ইলাস্টিক সার্চ সম্পূর্ণ গাইড 2026: অনুসন্ধান, সমষ্টি এবং ভেক্টর অনুসন্ধান

docker run -d   --name elasticsearch   -p 9200:9200   -e "discovery.type=single-node"   -e "xpack.security.enabled=false"   -e "ES_JAVA_OPTS=-Xms512m -Xmx512m"   elasticsearch:8.13.0

# Check health
curl http://localhost:9200/_cluster/health?pretty

pip install elasticsearch[async]

from elasticsearch import AsyncElasticsearch
import asyncio

es = AsyncElasticsearch("http://localhost:9200")

# Create index with mapping
async def setup_articles_index():
    mapping = {
        "mappings": {
            "properties": {
                "title": {
                    "type": "text",
                    "analyzer": "english",
                    "fields": {
                        "keyword": {"type": "keyword"},
                        "suggest": {"type": "completion"}
                    }
                },
                "content": {"type": "text", "analyzer": "english"},
                "author": {"type": "keyword"},
                "tags": {"type": "keyword"},
                "published_at": {"type": "date"},
                "views": {"type": "integer"},
                "embedding": {
                    "type": "dense_vector",
                    "dims": 384,
                    "index": True,
                    "similarity": "cosine"
                }
            }
        },
        "settings": {
            "number_of_shards": 1,
            "number_of_replicas": 0,
        }
    }
    await es.indices.create(index="articles", body=mapping, ignore=400)

# Index documents
async def index_article(article: dict):
    await es.index(
        index="articles",
        id=str(article["id"]),
        document={
            "title": article["title"],
            "content": article["content"],
            "author": article["author"],
            "tags": article["tags"],
            "published_at": article["published_at"],
            "views": article.get("views", 0),
        }
    )

# Bulk index
async def bulk_index_articles(articles: list[dict]):
    operations = []
    for article in articles:
        operations.append({"index": {"_index": "articles", "_id": str(article["id"])}})
        operations.append({
            "title": article["title"],
            "content": article["content"],
            "tags": article["tags"],
        })
    await es.bulk(operations=operations, refresh=True)

async def search_articles(query: str, tags: list[str] = None,
                         page: int = 1, size: int = 10) -> dict:
    must = [
        {
            "multi_match": {
                "query": query,
                "fields": ["title^3", "content", "tags^2"],  # title more important
                "type": "best_fields",
                "fuzziness": "AUTO",           # handle typos
                "minimum_should_match": "75%"
            }
        }
    ]

    filters = []
    if tags:
        filters.append({"terms": {"tags": tags}})
    filters.append({"range": {"published_at": {"gte": "now-1y"}}})

    body = {
        "query": {
            "bool": {
                "must": must,
                "filter": filters,
            }
        },
        "highlight": {
            "fields": {
                "title": {"number_of_fragments": 0},
                "content": {"fragment_size": 150, "number_of_fragments": 3}
            },
            "pre_tags": ["<strong>"],
            "post_tags": ["</strong>"]
        },
        "from": (page - 1) * size,
        "size": size,
        "_source": ["title", "author", "tags", "published_at", "views"],
    }

    result = await es.search(index="articles", body=body)
    return {
        "total": result["hits"]["total"]["value"],
        "hits": [
            {
                **hit["_source"],
                "id": hit["_id"],
                "score": hit["_score"],
                "highlights": hit.get("highlight", {})
            }
            for hit in result["hits"]["hits"]
        ]
    }

# Autocomplete suggest
async def suggest(prefix: str) -> list[str]:
    result = await es.search(
        index="articles",
        body={
            "suggest": {
                "title_suggest": {
                    "prefix": prefix,
                    "completion": {
                        "field": "title.suggest",
                        "size": 5,
                        "skip_duplicates": True,
                    }
                }
            }
        }
    )
    return [
        option["text"]
        for option in result["suggest"]["title_suggest"][0]["options"]
    ]

async def analytics_dashboard() -> dict:
    result = await es.search(
        index="articles",
        body={
            "size": 0,  # no hits, only aggregations
            "aggs": {
                # Count by tag
                "popular_tags": {
                    "terms": {
                        "field": "tags",
                        "size": 10,
                        "order": {"_count": "desc"}
                    }
                },
                # Articles per month
                "monthly_counts": {
                    "date_histogram": {
                        "field": "published_at",
                        "calendar_interval": "month",
                        "format": "yyyy-MM"
                    }
                },
                # Average views by author
                "avg_views_by_author": {
                    "terms": {"field": "author", "size": 5},
                    "aggs": {
                        "avg_views": {"avg": {"field": "views"}},
                        "total_views": {"sum": {"field": "views"}}
                    }
                },
                # Views percentiles
                "views_percentiles": {
                    "percentiles": {
                        "field": "views",
                        "percents": [50, 75, 90, 95, 99]
                    }
                }
            }
        }
    )
    return result["aggregations"]

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("all-MiniLM-L6-v2")

async def index_with_embedding(article: dict):
    embedding = model.encode(article["title"] + " " + article["content"][:500]).tolist()
    await es.index(
        index="articles",
        id=str(article["id"]),
        document={**article, "embedding": embedding}
    )

async def semantic_search(query: str, size: int = 10) -> list[dict]:
    query_embedding = model.encode(query).tolist()

    result = await es.search(
        index="articles",
        knn={
            "field": "embedding",
            "query_vector": query_embedding,
            "k": size,
            "num_candidates": 100,
        },
        source=["title", "content", "tags"]
    )
    return [h["_source"] for h in result["hits"]["hits"]]

# Hybrid search: keyword + vector
async def hybrid_search(query: str) -> list[dict]:
    embedding = model.encode(query).tolist()

    result = await es.search(
        index="articles",
        body={
            "query": {
                "multi_match": {
                    "query": query,
                    "fields": ["title^2", "content"],
                }
            },
            "knn": {
                "field": "embedding",
                "query_vector": embedding,
                "k": 10,
                "num_candidates": 50,
            },
            "rank": {
                "rrf": {}  # reciprocal rank fusion to combine scores
            }
        }
    )
    return [h["_source"] for h in result["hits"]["hits"]]