AbhigyaWangoo
commented
1 week ago Solution for Qdrant and Ollama Integration
- Corrected Configuration for Qdrant and Ollama:
import os import subprocess from mem0 import Memory
Ensure the path exists
os.makedirs("./qdrant_data", exist_ok=True)
config = { "vector_store": { "provider": "qdrant", "config": { "collection_name": "test_collection", "path": "./qdrant_data", # Local persistent storage "host": "localhost", "port": 6333, "embedding_model_dims": 768, # Verified dimension for nomic-embed-text }, }, "llm": { "provider": "ollama", "config": { "model": "llama3:latest", # Updated to a more reliable model "temperature": 0, "max_tokens": 8000, "ollama_base_url": "http://localhost:11434", }, }, "embedder": { "provider": "ollama", "config": { "model": "nomic-embed-text:latest", "ollama_base_url": "http://localhost:11434", }, }, }
Proper Memory initialization
m = Memory.from_config(config)
Adding memories with proper metadata
m.add("I'm visiting Paris", user_id="john", metadata={"type": "travel"}) m.add("I'm listening to music", user_id="john", metadata={"type": "activity"})
Retrieve memories
memories = m.get_all(user_id="john") print(memories)
### Solution for GraphRAG and Neo4j Integration
1. Corrected Configuration for GraphRAG with Ollama:
```python
from mem0 import Memory
config = {
"llm": {
"provider": "ollama",
"config": {
"model": "llama3:latest", # Updated model name
"temperature": 0,
"max_tokens": 8000,
"ollama_base_url": "http://localhost:11434",
},
},
"graph_store": {
"provider": "neo4j",
"config": {
"url": "neo4j+s://your-neo4j-instance.databases.neo4j.io",
"username": "neo4j",
# IMPORTANT: Use environment variables for sensitive credentials
"password": os.getenv("NEO4J_PASSWORD")
}
},
"version": "v1.1"
}
# Initialize Memory
m = Memory.from_config(config)
# Add memory with user context
m.add("I like pizza", user_id="alice")
# Retrieve and search memories
memories = m.get_all(user_id="alice")
search_result = m.search("tell me my name.", user_id="alice")Key Recommendations:
-
For Qdrant:
- Use a local path for persistent storage
- Ensure correct embedding model dimensions
- Add metadata to memories for better tracking
-
For GraphRAG and Neo4j:
- NEVER hardcode passwords (use environment variables)
- Use the latest Ollama models
- Verify Neo4j connection details
-
General Troubleshooting:
- Confirm Ollama is running (
ollama serve) - Verify model availability (
ollama list) - Check Qdrant and Neo4j services
- Set logging to DEBUG for more insights
- Confirm Ollama is running (
Specific Error Resolutions:
-
"ERROR:root:Error in new_memories_with_actions: 'event'":
- Add explicit metadata
- Ensure consistent path and permissions
- Verify Qdrant configuration
-
"OpenAI API key error":
- Remove OpenAI-specific configurations
- Use Ollama as the primary provider
- Set up local LLM entirely through Ollama
Let me know if any of that works. If there's any other data you con provide I can probably help you troubleshoot further.
microcoder-py
Cirr0e
🐛 Describe the bug
First sample is for Qdrant server and ollama
import os import subprocess from collections import deque from mem0 import Memory
Configuration for Mem0 and Ollama
config = { "vector_store": { "provider": "qdrant", "config": { "collection_name": "test", "host": "localhost", "port": 6333, "embedding_model_dims": 768, # Match this to your local model's embedding dimensions }, }, "llm": { "provider": "ollama", "config": { "model": "llama3.2:3b-instruct-q4_K_M", "temperature": 0, "max_tokens": 8000, "ollama_base_url": "http://localhost:11434", # Make sure Ollama is running at this URL }, }, "embedder": { "provider": "ollama", "config": { "model": "nomic-embed-text:latest", "ollama_base_url": "http://localhost:11434", }, }, "version": "v1.1" }
m = Memory.from_config(config)
Add a memory
m.add("I'm visiting Paris", user_id="john") m.add("I'm listening to music", user_id="john") m.add("I'm trying to learn French", user_id="john") m.add("I'm going to the beach", user_id="john") m.add("I'm going to the gym", user_id="john")
Retrieve memories
memories = m.get_all(user_id="john") print(memories)
Above code is giving error as """ERROR:root:Error in new_memories_with_actions: 'event'"""
Second sample is for graphrag and ollama
from mem0 import Memory
config = { "llm": { "provider": "ollama", "config": { "model": "llama3.2:3b-instruct-q4_K_M", "temperature": 0, "max_tokens": 8000, "ollama_base_url": "http://localhost:11434", # Make sure Ollama is running at this URL,
"api_key": "",
}
m = Memory.from_config(config_dict=config)
m.add("I like pizza", user_id="alice")
m.get_all(user_id="alice")
m.search("tell me my name.", user_id="alice")
Above sample giving error as """openai.OpenAIError: The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable
"""