SearchType

Enum defining the available search modes for cognee.search().

from cognee import SearchType

results = await cognee.search("query", query_type=SearchType.GRAPH_COMPLETION)

Values

The Retrieval source column shows what each type reads from: Vector (semantic similarity over embeddings), Graph (knowledge-graph traversal / Cypher), Vector + Graph (semantic seeds plus graph context — the “semantics + graph” combination), or Lexical (keyword matching, no embeddings). Types marked Varies pick or combine sources at runtime.

Value	Retrieval source	Description
`SearchType.SUMMARIES`	Vector	Pre-generated hierarchical summaries of content.
`SearchType.CHUNKS`	Vector	Raw text segments matching semantically.
`SearchType.CHUNKS_LEXICAL`	Lexical	Token-based lexical chunk search (BM25-style keyword ranking, no embeddings).
`SearchType.RAG_COMPLETION`	Vector	Traditional RAG: retrieve chunks by semantic similarity, then LLM answer.
`SearchType.HYBRID_COMPLETION`	Vector + Graph	Hybrid answer combining lexical chunks, semantic chunks, and graph/entity context.
`SearchType.TRIPLET_COMPLETION`	Vector	Semantic search over triplet embeddings, then LLM answer (needs `TRIPLET_EMBEDDING=true`).
`SearchType.GRAPH_COMPLETION`	Vector + Graph	Default. Semantic seeds expanded into graph context, then LLM completion.
`SearchType.GRAPH_COMPLETION_DECOMPOSITION`	Vector + Graph	Decomposes a complex query into focused subqueries, retrieves graph context per subquery, then synthesizes a final answer.
`SearchType.GRAPH_SUMMARY_COMPLETION`	Vector + Graph	Graph context condensed via summaries before completion.
`SearchType.GRAPH_COMPLETION_COT`	Vector + Graph	Graph completion with iterative chain-of-thought reasoning.
`SearchType.GRAPH_COMPLETION_CONTEXT_EXTENSION`	Vector + Graph	Graph completion that iteratively feeds each answer back as a new query to widen subgraph coverage.
`SearchType.TEMPORAL`	Vector + Graph	Time-aware search: extracts time constraints, then combines graph events with semantic retrieval.
`SearchType.CYPHER`	Graph	Direct Cypher query against the graph database.
`SearchType.NATURAL_LANGUAGE`	Graph	Natural language translated to Cypher, then executed against the graph.
`SearchType.CODING_RULES`	Graph	Retrieves coding rules stored in a graph node set.
`SearchType.AGENTIC_COMPLETION`	Varies	Agentic completion that chooses sources via skills and tools.
`SearchType.FEELING_LUCKY`	Varies	Auto-selects the best search type (and its source) for the query.

Speed vs. accuracy

The biggest cost driver is how many LLM calls a search type makes. Retrieval-only modes return matches without any generation step and are the fastest; single-completion modes add one LLM call; iterative modes make several calls and scale with their round/iteration settings. The table below orders types roughly from fastest to slowest. “Accuracy” here means how well-grounded and complete the answer tends to be — it depends on your data and query, so treat it as a relative guide, not a benchmark.

Search type	LLM calls	Relative speed	Accuracy / depth	Best for
`CHUNKS`	0	⚡️ Fastest	Raw passages, no synthesis	Display or post-process exact snippets
`CHUNKS_LEXICAL`	0	⚡️ Fastest	Keyword match, no synthesis	Exact-term / stopword-aware lookups
`SUMMARIES`	0	⚡️ Fastest	Precomputed summaries	Short, high-signal hits
`CYPHER`	0	⚡️ Fastest	Exact graph rows	You know the schema and write the query
`RAG_COMPLETION`	1	🟢 Fast	Good, text-only	Simple chunk-based RAG without graph structure
`GRAPH_COMPLETION` (default)	1	🟢 Fast	High — graph-grounded	Best balance for most questions
`HYBRID_COMPLETION`	1	🟢 Fast	High — chunk + graph context	Questions that benefit from lexical, semantic, and graph evidence together
`TRIPLET_COMPLETION`	1	🟢 Fast	High — triplet-level	Triplet context (needs `TRIPLET_EMBEDDING=true`)
`NATURAL_LANGUAGE`	1 query-generation call, retries up to 2 more times on empty/error results	🟡 Medium	Exact graph rows	Structured graph answers without writing Cypher
`TEMPORAL`	1 time-extraction call + answer completion	🟡 Medium	Time-aware	”before/after X”, year ranges, timelines
`GRAPH_SUMMARY_COMPLETION`	2 (summary + answer)	🟡 Medium	High, condensed	Noisy/large graphs needing a tighter context
`GRAPH_COMPLETION_DECOMPOSITION`	several (1 split + up to 5 subqueries + synthesis)	🟠 Slower	Higher on multi-part questions	Multi-entity / multi-aspect questions
`GRAPH_COMPLETION_CONTEXT_EXTENSION`	several (up to `context_extension_rounds`, default 4)	🔴 Slowest	Broader subgraph coverage	Open-ended, exploratory queries
`GRAPH_COMPLETION_COT`	many (up to `max_iter` rounds, default 4, several calls each)	🔴 Slowest	Highest on multi-hop reasoning	Complex `A → B → C` chains
`AGENTIC_COMPLETION`	varies (up to `max_iter`, default 6)	🔴 Slowest	Tool- and skill-assisted	Single-dataset agentic retrieval with skills/tools
`FEELING_LUCKY`	1 (selection) + chosen type	Varies	Matches chosen type	One-off queries when unsure which to use

To go faster: prefer CHUNKS, SUMMARIES, or CHUNKS_LEXICAL (no LLM call), or pass only_context=True to skip the final completion on any type. To go more accurate: start with GRAPH_COMPLETION, then escalate to GRAPH_COMPLETION_DECOMPOSITION for multi-part questions or GRAPH_COMPLETION_COT for multi-hop reasoning — both trade latency for depth. Lowering max_iter / context_extension_rounds via retriever_specific_config reduces cost for the iterative modes. See Search Basics — Advanced Parameters.

Choosing a Search Type

I want an LLM-generated answer grounded in my data

Use GRAPH_COMPLETION (default) for the best balance of accuracy and context. Use RAG_COMPLETION for a simpler chunk-based approach.

I want raw data, not an LLM answer

Use CHUNKS for semantic chunk retrieval or CHUNKS_LEXICAL for keyword-based. Use SUMMARIES for pre-generated summaries.

I want to query the graph directly

Use CYPHER for raw Cypher queries or NATURAL_LANGUAGE to have cognee translate your question to Cypher.

I'm not sure which to use

Use FEELING_LUCKY — cognee will pick the best search type for your query.

I need more accurate or comprehensive answers from the graph

All four graph-completion modes retrieve graph triplets and generate an LLM answer, but they differ in depth and latency:

Mode	Strategy	Best for	Trade-off
`GRAPH_COMPLETION`	Single-pass retrieval + completion	Most queries — good accuracy, low latency	Baseline
`GRAPH_COMPLETION_DECOMPOSITION`	Decomposes query into 1–5 subqueries, retrieves graph context per subquery, then synthesizes a final answer	Multi-entity or multi-aspect questions (e.g. “Tell me about A and B”)	One extra LLM call for decomposition; slightly higher latency
`GRAPH_SUMMARY_COMPLETION`	Graph context condensed via summaries before completion	Noisy or large graphs where a tighter context improves coherence	Slightly slower; summary quality matters
`GRAPH_COMPLETION_COT`	Iterative: retrieve → answer → validate → follow-up (up to `max_iter` rounds, default 4)	Complex multi-hop questions where stepwise reasoning helps	Higher latency; more LLM calls
`GRAPH_COMPLETION_CONTEXT_EXTENSION`	Iterative: retrieve → generate → use output as new query (up to `context_extension_rounds`, default 4)	Open-ended or exploratory queries needing a broader subgraph	Higher latency; early convergence stops extra rounds

See Search Basics — Advanced Parameters and Retrievers for full details.

Per-search-type parameters

Every type accepts the common parameters (query_text, top_k, system_prompt/system_prompt_path, only_context, verbose, include_references, datasets/dataset_ids, user, session_id) documented in Search Basics — Parameters Reference. The graph-completion family additionally honors the graph-ranking knobs (wide_search_top_k, triplet_distance_penalty, feedback_influence, neighborhood_depth, neighborhood_seed_top_k) and node-set filters. With recall(), use node_name and node_name_filter_operator; node_type is only exposed on lower-level search(). The accordions below list each search type’s type-specific parameters. Most entries are passed through retriever_specific_config; when a search type uses a common parameter in a special way, that is noted.

SUMMARIES

Parameter	Type	Default	Explanation
—	—	—	No `retriever_specific_config` keys. Uses common parameters only.

CHUNKS

Parameter	Type	Default	Explanation
`node_name`	`list[str] \| None`	`None`	Common parameter used to filter chunks by node set.
`node_name_filter_operator`	`"OR" \| "AND"`	`"OR"`	Common parameter controlling how multiple `node_name` filters are combined.

CHUNKS_LEXICAL

Parameter	Type	Default	Explanation
—	—	—	No `retriever_specific_config` keys. Uses common parameters only.

RAG_COMPLETION

Parameter	Type	Default	Explanation
`response_model`	`type`	`str`	Output type or Pydantic model for the completion response.
`node_name`	`list[str] \| None`	`None`	Common parameter used to filter chunks by node set.
`node_name_filter_operator`	`"OR" \| "AND"`	`"OR"`	Common parameter controlling how multiple `node_name` filters are combined.

HYBRID_COMPLETION

Parameter	Type	Default	Explanation
`response_model`	`type`	`str`	Output type or Pydantic model for the completion response.
`chunks_top_k`	`int`	`top_k`	Max merged lexical and semantic chunks included in context.
`entities_top_k`	`int`	`top_k`	Number of matched entities used for graph context.
`max_edges_per_entity`	`int`	`10`	Max connected edges listed per entity.
`facts_top_k`	`int`	`top_k`	Max edge-derived facts included in the related facts context.
`include_global_context_index`	`bool`	`False`	Adds global-context summaries to the retrieved context.
`global_context_index_top_k`	`int`	`3`	Number of global-context summaries to include when enabled.
`text_summaries_top_k`	`int \| None`	`None`	Optional limit for text summaries included by the hybrid retriever.
`use_importance_weight`	`bool`	`True`	Applies importance weighting when ranking hybrid context.

TRIPLET_COMPLETION

Parameter	Type	Default	Explanation
`response_model`	`type`	`str`	Output type or Pydantic model for the completion response.
`node_name`	`list[str] \| None`	`None`	Common parameter used to filter triplets by node set.
`node_name_filter_operator`	`"OR" \| "AND"`	`"OR"`	Common parameter controlling how multiple `node_name` filters are combined.

GRAPH_COMPLETION

Parameter	Type	Default	Explanation
`response_model`	`type`	`str`	Output type or Pydantic model for the completion response.
`include_global_context_index`	`bool`	`False`	Adds global-context summaries to the retrieved graph context.
`global_context_index_top_k`	`int`	`3`	Number of global-context summaries to include when enabled.

GRAPH_COMPLETION_DECOMPOSITION

Parameter	Type	Default	Explanation
`response_model`	`type`	`str`	Output type or Pydantic model for the completion response.
`decomposition_mode`	`"answer_per_subquery" \| "combined_triplets_context"`	`"answer_per_subquery"`	Controls whether each subquery is answered separately or all retrieved triplets are merged before the final answer.

GRAPH_SUMMARY_COMPLETION

Parameter	Type	Default	Explanation
`summarize_prompt_path`	`str`	`"summarize_search_results.txt"`	Prompt file used to summarize graph context before completion.

GRAPH_COMPLETION_COT

Parameter	Type	Default	Explanation
`max_iter`	`int`	`4`	Number of follow-up reasoning rounds after the initial retrieval.
`response_model`	`type`	`str`	Output type or Pydantic model for the completion response.
`validation_system_prompt_path`	`str`	`"cot_validation_system_prompt.txt"`	System prompt used to validate the current answer.
`validation_user_prompt_path`	`str`	`"cot_validation_user_prompt.txt"`	User prompt used to validate the current answer.
`followup_system_prompt_path`	`str`	`"cot_followup_system_prompt.txt"`	System prompt used to generate follow-up questions.
`followup_user_prompt_path`	`str`	`"cot_followup_user_prompt.txt"`	User prompt used to generate follow-up questions.

GRAPH_COMPLETION_CONTEXT_EXTENSION

Parameter	Type	Default	Explanation
`context_extension_rounds`	`int`	`4`	Max rounds for extending context by using the previous output as the next query.
`response_model`	`type`	`str`	Output type or Pydantic model for the completion response.

TEMPORAL

Parameter	Type	Default	Explanation
`response_model`	`type`	`str`	Output type or Pydantic model for the completion response.
`user_prompt_path`	`str`	`"graph_context_for_question.txt"`	Prompt file used to format temporal graph context for the question.
`system_prompt_path`	`str`	`"answer_simple_question.txt"`	System prompt file used for the final answer.
`time_extraction_prompt_path`	`str`	`"extract_query_time.txt"`	Prompt file used to extract temporal constraints from the query.

CYPHER

Parameter	Type	Default	Explanation
`user_prompt_path`	`str`	`"context_for_question.txt"`	Prompt file used to format Cypher results.
`system_prompt_path`	`str`	`"answer_simple_question.txt"`	System prompt file used for result formatting.

NATURAL_LANGUAGE

Parameter	Type	Default	Explanation
`system_prompt_path`	`str`	`"natural_language_retriever_system.txt"`	System prompt file used to translate natural language to Cypher.
`max_attempts`	`int`	`3`	Max attempts to generate and run a useful graph query.

CODING_RULES

Parameter	Type	Default	Explanation
`node_name`	`list[str] \| None`	`None`	Common parameter interpreted as the coding-rules node set name.

AGENTIC_COMPLETION

Requires the resolved scope to contain exactly one dataset.

Parameter	Type	Default	Explanation
`skills`	`list[str \| Skill] \| None`	`None`	Skill names or `Skill` objects to load into the agentic retriever.
`tools`	`list[str] \| None`	`None`	Optional whitelist of tool names available to the agent.
`max_iter`	`int`	`6`	Max tool-call iterations before forcing a final answer.
`agentic_system_prompt_path`	`str`	`"agentic_system.txt"`	System prompt file used inside the agent loop.
`agentic_user_prompt_path`	`str`	`"agentic_user.txt"`	User prompt file used inside the agent loop.
`response_model`	`type`	`str`	Output type or Pydantic model for the final response.

FEELING_LUCKY

Parameter	Type	Default	Explanation
—	—	—	No direct `retriever_specific_config` keys. It selects another search type and inherits that type’s parameters.

# Example: tune the chain-of-thought depth and request typed output
await cognee.recall(
    query_text="How did the incident propagate across services?",
    query_type=SearchType.GRAPH_COMPLETION_COT,
    retriever_specific_config={"max_iter": 2},
)

See Search Basics — Advanced Parameters for retriever_specific_config usage and Retrievers for per-retriever behavior.

​SearchType

​Values

​Speed vs. accuracy

​Choosing a Search Type

​Per-search-type parameters

SearchType

Values

Speed vs. accuracy

Choosing a Search Type

Per-search-type parameters