Absolutely! Meilisearch can absolutely handle content indexing and search for PDF and DOCX files—you just need to do a bit of preprocessing first, since Meilisearch works best with structured text data. Let me break down the full indexing and search workflow clearly for you.

Short answer: Yes, but Meilisearch doesn't directly parse binary files like PDFs or DOCXs out of the box. You'll need to extract the text content from these files first, then feed that structured text (plus any metadata you care about) into Meilisearch for indexing.

Here's a step-by-step guide to getting your PDF/DOCX content into Meilisearch:

1. Extract Text from Files #

First, you need to pull the readable text out of your PDF and DOCX files. You can use tools tailored to each file type:

• For PDFs: Use libraries like PyPDF2 (Python) or pdftotext (command-line)
• For DOCXs: Use python-docx (Python) or pandoc (command-line)

Here's a quick Python example to extract text from both file types:

import PyPDF2
from docx import Document

def extract_text_from_pdf(pdf_path):
    text = ""
    with open(pdf_path, 'rb') as f:
        reader = PyPDF2.PdfReader(f)
        for page in reader.pages:
            text += page.extract_text() or ""
    return text

def extract_text_from_docx(docx_path):
    doc = Document(docx_path)
    return "\n".join([para.text for para in doc.paragraphs])

# Usage
pdf_text = extract_text_from_pdf("example.pdf")
docx_text = extract_text_from_docx("example.docx")

2. Structure Your Data #

Next, format the extracted text into a structured document that Meilisearch can ingest. Include metadata to make search results more useful (like filename, file type, upload date, etc.):

documents = [
    {
        "id": 1,
        "filename": "example.pdf",
        "file_type": "pdf",
        "content": pdf_text,
        "uploaded_at": "2024-05-20"
    },
    {
        "id": 2,
        "filename": "example.docx",
        "file_type": "docx",
        "content": docx_text,
        "uploaded_at": "2024-05-20"
    }
]

3. Import to Meilisearch #

Use Meilisearch's SDK (or REST API) to create an index and add your documents. Here's a Python SDK example:

from meilisearch import Client

# Initialize Meilisearch client (assuming Meilisearch is running locally on port 7700)
client = Client('http://localhost:7700', 'your_master_key')

# Create an index (if it doesn't exist)
index = client.create_index('document_search', {'primaryKey': 'id'})

# Add documents to the index
index.add_documents(documents)

Wait for the indexing to complete (you can check the status with index.get_task(task_uid) if needed).

Once your documents are indexed, searching is straightforward:

1. Basic Full-Text Search #

Use the search method to query across the content field (and any other fields you want):

# Search for a keyword in all fields
results = index.search("your search query")

# Print the results
for hit in results['hits']:
    print(f"Found in {hit['filename']}: {hit['content'][:200]}...")

2. Advanced Search & Filtering #

You can refine searches with filters, sort options, or field-specific queries:

# Search only in PDF files
pdf_results = index.search("your query", {'filter': ['file_type = pdf']})

# Sort results by upload date
sorted_results = index.search("your query", {'sort': ['uploaded_at:desc']})

• Clean Up Extracted Text: Sometimes extracted text has extra newlines, special characters, or formatting artifacts. Run a quick cleanup (like stripping whitespace, removing redundant line breaks) to improve search accuracy.
• Handle Large Files: For very large documents, consider splitting the content into smaller chunks (e.g., by page or section) and indexing each chunk as a separate document with shared metadata—this makes search results more precise.
• Configure Index Settings: Tweak Meilisearch's index settings (like enabling synonyms, adjusting ranking rules) to tailor search behavior to your needs. For example, you can boost the filename field so matches there appear higher in results.

内容的提问来源于stack exchange，提问作者Rakshitha Vasishta