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RAG and Memory
Tools for semantic retrieval, knowledge bases, and long-term agent memory.
| Tool | Extra | Description |
|---|---|---|
retrieve | base | Semantic search over Amazon Bedrock Knowledge Bases. |
memory | base | CRUD over Knowledge Base documents. |
agent_core_memory | base (AWS) | Bedrock AgentCore Memory. Register via AgentCoreMemoryToolProvider. |
mem0_memory | mem0-memory | Long-term memory and personalization via Mem0. |
elasticsearch_memory | elasticsearch-memory | Agent memory with Elasticsearch vector search. |
mongodb_memory | mongodb-memory | Agent memory with MongoDB Atlas Vector Search. |
retrieve
Semantic search over Amazon Bedrock Knowledge Bases.
python
from elsai_tools.retrieve import retrieve| Parameter | Type | Required | Description |
|---|---|---|---|
text | str | Yes | Search query |
knowledgeBaseId | str | No | Knowledge Base ID |
numberOfResults | int | No | Max results (default 5) |
region | str | No | AWS region |
score | float | No | Minimum relevance score |
memory
CRUD over Knowledge Base documents.
python
from elsai_tools.memory import memory| Parameter | Type | Required | Description |
|---|---|---|---|
action | str | Yes | store, delete, list, get, or retrieve |
content | str | No | Document content (for store) |
title | str | No | Document title |
document_id | str | No | Document ID |
query | str | No | Search query (for retrieve) |
knowledge_base_id | str | No | Knowledge Base ID |
max_results | int | No | Max retrieve results |
agent_core_memory
Bedrock AgentCore Memory. Register via AgentCoreMemoryToolProvider.
python
from elsai_tools.agent_core_memory import AgentCoreMemoryToolProvider
provider = AgentCoreMemoryToolProvider(
memory_id="memory-123",
actor_id="user-456",
session_id="session-789",
namespace="default",
)
agent = Agent(tools=provider.tools)| Parameter | Type | Required | Description |
|---|---|---|---|
action | str | Yes | record, retrieve, list, get, or delete |
content | str | No | Content to record |
query | str | No | Semantic search query |
memory_record_id | str | No | Record ID for get/delete |
max_results | int | No | Max retrieve results |
mem0_memory
Long-term memory and personalization via Mem0.
python
from elsai_tools.mem0_memory import mem0_memoryExtra: mem0-memory
| Parameter | Type | Required | Description |
|---|---|---|---|
action | str | Yes | Memory action (add, search, delete, etc.) |
content | Any | No | Content to store |
query | str | No | Search query |
memory_id | str | No | Specific memory ID |
user_id | str | No | User identifier |
agent_id | str | No | Agent identifier |
elasticsearch_memory
Agent memory with Elasticsearch vector search.
python
from elsai_tools.elasticsearch_memory import elasticsearch_memoryExtra: elasticsearch-memory
| Parameter | Type | Required | Description |
|---|---|---|---|
action | str | Yes | Memory action |
content | str | No | Content to store |
query | str | No | Search query |
memory_id | str | No | Memory ID |
max_results | int | No | Max results |
index_name | str | No | Elasticsearch index |
mongodb_memory
Agent memory with MongoDB Atlas Vector Search.
python
from elsai_tools.mongodb_memory import mongodb_memoryExtra: mongodb-memory
| Parameter | Type | Required | Description |
|---|---|---|---|
action | str | Yes | Memory action |
content | str | No | Content to store |
query | str | No | Search query |
memory_id | str | No | Memory ID |
max_results | int | No | Max results |
namespace | str | No | MongoDB namespace |