Recall

Which recall endpoint should I use?

Endpoint reference

Endpoint	Method	Purpose	Returns
`/recall/full_recall`	`POST`	Hybrid recall over knowledge	Ranked chunks + optional graph context
`/recall/recall_preferences`	`POST`	Hybrid recall over user memories	Ranked memory chunks + optional graph context
`/recall/boolean_recall`	`POST`	Exact-match full-text search	BM25-ranked chunks

Side-by-side comparison

	`full_recall`	`recall_preferences`	`boolean_recall`
Searches	Knowledge	Memories	Either (via `search_mode`)
Match type	Semantic + graph	Semantic + graph	Exact term/phrase
Returns	Chunks	Chunks	Chunks
Latency	Medium	Medium	Lowest
Best for	Document Q&A, RAG context	Personalization	Compliance lookups, exact matches

Modes

Two of the three endpoints (full_recall, recall_preferences) accept a mode parameter:

Mode	Behavior	When to use
`fast` (default)	Single query pass, no reranking	Real-time chat, autocomplete, simple lookups
`thinking`	Multi-query expansion, reranking, forceful-relation context	Complex queries, customer-facing answers, anything where quality matters

boolean_recall doesn’t have modes – it’s deterministic by design.

Common parameters

full_recall and recall_preferences share most parameters:

Parameter	Default	Purpose
`query`	–	The user’s input
`mode`	`fast`	Retrieval quality vs latency
`alpha`	`0.8`	Hybrid weight (semantic vs keyword)
`recency_bias`	`0.0`	Prefer newer content
`metadata_filters`	–	Deterministic scoping
`graph_context`	`false`	Include entity relationships

Typical patterns

Personalized Q&A

# 1. Recall user's preferences first
prefs = client.recall.recall_preferences(
    tenant_id="acme",
    sub_tenant_id="user_alex",
    query="how does the user like answers formatted?",
)

# 2. Then recall the actual knowledge
result = client.recall.full_recall(
    tenant_id="acme",
    query="What's our refund policy?",
    additional_context=format_prefs(prefs),  # use prefs as extra signal
    mode="thinking",
)

Document Q&A with citations

# Retrieve context, then pass to your LLM of choice
result = client.recall.full_recall(
    tenant_id="acme",
    query="What's our refund policy?",
    mode="thinking",
    graph_context=True,
)

# Build prompt for your LLM with retrieved chunks
context = "\n\n".join(c.chunk_content for c in result.chunks)
sources = [c.source_title for c in result.chunks]

# answer = your_llm.complete(f"Context:\n{context}\n\nQuestion: ...")
# cite using `sources`

Compliance lookup

# Exact match for a regulatory term
result = client.recall.boolean_recall(
    tenant_id="acme",
    query="GDPR Article 17",
    operator="phrase",
    search_mode="sources",
)

Key concepts

Hybrid retrieval – Combines semantic similarity (embeddings) with keyword matching (BM25). The alpha parameter controls the weight. Default 0.8 favors semantic. Graph context – Entity relationships extracted from your data. Optional. Helps surface “how things connect” in addition to “what’s similar.” thinking mode – Expands the query into multiple sub-queries, retrieves for each, and reranks the combined results. Higher quality, higher latency. Forceful relations – Sources can be explicitly linked at ingestion. In thinking mode, recall surfaces context from these linked sources via the additional_context field.

Essentials → Recall – conceptual overview, the multi-stage pipeline
Essentials → Context Graphs – how graph context is built and used
API Reference → Memories – ingest memories before recall
API Reference → Ingestion – ingest knowledge before recall

API Documentation

Tenants

Knowledge & Memories

Managing Data

Recall – Overview

Which recall endpoint should I use?

Endpoint reference

Side-by-side comparison

Modes

Common parameters

Typical patterns

Personalized Q&A

Document Q&A with citations

Compliance lookup

Key concepts

​Which recall endpoint should I use?

​Endpoint reference

​Side-by-side comparison

​Modes

​Common parameters

​Typical patterns

​Personalized Q&A

​Document Q&A with citations

​Compliance lookup

​Key concepts

​Related sections

Which recall endpoint should I use?

Endpoint reference

Side-by-side comparison

Modes

Common parameters

Typical patterns

Personalized Q&A

Document Q&A with citations

Compliance lookup

Key concepts

Related sections