge can attribute to a chunk.

Last updated: 05 Oct, 2026 · DeepEval 4.2.8

Contextual precision grades the order of the chunks, and it cannot see a chunk that never came back. Contextual recall starts from the other end: it takes the expected answer and looks for each part of it in the chunks.

Context recall and the missing claim · from the Production RAG Live Marathon · 3:59:05 to 4:04:51

Claims from the reference, found in the chunks

The video lines up the metrics so far. Faithfulness works on the hallucination side of the pipeline; answer relevancy checks that the answer addresses the user's question. An answer can be faithful and still not relevant, so you need both. Context recall covers a third side, and it needs two pieces of data: the reference, the ground-truth answer from the goldens, and the retrieved context. Its goal is to check whether the retrieved context is sufficient.

The judge does it in two steps. It extracts the claims from the reference, the correct answer, then checks whether the retrieved chunks cover each claim. For a ProBook X1 question, the reference says 16GB DDR5 RAM and a 512GB NVMe SSD, so the chunks must say both. In the bank example the reference makes four claims, and no chunk contains the third, "rural branch minimum balance is ₹2,500". Three supported out of four is 0.75. The gap points at the ingestion pipeline or the semantic search, not at the answer.

The video scores this with RAGAS; DeepEval's metric works the same way: it reads expected_output and retrieval_context, the judge goes through the expected output sentence by sentence and says yes when a sentence can be attributed to a node of the context, and the score is attributable sentences over all sentences.

The ContextualRecallMetric API

python
from deepeval.metrics import ContextualRecallMetric

metric = ContextualRecallMetric(model=judge, threshold=0.5)
metric.measure(LLMTestCase(
    input=...,
    expected_output=...,      # split into sentences, each one looked for in the chunks
    retrieval_context=[...],  # the order does not matter here
))
metric.score, metric.verdicts  # one yes or no per sentence, with the node it came from

The video's reference and the chunks without the rural balance

The reference has four sentences, one per claim on the video's slide. The chunks leave out the rural one.

python
expected = ("Urban branches require a ₹10,000 minimum balance. The non-maintenance fee is ₹350 + taxes "
            "when the balance falls below the limit. Rural branch minimum balance is ₹2,500. "
            "Semi-urban branch minimum balance is ₹5,000.")
chunks = [
    "Minimum balance is ₹10,000 for urban branches.",
    "Non-maintenance fee is ₹350 + taxes if the balance falls below the minimum.",
    "KYC update is required every 8 years.",
    "Semi-urban branch minimum balance is ₹5,000.",
]
Project files used on this pageThis lesson builds on a project from earlier lessons. The code below imports these files. Click a file to see its code, or follow the link to the lesson that wrote it. To run the code yourself, keep them in the same folder.
View the code here
judge.py
import os

from deepeval.models import DeepEvalBaseLLM
from openai import AsyncOpenAI, OpenAI

GROQ_URL = "https://api.groq.com/openai/v1"


class GroqJudge(DeepEvalBaseLLM):
    """A DeepEval judge model that runs on Groq."""

    def __init__(self, model="openai/gpt-oss-120b"):
        self.model_name = model
        key = os.environ["GROQ_API_KEY"]
        # on a 429 (rate limit) the client waits and tries again, up to 8 times
        self.client = OpenAI(api_key=key, base_url=GROQ_URL, max_retries=8)
        self.async_client = AsyncOpenAI(api_key=key, base_url=GROQ_URL, max_retries=8)

    def load_model(self):
        return self.client

    def get_model_name(self):
        return self.model_name

    def request(self, prompt, schema):
        request = {"model": self.model_name, "messages": [{"role": "user", "content": prompt}], "temperature": 0}
        if schema is not None:
            # ask Groq for JSON in the shape of the metric's Pydantic schema
            json_schema = {"name": schema.__name__, "schema": schema.model_json_schema()}
            request["response_format"] = {"type": "json_schema", "json_schema": json_schema}
        return request

    def generate(self, prompt, schema=None):
        reply = self.client.chat.completions.create(**self.request(prompt, schema))
        text = reply.choices[0].message.content
        return schema.model_validate_json(text) if schema else text

    async def a_generate(self, prompt, schema=None):
        reply = await self.async_client.chat.completions.create(**self.request(prompt, schema))
        text = reply.choices[0].message.content
        return schema.model_validate_json(text) if schema else text


judge = GroqJudge(os.environ.get("JUDGE_MODEL", "openai/gpt-oss-120b"))
technest.py
import json
import os
import re

from openai import OpenAI

groq = OpenAI(api_key=os.environ["GROQ_API_KEY"], base_url="https://api.groq.com/openai/v1")
CHAT_MODEL = "qwen/qwen3.8-27b"

SYSTEM_PROMPT = """You are a helpful customer support assistant for TechNest, an online electronics store.
Answer the customer's question using ONLY the information provided in the context below.
If the context does not contain enough information to answer fully, say so honestly.
Keep your answer concise, factual, and friendly. Do not invent any details not present in the context.
Reply in two or three plain sentences, with no lists or tables."""

with open("catalog.json", encoding="utf-8") as f:
    CATALOG = json.load(f)

SKIP = {"a", "an", "and", "are", "can", "do", "does", "for", "how", "i", "in", "is", "it",
        "long", "much", "my", "of", "on", "s", "technest", "the", "to", "what", "with", "you", "your"}


def words(text):
    """The words in a text that carry meaning, in lower case."""
    return {w for w in re.findall(r"[a-z0-9]+", text.lower()) if w not in SKIP}


def retrieve(question, top_k=3):
    asked = words(question)
    ranked = sorted(CATALOG, key=lambda item: len(asked & words(item["title"] + " " + item["content"])), reverse=True)
    return [item["content"] for item in ranked[:top_k]]


def generate(question, contexts):
    context_block = "\n\n".join(f"[{i+1}] {c}" for i, c in enumerate(contexts))
    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": f"Context:\n{context_block}\n\nCustomer question: {question}"},
    ]
    response = groq.chat.completions.create(model=CHAT_MODEL, messages=messages, temperature=0)
    return response.choices[0].message.content.strip()


def answer(question, top_k=3):
    contexts = retrieve(question, top_k)
    return generate(question, contexts), contexts
catalog.json
[
  {
    "id": "prod_001",
    "category": "product",
    "title": "ProBook X1 Laptop",
    "content": "The TechNest ProBook X1 is a 14-inch laptop featuring an Intel Core i7-13th Gen processor, 16GB DDR5 RAM, and a 512GB NVMe SSD. It has a battery life of 12 hours, weighs 1.4kg, and comes with a backlit keyboard. Price: $1,299. Includes a 2-year manufacturer warranty."
  },
  {
    "id": "prod_002",
    "category": "product",
    "title": "PixelPhone 15",
    "content": "The TechNest PixelPhone 15 is a 6.7-inch AMOLED smartphone with a 50MP triple camera system, 8GB RAM, 256GB storage, and a 5,000mAh battery supporting 65W fast charging. Available in Midnight Black and Arctic White. Price: $899. Includes a 1-year warranty."
  },
  {
    "id": "prod_003",
    "category": "product",
    "title": "SoundPods Pro",
    "content": "The TechNest SoundPods Pro are true wireless earbuds with active noise cancellation (ANC), 8 hours of playback per charge plus 24 hours with the case, and IPX4 water resistance. They connect via Bluetooth 5.3 and support multipoint pairing with two devices simultaneously. Price: $149."
  },
  {
    "id": "prod_004",
    "category": "product",
    "title": "UltraTab S2 Tablet",
    "content": "The TechNest UltraTab S2 is a 11-inch tablet powered by a Snapdragon 870 processor with 8GB RAM and 128GB storage expandable via microSD. It features a 120Hz display, a 7,500mAh battery, and supports the TechNest Stylus Pen sold separately. Price: $549. Includes a 1-year warranty."
  },
  {
    "id": "prod_005",
    "category": "product",
    "title": "SmartWatch X",
    "content": "The TechNest SmartWatch X features continuous heart rate monitoring, SpO2 tracking, GPS, and 7-day battery life. It is water-resistant up to 50 metres. Compatible with both Android and iOS. Price: $299. Includes a 1-year warranty and a free extra silicone band."
  },
  {
    "id": "prod_006",
    "category": "product",
    "title": "ProCam 4K Action Camera",
    "content": "The TechNest ProCam 4K shoots 4K video at 60fps and 20MP photos. It is waterproof up to 10 metres without a case, has built-in image stabilisation (EIS), and includes a touch screen. Battery life is 90 minutes of 4K recording. Price: $229. Includes a 1-year warranty."
  },
  {
    "id": "prod_007",
    "category": "product",
    "title": "BassBuds Max Headphones",
    "content": "The TechNest BassBuds Max are over-ear wireless headphones with 40-hour battery life, hybrid active noise cancellation, and a premium 40mm driver for deep bass. They fold flat for travel and include a carrying case. Price: $199. Compatible with all Bluetooth devices."
  },
  {
    "id": "prod_008",
    "category": "product",
    "title": "SoundBar 360",
    "content": "The TechNest SoundBar 360 is a 2.1 soundbar with a 120W output, built-in subwoofer, Dolby Atmos support, and HDMI ARC connectivity. It also supports Bluetooth streaming and has an optical audio input. Dimensions: 90cm wide. Price: $349. Includes a 2-year warranty."
  },
  {
    "id": "policy_001",
    "category": "policy",
    "title": "Return Policy",
    "content": "TechNest accepts returns within 30 days of the original purchase date. Items must be in their original packaging with all accessories included. Customers are responsible for return shipping costs unless the item arrives defective or damaged. Refunds are processed within 5 to 7 business days of receiving the returned item. Digital downloads and opened software are non-refundable."
  },
  {
    "id": "policy_002",
    "category": "policy",
    "title": "Shipping Policy",
    "content": "TechNest offers free standard shipping on all orders over $50 within the continental US. Standard shipping takes 3 to 5 business days. Expedited shipping (1 to 2 business days) is available for $9.99. Same-day delivery is available in select cities for $19.99. Orders placed before 2pm local time are dispatched the same day."
  },
  {
    "id": "policy_003",
    "category": "policy",
    "title": "Warranty Policy",
    "content": "All TechNest products include a minimum 1-year manufacturer warranty covering defects in materials and workmanship. The ProBook X1 and SoundBar 360 include a 2-year warranty. Warranty does not cover physical damage, water damage (unless the product is rated waterproof), or damage from unauthorised modifications. To make a warranty claim, contact support@technest.com with your order number and a description of the issue."
  },
  {
    "id": "policy_004",
    "category": "policy",
    "title": "Payment Policy",
    "content": "TechNest accepts Visa, Mastercard, American Express, PayPal, and Apple Pay. All transactions are encrypted using 256-bit SSL. Buy Now Pay Later is available via Klarna for orders over $100, with 0% interest for 3 monthly instalments. TechNest does not store full card details — payments are processed securely by Stripe."
  },
  {
    "id": "faq_001",
    "category": "faq",
    "title": "Order Tracking",
    "content": "To track your order, visit technest.com/orders and enter your order number and email address. A shipping confirmation email with a tracking link is sent within 24 hours of dispatch. If you have not received your tracking email after 48 hours, check your spam folder or contact support@technest.com."
  },
  {
    "id": "faq_002",
    "category": "faq",
    "title": "International Shipping",
    "content": "TechNest ships to over 40 countries. International shipping rates start at $14.99 and delivery takes 7 to 14 business days. Import duties and taxes are the responsibility of the customer and are not included in the product price. Free shipping promotions apply to US orders only."
  },
  {
    "id": "faq_003",
    "category": "faq",
    "title": "Bulk and Business Orders",
    "content": "TechNest offers volume discounts for businesses purchasing 10 or more units of any single product. Discounts range from 10% for 10 to 49 units up to 25% for 100 or more units. Contact business@technest.com with your requirements for a custom quote. A dedicated account manager is assigned for orders over $10,000."
  }
]

The video's missing rural balance, run on Groq

ExampleAPI keyFrom the video, run on Groq
from deepeval.metrics import ContextualRecallMetric
from deepeval.test_case import LLMTestCase
from judge import judge

question = "What is the minimum balance for my savings account?"
expected = ("Urban branches require a ₹10,000 minimum balance. The non-maintenance fee is ₹350 + taxes "
            "when the balance falls below the limit. Rural branch minimum balance is ₹2,500. "
            "Semi-urban branch minimum balance is ₹5,000.")
chunks = [
    "Minimum balance is ₹10,000 for urban branches.",
    "Non-maintenance fee is ₹350 + taxes if the balance falls below the minimum.",
    "KYC update is required every 8 years.",
    "Semi-urban branch minimum balance is ₹5,000.",
]

metric = ContextualRecallMetric(model=judge)
metric.measure(LLMTestCase(input=question, expected_output=expected, retrieval_context=chunks))
print(f"recall: {metric.score:.2f}")
for verdict in metric.verdicts:
    print(f"   {verdict.verdict:3} {verdict.reason}")

The judge went through the four sentences and attributed three of them to a node, naming the node each time. The rural balance gets no: no node mentions it. Three out of four is 0.75, the same score as the video's slide. Each reason names a node by its position, which tells you where a fact came from.

A two-part TechNest question

The question asks about two policies, shipping and returns, so a complete answer needs two catalog entries.

python
question = "What are TechNest's shipping options and how long do returns take to process?"
expected = ("Standard shipping is free on orders over $50 and takes 3 to 5 business days. Expedited shipping "
            "takes 1 to 2 business days for $9.99, and same-day delivery costs $19.99 in select cities. "
            "Refunds are processed within 5 to 7 business days of receiving the returned item.")

Three chunks, then one

python
for top_k in [3, 1]:
    contexts = retrieve(question, top_k=top_k)
    metric.measure(LLMTestCase(input=question, expected_output=expected, retrieval_context=contexts))

Recall of the TechNest search at top_k=3 and top_k=1

ExampleAPI key
from deepeval.metrics import ContextualRecallMetric
from deepeval.test_case import LLMTestCase
from judge import judge
from technest import retrieve

question = "What are TechNest's shipping options and how long do returns take to process?"
expected = ("Standard shipping is free on orders over $50 and takes 3 to 5 business days. Expedited shipping "
            "takes 1 to 2 business days for $9.99, and same-day delivery costs $19.99 in select cities. "
            "Refunds are processed within 5 to 7 business days of receiving the returned item.")

metric = ContextualRecallMetric(model=judge)
for top_k in [3, 1]:
    contexts = retrieve(question, top_k=top_k)
    metric.measure(LLMTestCase(input=question, expected_output=expected, retrieval_context=contexts))
    print(f"top_k={top_k}  recall={metric.score:.2f}  first chunk: {contexts[0][:45]}...")
    for verdict in metric.verdicts:
        print(f"   {verdict.verdict:3} {verdict.reason}")

What top_k=1 cut off

  • top_k=3 scores 1.00. The two shipping sentences are found in the 2nd node, the shipping policy, and the refund sentence in the 1st, the return policy.
  • top_k=1 scores 0.33. The first chunk is the return policy, so the refund sentence still gets yes, and both shipping sentences get no: the shipping policy ranked second and was cut off.
  • The search ranked the right entries. Recall dropped because top_k kept too few of them, the first lever the docs name for a low recall.

Contextual recall vs faithfulness

Both metrics split a text into parts and look for each part in the chunks. They start from opposite ends.

Contextual recallFaithfulness
Parts come fromThe expected answerThe bot's answer
Checked againstThe retrieved chunksThe retrieved chunks
GradesThe retrieverThe generator
Needs an expected answer?YesNo

When to use contextual recall

  • After changing chunk size, top_k or the search, to check that nothing an answer needs stopped coming back.
  • When answers are incomplete and you need to know whether the missing fact was ever retrieved.
Watch out. Recall is only as good as the expected answer. It never reads the bot's answer, so a bad answer cannot lower it, but an expected answer with a fact the catalog does not hold scores low on every search. Check the expected answers against the knowledge base first.
Try it yourself
  • Add "Rural branch minimum balance is ₹2,500." to the bank chunks and check that recall reaches 1.00.
  • Run the TechNest question with top_k=2: the return and shipping policies both come back.
  • Ask "Do you ship to Canada?" with the expected answer "TechNest offers free standard shipping on orders over $50 within the continental US." and read why recall is 0.

You understood something today that you didn't yesterday.