Semantic 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}")

const results = await client.memories.search({
  space_id: 'space_123',
  query: 'What are the user\'s communication preferences?',
  limit: 10,
});

for (const memory of results.memories) {
  console.log(`Score: ${memory.score}`);
  console.log(`Content: ${memory.content}`);
}

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

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

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}"

async function getContextForQuery(userQuery: string, spaceId: string) {
  const results = await client.memories.search({
    space_id: spaceId,
    query: userQuery,
    limit: 5,
  });
  return results.memories.map(m => m.content).join('\n\n');
}

// Use in AI prompt
const context = await getContextForQuery('How should I configure the database?', 'docs_space');
const prompt = `Context: ${context}\n\nQuestion: ${userQuery}`;