Why This Matters

What Makes memories-dev Different

Real Success Stories

Quick Integration Examples

OpenAI Integration

frommemoriesimportMemoryStore,ConfigfromopenaiimportOpenAI# Initialize memory systemconfig=Config()memory_store=MemoryStore(config)client=OpenAI()defsafe_ai_response(prompt):# Store relevant contextmemory_store.store("conversation_context", {"prompt":prompt,"timestamp":datetime.now().isoformat(),"domain":"general"    },tier="hot")# Get AI responseresponse=client.chat.completions.create(model="gpt-4",messages=[{"role":"user","content":prompt}],max_tokens=150    )# Cross-reference with stored knowledgeai_text=response.choices[0].message.contentcontext=memory_store.retrieve("conversation_context")return {"response":ai_text,"context_verified":True,"stored_context":context    }

Anthropic Claude Integration

frommemoriesimportMemoryStore,ConfigfromanthropicimportAnthropicclient=Anthropic()config=Config()memory_store=MemoryStore(config)defclaude_with_memory(message):# Store conversation historymemory_store.store("claude_conversation", {"user_message":message,"timestamp":datetime.now().isoformat(),"session_id":"unique_session_id"    },tier="warm")response=client.messages.create(model="claude-3-opus-20240229",max_tokens=1024,messages=[{"role":"user","content":message}]    )# Store Claude's response for contextmemory_store.store("claude_response", {"response":response.content[0].text,"original_query":message    },tier="warm")returnresponse.content[0].text

Custom Model Integration

frommemoriesimportMemoryStore,LoadModel,Config# Initialize memory and model systemsconfig=Config()memory_store=MemoryStore(config)model_loader=LoadModel()# Works with any model that outputs textdefprocess_with_memory(text_input,domain="general"):# Store domain-specific knowledgememory_store.store(f"{domain}_context", {"input":text_input,"domain":domain,"processing_time":datetime.now().isoformat()    },tier="hot")# Process with your modelresult=your_model_process(text_input)# Cross-reference with memorycontext=memory_store.retrieve(f"{domain}_context")return {"result":result,"domain":domain,"memory_verified":True,"context":context    }

Performance Benchmarks

Advanced Features

Architecture Overview

Deployment Architectures

Production-Scale Deployment

# docker-compose.ymlversion:'3.8'services:memories-api:image:memories-dev:latestports:      -"8080:8080"environment:      -REDIS_URL=redis://redis:6379      -VECTOR_STORE=qdrant      -BATCH_SIZE=100deploy:replicas:3resources:limits:memory:4Gcpus:"2.0"redis:image:redis:alpinevolumes:      -redis_data:/dataqdrant:image:qdrant/qdrantports:      -"6333:6333"

Kubernetes Deployment

apiVersion:apps/v1kind:Deploymentmetadata:name:memories-verifierspec:replicas:5selector:matchLabels:app:memories-verifiertemplate:metadata:labels:app:memories-verifierspec:containers:      -name:verifierimage:memories-dev:latestports:        -containerPort:8080env:        -name:MEMORY_TIER_CONFIGvalue:"production"resources:requests:memory:"2Gi"cpu:"1"limits:memory:"4Gi"cpu:"2"

Memory System Architecture

API Specifications

REST API

# Verify single responsecurl -X POST"https://api.memories.dev/verify" \  -H"Authorization: Bearer YOUR_API_KEY" \  -H"Content-Type: application/json" \  -d'{    "content": "The capital of France is Lyon",    "domain": "geography",    "confidence_threshold": 0.9  }'# Response{"verified_content":"The capital of France is Paris","confidence_score": 0.95,"corrections_made": 1,"verification_time_ms": 87,"sources": ["geography_db","current_facts"]}

gRPC API

serviceMemoryVerifier {rpcVerifyContent(VerificationRequest)returns (VerificationResponse);rpcBatchVerify(BatchVerificationRequest)returns (streamVerificationResponse);}messageVerificationRequest {stringcontent=1;stringdomain=2;floatconfidence_threshold=3;repeatedstringcontext_sources=4;}

Performance Specifications

OpenAI GPT Integration

frommemoriesimportMemoryStore,ConfigfromopenaiimportOpenAIclient=OpenAI()config=Config()memory_store=MemoryStore(config)defsafe_gpt_call(prompt):# Store the conversationmemory_store.store("gpt_conversation", {"prompt":prompt,"timestamp":datetime.now().isoformat(),"model":"gpt-4"    },tier="hot")response=client.chat.completions.create(model="gpt-4",messages=[{"role":"user","content":prompt}]    )returnresponse.choices[0].message.content

Anthropic Claude Integration

frommemoriesimportMemoryVerifierfromanthropicimportAnthropicclient=Anthropic()verifier=MemoryVerifier()defsafe_claude_call(message):response=client.messages.create(model="claude-3-opus-20240229",max_tokens=1024,messages=[{"role":"user","content":message}]    )returnverifier.verify_and_correct(response.content[0].text    )

Google Gemini Integration

frommemoriesimportMemoryVerifierimportgoogle.generativeaiasgenaigenai.configure(api_key="YOUR_API_KEY")model=genai.GenerativeModel('gemini-pro')verifier=MemoryVerifier()defsafe_gemini_call(prompt):response=model.generate_content(prompt)returnverifier.verify_and_correct(response.text)

FastAPI Integration

fromfastapiimportFastAPIfrommemoriesimportMemoryVerifierapp=FastAPI()verifier=MemoryVerifier()@app.post("/chat")asyncdefchat_endpoint(message:str):# Your AI processingai_response=awaityour_ai_model(message)# Verify before returningverified_response=verifier.verify_and_correct(ai_response)return {"response":verified_response}

Flask Integration

fromflaskimportFlask,request,jsonifyfrommemoriesimportMemoryVerifierapp=Flask(__name__)verifier=MemoryVerifier()@app.route('/chat',methods=['POST'])defchat():message=request.json['message']ai_response=your_ai_model(message)verified_response=verifier.verify_and_correct(ai_response)returnjsonify({'response':verified_response})