Examples

from nia_ai_api_client import AuthenticatedClient
from nia_ai_api_client.api.repositories import index_repository
from nia_ai_api_client.models import RepositoryRequest

client = AuthenticatedClient(
    base_url="https://apigcp.trynia.ai/v2",
    token="nia_your_api_key"
)

# Index a public repository
request = RepositoryRequest(repository="vercel/ai")
result = index_repository.sync(client=client, body=request)
print(f"Indexing started: {result.data.repository} - {result.data.status}")

from nia_ai_api_client.api.repositories import get_repository_status

status = get_repository_status.sync(
    client=client,
    repository_id="vercel/ai"
)

print(f"Status: {status.status}")
if status.progress:
    print(f"Progress: {status.progress.percentage}%")

from nia_ai_api_client.api.repositories import get_repository_tree

tree = get_repository_tree.sync(
    client=client,
    repository_id="vercel/ai"
)

for item in tree.items:
    print(f"{'📁' if item.type == 'directory' else '📄'} {item.path}")

from nia_ai_api_client.api.repositories import get_repository_content

content = get_repository_content.sync(
    client=client,
    repository_id="vercel/ai",
    path="packages/ai/src/index.ts"
)

print(content.content)

from nia_ai_api_client.api.repositories import grep_repository
from nia_ai_api_client.models import CodeGrepRequest

request = CodeGrepRequest(
    pattern="async function",
    path="src/",  # optional path filter
    context_lines=2
)

results = grep_repository.sync(
    client=client,
    repository_id="vercel/ai",
    body=request
)

for match in results.matches:
    print(f"{match.file}:{match.line_number}")
    print(f"  {match.content}")

from nia_ai_api_client.api.search import search_query
from nia_ai_api_client.models import QueryRequest, QueryRequestMessagesItem

request = QueryRequest(
    messages=[
        QueryRequestMessagesItem(
            role="user",
            content="How does streaming work in the AI SDK?"
        )
    ],
    repositories=[{"repository": "vercel/ai"}],
    include_sources=True
)

result = search_query.sync(client=client, body=request)

print("Answer:", result.content)
print("\nSources:")
for source in result.sources:
    print(f"  - {source.path}")

from nia_ai_api_client.api.search import search_universal
from nia_ai_api_client.models import UniversalSearchRequest

request = UniversalSearchRequest(
    query="How to implement tool calling?"
)

result = search_universal.sync(client=client, body=request)
print(result.content)

from nia_ai_api_client.api.search import search_web
from nia_ai_api_client.models import WebSearchRequest

request = WebSearchRequest(
    query="latest LLM developments 2024",
    num_results=5
)

results = search_web.sync(client=client, body=request)
for item in results.results:
    print(f"{item.title}: {item.url}")

from nia_ai_api_client.api.data_sources import create_data_source
from nia_ai_api_client.models import DataSourceRequest

request = DataSourceRequest(
    url="https://docs.anthropic.com",
    display_name="Anthropic Docs"
)

result = create_data_source.sync(client=client, body=request)
print(f"Indexing: {result.source_id}")

from nia_ai_api_client.api.data_sources import list_data_sources

sources = list_data_sources.sync(client=client)

for source in sources.sources:
    print(f"{source.display_name} ({source.status})")

from nia_ai_api_client.api.search import search_query
from nia_ai_api_client.models import QueryRequest, QueryRequestMessagesItem

request = QueryRequest(
    messages=[
        QueryRequestMessagesItem(
            role="user",
            content="How to use Claude's tool use feature?"
        )
    ],
    data_sources=["Anthropic Docs"],  # by display name
    search_mode="sources"
)

result = search_query.sync(client=client, body=request)
print(result.content)

from nia_ai_api_client.api.research_papers import index_research_paper
from nia_ai_api_client.models import ResearchPaperRequest

request = ResearchPaperRequest(
    url="https://arxiv.org/abs/2303.08774"  # GPT-4 paper
)

result = index_research_paper.sync(client=client, body=request)
print(f"Paper: {result.title}")
print(f"Authors: {', '.join(result.authors)}")

from nia_ai_api_client.api.package_search import package_search_hybrid
from nia_ai_api_client.models import PackageSearchHybridRequest

request = PackageSearchHybridRequest(
    registry="py_pi",
    package_name="openai",
    semantic_queries=["How does streaming work?"]
)

results = package_search_hybrid.sync(client=client, body=request)
for result in results.results:
    print(f"{result.file}: {result.snippet}")

from nia_ai_api_client.api.oracle_research import create_oracle_job
from nia_ai_api_client.models import OracleResearchRequest

request = OracleResearchRequest(
    query="Compare React Server Components vs traditional SSR",
    repositories=["vercel/next.js"],
    output_format="comparison table"
)

job = create_oracle_job.sync(client=client, body=request)
print(f"Job ID: {job.job_id}")

from nia_ai_api_client.api.oracle_research import get_oracle_job

job = get_oracle_job.sync(client=client, job_id="job_123")

print(f"Status: {job.status}")
if job.status == "completed":
    print(f"Result: {job.result}")

from nia_ai_api_client.api.context_sharing import save_context, search_contexts
from nia_ai_api_client.models import ContextShareRequest, ContextSearchRequest

# Save context
request = ContextShareRequest(
    title="Auth implementation notes",
    summary="Implemented OAuth2 flow with refresh tokens",
    content="Detailed implementation notes...",
    tags=["auth", "oauth"]
)

context = save_context.sync(client=client, body=request)
print(f"Saved: {context.id}")

# Search contexts
search_request = ContextSearchRequest(query="authentication")
results = search_contexts.sync(client=client, body=search_request)

for ctx in results:
    print(f"{ctx.title}: {ctx.summary}")