cognee/docs/kr/guides/search-basics.md

Search Basics

Step-by-step guide to running your first Cognee search and understanding core parameters

A minimal guide to using cognee.search() to ask questions against your processed datasets. This guide shows the basic call and what each parameter does so you know which knob to turn.

Before you start:

• Complete Quickstart to understand basic operations
• Ensure you have LLM Providers configured for LLM-backed search types
• Run cognee.cognify(...) to build the graph before searching
• Keep at least one dataset with read permission for the user running the search

Code in Action

import asyncio
import cognee

async def main():
    # Make sure you've already run cognee.cognify(...) so the graph has content
    answers = await cognee.search(
        query_text="What are the main themes in my data?"
    )
    for answer in answers:
        print(answer)

asyncio.run(main())

`SearchType.GRAPH_COMPLETION` is the default, so you get an LLM-backed answer plus supporting context as soon as you have data in your graph.

What Just Happened

The search call uses the default SearchType.GRAPH_COMPLETION mode to provide LLM-backed answers with supporting context from your knowledge graph. The results are returned as a list that you can iterate through and process as needed.

Parameters Reference

Most examples below assume you are inside an async function. Import helpers when you need them:

from cognee import SearchType
from cognee.modules.engine.models.node_set import NodeSet

* **`query_text`** (str, required): The question or phrase you want answered. ```python theme={null} answers = await cognee.search(query_text="Who owns the rollout plan?") ``` * **`query_type`** (SearchType, optional, default: `SearchType.GRAPH_COMPLETION`): Switch search modes without changing your code flow. See [Search Types](../core-concepts/main-operations/search) for the complete list. ```python theme={null} await cognee.search( query_text="List coding guidelines", query_type=SearchType.CODING_RULES, ) ``` * **`top_k`** (int, optional, default: 10): Cap how many ranked results you want back. ```python theme={null} await cognee.search(query_text="Summaries please", top_k=3) ``` * **`system_prompt_path`** (str, optional, default: `"answer_simple_question.txt"`): Point to a prompt file packaged with your project. ```python theme={null} await cognee.search( query_text="Explain the roadmap in bullet points", system_prompt_path="prompts/bullets.txt", ) ``` * **`system_prompt`** (Optional\[str]): Inline override for experiments or dynamically generated prompts. ```python theme={null} await cognee.search( query_text="Give me a confident answer", system_prompt="Answer succinctly and state confidence at the end.", ) ``` * **`only_context`** (bool, optional, default: False): Skip LLM generation and just fetch supporting context chunks. ```python theme={null} context = await cognee.search( query_text="What did we promise the client?", only_context=True, ) ``` * **`use_combined_context`** (bool, optional, default: False): Collapse results into a single combined response when you query multiple datasets. ```python theme={null} combined = await cognee.search( query_text="Quarterly financial highlights", datasets=["finance_q1", "finance_q2"], use_combined_context=True, ) ```

<Info>
  `use_combined_context` should only be set when `ENABLE_BACKEND_ACCESS_CONTROL` is turned on. When access control is disabled, this parameter has no meaningful effect on dataset scoping.
</Info>

These options filter the graph down to the node sets you care about. In most workflows you set **both**: keep `node_type=NodeSet` and pass one or more set names in `node_name`—the same labels you used when calling `cognee.add(..., node_set=[...])`.

* **`node_type`** (Optional\[Type], optional, default: `NodeSet`): Controls which graph model to search. Leave this as `NodeSet` unless you’ve built a custom node model.
* **`node_name`** (Optional\[List\[str]]): Names of the node sets to include. Cognee treats each string as a logical bucket of memories.
  ```python  theme={null}
  await cognee.search(
      query_text="What discounts did TechSupply offer?",
      node_type=NodeSet,
      node_name=["vendor_conversations"],
  )
  ```
  ```python  theme={null}
  await cognee.search(
      query_text="Summarize procurement rules",
      node_type=NodeSet,
      node_name=["procurement_policies", "purchase_history"],
  )
  ```

* **`session_id`** (Optional\[str]): Maintain conversation history across searches. When you use the same `session_id`, Cognee includes previous interactions in the LLM prompt, enabling contextual follow-up questions. ```python theme={null} await cognee.search( query_text="Where does Alice live?", session_id="conversation_1" ) # Later, same session remembers previous context await cognee.search( query_text="What does she do for work?", session_id="conversation_1" # "she" refers to Alice ) ``` See [Sessions Guide](/guides/sessions) for complete examples. * **`save_interaction`** (bool, optional, default: False): Persist the Q\&A as a graph interaction for auditing or later review. ```python theme={null} await cognee.search( query_text="Draft the release note", save_interaction=True, ) ``` * **`last_k`** (Optional\[int], optional, default: 1): When using `SearchType.FEEDBACK`, choose how many recent interactions to update with your feedback. ```python theme={null} await cognee.search( query_text="Please improve the last answer", query_type=SearchType.FEEDBACK, last_k=3, ) ``` * **`datasets`** (Optional\[Union\[list\[str], str]]): Limit search to dataset names you already know. ```python theme={null} await cognee.search( query_text="Key risks", datasets=["risk_register", "exec_summary"], ) ``` * **`dataset_ids`** (Optional\[Union\[list\[UUID], UUID]]): Same as `datasets`, but with explicit UUIDs when names collide. ```python theme={null} from uuid import UUID await cognee.search( query_text="Customer feedback", dataset_ids=[UUID("aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee")], ) ``` * **`user`** (Optional\[User]): Provide a user object when running multi-tenant flows or background jobs.

  ```python  theme={null}
  from cognee.modules.users.methods import get_user
  user = await get_user(user_id)
  await cognee.search(query_text="Team OKRs", user=user)
  ```

  **When** `ENABLE_BACKEND_ACCESS_CONTROL=true`:

  * **Result shape**: Searches run only on datasets the user can access and return either:
    * **Per dataset**: list of `{dataset_name, dataset_id, search_result}`
    * **Combined**: single `CombinedSearchResult` with merged snippets (`use_combined_context=True`)

  * If no `user` is given, `get_default_user()` is used (created if missing); errors only if this user lacks dataset permissions.

  * If `datasets` is not set, all datasets readable by the user are searched; errors if none are accessible or if requested datasets are forbidden.

  <Warning>
    `PermissionDeniedError` will be raised unless you search with the same user that added the data or grant access to the default user.
  </Warning>

  **When** `ENABLE_BACKEND_ACCESS_CONTROL=false`

  * Dataset filters (`datasets`, `dataset_ids`) are ignored — everything is searched.
  * Results normally come back as a plain list (`["answer1", "answer2"]`).
  * Setting `use_combined_context=True` here just wraps the same results in a `CombinedSearchResult` without changing them.

Learn about custom prompts for tailored answers Multi-tenant deployment patterns Explore all search types and parameters Enable conversational memory with sessions

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To find navigation and other pages in this documentation, fetch the llms.txt file at: https://docs.cognee.ai/llms.txt