Easily find the most relevant information from trusted sources and your own data. Use it to power agents that give accurate, real-time answers. Ready to start using the Search Service? Let’s dive in! We will create a query with a criteria to find insights and then create and run the search. Search parameters:
  • query: A query object that represents the search criteria. It can contain several query filters combined with standard operators. More details in Query Filters
  • rerank_threshold (Optional): A float between 0 and 1 for an optional re-ranking phase. More details in Rerank search
  • scope (Optional): An object that filters the search results by content type (news, files, etc.). More details in Scope.
  • date_range (Optional): A date range object that specifies the time frame of the search. More details in Date Range.
  • sortby (Optional): An enum that specifies the sorting criteria of the search results, either by SortBy.DATE or SortBy.RELEVANCE.
Example: The following example searches for news about Pfizer’s partnerships and returns one document. 
from bigdata_client.query import Entity
from bigdata_client.query import Similarity
from bigdata_client.daterange import RollingDateRange

# Find Pfizer ID
pfizer_id = bigdata.knowledge_graph.find_companies("Pfizer")[0].id

# Craft the query: Partnership news where Pfizer has been identified
query = Similarity("Partnership") & Entity(pfizer_id)

# Create the search
search = bigdata.search.new(query, rerank_threshold=0.2, date_range=RollingDateRange.LAST_NINETY_DAYS)

# Run the search to retrieve one document and print an overview of the document
documents = search.run(1) 

# Loop through the list of retrieved documents
for doc in documents:
    print(doc)
Output: 
Document ID:        EB961CB16ECEDF294F77AB943DE144B6
Timestamp:                       2025-07-10 09:43:35
Scope:                                          News
Source (Rank):                    Industry Today (3)
Title: Biosimilars Partnering Market to Reach USD 24.5 Billion by 2032, Expanding at 12.5% CAGR
Document Url: https://industrytoday.co.uk/health_and_safety/biosimilars-partnering-market-to-reach-usd-245-billion-by-2032-expanding-at-125-cagr
Language:                                    English
Sentiment:                                      0.57
====Sentence matches====
*A B2B Market Fueled by Strategic Collaboration
Biosimilars-highly similar, cost-effective alternatives to original biologic therapies-have become the cornerstone of next-generation therapeutic strategies. In the Biosimilars Partnering Market, business-to-business (B2B) collaboration is the foundation of success. Leading pharmaceutical and biotech companies such as Pfizer, Sandoz, Amgen, and Biocon are forming partnerships to leverage each other's R&D capabilities, manufacturing strengths, and global distribution networks. These strategic alliances are not only helping to reduce development costs but also facilitating faster market entry.
--
*India and South Korea, in particular, are becoming biotech innovation hubs, attracting collaborations from global pharmaceutical leaders. Meanwhile, Latin America and the Middle East & Africa (MEA) regions are slowly entering the landscape, driven by efforts to improve drug affordability and healthcare access.
Competitive Landscape: Major Players Shaping the Future
The market's competitive landscape is dominated by key players including Sandoz, Pfizer, Merck, Roche, Celltrion, Samsung Bioepis, Biocon, and Amgen. These companies are investing heavily in strategic partnerships to streamline biosimilar development and distribution.
--
The quickest way to test the Search Service is with the Search Quickstart GuideThen, you can explore the different search parameters (Query filters, Scope, Date Range, SortBy) on their respective pages.The search will return a list of documents, and the page Response - Document will show you how to access the document content.Finally, but not least, check out the Co-mentions feature. It will tell you which Knowledge Graph Objects (Entities, Topics, Sources, etc.) are mentioned the most in your search. That’s a quick way to see your query’s exposure!
The method search.run(x) requests x documents with chunks. Suppose you prefer to control the number of retrieved chunks instead of documents. You can use the object ChunkLimit(y). Check out Retrieved limited chunks for more details.