Elasticsearch Search Engine

Elasticsearch Search Engine

This document describes how to use and configure the Elasticsearch search engine within the Local Deep Research project.

Overview

The Elasticsearch search engine allows you to search for documents within Elasticsearch indices. This is particularly useful for scenarios requiring searches across large volumes of structured or unstructured text data.

Prerequisites

1. A running Elasticsearch server (local or remote).
2. Elasticsearch Python client library: elasticsearch>=8.10.0.

Installation

Ensure that you have installed the elasticsearch Python package. If you are installing Local Deep Research from source, you can run:

pip install elasticsearch>=8.10.0

Basic Usage

Usage in Code

from local_deep_research.web_search_engines.engines.search_engine_elasticsearch import ElasticsearchSearchEngine

# Create search engine instance
es_search = ElasticsearchSearchEngine(
    hosts=["http://localhost:9200"],  # List of Elasticsearch hosts
    index_name="my_index",            # Name of the index to search
    username="user",                  # Optional: Authentication username
    password="pass",                  # Optional: Authentication password
    max_results=10                    # Maximum number of results to return
)

# Execute search
results = es_search.run("Your search query")

# Process results
for result in results:
    print(f"Title: {result.get('title')}")
    print(f"Snippet: {result.get('snippet')}")
    print(f"Content: {result.get('content')}")

Advanced Search

The ES search engine supports various advanced search methods:

# Use Elasticsearch Query String syntax
results = es_search.search_by_query_string("title:keyword AND content:search_text")

# Use Elasticsearch DSL (Domain Specific Language)
results = es_search.search_by_dsl({
    "query": {
        "bool": {
            "must": {"match": {"content": "search term"}},
            "filter": {"term": {"category": "technology"}}
        }
    }
})

Configuration

Search Engine Parameters

Indexing Data

For ease of use, we provide the ElasticsearchManager utility class to help index data:

from local_deep_research.utilities.es_utils import ElasticsearchManager

# Create ES Manager
es_manager = ElasticsearchManager(
    hosts=["http://localhost:9200"]
)

# Create index
es_manager.create_index("my_index")

# Index a single document
es_manager.index_document(
    index_name="my_index",
    document={
        "title": "Document Title",
        "content": "Document content...",
    }
)

# Bulk index documents
documents = [
    {"title": "Document 1", "content": "Content 1"},
    {"title": "Document 2", "content": "Content 2"}
]
es_manager.bulk_index_documents("my_index", documents)

# Index file (automatically extract content)
es_manager.index_file("my_index", "path/to/document.pdf")

# Index all files in a directory
es_manager.index_directory(
    "my_index",
    "path/to/docs",
    file_patterns=["*.pdf", "*.docx", "*.txt"]
)

Examples

Refer to examples/elasticsearch_search_example.py for a complete usage example.

Running Examples

Ensure Elasticsearch is running, then execute:

python examples/elasticsearch_search_example.py

Troubleshooting

Cannot connect to Elasticsearch

• Ensure the Elasticsearch server is running.
• Verify the host address and port.
• Verify authentication credentials (if required).
• Check network connection and firewall settings.

Empty Search Results

• Ensure the index exists and contains data.
• Check if the search fields are correct.
• Try using a simpler query.
• Check Elasticsearch logs for more information.