Semantic search across your documents. Upload files and let AI find and retrieve the exact information you need.
Upload files to a vector store
Files are split into chunks
Chunks are embedded into vectors
AI retrieves relevant chunks
from mythicdot import MythicDot
client = MythicDot()
# Create a vector store
vector_store = client.beta.vector_stores.create(
name="Company Docs"
)
# Upload files
file = client.files.create(
file=open("handbook.pdf", "rb"),
purpose="assistants"
)
# Add to vector store
client.beta.vector_stores.files.create(
vector_store_id=vector_store.id,
file_id=file.id
)
# Create assistant with file search
assistant = client.beta.assistants.create(
name="Doc Assistant",
model="mythic-4",
tools=[{"type": "file_search"}],
tool_resources={
"file_search": {"vector_store_ids": [vector_store.id]}
}
)
Finds information based on meaning, not just keywords. Ask questions in natural language.
Responses include citations back to specific passages in your documents.
Vector search retrieves relevant chunks in milliseconds, even across thousands of files.
Re-upload files and the vector store automatically updates with new content.
For best results, use documents with clear structure (headings, paragraphs). The default chunk size works well for most documents, but you can customize it for specialized use cases.
Enable semantic search in your applications.