Narrowing search with Agentic

import os
import requests

headers = {"Authorization": f"Bearer {os.environ['LIGHTON_API_KEY']}"}

# Infer content type and attribute filters for a search query.
# The model parameter tells the API to call the LLM and return
# parsed, normalized filters in scope_completion.
response = requests.post(
    "https://api.lighton.ai/api/v3/content-types/scope",
    headers=headers,
    json={
        "query": "rejected electronics patents filed after 2023",
        "model": "mistral-large-latest",  # replace with your model
    },
)
print(response.json())

import os
import requests

headers = {"Authorization": f"Bearer {os.environ['LIGHTON_API_KEY']}"}

query = "rejected electronics patents filed after 2023"

# Step 1: Infer scope from the query (completion mode)
scope_response = requests.post(
    "https://api.lighton.ai/api/v3/content-types/scope",
    headers=headers,
    json={
        "query": query,
        "model": "mistral-large-latest",  # replace with your model
    },
)

# Step 2: Build a scoped search from the inferred filters
scope = scope_response.json().get("scope_completion", {})

search_body = {"query": query}
if scope.get("content_type"):
    search_body["content_type"] = [scope["content_type"]]
if scope.get("attribute"):
    search_body["attribute"] = scope["attribute"]

# Step 3: Search within the inferred scope
response = requests.post(
    "https://api.lighton.ai/api/v3/search",
    headers=headers,
    json=search_body,
)
print(response.json())

import os
import requests

headers = {"Authorization": f"Bearer {os.environ['LIGHTON_API_KEY']}"}

# Get prompt_context: an LLM-ready text block with ranked content types,
# attribute definitions, filter syntax, and inference rules.
# Feed this to your own LLM alongside the user query.
response = requests.post(
    "https://api.lighton.ai/api/v3/content-types/scope",
    headers=headers,
    json={
        "query": "rejected electronics patents filed after 2023",
    },
)
print(response.json())
```python
prompt_context = response.json()["prompt_context"]
user_query = "rejected electronics patents filed after 2023"

# Add business rules only someone familiar with this corpus would know
domain_rules = """
ADDITIONAL RULES:
- "Project Aurora" is our internal name for the renewable energy initiative: always map it to patent:electricity:h02
- Our fiscal year starts in April: "this year" means >= 2025-04-01, "last year" means >= 2024-04-01 AND <= 2025-03-31
- When users say "pending", they mean decision_status:Accepted (our process labels accepted-but-not-granted as "pending")
"""

# Few-shot examples that correct patterns your LLM gets wrong
examples = """
Examples:
  Query: "Project Aurora patents from last year"
  Answer: {"content_type": "patent:electricity:h02", "attribute": ["filing_date:>=2024-04-01", "filing_date:<=2025-03-31"]}

  Query: "pending applications from the energy team"
  Answer: {"content_type": "patent:electricity", "attribute": ["decision_status:Accepted"]}
"""

prompt = f"""{prompt_context}

{domain_rules}

{examples}

Query: "{user_query}"

Respond with ONLY JSON:
{{"content_type": "path or null", "attribute": ["filter1", "filter2"] or []}}"""