nosible 0.2.1

Python client for the NOSIBLE Search API

NOSIBLE Search Client

A high-level Python client for the NOSIBLE Search API. Easily integrate the Nosible Search API into your Python projects.

📄 Documentation

You can find the full NOSIBLE Search Client documentation here.

📦 Installation

pip install nosible

⚡ Installing with uv

uv pip install nosible

Requirements:

• Python 3.9+
• requests
• polars
• cryptography
• tenacity
• pyrate-limiter
• tantivy
• openai

🔑 Authentication

1. Sign in to NOSIBLE.AI and grab your free API key.
2. Set it as an environment variable or pass directly:

On Windows

$Env:NOSIBLE_API_KEY="basic|abcd1234..."
$Env:LLM_API_KEY="sk-..."  # for query expansions (optional)

On Linux

export NOSIBLE_API_KEY="basic|abcd1234..."
export LLM_API_KEY="sk-..."  # for query expansions (optional)

Or in code:

• As an argument:

from nosible import Nosible

client = Nosible(
    nosible_api_key="basic|abcd1234...",
    llm_api_key="sk-...",
)

• As an environment variable:

from nosible import Nosible
import os

os.environ["NOSIBLE_API_KEY"] = "basic|abcd1234..."
os.environ["LLM_API_KEY"] = "sk-..."

🎯 Core Workflows

I need	Method	Use case
Single query, up to 100 results	search	Interactive lookups
Multiple queries in parallel	searches	Dashboards, comparisons
Thousands of results (100–10k)	bulk_search	Analytics, offline jobs

🚀 Examples

Fast Search

Retrieve up to 100 results with optional filters:

from nosible import Nosible

with Nosible(
    nosible_api_key="basic|abcd1234...",
    llm_api_key="sk-...",
    openai_base_url="https://api.openrouter.ai/v1"
) as client:
    results = client.search(
        question="What are the terms of the partnership between Microsoft and OpenAI?",
        n_results=20,
        publish_start="2020-06-01",
        publish_end="2025-06-30",
        include_netlocs=["nytimes.com", "techcrunch.com"],
        exclude_netlocs=["example.com"],
        visited_start="2023-06-01",
        visited_end="2025-06-29",
        include_languages=["en", "fr"],
        exclude_languages=["de"],
        include_companies=["/m/04sv4"],  # Microsoft's GKID
        exclude_companies=["/m/045c7b"]  # Google GKID
    )
    print([r.title for r in results])

Parallel Searches

Run multiple queries concurrently:

from nosible import Nosible

with Nosible(nosible_api_key="basic|abcd1234...", llm_api_key="sk-...") as client:
    for batch in client.searches(
        questions=[
            "What are the terms of the partnership between Microsoft and OpenAI?",
            "What exclusivity or non-compete clauses are included in their partnership?"
        ],
        n_results=10,
        publish_start="2025-06-01"
    ):
        print(batch[0].title)

Bulk Search

Fetch thousands of results for offline analysis:

from nosible import Nosible

with Nosible(nosible_api_key="basic|abcd1234...") as client:
    bulk = client.bulk_search(
        question="What chip-development responsibilities has Intel committed to under its deal with Apple?",
        n_results=2000
    )
    print(len(bulk))
print(bulk)

Combine Results

Add two ResultSets together:

from nosible import Nosible

with Nosible(nosible_api_key="basic|abcd1234...") as client:
    r1 = client.search(
        question="What are the terms of the partnership between Microsoft and OpenAI?",
        n_results=5
    )
    r2 = client.search(
        question="How is research governance and decision-making structured between Google and DeepMind?",
        n_results=5
    )
    combined = r1 + r2
    print(combined)

Search Object

Use the Search class to encapsulate parameters:

from nosible import Nosible, Search

with Nosible(nosible_api_key="basic|abcd1234...") as client:
    search = Search(
        question="What are the terms of the partnership between Microsoft and OpenAI?",
        n_results=3,
        publish_start="2020-01-15",
        publish_end="2025-07-20",
        include_netlocs=["arxiv.org", "bbc.com"],
        certain=True
    )
    results = client.search(search=search)
    print([r for r in results])

Sentiment Analysis

Compute sentiment for a single result (uses GPT-4o; requires an LLM API key):

from nosible import Nosible

with Nosible(nosible_api_key="basic|abcd1234...", llm_api_key="sk-...") as client:
    results = client.search(
        question="What are the terms of the partnership between Microsoft and OpenAI?",
        n_results=1
    )
    score = results[0].sentiment(client)
    print(f"Sentiment score: {score:.2f}")

Save & Load Formats

Supported formats for saving and loading:

from nosible import Nosible, ResultSet

with Nosible(nosible_api_key="basic|abcd1234...") as client:
    combined = client.search(
        question="What are the terms of the partnership between Microsoft and OpenAI?",
        n_results=5
    ) + client.search(
        question="How is research governance and decision-making structured between Google and DeepMind?",
        n_results=5
    )

    # Save
    combined.to_csv("all_news.csv")
    combined.to_json("all_news.json")
    combined.to_parquet("all_news.parquet")
    combined.to_arrow("all_news.arrow")
    combined.to_duckdb("all_news.duckdb", table_name="news")
    combined.to_ndjson("all_news.ndjson")

    # Load
    rs_csv    = ResultSet.from_csv("all_news.csv")
    rs_json   = ResultSet.from_json("all_news.json")
    rs_parq   = ResultSet.from_parquet("all_news.parquet")
    rs_arrow  = ResultSet.from_arrow("all_news.arrow")
    rs_duckdb = ResultSet.from_duckdb("all_news.duckdb")
    rs_ndjson = ResultSet.from_ndjson("all_news.ndjson")

More Examples

For more examples, checkout /examples for in-depth usage of the NOSIBLE Client Package

📡 Swagger Docs

You can find online endpoints to the NOSIBLE Search API Swagger Docs here.

⚙️ Rate Limiting

Inspect your current limits at runtime:

client.get_ratelimits()

Or you can view them on the docs.

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