Documentation Index
Fetch the complete documentation index at: https://docs.maximem.ai/llms.txt
Use this file to discover all available pages before exploring further.
await sdk.customer.context.fetch(
customer_id,
conversation_id=None,
search_query=None,
max_results=10,
types=None,
mode="fast",
)
Parameters
The customer identifier to fetch context for.
Optional conversation identifier. When provided, results are biased toward memories relevant to the active conversation.
One or more search queries to find relevant customer memories. If omitted, returns the most recent and highest-confidence customer-scoped memories.
Maximum number of memory items to return. Defaults to 10. Maximum 50.
Filter results to specific memory types. If omitted, all types are included.| Value | Description |
|---|
fact | Factual information about the customer organization |
preference | Customer organizational preferences |
episode | Significant events within the customer organization |
emotion | Sentiment and emotional context within the organization |
temporal_event | Time-bound events (project deadlines, fiscal year, etc.) |
Retrieval mode that controls the speed-quality tradeoff.| Value | Description |
|---|
fast | Vector search only. Lowest latency (~50-100ms). Default. |
accurate | Full vector + graph + re-ranking. Highest quality (~200-500ms). |
Returns
A ContextResponse with the following fields:
Array of fact memories relevant to the query.
Array of preference memories.
Array of episode memories.
Array of emotion memories.
Response metadata including total_results, query_time_ms, tokens_used, scope, and mode.
Example
from maximem_synap import MaximemSynapSDK
sdk = MaximemSynapSDK(api_key="synap_your_key_here")
await sdk.initialize()
context = await sdk.customer.context.fetch(
customer_id="cust_acme_corp",
search_query=["project management tools", "engineering stack"],
max_results=5,
types=["fact", "preference"],
mode="accurate",
)
for fact in context.facts:
print(f"[{fact.confidence:.2f}] {fact.content}")
{
"facts": [
{
"id": "mem_cust_f1a2b3c4",
"content": "Acme Corp uses Jira for project management and Slack for communication",
"confidence": 0.94,
"entities": [
{ "canonical_name": "Acme Corp", "type": "organization", "entity_id": "ent_acme001" }
],
"source": {
"ingestion_id": "ing_cust_7a8b9c0d",
"document_type": "ai-chat-conversation",
"document_created_at": "2025-01-12T14:30:00Z"
},
"relevance_score": 0.92,
"scope": "customer"
},
{
"id": "mem_cust_d5e6f7a8",
"content": "Engineering team uses Python 3.11 and PostgreSQL for all services",
"confidence": 0.91,
"entities": [
{ "canonical_name": "Acme Corp Engineering", "type": "team", "entity_id": "ent_acme_eng" }
],
"source": {
"ingestion_id": "ing_cust_0a9b8c7d",
"document_type": "document",
"document_created_at": "2025-01-08T09:00:00Z"
},
"relevance_score": 0.88,
"scope": "customer"
}
],
"preferences": [
{
"id": "mem_cust_b9c0d1e2",
"content": "Team prefers detailed code reviews over quick approvals",
"confidence": 0.87,
"entities": [],
"source": {
"ingestion_id": "ing_cust_7a8b9c0d",
"document_type": "ai-chat-conversation",
"document_created_at": "2025-01-12T14:30:00Z"
},
"relevance_score": 0.79,
"scope": "customer"
}
],
"episodes": [],
"emotions": [],
"metadata": {
"total_results": 3,
"query_time_ms": 187,
"tokens_used": 456,
"scope": {
"customer_id": "cust_acme_corp",
"client_id": "cli_a1b2c3d4e5f67890"
},
"mode": "accurate"
}
}
Customer-scoped memories take priority over client-scoped memories when they cover the same topic, following the scope chain priority rules. To merge customer and client context in a single call, use sdk.context.fetch. Raises
InvalidInputError — when mode is not "fast" or "accurate".SDKNotInitializedError — when called before await sdk.initialize().AuthenticationError — when the API key is invalid or revoked.ContextNotFoundError — when customer_id does not exist for this instance.
See also
