Chapter 73

Hybrid search example

NotebookPython 3 (ipykernel)42 cells

In [1]python · cell 1

python

import json
import pandas as pd
from tqdm.auto import tqdm
from sentence_transformers import SentenceTransformer
from elasticsearch import Elasticsearch

Output

/usr/local/python/3.10.13/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

In [3]python · cell 2

python

with open('documents-with-ids.json', 'rt') as f_in:
    documents = json.load(f_in)

In [4]python · cell 3

python

model_name = 'multi-qa-MiniLM-L6-cos-v1'
model = SentenceTransformer(model_name)

Output

/usr/local/python/3.10.13/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884
  warnings.warn(

In [5]python · cell 4

python

for doc in tqdm(documents):
    question = doc['question']
    text = doc['text']
    qt = question + ' ' + text

    doc['question_vector'] = model.encode(question)
    doc['text_vector'] = model.encode(text)
    doc['question_text_vector'] = model.encode(qt)

Output

100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 948/948 [01:55<00:00,  8.19it/s]

In [6]python · cell 5

python

es_client = Elasticsearch('http://localhost:9200') 

index_settings = {
    "settings": {
        "number_of_shards": 1,
        "number_of_replicas": 0
    },
    "mappings": {
        "properties": {
            "text": {"type": "text"},
            "section": {"type": "text"},
            "question": {"type": "text"},
            "course": {"type": "keyword"},
            "id": {"type": "keyword"},
            "question_vector": {
                "type": "dense_vector",
                "dims": 384,
                "index": True,
                "similarity": "cosine"
            },
            "text_vector": {
                "type": "dense_vector",
                "dims": 384,
                "index": True,
                "similarity": "cosine"
            },
            "question_text_vector": {
                "type": "dense_vector",
                "dims": 384,
                "index": True,
                "similarity": "cosine"
            },
        }
    }
}

index_name = "course-questions"

es_client.indices.delete(index=index_name, ignore_unavailable=True)
es_client.indices.create(index=index_name, body=index_settings)

Output

ObjectApiResponse({'acknowledged': True, 'shards_acknowledged': True, 'index': 'course-questions'})

In [7]python · cell 6

python

for doc in tqdm(documents):
    es_client.index(index=index_name, document=doc)

Output

100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 948/948 [00:26<00:00, 36.07it/s]

Hybrid search example

In [8]python · cell 8

python

course = "data-engineering-zoomcamp"

In [9]python · cell 9

python

query = 'I just discovered the course. Can I still join it?'

In [10]python · cell 10

python

v_q = model.encode(query)

In [35]python · cell 11

python

knn_query = {
    "field": "text_vector",
    "query_vector": v_q,
    "k": 5,
    "num_candidates": 10000,
    "boost": 0.5,
    "filter": {
        "term": {
            "course": course
        }
    }
}

In [36]python · cell 12

python

keyword_query = {
    "bool": {
        "must": {
            "multi_match": {
                "query": query,
                "fields": ["question^3", "text", "section"],
                "type": "best_fields",
                "boost": 0.5,
            }
        },
        "filter": {
            "term": {
                "course": course
            }
        }
    }
}

In [37]python · cell 13

python

response = es_client.search(
    index=index_name,
    query=keyword_query,
    knn=knn_query,
    size=5
)

In [38]python · cell 14

python

response["hits"]["hits"]

Output

[{'_index': 'course-questions',
  '_id': 'ICPvR5EBO7bpWV6zkHVw',
  '_score': 36.424633,
  '_source': {'text': "Yes, even if you don't register, you're still eligible to submit the homeworks.\nBe aware, however, that there will be deadlines for turning in the final projects. So don't leave everything for the last minute.",
   'section': 'General course-related questions',
   'question': 'Course - Can I still join the course after the start date?',
   'course': 'data-engineering-zoomcamp',
   'id': '7842b56a',
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 {'_index': 'course-questions',
  '_id': 'JSPvR5EBO7bpWV6zkXU7',
  '_score': 27.396076,
  '_source': {'text': 'Yes, we will keep all the materials after the course finishes, so you can follow the course at your own pace after it finishes.\nYou can also continue looking at the homeworks and continue preparing for the next cohort. I guess you can also start working on your final capstone project.',
   'section': 'General course-related questions',
   'question': 'Course - Can I follow the course after it finishes?',
   'course': 'data-engineering-zoomcamp',
   'id': 'a482086d',
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 {'_index': 'course-questions',
  '_id': 'IiPvR5EBO7bpWV6zkHXB',
  '_score': 21.920742,
  '_source': {'text': 'You can start by installing and setting up all the dependencies and requirements:\nGoogle cloud account\nGoogle Cloud SDK\nPython 3 (installed with Anaconda)\nTerraform\nGit\nLook over the prerequisites and syllabus to see if you are comfortable with these subjects.',
   'section': 'General course-related questions',
   'question': 'Course - What can I do before the course starts?',
   'course': 'data-engineering-zoomcamp',
   'id': '63394d91',
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 {'_index': 'course-questions',
  '_id': 'JiPvR5EBO7bpWV6zkXVu',
  '_score': 21.325657,
  '_source': {'text': 'Yes, the slack channel remains open and you can ask questions there. But always sDocker containers exit code w search the channel first and second, check the FAQ (this document), most likely all your questions are already answered here.\nYou can also tag the bot @ZoomcampQABot to help you conduct the search, but don’t rely on its answers 100%, it is pretty good though.',
   'section': 'General course-related questions',
   'question': 'Course - Can I get support if I take the course in the self-paced mode?',
   'course': 'data-engineering-zoomcamp',
   'id': 'eb56ae98',
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 {'_index': 'course-questions',
  '_id': 'ISPvR5EBO7bpWV6zkHWd',
  '_score': 18.261362,
  '_source': {'text': "You don't need it. You're accepted. You can also just start learning and submitting homework without registering. It is not checked against any registered list. Registration is just to gauge interest before the start date.",
   'section': 'General course-related questions',
   'question': 'Course - I have registered for the Data Engineering Bootcamp. When can I expect to receive the confirmation email?',
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    -0.027297912165522575,
    -0.08384589850902557]}}]

Hybrid search pipeline

In [25]python · cell 16

python

df_ground_truth = pd.read_csv('ground-truth-data.csv')

In [26]python · cell 17

python

ground_truth = df_ground_truth.to_dict(orient='records')

In [27]python · cell 18

python

def hit_rate(relevance_total):
    cnt = 0

    for line in relevance_total:
        if True in line:
            cnt = cnt + 1

    return cnt / len(relevance_total)

In [28]python · cell 19

python

def mrr(relevance_total):
    total_score = 0.0

    for line in relevance_total:
        for rank in range(len(line)):
            if line[rank] == True:
                total_score = total_score + 1 / (rank + 1)

    return total_score / len(relevance_total)

In [44]python · cell 20

python

def elastic_search_hybrid(field, query, vector, course):
    knn_query = {
        "field": field,
        "query_vector": vector,
        "k": 5,
        "num_candidates": 10000,
        "boost": 0.5,
        "filter": {
            "term": {
                "course": course
            }
        }
    }

    keyword_query = {
        "bool": {
            "must": {
                "multi_match": {
                    "query": query,
                    "fields": ["question", "text", "section"],
                    "type": "best_fields",
                    "boost": 0.5,
                }
            },
            "filter": {
                "term": {
                    "course": course
                }
            }
        }
    }

    search_query = {
        "knn": knn_query,
        "query": keyword_query,
        "size": 5,
        "_source": ["text", "section", "question", "course", "id"]
    }

    es_results = es_client.search(
        index=index_name,
        body=search_query
    )
    
    result_docs = []
    
    for hit in es_results['hits']['hits']:
        result_docs.append(hit['_source'])

    return result_docs

In [49]python · cell 21

python

def question_hybrid(q):
    question = q['question']
    course = q['course']

    v_q = model.encode(question)

    return elastic_search_hybrid('question_vector', question, v_q, course)

In [23]python · cell 22

python

def evaluate(ground_truth, search_function):
    relevance_total = []

    for q in tqdm(ground_truth):
        doc_id = q['document']
        results = search_function(q)
        relevance = [d['id'] == doc_id for d in results]
        relevance_total.append(relevance)

    return {
        'hit_rate': hit_rate(relevance_total),
        'mrr': mrr(relevance_total),
    }

In [51]python · cell 23

python

evaluate(ground_truth, question_hybrid)

Output

100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4627/4627 [02:00<00:00, 38.35it/s]

{'hit_rate': 0.9234925437648585, 'mrr': 0.8481665586052878}

ES knn on questions: {'hit_rate': 0.773071104387292, 'mrr': 0.6666810748505158}

In [52]python · cell 25

python

def text_hybrid(q):
    question = q['question']
    course = q['course']

    v_q = model.encode(question)

    return elastic_search_hybrid('text_vector', question, v_q, course)

In [53]python · cell 26

python

evaluate(ground_truth, text_hybrid)

Output

100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4627/4627 [01:51<00:00, 41.43it/s]

{'hit_rate': 0.9234925437648585, 'mrr': 0.8461710251422809}

ES knn on texts: {'hit_rate': 0.8286146531229739, 'mrr': 0.7062315395144454}

In [54]python · cell 28

python

def question_text_hybrid(q):
    question = q['question']
    course = q['course']

    v_q = model.encode(question)

    return elastic_search_hybrid('question_text_vector', question, v_q, course)

evaluate(ground_truth, question_text_hybrid)

Output

100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4627/4627 [01:48<00:00, 42.46it/s]

{'hit_rate': 0.9250054030689432, 'mrr': 0.8506231539514445}

ES knn on questions and answers: {'hit_rate': 0.9172249837907932, 'mrr': 0.824306606152295}

Reranking

To use the Reciprocal rank fusion (RRF) score we need to pull the docker image with a more recent version of Elasticsearch:

bash

docker run -it \
    --rm \
    --name elasticsearch \
    -m 4GB \
    -p 9200:9200 \
    -p 9300:9300 \
    -e "discovery.type=single-node" \
    -e "xpack.security.enabled=false" \
    docker.elastic.co/elasticsearch/elasticsearch:8.9.0

In [16]python · cell 32

python

def elastic_search_hybrid_rrf(field, query, vector, course):
    knn_query = {
        "field": field,
        "query_vector": vector,
        "k": 5,
        "num_candidates": 10000,
        "boost": 0.5,
        "filter": {
            "term": {
                "course": course
            }
        }
    }

    keyword_query = {
        "bool": {
            "must": {
                "multi_match": {
                    "query": query,
                    "fields": ["question", "text", "section"],
                    "type": "best_fields",
                    "boost": 0.5,
                }
            },
            "filter": {
                "term": {
                    "course": course
                }
            }
        }
    }

    search_query = {
        "knn": knn_query,
        "query": keyword_query,
        "size": 5,
        "rank": {
            "rrf": {}
        },
        "_source": ["text", "section", "question", "course", "id"]
    }

    es_results = es_client.search(
        index=index_name,
        body=search_query
    )
    
    result_docs = []
    
    for hit in es_results['hits']['hits']:
        result_docs.append(hit['_source'])

    return result_docs

In [17]python · cell 33

python

course = "data-engineering-zoomcamp"

In [18]python · cell 34

python

query = 'I just discovered the course. Can I still join it?'

In [19]python · cell 35

python

v_q = model.encode(query)

In [ ]python · cell 36

python

elastic_search_hybrid_rrf('question_text_vector', query, v_q, course)

By default, RRF isn't available in a free-tier subscription. But you can try to use 30-day trial or upgrade the subscription plan.

RRF implementation

In [30]python · cell 39

python

def compute_rrf(rank, k=60):
    """ Our own implementation of the relevance score """
    return 1 / (k + rank)

def elastic_search_hybrid_rrf(field, query, vector, course, k=60):
    knn_query = {
        "field": field,
        "query_vector": vector,
        "k": 10,
        "num_candidates": 10000,
        "boost": 0.5,
        "filter": {
            "term": {
                "course": course
            }
        }
    }

    keyword_query = {
        "bool": {
            "must": {
                "multi_match": {
                    "query": query,
                    "fields": ["question", "text", "section"],
                    "type": "best_fields",
                    "boost": 0.5,
                }
            },
            "filter": {
                "term": {
                    "course": course
                }
            }
        }
    }

    knn_results = es_client.search(
        index=index_name, 
        body={
            "knn": knn_query, 
            "size": 10
        }
    )['hits']['hits']
    
    keyword_results = es_client.search(
        index=index_name, 
        body={
            "query": keyword_query, 
            "size": 10
        }
    )['hits']['hits']
    
    rrf_scores = {}
    # Calculate RRF using vector search results
    for rank, hit in enumerate(knn_results):
        doc_id = hit['_id']
        rrf_scores[doc_id] = compute_rrf(rank + 1, k)

    # Adding keyword search result scores
    for rank, hit in enumerate(keyword_results):
        doc_id = hit['_id']
        if doc_id in rrf_scores:
            rrf_scores[doc_id] += compute_rrf(rank + 1, k)
        else:
            rrf_scores[doc_id] = compute_rrf(rank + 1, k)

    # Sort RRF scores in descending order
    reranked_docs = sorted(rrf_scores.items(), key=lambda x: x[1], reverse=True)
    
    # Get top-K documents by the score
    final_results = []
    for doc_id, score in reranked_docs[:5]:
        doc = es_client.get(index=index_name, id=doc_id)
        final_results.append(doc['_source'])
    
    return final_results

In [29]python · cell 40

python

def question_text_hybrid_rrf(q):
    question = q['question']
    course = q['course']

    v_q = model.encode(question)

    return elastic_search_hybrid_rrf('question_text_vector', question, v_q, course)

evaluate(ground_truth, question_text_hybrid_rrf)

Output

100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4627/4627 [03:08<00:00, 24.49it/s]

{'hit_rate': 0.9520207477847418, 'mrr': 0.8745911677833017}

ES hybrid search scores: {'hit_rate': 0.9250054030689432, 'mrr': 0.8506231539514445}

In [ ]python · cell 42

python

Chapter 73

Hybrid search example

NotebookPython 3 (ipykernel)42 cells

In [1]python · cell 1

python

import json
import pandas as pd
from tqdm.auto import tqdm
from sentence_transformers import SentenceTransformer
from elasticsearch import Elasticsearch

Output

/usr/local/python/3.10.13/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

In [3]python · cell 2

python

with open('documents-with-ids.json', 'rt') as f_in:
    documents = json.load(f_in)

In [4]python · cell 3

python

model_name = 'multi-qa-MiniLM-L6-cos-v1'
model = SentenceTransformer(model_name)

Output

/usr/local/python/3.10.13/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884
  warnings.warn(

In [5]python · cell 4

python

for doc in tqdm(documents):
    question = doc['question']
    text = doc['text']
    qt = question + ' ' + text

    doc['question_vector'] = model.encode(question)
    doc['text_vector'] = model.encode(text)
    doc['question_text_vector'] = model.encode(qt)

Output

100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 948/948 [01:55<00:00,  8.19it/s]

In [6]python · cell 5

python

es_client = Elasticsearch('http://localhost:9200') 

index_settings = {
    "settings": {
        "number_of_shards": 1,
        "number_of_replicas": 0
    },
    "mappings": {
        "properties": {
            "text": {"type": "text"},
            "section": {"type": "text"},
            "question": {"type": "text"},
            "course": {"type": "keyword"},
            "id": {"type": "keyword"},
            "question_vector": {
                "type": "dense_vector",
                "dims": 384,
                "index": True,
                "similarity": "cosine"
            },
            "text_vector": {
                "type": "dense_vector",
                "dims": 384,
                "index": True,
                "similarity": "cosine"
            },
            "question_text_vector": {
                "type": "dense_vector",
                "dims": 384,
                "index": True,
                "similarity": "cosine"
            },
        }
    }
}

index_name = "course-questions"

es_client.indices.delete(index=index_name, ignore_unavailable=True)
es_client.indices.create(index=index_name, body=index_settings)

Output

ObjectApiResponse({'acknowledged': True, 'shards_acknowledged': True, 'index': 'course-questions'})

In [7]python · cell 6

python

for doc in tqdm(documents):
    es_client.index(index=index_name, document=doc)

Output

100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 948/948 [00:26<00:00, 36.07it/s]

Hybrid search example

In [8]python · cell 8

python

course = "data-engineering-zoomcamp"

In [9]python · cell 9

python

query = 'I just discovered the course. Can I still join it?'

In [10]python · cell 10

python

v_q = model.encode(query)

In [35]python · cell 11

python

knn_query = {
    "field": "text_vector",
    "query_vector": v_q,
    "k": 5,
    "num_candidates": 10000,
    "boost": 0.5,
    "filter": {
        "term": {
            "course": course
        }
    }
}

In [36]python · cell 12

python

keyword_query = {
    "bool": {
        "must": {
            "multi_match": {
                "query": query,
                "fields": ["question^3", "text", "section"],
                "type": "best_fields",
                "boost": 0.5,
            }
        },
        "filter": {
            "term": {
                "course": course
            }
        }
    }
}

In [37]python · cell 13

python

response = es_client.search(
    index=index_name,
    query=keyword_query,
    knn=knn_query,
    size=5
)

In [38]python · cell 14

python

response["hits"]["hits"]

Output

[{'_index': 'course-questions',
  '_id': 'ICPvR5EBO7bpWV6zkHVw',
  '_score': 36.424633,
  '_source': {'text': "Yes, even if you don't register, you're still eligible to submit the homeworks.\nBe aware, however, that there will be deadlines for turning in the final projects. So don't leave everything for the last minute.",
   'section': 'General course-related questions',
   'question': 'Course - Can I still join the course after the start date?',
   'course': 'data-engineering-zoomcamp',
   'id': '7842b56a',
   'question_vector': [0.0030358789954334497,
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    0.0358816422522068,
    0.020998863503336906,
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 {'_index': 'course-questions',
  '_id': 'JSPvR5EBO7bpWV6zkXU7',
  '_score': 27.396076,
  '_source': {'text': 'Yes, we will keep all the materials after the course finishes, so you can follow the course at your own pace after it finishes.\nYou can also continue looking at the homeworks and continue preparing for the next cohort. I guess you can also start working on your final capstone project.',
   'section': 'General course-related questions',
   'question': 'Course - Can I follow the course after it finishes?',
   'course': 'data-engineering-zoomcamp',
   'id': 'a482086d',
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 {'_index': 'course-questions',
  '_id': 'IiPvR5EBO7bpWV6zkHXB',
  '_score': 21.920742,
  '_source': {'text': 'You can start by installing and setting up all the dependencies and requirements:\nGoogle cloud account\nGoogle Cloud SDK\nPython 3 (installed with Anaconda)\nTerraform\nGit\nLook over the prerequisites and syllabus to see if you are comfortable with these subjects.',
   'section': 'General course-related questions',
   'question': 'Course - What can I do before the course starts?',
   'course': 'data-engineering-zoomcamp',
   'id': '63394d91',
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 {'_index': 'course-questions',
  '_id': 'JiPvR5EBO7bpWV6zkXVu',
  '_score': 21.325657,
  '_source': {'text': 'Yes, the slack channel remains open and you can ask questions there. But always sDocker containers exit code w search the channel first and second, check the FAQ (this document), most likely all your questions are already answered here.\nYou can also tag the bot @ZoomcampQABot to help you conduct the search, but don’t rely on its answers 100%, it is pretty good though.',
   'section': 'General course-related questions',
   'question': 'Course - Can I get support if I take the course in the self-paced mode?',
   'course': 'data-engineering-zoomcamp',
   'id': 'eb56ae98',
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 {'_index': 'course-questions',
  '_id': 'ISPvR5EBO7bpWV6zkHWd',
  '_score': 18.261362,
  '_source': {'text': "You don't need it. You're accepted. You can also just start learning and submitting homework without registering. It is not checked against any registered list. Registration is just to gauge interest before the start date.",
   'section': 'General course-related questions',
   'question': 'Course - I have registered for the Data Engineering Bootcamp. When can I expect to receive the confirmation email?',
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Hybrid search pipeline

In [25]python · cell 16

python

df_ground_truth = pd.read_csv('ground-truth-data.csv')

In [26]python · cell 17

python

ground_truth = df_ground_truth.to_dict(orient='records')

In [27]python · cell 18

python

def hit_rate(relevance_total):
    cnt = 0

    for line in relevance_total:
        if True in line:
            cnt = cnt + 1

    return cnt / len(relevance_total)

In [28]python · cell 19

python

def mrr(relevance_total):
    total_score = 0.0

    for line in relevance_total:
        for rank in range(len(line)):
            if line[rank] == True:
                total_score = total_score + 1 / (rank + 1)

    return total_score / len(relevance_total)

In [44]python · cell 20

python

def elastic_search_hybrid(field, query, vector, course):
    knn_query = {
        "field": field,
        "query_vector": vector,
        "k": 5,
        "num_candidates": 10000,
        "boost": 0.5,
        "filter": {
            "term": {
                "course": course
            }
        }
    }

    keyword_query = {
        "bool": {
            "must": {
                "multi_match": {
                    "query": query,
                    "fields": ["question", "text", "section"],
                    "type": "best_fields",
                    "boost": 0.5,
                }
            },
            "filter": {
                "term": {
                    "course": course
                }
            }
        }
    }

    search_query = {
        "knn": knn_query,
        "query": keyword_query,
        "size": 5,
        "_source": ["text", "section", "question", "course", "id"]
    }

    es_results = es_client.search(
        index=index_name,
        body=search_query
    )
    
    result_docs = []
    
    for hit in es_results['hits']['hits']:
        result_docs.append(hit['_source'])

    return result_docs

In [49]python · cell 21

python

def question_hybrid(q):
    question = q['question']
    course = q['course']

    v_q = model.encode(question)

    return elastic_search_hybrid('question_vector', question, v_q, course)

In [23]python · cell 22

python

def evaluate(ground_truth, search_function):
    relevance_total = []

    for q in tqdm(ground_truth):
        doc_id = q['document']
        results = search_function(q)
        relevance = [d['id'] == doc_id for d in results]
        relevance_total.append(relevance)

    return {
        'hit_rate': hit_rate(relevance_total),
        'mrr': mrr(relevance_total),
    }

In [51]python · cell 23

python

evaluate(ground_truth, question_hybrid)

Output

100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4627/4627 [02:00<00:00, 38.35it/s]

{'hit_rate': 0.9234925437648585, 'mrr': 0.8481665586052878}

ES knn on questions: {'hit_rate': 0.773071104387292, 'mrr': 0.6666810748505158}

In [52]python · cell 25

python

def text_hybrid(q):
    question = q['question']
    course = q['course']

    v_q = model.encode(question)

    return elastic_search_hybrid('text_vector', question, v_q, course)

In [53]python · cell 26

python

evaluate(ground_truth, text_hybrid)

Output

100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4627/4627 [01:51<00:00, 41.43it/s]

{'hit_rate': 0.9234925437648585, 'mrr': 0.8461710251422809}

ES knn on texts: {'hit_rate': 0.8286146531229739, 'mrr': 0.7062315395144454}

In [54]python · cell 28

python

def question_text_hybrid(q):
    question = q['question']
    course = q['course']

    v_q = model.encode(question)

    return elastic_search_hybrid('question_text_vector', question, v_q, course)

evaluate(ground_truth, question_text_hybrid)

Output

100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4627/4627 [01:48<00:00, 42.46it/s]

{'hit_rate': 0.9250054030689432, 'mrr': 0.8506231539514445}

ES knn on questions and answers: {'hit_rate': 0.9172249837907932, 'mrr': 0.824306606152295}

Reranking

To use the Reciprocal rank fusion (RRF) score we need to pull the docker image with a more recent version of Elasticsearch:

bash

docker run -it \
    --rm \
    --name elasticsearch \
    -m 4GB \
    -p 9200:9200 \
    -p 9300:9300 \
    -e "discovery.type=single-node" \
    -e "xpack.security.enabled=false" \
    docker.elastic.co/elasticsearch/elasticsearch:8.9.0

In [16]python · cell 32

python

def elastic_search_hybrid_rrf(field, query, vector, course):
    knn_query = {
        "field": field,
        "query_vector": vector,
        "k": 5,
        "num_candidates": 10000,
        "boost": 0.5,
        "filter": {
            "term": {
                "course": course
            }
        }
    }

    keyword_query = {
        "bool": {
            "must": {
                "multi_match": {
                    "query": query,
                    "fields": ["question", "text", "section"],
                    "type": "best_fields",
                    "boost": 0.5,
                }
            },
            "filter": {
                "term": {
                    "course": course
                }
            }
        }
    }

    search_query = {
        "knn": knn_query,
        "query": keyword_query,
        "size": 5,
        "rank": {
            "rrf": {}
        },
        "_source": ["text", "section", "question", "course", "id"]
    }

    es_results = es_client.search(
        index=index_name,
        body=search_query
    )
    
    result_docs = []
    
    for hit in es_results['hits']['hits']:
        result_docs.append(hit['_source'])

    return result_docs

In [17]python · cell 33

python

course = "data-engineering-zoomcamp"

In [18]python · cell 34

python

query = 'I just discovered the course. Can I still join it?'

In [19]python · cell 35

python

v_q = model.encode(query)

In [ ]python · cell 36

python

elastic_search_hybrid_rrf('question_text_vector', query, v_q, course)

By default, RRF isn't available in a free-tier subscription. But you can try to use 30-day trial or upgrade the subscription plan.

RRF implementation

In [30]python · cell 39

python

def compute_rrf(rank, k=60):
    """ Our own implementation of the relevance score """
    return 1 / (k + rank)

def elastic_search_hybrid_rrf(field, query, vector, course, k=60):
    knn_query = {
        "field": field,
        "query_vector": vector,
        "k": 10,
        "num_candidates": 10000,
        "boost": 0.5,
        "filter": {
            "term": {
                "course": course
            }
        }
    }

    keyword_query = {
        "bool": {
            "must": {
                "multi_match": {
                    "query": query,
                    "fields": ["question", "text", "section"],
                    "type": "best_fields",
                    "boost": 0.5,
                }
            },
            "filter": {
                "term": {
                    "course": course
                }
            }
        }
    }

    knn_results = es_client.search(
        index=index_name, 
        body={
            "knn": knn_query, 
            "size": 10
        }
    )['hits']['hits']
    
    keyword_results = es_client.search(
        index=index_name, 
        body={
            "query": keyword_query, 
            "size": 10
        }
    )['hits']['hits']
    
    rrf_scores = {}
    # Calculate RRF using vector search results
    for rank, hit in enumerate(knn_results):
        doc_id = hit['_id']
        rrf_scores[doc_id] = compute_rrf(rank + 1, k)

    # Adding keyword search result scores
    for rank, hit in enumerate(keyword_results):
        doc_id = hit['_id']
        if doc_id in rrf_scores:
            rrf_scores[doc_id] += compute_rrf(rank + 1, k)
        else:
            rrf_scores[doc_id] = compute_rrf(rank + 1, k)

    # Sort RRF scores in descending order
    reranked_docs = sorted(rrf_scores.items(), key=lambda x: x[1], reverse=True)
    
    # Get top-K documents by the score
    final_results = []
    for doc_id, score in reranked_docs[:5]:
        doc = es_client.get(index=index_name, id=doc_id)
        final_results.append(doc['_source'])
    
    return final_results

In [29]python · cell 40

python

def question_text_hybrid_rrf(q):
    question = q['question']
    course = q['course']

    v_q = model.encode(question)

    return elastic_search_hybrid_rrf('question_text_vector', question, v_q, course)

evaluate(ground_truth, question_text_hybrid_rrf)

Output

100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4627/4627 [03:08<00:00, 24.49it/s]

{'hit_rate': 0.9520207477847418, 'mrr': 0.8745911677833017}

ES hybrid search scores: {'hit_rate': 0.9250054030689432, 'mrr': 0.8506231539514445}

In [ ]python · cell 42

python