- Python SDK
- JavaScript SDK
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from gravixlayer import GravixLayer
client = GravixLayer()
memory = client.memory
# Add simple text
result = memory.add("I love pizza", user_id="alice")
print(f"Added memory: {result['results'][0]['memory']}")
print(f"Memory ID: {result['results'][0]['id']}")
# Add with metadata
result = memory.add("User prefers dark mode", user_id="alice", metadata={"type": "preference"})
print(f"Added preference: {result['results'][0]['memory']}")
print(f"Metadata: {result['results'][0]['metadata']}")
# Get all memories to verify
all_memories = memory.get_all(user_id="alice")
print(f"\nTotal memories for alice: {len(all_memories['results'])}")
for i, mem in enumerate(all_memories['results'], 1):
print(f"{i}. {mem['memory']}")
if mem.get('metadata'):
print(f" Metadata: {mem['metadata']}")
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import { GravixLayer } from 'gravixlayer';
const client = new GravixLayer();
const memory = client.memory;
async function addMemories() {
// Add simple text
const result1 = await memory.add("I love pizza", "alice");
console.log(`Added memory: ${result1.results[0].memory}`);
console.log(`Memory ID: ${result1.results[0].id}`);
// Add with metadata
const result2 = await memory.add("User prefers dark mode", "alice", {
metadata: {type: "preference"}
});
console.log(`Added preference: ${result2.results[0].memory}`);
console.log(`Metadata: ${JSON.stringify(result2.results[0].metadata)}`);
// Get all memories to verify
const allMemories = await memory.getAll("alice");
console.log(`\nTotal memories for alice: ${allMemories.results.length}`);
allMemories.results.forEach((mem, index) => {
console.log(`${index + 1}. ${mem.memory}`);
if (mem.metadata) {
console.log(` Metadata: ${JSON.stringify(mem.metadata)}`);
}
});
}
addMemories().catch(console.error);
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Memory index 'gravixlayer_memories' not found
Embedding model: baai/bge-large-en-v1.5
Dimension: 1024
Cloud config: {'cloud_provider': 'AWS', 'region': 'us-east-1', 'index_type': 'serverless'}
Creating memory index...
Successfully created memory index: 1dc9e3a7-ffba-46b6-ad65-7aa3c55685e3
I love pizza
Extracted 2 memories from conversation
Add Memory with Custom Configuration
Custom Configuration: When you specify custom parameters, they override the defaults for that memory instance.
- Python SDK
- JavaScript SDK
Copy
from gravixlayer import GravixLayer
client = GravixLayer()
memory = client.memory
# Store a conversation with AI inference
conversation = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
result = memory.add(conversation, user_id="alice", infer=True, metadata={"type": "conversation"})
print(f"AI extracted {len(result['results'])} memories from conversation:")
for i, extracted_memory in enumerate(result['results'], 1):
print(f"{i}. {extracted_memory['memory']}")
print(f" ID: {extracted_memory['id']}")
if extracted_memory.get('metadata'):
print(f" Metadata: {extracted_memory['metadata']}")
# Verify by searching for movie preferences
search_results = memory.search("movie preferences", user_id="alice")
print(f"\nFound {len(search_results['results'])} movie-related memories:")
for result in search_results['results']:
print(f"- {result['memory']}")
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import { GravixLayer } from 'gravixlayer';
const client = new GravixLayer();
const memory = client.memory;
async function addConversation() {
// Store a conversation with AI inference
const conversation = [
{role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?"},
{role: "assistant", content: "How about thriller movies? They can be quite engaging."},
{role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies."},
{role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
];
const result = await memory.add(conversation, "alice", {
infer: true,
metadata: {type: "conversation"}
});
console.log(`AI extracted ${result.results.length} memories from conversation:`);
result.results.forEach((extractedMemory, index) => {
console.log(`${index + 1}. ${extractedMemory.memory}`);
console.log(` ID: ${extractedMemory.id}`);
if (extractedMemory.metadata) {
console.log(` Metadata: ${JSON.stringify(extractedMemory.metadata)}`);
}
});
// Verify by searching for movie preferences
const searchResults = await memory.search("movie preferences", "alice");
console.log(`\nFound ${searchResults.results.length} movie-related memories:`);
searchResults.results.forEach(result => {
console.log(`- ${result.memory}`);
});
}
addConversation().catch(console.error);