Semantic search

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How it works

• Embedding-based similarity for meaning
• Knowledge graph traversal for connected information
• Entity recognition for precise matches
• Metadata filtering for refined results

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Basic search

results = client.memories.search(
    space_id="space_123",
    query="What are the user's communication preferences?",
    limit=10
)

for memory in results.memories:
    print(f"Score: {memory.score}")
    print(f"Content: {memory.content}")

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Search results

• Relevance score (0-1): Semantic similarity to query
• Content: The extracted information
• Metadata: Context and tags
• Entities: Extracted people, places, technologies
• Source: Original file, timestamp, page number

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Query patterns

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Natural questions

"What does the user like for breakfast?"
"Who works in the engineering team?"
"How do I authenticate API requests?"

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Entity searches

"Sarah Chen"
"Acme Corp"
"Kubernetes deployment"

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Conceptual queries

"user preferences"
"team structure"
"authentication methods"

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Search with filters

results = client.memories.search(
    space_id="space_123",
    query="bug reports",
    filters={
        "type": "bug_report",
        "severity": "high"
    },
    limit=20
)

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Building AI context

def get_context_for_query(user_query: str, space_id: str) -> str:
    results = client.memories.search(
        space_id=space_id,
        query=user_query,
        limit=5
    )
    return "\n\n".join([m.content for m in results.memories])

# Use in AI prompt
context = get_context_for_query("How should I configure the database?", "docs_space")
prompt = f"Context: {context}\n\nQuestion: {user_query}"

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Best practices

• Be specific: “What is the user’s preferred auth method?” vs “auth”
• Use natural language: Ask complete questions
• Adjust limit: 5-10 for AI context, 20-50 for comprehensive search
• Combine with filters: Use metadata to narrow results
• Check scores: Filter by relevance threshold if needed

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Next steps