# FAR Search Tool for Government Contractors

Government contractors, compliance officers, and proposal writers use FAR Search Tool to verify regulatory requirements and cite accurate compliance clauses.

## Problem: LLM Hallucination in Government Contracts

**Scenario**: Your proposal team asks ChatGPT to cite FAR clauses for a $5M DoD contract. ChatGPT returns:

> "Per FAR 52.234-1 (Cost Accounting Standards), contractors must..."

You cite this in your proposal. **But FAR 52.234-1 doesn't exist.** The evaluator catches it. Your proposal is rejected.

**Cost**: $500K+ lost proposal value.

**Root cause**: LLMs don't have real-time access to regulatory text and often "hallucinate" plausible-sounding regulation numbers.

## Solution: Ground-Truth FAR Data

FAR Search Tool provides verified, official FAR clauses directly from acquisition.gov. Every citation is verifiable.

```python
from far_search import FARSearchTool

tool = FARSearchTool()

# Query: What are the cybersecurity requirements for DoD contracts?
result = tool.invoke({
    "query": "DFARS cybersecurity requirements DoD contracts",
    "top_k": 3
})

# Response includes:
# - Clause ID (e.g., "252.204-7012")
# - Title: "Safeguarding Covered Contractor Information Systems"
# - Official text from acquisition.gov
# - Direct URL to source
# - Similarity score
```

Now you can cite: **"Per DFARS 252.204-7012 (sourced from acquisition.gov)..."**

No hallucination. No risk of rejection.

---

## Use Case 1: Proposal Writers

**Task**: Write compliance section for government proposal.

**Before**: Search acquisition.gov manually, read 100 pages of PDF regulations, cherry-pick clauses that sound relevant.

**After**: Query FAR Search Tool:

```python
from far_search import FARSearchTool

tool = FARSearchTool()

# Find all relevant clauses for your proposal
result = tool.invoke({
    "query": "small business subcontracting requirements federal contracts",
    "top_k": 10
})

print(result)
# Output: Top 10 relevant FAR clauses with official citations
# Use these directly in your compliance section
```

**Benefit**: 
- 80% faster than manual search
- 100% verified citations
- Confidence in accuracy (no hallucination risk)

---

## Use Case 2: Compliance Officers

**Task**: Audit AI-generated contract language before submission.

**Before**: Ask ChatGPT to draft terms, manually verify each citation in acquisition.gov, waste hours checking.

**After**: Use FAR Search Tool to verify claims:

```python
from far_search import FARSearchTool

# ChatGPT says: "Per FAR 49.201-1, cost reimbursement contracts require..."
# Verify it exists:

tool = FARSearchTool()
result = tool.invoke({"query": "FAR 49.201 termination cost reimbursement"})

# Check if the cited clause exists and matches context
print(result)
# Review returned clauses to verify ChatGPT's citation
```

**Benefit**:
- Catch hallucinated citations before submission
- Real-time compliance verification
- Reduce proposal rejection risk

---

## Use Case 3: Compliance Training

**Task**: Train proposal team on regulatory requirements for upcoming contracts.

**Before**: Send PDF of regulations, hope team reads it.

**After**: Use FAR Search Tool to surface relevant clauses:

```python
from far_search import FARSearchTool

tool = FARSearchTool()

# "Show me cybersecurity clauses relevant to our contract"
result = tool.invoke({
    "query": "cybersecurity information system protection contractor requirements",
    "top_k": 20
})

# Use results to brief team on the regulatory landscape
for clause in result[:5]:  # Top 5 most relevant
    print(f"- {clause['id']}: {clause['title']}")
```

**Benefit**:
- Team stays current on relevant regulations
- AI-powered briefings on complex regulations
- Track which clauses affect your contracts most

---

## Use Case 4: AI Agent Integration (RAG Pipeline)

**Task**: Build a compliance AI bot that generates accurate FAR citations.

**Before**: Feed ChatGPT raw regulations, it hallucinates anyway.

**After**: Use FAR Search Tool as ground-truth retrieval in your RAG pipeline:

```python
from langchain.agents import initialize_agent, AgentType
from langchain_openai import ChatOpenAI
from far_search import FARSearchTool

# Create agent with FAR search capability
llm = ChatOpenAI(model="gpt-4")
tools = [FARSearchTool()]

agent = initialize_agent(
    tools=tools,
    llm=llm,
    agent=AgentType.OPENAI_FUNCTIONS,
    verbose=True
)

# Now agent has access to verified FAR data
response = agent.run(
    "Draft a cybersecurity compliance section for a DoD proposal, "
    "citing relevant FAR clauses I can verify on acquisition.gov"
)

print(response)
```

**Benefit**:
- AI bot generates proposals with verified citations
- 100% traceable, auditable, verifiable
- Proposal quality improves

---

## Compliance & Audit Trail

Government contractors often need to demonstrate that compliance research followed proper procedures (DFARS, SOC 2 Type II, ISO 27001).

**FAR Search Tool + Agent Observability = Complete Audit Trail**

```python
from far_search import FARSearchTool
from agent_observability import AgentLogger
from datetime import datetime

tool = FARSearchTool()
logger = AgentLogger()

# Search for compliance info
query = "DFARS cybersecurity requirements"
result = tool.invoke({"query": query})

# Log it for audit trail
logger.log("compliance_research", {
    "query": query,
    "results_count": len(result) if isinstance(result, list) else 1,
    "timestamp": datetime.now().isoformat(),
    "user": "compliance-officer-1",
    "contract": "DoD-2025-12345"
})

# Now you have:
# - Verified citations (from FAR Search Tool)
# - Audit log of when research was done (from Agent Observability)
# - Who did it, what they searched for, what they found
# = DFARS compliance proof
```

**Why combine these tools?**
- **DFARS Compliance**: Government contracts require audit trails
- **Cost Tracking**: Know how much time is spent on regulatory research
- **Team Analytics**: See what your team searches most frequently
- **SOC 2 / ISO 27001**: Demonstrate due diligence in compliance

[Learn more about Agent Observability](https://github.com/blueskylineassets/agent-observability)

---

## Pricing for Government Contractors

| Plan | Price | Queries/Month | Use Case |
|------|-------|---------------|----------|
| **Free** | $0 | 500 | Evaluation, small projects |
| **Pro** | $29/mo | 5,000 | Small team, 1-2 proposals/month |
| **Ultra** | $199/mo | 150,000 | Enterprise, high-volume proposals |

**Pro tip**: 1 large proposal typically requires 200-500 FAR searches. Start with Pro if you have 3+ proposals/year.

[Upgrade on RapidAPI](https://rapidapi.com/yschang/api/far-rag-federal-acquisition-regulation-search)

---

## FAQ

**Q: Is FAR data current?**

A: Yes. FAR Search Tool uses data from acquisition.gov. The vector database is regularly updated to reflect regulatory changes.

**Q: Can I use this for commercial contracts?**

A: Yes. FAR applies to federal contracts. If your commercial contracts reference federal standards, FAR Search Tool helps you cite them accurately.

**Q: Does this work with ChatGPT?**

A: Yes. Use FAR Search Tool as a custom GPT action, or use LangChain agents with GPT-4 as shown in the examples above.

**Q: What about liability?**

A: This tool is for informational purposes. Always verify critical citations on acquisition.gov before submission. FAR Search Tool provides ground truth, but you're responsible for compliance decisions.

**Q: How do I suppress usage warnings in production?**

A: Set the environment variable `FAR_QUIET=1` to suppress usage warning messages.

**Q: Can I self-host this?**

A: The API is hosted, but you can use the `base_url` parameter to point to a self-hosted instance if you deploy your own.

---

## Get Started

1. **Install**: `pip install far-search-tool`
2. **Use**: First run auto-registers you on free tier (500 queries/month)
3. **Search**: `tool.invoke({"query": "your compliance question"})`
4. **Upgrade**: When ready, [get Pro plan ($29/month)](https://rapidapi.com/yschang/api/far-rag-federal-acquisition-regulation-search)

[Back to README](../README.md)

