llm-web-research 0.0.1

A precision-focused LLM-powered web research tool that prioritizes accuracy over quantity

llm-web-research

A precision-focused LLM-powered web research tool that prioritizes accuracy over quantity.

Philosophy

Unlike traditional web scraping or search tools that aim to return as much information as possible, llm-web-research is designed with a different goal: reducing false positives.

This tool is built for use cases where:

• Accuracy matters more than completeness - You'd rather get fewer results that are correct than many results with errors
• Confidence thresholds are important - The tool will return a non-answer rather than provide uncertain information
• Verification is built-in - Results are cross-checked and validated before being returned

Installation

pip install llm-web-research

Quick Start

import llm_web_research as lwr

# Search for CEO information with confidence scoring
results = lwr.web_research(
    search_question="current CEO",
    search_input=["Apple Inc", "Microsoft", "Google"],
    api_key="your-anthropic-api-key",
    model_source="anthropic"
)

# Returns a pandas DataFrame with answer and source URL for each input
print(results[['search_input', 'answer', 'url']])

Using Different Providers

# Google Gemini with grounded search
results = lwr.web_research(
    search_question="founding year",
    search_input=["Tesla", "SpaceX"],
    api_key="your-google-api-key",
    model_source="google",
    user_model="gemini-2.5-flash"
)

# Perplexity for enhanced search
results = lwr.web_research(
    search_question="headquarters location",
    search_input=["OpenAI", "Anthropic"],
    api_key="your-perplexity-api-key",
    model_source="perplexity"
)

Advanced Search with Tavily

For deeper searches, use Tavily's advanced search:

results = lwr.web_research(
    search_question="recent acquisitions",
    search_input=["Amazon", "Meta"],
    api_key="your-anthropic-api-key",
    model_source="anthropic",
    search_depth="advanced",
    tavily_api="your-tavily-api-key"
)

Date Filtering

results = lwr.web_research(
    search_question="stock price",
    search_input=["NVIDIA"],
    api_key="your-api-key",
    model_source="anthropic",
    start_date="2024-01-01",
    end_date="2024-12-31"
)

The Problem: LLM Bias Toward Finding Answers

Large Language Models have a well-documented tendency to provide answers even when the best response is "I don't know." This bias becomes particularly problematic in web research scenarios:

Answer Hallucination Under Ambiguity

When faced with ambiguous queries, LLMs tend to latch onto the most prominent entity rather than acknowledging uncertainty. For example:

• "Michael Johnson height" - The model may confidently return the height of Michael Johnson the Olympic sprinter, even though there are thousands of people named Michael Johnson. Without additional context specifying which Michael Johnson, any answer is potentially wrong.

• "Springfield population" - The model might return data for Springfield, Illinois (the state capital) when the user meant Springfield, Massachusetts, or any of the 30+ other Springfields in the US.

• "Washington County median income" - With Washington County existing in 30+ US states, the model often defaults to the most populous or most-searched one rather than flagging the ambiguity.

The Confidence Illusion

Traditional LLM responses often sound confident regardless of actual certainty. A model might state "Michael Johnson is 6'1" tall" with the same tone it would use for "The Earth orbits the Sun" - even though the former is answering an fundamentally ambiguous question.

Our Solution: Funnel of Verification (FoVe)

This package attempts to correct for these biases through a novel Funnel of Verification (FoVe) prompting method combined with algorithmic post-processing.

How FoVe Works

The Funnel of Verification is a multi-step pipeline that progressively narrows down and validates information:

1. Step 1 - Broad Information Gathering (web search): Cast a wide net to understand what the entity is and gather context. Includes an early exit if no information is found ("ANSWER NOT FOUND").

2. Step 2 - Critical Ambiguity Check (no web search): Analyze the gathered context with explicit instructions to flag ambiguity. The model is prompted to identify:

   • Multiple entities with the same name
   • Common names (people, places, counties, companies)
   • Conflicting information
   • Insufficient qualifying information

   If ambiguity is detected, the pipeline exits early with "RESPONSE NOT CONFIDENT" rather than guessing.

3. Step 3 - Skeptical Verification (web search): For queries that pass the ambiguity check, perform a verification search specifically looking for contradicting information or potential confusions.

4. Step 4 - Structured Output (no web search): Format the final answer as strict JSON with binary confidence scoring.

Internal Confidence Scoring

Internally, FoVe uses binary confidence scoring (0 or 1) to make decisions:

• 1 = The answer has been verified, applies to a uniquely identified entity, and no contradicting information was found
• 0 = Any doubt exists, including ambiguity, conflicting sources, or uncertainty about which entity is being discussed

When confidence is 0, the answer is automatically set to "Information unclear" rather than returning a potentially wrong answer. This binary approach forces the system to make a clear decision: either we're confident enough to stand behind this answer, or we're not.

Early Exit Strategy

A key feature of FoVe is its early exit strategy. Rather than pushing through ambiguous queries and returning potentially wrong answers, the pipeline exits as soon as uncertainty is detected:

Query: "John Smith net worth"
Step 1: Gathers information about various John Smiths
Step 2: Detects "John Smith" is a common name with multiple possible referents
→ EARLY EXIT: Returns {"answer": "Information unclear", "url": ""}

This is intentional - no answer is better than a wrong answer for precision-focused use cases.

Key Features

• Structured DataFrame Output: Simply provide a question and a list of inputs, and receive a clean pandas DataFrame with answers and source URLs
• Incremental Saving (Safety Mode): Save results to CSV after each query, protecting against API failures or interruptions during long-running searches
• Funnel of Verification: Multi-step pipeline that catches ambiguity before it becomes error
• Early exit on ambiguity: Returns "Information unclear" rather than guessing
• Multi-provider support: Works with Anthropic, Google, and Perplexity APIs
• Citation tracking: Source URLs included with every result
• Verbose mode: Debug output showing each prompt and response in the pipeline
• Date filtering: Constrain results to specific time periods

Structured Output

The package returns results as a pandas DataFrame, making it easy to integrate into data analysis workflows:

results = lwr.web_research(
    search_question="founding year",
    search_input=["Apple", "Microsoft", "Google", "Amazon"],
    api_key="your-api-key",
    model_source="anthropic"
)

# Results DataFrame:
#   search_input    answer              url
# 0 Apple           1976                https://...
# 1 Microsoft       1975                https://...
# 2 Google          1998                https://...
# 3 Amazon          1994                https://...

Safety Mode: Incremental Saving

For large batch searches, enable safety mode to save progress after each query. This prevents data loss if the API fails mid-run:

results = lwr.web_research(
    search_question="current CEO",
    search_input=large_company_list,  # hundreds of companies
    api_key="your-api-key",
    model_source="anthropic",
    safety=True,
    filename="ceo_research.csv"  # saves after each query
)

If the process is interrupted, you'll have all completed results saved to the CSV file.

Use Cases

• Fact-checking claims with high accuracy requirements
• Academic research requiring verifiable sources
• Building high-quality datasets where precision matters
• Automated due diligence tasks

Related Projects

This package is part of the cat-llm ecosystem. For text and image categorization tasks, see cat-llm.

License

MIT License - see LICENSE file for details.

Author

Chris Soria ([e-mail])