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This guide provides a quick overview of how to set up and start using GroupDocs.Search for Python via .NET. The library builds an index over your documents, searches it with a rich query language, and shows where in each document the found words are.
To proceed, make sure you have:
- Configured environment as described in the System Requirements topic.
- Optionally you may Get a Temporary License to test all the product features.
For best practices, use a virtual environment to manage dependencies in Python applications. Learn more about virtual environments at the Create and Use Virtual Environments documentation topic.
Create a virtual environment:
py -m venv .venv
python3 -m venv .venv
python3 -m venv .venv
Activate a virtual environment:
.venv\Scripts\activate
source .venv/bin/activate
source .venv/bin/activate
After activating the virtual environment, run the following command in your terminal to install the latest version of the package:
py -m pip install groupdocs-search-net
python3 -m pip install groupdocs-search-net
python3 -m pip install groupdocs-search-net
Ensure the package is installed successfully. You should see the message
Successfully installed groupdocs-search-net-*
NoteUntil PyPI carries the 26.10 wheels, install the wheel for your platform from the GroupDocs Releases website instead, as described in Install from a Pre-Downloaded Wheel.
To quickly test the library, let’s index a folder of documents and search it for a phrase.
import os
from groupdocs.search import Index, License
def search_documents():
# Optionally set a license
license_path = os.path.abspath("./GroupDocs.Search.lic")
if os.path.exists(license_path):
License().set_license(license_path)
# Create an index in the "index" folder and index the "documents" folder
with Index("./index") as index:
index.add("./documents")
# A phrase in double quotes matches these words in this order
result = index.search('"theory of relativity"')
print("Documents found:", result.document_count)
print("Occurrences found:", result.occurrence_count)
# Most relevant first: the result lists the documents in the order they were indexed
for document in sorted(result, key=lambda found: found.relevance, reverse=True):
print(os.path.basename(document.document_info.file_path), "-", document.occurrence_count, "occurrences")
if __name__ == "__main__":
search_documents()
The following sample files are used in this example: documents/einstein.txt, documents/nobel-prize-1921.txt, documents/relativity.txt, documents/time.txt
Documents found: 2
Occurrences found: 3
einstein.txt - 2 occurrences
relativity.txt - 1 occurrences
Your folder tree should look similar to the following directory structure:
📂 demo-app
├──search_documents.py
├──📂 documents
│ ├──einstein.txt
│ ├──nobel-prize-1921.txt
│ ├──relativity.txt
│ └──time.txt
└──GroupDocs.Search.lic (Optionally)
py search_documents.py
python3 search_documents.py
python3 search_documents.py
The first run creates the index folder next to the script. Later runs open the index from there, and add() indexes only the documents that are new or have changed. After running the app you can deactivate the virtual environment by executing deactivate or closing your shell.
Index("./index"): Creates an index in theindexfolder, or opens the index already stored there. Used as a context manager so the index is closed when the block ends.index.add("./documents"): Indexes every document of a supported format in the folder and its subfolders.index.search('"theory of relativity"'): Runs a query. The double quotes make it a phrase: the words must appear together and in this order.result.document_count/result.occurrence_count: The numbers of documents found and of occurrences in all of them. Iterating overresultgives each found document, in the order the documents were indexed, which follows the file system: name order on Windows, but not on Linux. Sorting byrelevancelists the most relevant first on every system.
In this example we search for a misspelled name. Fuzzy search finds the words that differ from the query by a few characters.
import os
from groupdocs.search import Index, License
from groupdocs.search.options import SearchOptions
def fuzzy_search():
# Optionally set a license
license_path = os.path.abspath("./GroupDocs.Search.lic")
if os.path.exists(license_path):
License().set_license(license_path)
with Index("./index") as index:
index.add("./documents")
# Fuzzy search also finds words that differ from the query by a few characters
options = SearchOptions()
options.fuzzy_search.enabled = True
result = index.search("Einstien", options)
print("Documents found:", result.document_count)
for document in sorted(result, key=lambda found: found.relevance, reverse=True):
# The words of the document that matched the query
words = set()
for field in document.found_fields:
words.update(field.terms)
print(os.path.basename(document.document_info.file_path), "-", ", ".join(sorted(words)))
if __name__ == "__main__":
fuzzy_search()
The following sample files are used in this example: documents/einstein.txt, documents/nobel-prize-1921.txt, documents/relativity.txt, documents/time.txt
Documents found: 2
einstein.txt - einstein
nobel-prize-1921.txt - einstein
py fuzzy_search.py
python3 fuzzy_search.py
python3 fuzzy_search.py
SearchOptions(): Holds the settings of one search.fuzzy_search.enabled = Trueturns on fuzzy search with its default similarity level.index.search("Einstien", options): Searches with these options. Without them, the misspelled query finds nothing.document.found_fields: The fields of a found document that matched, such ascontentandfilename, each with the words it found interms.
Sometimes you need to show where in a document a word was found. A fragment highlighter returns the found words with a few words of context around each.
import os
from groupdocs.search import Index, License
from groupdocs.search.highlighters import FragmentHighlighter
from groupdocs.search.options import HighlightOptions, OutputFormat
def highlight_results():
# Optionally set a license
license_path = os.path.abspath("./GroupDocs.Search.lic")
if os.path.exists(license_path):
License().set_license(license_path)
with Index("./index") as index:
index.add("./documents")
result = index.search("gravity")
# Four words of context on each side of a found word, which is put in brackets
options = HighlightOptions()
options.terms_before = 4
options.terms_after = 4
options.term_highlight_start_tag = "["
options.term_highlight_end_tag = "]"
for document in sorted(result, key=lambda found: found.relevance, reverse=True):
highlighter = FragmentHighlighter(OutputFormat.PLAIN_TEXT)
index.highlight(document, highlighter, options)
print(os.path.basename(document.document_info.file_path))
for container in highlighter.get_result():
for fragment in container.get_fragments():
print(" ", " ".join(fragment.split()))
if __name__ == "__main__":
highlight_results()
The following sample files are used in this example: documents/einstein.txt, documents/nobel-prize-1921.txt, documents/relativity.txt, documents/time.txt
relativity.txt
in the absence of [gravity]. General relativity explains the
einstein.txt
theory of relativity described [gravity] as the curvature of
py highlight_results.py
python3 highlight_results.py
python3 highlight_results.py
HighlightOptions(): How many words of context to keep before and after each found word (terms_before,terms_after), and the tags put around the found words.FragmentHighlighter(OutputFormat.PLAIN_TEXT): Collects the fragments as plain text.OutputFormat.HTMLmarks the found words with HTML instead.index.highlight(document, highlighter, options): Highlights the found words of one found document.get_result()then gives the fragments of each field.
After completing the basics, explore additional resources to enhance your usage:
- Supported Document Formats: Review the full list of supported file types.
- Licensing and Evaluation: Check details on licensing and the evaluation limits.
- Developer Guide: Dive into indexing, every search type, highlighting, dictionaries and index events.
- Technical Support: Contact support for assistance if you encounter issues.
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