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Fix user_id functionality (#2548)

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This commit is contained in:
Dev Khant
2025-04-16 13:32:33 +05:30
committed by GitHub
parent 541030d69c
commit 3613e2f14a
9 changed files with 86 additions and 49 deletions
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4
mem0/client/main.py
View File

@@ -1,6 +1,7 @@
import logging
import os
import warnings
import hashlib
from functools import wraps
from typing import Any, Dict, List, Optional, Union
@@ -83,6 +84,9 @@ class MemoryClient:
if not self.api_key:
raise ValueError("Mem0 API Key not provided. Please provide an API Key.")
# Create MD5 hash of API key for user_id
self.user_id = hashlib.md5(self.api_key.encode()).hexdigest()
self.client = httpx.Client(
base_url=self.host,
headers={"Authorization": f"Token {self.api_key}", "Mem0-User-ID": self.user_id},

5
mem0/configs/base.py
View File

@@ -6,9 +6,12 @@ from pydantic import BaseModel, Field
from mem0.embeddings.configs import EmbedderConfig
from mem0.graphs.configs import GraphStoreConfig
from mem0.llms.configs import LlmConfig
from mem0.memory.setup import mem0_dir
from mem0.vector_stores.configs import VectorStoreConfig
# Set up the directory path
home_dir = os.path.expanduser("~")
mem0_dir = os.environ.get("MEM0_DIR") or os.path.join(home_dir, ".mem0")
class MemoryItem(BaseModel):
id: str = Field(..., description="The unique identifier for the text data")

2
mem0/configs/vector_stores/azure_ai_search.py
View File

@@ -7,7 +7,7 @@ class AzureAISearchConfig(BaseModel):
collection_name: str = Field("mem0", description="Name of the collection")
service_name: str = Field(None, description="Azure AI Search service name")
api_key: str = Field(None, description="API key for the Azure AI Search service")
embedding_model_dims: int = Field(None, description="Dimension of the embedding vector")
embedding_model_dims: int = Field(1536, description="Dimension of the embedding vector")
compression_type: Optional[str] = Field(
None, description="Type of vector compression to use. Options: 'scalar', 'binary', or None"
)

4
mem0/configs/vector_stores/langchain.py
View File

@@ -7,7 +7,9 @@ class LangchainConfig(BaseModel):
try:
from langchain_community.vectorstores import VectorStore
except ImportError:
raise ImportError("The 'langchain_community' library is required. Please install it using 'pip install langchain_community'.")
raise ImportError(
"The 'langchain_community' library is required. Please install it using 'pip install langchain_community'."
)
VectorStore: ClassVar[type] = VectorStore
client: VectorStore = Field(description="Existing VectorStore instance")

4
mem0/memory/graph_memory.py
View File

@@ -35,9 +35,7 @@ class MemoryGraph:
self.config.graph_store.config.password,
)
self.embedding_model = EmbedderFactory.create(
self.config.embedder.provider,
self.config.embedder.config,
self.config.vector_store.config
self.config.embedder.provider, self.config.embedder.config, self.config.vector_store.config
)
self.llm_provider = "openai_structured"

13
mem0/memory/main.py
View File

@@ -1,3 +1,4 @@
import os
import asyncio
import concurrent
import hashlib
@@ -18,7 +19,7 @@ from mem0.configs.prompts import (
get_update_memory_messages,
)
from mem0.memory.base import MemoryBase
from mem0.memory.setup import setup_config
from mem0.memory.setup import setup_config, mem0_dir
from mem0.memory.storage import SQLiteManager
from mem0.memory.telemetry import capture_event
from mem0.memory.utils import (
@@ -62,6 +63,16 @@ class Memory(MemoryBase):
self.graph = MemoryGraph(self.config)
self.enable_graph = True
self.config.vector_store.config.collection_name = "mem0_migrations"
if self.config.vector_store.provider in ["faiss", "qdrant"]:
provider_path = f"migrations_{self.config.vector_store.provider}"
self.config.vector_store.config.path = os.path.join(mem0_dir, provider_path)
os.makedirs(self.config.vector_store.config.path, exist_ok=True)
self._telemetry_vector_store = VectorStoreFactory.create(
self.config.vector_store.provider, self.config.vector_store.config
)
capture_event("mem0.init", self, {"sync_type": "sync"})
@classmethod

25
mem0/memory/setup.py
View File

@@ -3,6 +3,7 @@ import os
import uuid
# Set up the directory path
VECTOR_ID = str(uuid.uuid4())
home_dir = os.path.expanduser("~")
mem0_dir = os.environ.get("MEM0_DIR") or os.path.join(home_dir, ".mem0")
os.makedirs(mem0_dir, exist_ok=True)
@@ -29,3 +30,27 @@ def get_user_id():
return user_id
except Exception:
return "anonymous_user"
def get_or_create_user_id(vector_store):
"""Store user_id in vector store and return it."""
user_id = get_user_id()
# Try to get existing user_id from vector store
try:
existing = vector_store.get(vector_id=VECTOR_ID)
if existing and hasattr(existing, "payload") and existing.payload and "user_id" in existing.payload:
return existing.payload["user_id"]
except:
pass
# If we get here, we need to insert the user_id
try:
dims = getattr(vector_store, "embedding_model_dims", 1)
vector_store.insert(
vectors=[[0.0] * dims], payloads=[{"user_id": user_id, "type": "user_identity"}], ids=[VECTOR_ID]
)
except:
pass
return user_id

30
mem0/memory/telemetry.py
View File

@@ -6,9 +6,11 @@ import sys
from posthog import Posthog
import mem0
from mem0.memory.setup import get_user_id, setup_config
from mem0.memory.setup import get_or_create_user_id
MEM0_TELEMETRY = os.environ.get("MEM0_TELEMETRY", "True")
PROJECT_API_KEY="phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX"
HOST="https://us.i.posthog.com"
if isinstance(MEM0_TELEMETRY, str):
MEM0_TELEMETRY = MEM0_TELEMETRY.lower() in ("true", "1", "yes")
@@ -21,11 +23,11 @@ logging.getLogger("urllib3").setLevel(logging.CRITICAL + 1)
class AnonymousTelemetry:
def __init__(self, project_api_key, host):
self.posthog = Posthog(project_api_key=project_api_key, host=host)
# Call setup config to ensure that the user_id is generated
setup_config()
self.user_id = get_user_id()
def __init__(self, vector_store=None):
self.posthog = Posthog(project_api_key=PROJECT_API_KEY, host=HOST)
self.user_id = get_or_create_user_id(vector_store)
if not MEM0_TELEMETRY:
self.posthog.disabled = True
@@ -50,14 +52,16 @@ class AnonymousTelemetry:
self.posthog.shutdown()
# Initialize AnonymousTelemetry
telemetry = AnonymousTelemetry(
project_api_key="phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX",
host="https://us.i.posthog.com",
)
client_telemetry = AnonymousTelemetry()
def capture_event(event_name, memory_instance, additional_data=None):
oss_telemetry = AnonymousTelemetry(
vector_store=memory_instance._telemetry_vector_store
if hasattr(memory_instance, "_telemetry_vector_store")
else None,
)
event_data = {
"collection": memory_instance.collection_name,
"vector_size": memory_instance.embedding_model.config.embedding_dims,
@@ -73,7 +77,7 @@ def capture_event(event_name, memory_instance, additional_data=None):
if additional_data:
event_data.update(additional_data)
telemetry.capture_event(event_name, event_data)
oss_telemetry.capture_event(event_name, event_data)
def capture_client_event(event_name, instance, additional_data=None):
@@ -83,4 +87,4 @@ def capture_client_event(event_name, instance, additional_data=None):
if additional_data:
event_data.update(additional_data)
telemetry.capture_event(event_name, event_data, instance.user_email)
client_telemetry.capture_event(event_name, event_data, instance.user_email)

48
mem0/vector_stores/langchain.py
View File

@@ -5,7 +5,9 @@ from pydantic import BaseModel
try:
from langchain_community.vectorstores import VectorStore
except ImportError:
raise ImportError("The 'langchain_community' library is required. Please install it using 'pip install langchain_community'.")
raise ImportError(
"The 'langchain_community' library is required. Please install it using 'pip install langchain_community'."
)
from mem0.vector_stores.base import VectorStoreBase
@@ -15,11 +17,12 @@ class OutputData(BaseModel):
score: Optional[float] # distance
payload: Optional[Dict] # metadata
class Langchain(VectorStoreBase):
def __init__(self, client: VectorStore, collection_name: str = "mem0"):
self.client = client
self.collection_name = collection_name
def _parse_output(self, data: Dict) -> List[OutputData]:
"""
Parse the output data.
@@ -31,17 +34,17 @@ class Langchain(VectorStoreBase):
List[OutputData]: Parsed output data.
"""
# Check if input is a list of Document objects
if isinstance(data, list) and all(hasattr(doc, 'metadata') for doc in data if hasattr(doc, '__dict__')):
if isinstance(data, list) and all(hasattr(doc, "metadata") for doc in data if hasattr(doc, "__dict__")):
result = []
for doc in data:
entry = OutputData(
id=getattr(doc, "id", None),
score=None, # Document objects typically don't include scores
payload=getattr(doc, "metadata", {})
payload=getattr(doc, "metadata", {}),
)
result.append(entry)
return result
# Original format handling
keys = ["ids", "distances", "metadatas"]
values = []
@@ -70,26 +73,20 @@ class Langchain(VectorStoreBase):
self.collection_name = name
return self.client
def insert(self, vectors: List[List[float]], payloads: Optional[List[Dict]] = None, ids: Optional[List[str]] = None):
def insert(
self, vectors: List[List[float]], payloads: Optional[List[Dict]] = None, ids: Optional[List[str]] = None
):
"""
Insert vectors into the LangChain vectorstore.
"""
# Check if client has add_embeddings method
if hasattr(self.client, "add_embeddings"):
# Some LangChain vectorstores have a direct add_embeddings method
self.client.add_embeddings(
embeddings=vectors,
metadatas=payloads,
ids=ids
)
self.client.add_embeddings(embeddings=vectors, metadatas=payloads, ids=ids)
else:
# Fallback to add_texts method
texts = [payload.get("data", "") for payload in payloads] if payloads else [""] * len(vectors)
self.client.add_texts(
texts=texts,
metadatas=payloads,
ids=ids
)
self.client.add_texts(texts=texts, metadatas=payloads, ids=ids)
def search(self, query: str, vectors: List[List[float]], limit: int = 5, filters: Optional[Dict] = None):
"""
@@ -97,16 +94,9 @@ class Langchain(VectorStoreBase):
"""
# For each vector, perform a similarity search
if filters:
results = self.client.similarity_search_by_vector(
embedding=vectors,
k=limit,
filter=filters
)
results = self.client.similarity_search_by_vector(embedding=vectors, k=limit, filter=filters)
else:
results = self.client.similarity_search_by_vector(
embedding=vectors,
k=limit
)
results = self.client.similarity_search_by_vector(embedding=vectors, k=limit)
final_results = self._parse_output(results)
return final_results
@@ -133,26 +123,26 @@ class Langchain(VectorStoreBase):
doc = docs[0]
return self._parse_output([doc])[0]
return None
def list_cols(self):
"""
List all collections.
"""
# LangChain doesn't have collections
return [self.collection_name]
def delete_col(self):
"""
Delete a collection.
"""
self.client.delete(ids=None)
def col_info(self):
"""
Get information about a collection.
"""
return {"name": self.collection_name}
def list(self, filters=None, limit=None):
"""
List all vectors in a collection.