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Fix: Add Google Genai library support (#2941)

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This commit is contained in:
Akshat Jain
2025-06-17 17:47:09 +05:30
committed by GitHub
parent e0003247c3
commit c70dc7614b
7 changed files with 589 additions and 276 deletions
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22
docs/components/llms/models/gemini.mdx
View File

@@ -4,7 +4,11 @@ title: Gemini
<Snippet file="paper-release.mdx" />
To use Gemini model, you have to set the `GEMINI_API_KEY` environment variable. You can obtain the Gemini API key from the [Google AI Studio](https://aistudio.google.com/app/apikey)
To use the Gemini model, set the `GEMINI_API_KEY` environment variable. You can obtain the Gemini API key from [Google AI Studio](https://aistudio.google.com/app/apikey).
> **Note:** As of the latest release, Mem0 uses the new `google.genai` SDK instead of the deprecated `google.generativeai`. All message formatting and model interaction now use the updated `types` module from `google.genai`.
> **Note:** Some Gemini models are being deprecated and will retire soon. It is recommended to migrate to the latest stable models like `"gemini-2.0-flash-001"` or `"gemini-2.0-flash-lite-001"` to ensure ongoing support and improvements.
## Usage
@@ -12,28 +16,32 @@ To use Gemini model, you have to set the `GEMINI_API_KEY` environment variable.
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
os.environ["GEMINI_API_KEY"] = "your-api-key"
os.environ["OPENAI_API_KEY"] = "your-openai-api-key" # Used for embedding model
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"
config = {
"llm": {
"provider": "gemini",
"config": {
"model": "gemini-1.5-flash-latest",
"model": "gemini-2.0-flash-001",
"temperature": 0.2,
"max_tokens": 2000,
"top_p": 1.0
}
}
}
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a 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."}
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I’m not a big fan of thrillers, but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thrillers and suggest sci-fi movies instead."}
]
m.add(messages, user_id="alice", metadata={"category": "movies"})
```
## Config

78
docs/open-source/graph_memory/overview.mdx
View File

@@ -238,16 +238,24 @@ The Mem0's graph supports the following operations:
### Add Memories
<Note>
If you are using Mem0 with Graph Memory, it is recommended to pass `user_id`. Use `userId` in NodeSDK.
Mem0 with Graph Memory supports both `user_id` and `agent_id` parameters. You can use either or both to organize your memories. Use `userId` and `agentId` in NodeSDK.
</Note>
<CodeGroup>
```python Python
# Using only user_id
m.add("I like pizza", user_id="alice")
# Using both user_id and agent_id
m.add("I like pizza", user_id="alice", agent_id="food-assistant")
```
```typescript TypeScript
// Using only userId
memory.add("I like pizza", { userId: "alice" });
// Using both userId and agentId
memory.add("I like pizza", { userId: "alice", agentId: "food-assistant" });
```
```json Output
@@ -260,11 +268,19 @@ memory.add("I like pizza", { userId: "alice" });
<CodeGroup>
```python Python
# Get all memories for a user
m.get_all(user_id="alice")
# Get all memories for a specific agent belonging to a user
m.get_all(user_id="alice", agent_id="food-assistant")
```
```typescript TypeScript
// Get all memories for a user
memory.getAll({ userId: "alice" });
// Get all memories for a specific agent belonging to a user
memory.getAll({ userId: "alice", agentId: "food-assistant" });
```
```json Output
@@ -277,7 +293,8 @@ memory.getAll({ userId: "alice" });
'metadata': None,
'created_at': '2024-08-20T14:09:27.588719-07:00',
'updated_at': None,
'user_id': 'alice'
'user_id': 'alice',
'agent_id': 'food-assistant'
}
],
'entities': [
@@ -295,11 +312,19 @@ memory.getAll({ userId: "alice" });
<CodeGroup>
```python Python
# Search memories for a user
m.search("tell me my name.", user_id="alice")
# Search memories for a specific agent belonging to a user
m.search("tell me my name.", user_id="alice", agent_id="food-assistant")
```
```typescript TypeScript
// Search memories for a user
memory.search("tell me my name.", { userId: "alice" });
// Search memories for a specific agent belonging to a user
memory.search("tell me my name.", { userId: "alice", agentId: "food-assistant" });
```
```json Output
@@ -312,7 +337,8 @@ memory.search("tell me my name.", { userId: "alice" });
'metadata': None,
'created_at': '2024-08-20T14:09:27.588719-07:00',
'updated_at': None,
'user_id': 'alice'
'user_id': 'alice',
'agent_id': 'food-assistant'
}
],
'entities': [
@@ -331,11 +357,19 @@ memory.search("tell me my name.", { userId: "alice" });
<CodeGroup>
```python Python
# Delete all memories for a user
m.delete_all(user_id="alice")
# Delete all memories for a specific agent belonging to a user
m.delete_all(user_id="alice", agent_id="food-assistant")
```
```typescript TypeScript
// Delete all memories for a user
memory.deleteAll({ userId: "alice" });
// Delete all memories for a specific agent belonging to a user
memory.deleteAll({ userId: "alice", agentId: "food-assistant" });
```
</CodeGroup>
@@ -516,6 +550,42 @@ memory.search("Who is spiderman?", { userId: "alice123" });
> **Note:** The Graph Memory implementation is not standalone. You will be adding/retrieving memories to the vector store and the graph store simultaneously.
## Using Multiple Agents with Graph Memory
When working with multiple agents, you can use the `agent_id` parameter to organize memories by both user and agent. This allows you to:
1. Create agent-specific knowledge graphs
2. Share common knowledge between agents
3. Isolate sensitive or specialized information to specific agents
### Example: Multi-Agent Setup
<CodeGroup>
```python Python
# Add memories for different agents
m.add("I prefer Italian cuisine", user_id="bob", agent_id="food-assistant")
m.add("I'm allergic to peanuts", user_id="bob", agent_id="health-assistant")
m.add("I live in Seattle", user_id="bob") # Shared across all agents
# Search within specific agent context
food_preferences = m.search("What food do I like?", user_id="bob", agent_id="food-assistant")
health_info = m.search("What are my allergies?", user_id="bob", agent_id="health-assistant")
location = m.search("Where do I live?", user_id="bob") # Searches across all agents
```
```typescript TypeScript
// Add memories for different agents
memory.add("I prefer Italian cuisine", { userId: "bob", agentId: "food-assistant" });
memory.add("I'm allergic to peanuts", { userId: "bob", agentId: "health-assistant" });
memory.add("I live in Seattle", { userId: "bob" }); // Shared across all agents
// Search within specific agent context
const foodPreferences = memory.search("What food do I like?", { userId: "bob", agentId: "food-assistant" });
const healthInfo = memory.search("What are my allergies?", { userId: "bob", agentId: "health-assistant" });
const location = memory.search("Where do I live?", { userId: "bob" }); // Searches across all agents
```
</CodeGroup>
If you want to use a managed version of Mem0, please check out [Mem0](https://mem0.dev/pd). If you have any questions, please feel free to reach out to us using one of the following methods:
<Snippet file="get-help.mdx" />
<Snippet file="get-help.mdx" />

4
embedchain/poetry.lock generated
View File

@@ -2552,7 +2552,7 @@ azure = ["adlfs (>=2024.2.0)"]
clip = ["open-clip", "pillow", "torch"]
dev = ["pre-commit", "ruff"]
docs = ["mkdocs", "mkdocs-jupyter", "mkdocs-material", "mkdocstrings[python]"]
embeddings = ["awscli (>=1.29.57)", "boto3 (>=1.28.57)", "botocore (>=1.31.57)", "cohere", "google-generativeai", "huggingface-hub", "instructorembedding", "open-clip-torch", "openai (>=1.6.1)", "pillow", "sentence-transformers", "torch"]
embeddings = ["awscli (>=1.29.57)", "boto3 (>=1.28.57)", "botocore (>=1.31.57)", "cohere", "google-generativeai", "huggingface-hub", "instructorembedding", "open-clip-torch", "openai (>=1.6.1)", "pillow", "sentence-transformers", "torch", "google-genai"]
tests = ["aiohttp", "boto3", "duckdb", "pandas (>=1.4)", "polars (>=0.19)", "pytest", "pytest-asyncio", "pytest-mock", "pytz", "tantivy"]
[[package]]
@@ -7129,7 +7129,7 @@ cffi = ["cffi (>=1.11)"]
aws = ["langchain-aws"]
elasticsearch = ["elasticsearch"]
gmail = ["google-api-core", "google-api-python-client", "google-auth", "google-auth-httplib2", "google-auth-oauthlib", "requests"]
google = ["google-generativeai"]
google = ["google-generativeai", "google-genai"]
googledrive = ["google-api-python-client", "google-auth-httplib2", "google-auth-oauthlib"]
lancedb = ["lancedb"]
llama2 = ["replicate"]

98
mem0/llms/gemini.py
View File

@@ -2,9 +2,9 @@ import os
from typing import Dict, List, Optional
try:
import google.generativeai as genai
from google.generativeai import GenerativeModel, protos
from google.generativeai.types import content_types
from google import genai
from google.genai import types
except ImportError:
raise ImportError(
"The 'google-generativeai' library is required. Please install it using 'pip install google-generativeai'."
@@ -22,66 +22,71 @@ class GeminiLLM(LLMBase):
self.config.model = "gemini-1.5-flash-latest"
api_key = self.config.api_key or os.getenv("GEMINI_API_KEY")
genai.configure(api_key=api_key)
self.client = GenerativeModel(model_name=self.config.model)
self.client_gemini = genai.Client(
api_key=api_key,
)
def _parse_response(self, response, tools):
"""
Process the response based on whether tools are used or not.
Args:
response: The raw response from API.
response: The raw response from the API.
tools: The list of tools provided in the request.
Returns:
str or dict: The processed response.
"""
candidate = response.candidates[0]
content = candidate.content.parts[0].text if candidate.content.parts else None
if tools:
processed_response = {
"content": (content if (content := response.candidates[0].content.parts[0].text) else None),
"content": content,
"tool_calls": [],
}
for part in response.candidates[0].content.parts:
if fn := part.function_call:
if isinstance(fn, protos.FunctionCall):
fn_call = type(fn).to_dict(fn)
processed_response["tool_calls"].append({"name": fn_call["name"], "arguments": fn_call["args"]})
continue
processed_response["tool_calls"].append({"name": fn.name, "arguments": fn.args})
for part in candidate.content.parts:
fn = getattr(part, "function_call", None)
if fn:
processed_response["tool_calls"].append({
"name": fn.name,
"arguments": fn.args,
})
return processed_response
else:
return response.candidates[0].content.parts[0].text
def _reformat_messages(self, messages: List[Dict[str, str]]):
return content
def _reformat_messages(self, messages: List[Dict[str, str]]) -> List[types.Content]:
"""
Reformat messages for Gemini.
Reformat messages for Gemini using google.genai.types.
Args:
messages: The list of messages provided in the request.
Returns:
list: The list of messages in the required format.
list: A list of types.Content objects with proper role and parts.
"""
new_messages = []
for message in messages:
if message["role"] == "system":
content = "THIS IS A SYSTEM PROMPT. YOU MUST OBEY THIS: " + message["content"]
else:
content = message["content"]
new_messages.append(
{
"parts": content,
"role": "model" if message["role"] == "model" else "user",
}
types.Content(
role="model" if message["role"] == "model" else "user",
parts=[types.Part(text=content)]
)
)
return new_messages
def _reformat_tools(self, tools: Optional[List[Dict]]):
"""
Reformat tools for Gemini.
@@ -126,6 +131,7 @@ class GeminiLLM(LLMBase):
tools: Optional[List[Dict]] = None,
tool_choice: str = "auto",
):
"""
Generate a response based on the given messages using Gemini.
@@ -149,23 +155,37 @@ class GeminiLLM(LLMBase):
params["response_mime_type"] = "application/json"
if "schema" in response_format:
params["response_schema"] = response_format["schema"]
tool_config = None
if tool_choice:
tool_config = content_types.to_tool_config(
{
"function_calling_config": {
"mode": tool_choice,
"allowed_function_names": (
[tool["function"]["name"] for tool in tools] if tool_choice == "any" else None
),
}
}
tool_config = types.ToolConfig(
function_calling_config=types.FunctionCallingConfig(
mode=tool_choice.upper(), # Assuming 'any' should become 'ANY', etc.
allowed_function_names=[
tool["function"]["name"] for tool in tools
] if tool_choice == "any" else None
)
)
response = self.client.generate_content(
contents=self._reformat_messages(messages),
tools=self._reformat_tools(tools),
generation_config=genai.GenerationConfig(**params),
tool_config=tool_config,
)
print(f"Tool config: {tool_config}")
print(f"Params: {params}" )
print(f"Messages: {messages}")
print(f"Tools: {tools}")
print(f"Reformatted messages: {self._reformat_messages(messages)}")
print(f"Reformatted tools: {self._reformat_tools(tools)}")
response = self.client_gemini.models.generate_content(
model=self.config.model,
contents=self._reformat_messages(messages),
config=types.GenerateContentConfig(
temperature= self.config.temperature,
max_output_tokens= self.config.max_tokens,
top_p= self.config.top_p,
tools=self._reformat_tools(tools),
tool_config=tool_config,
),
)
print(f"Response test: {response}")
return self._parse_response(response, tools)

364
mem0/memory/graph_memory.py
View File

@@ -80,8 +80,8 @@ class MemoryGraph:
# TODO: Batch queries with APOC plugin
# TODO: Add more filter support
deleted_entities = self._delete_entities(to_be_deleted, filters["user_id"])
added_entities = self._add_entities(to_be_added, filters["user_id"], entity_type_map)
deleted_entities = self._delete_entities(to_be_deleted, filters)
added_entities = self._add_entities(to_be_added, filters, entity_type_map)
return {"deleted_entities": deleted_entities, "added_entities": added_entities}
@@ -122,32 +122,35 @@ class MemoryGraph:
return search_results
def delete_all(self, filters):
cypher = f"""
MATCH (n {self.node_label} {{user_id: $user_id}})
DETACH DELETE n
"""
params = {"user_id": filters["user_id"]}
if filters.get("agent_id"):
cypher = f"""
MATCH (n {self.node_label} {{user_id: $user_id, agent_id: $agent_id}})
DETACH DELETE n
"""
params = {"user_id": filters["user_id"], "agent_id": filters["agent_id"]}
else:
cypher = f"""
MATCH (n {self.node_label} {{user_id: $user_id}})
DETACH DELETE n
"""
params = {"user_id": filters["user_id"]}
self.graph.query(cypher, params=params)
def get_all(self, filters, limit=100):
"""
Retrieves all nodes and relationships from the graph database based on optional filtering criteria.
Args:
filters (dict): A dictionary containing filters to be applied during the retrieval.
limit (int): The maximum number of nodes and relationships to retrieve. Defaults to 100.
Returns:
list: A list of dictionaries, each containing:
- 'contexts': The base data store response for each memory.
- 'entities': A list of strings representing the nodes and relationships
"""
# return all nodes and relationships
def get_all(self, filters, limit=100):
agent_filter = ""
params = {"user_id": filters["user_id"], "limit": limit}
if filters.get("agent_id"):
agent_filter = "AND n.agent_id = $agent_id AND m.agent_id = $agent_id"
params["agent_id"] = filters["agent_id"]
query = f"""
MATCH (n {self.node_label} {{user_id: $user_id}})-[r]->(m {self.node_label} {{user_id: $user_id}})
WHERE 1=1 {agent_filter}
RETURN n.name AS source, type(r) AS relationship, m.name AS target
LIMIT $limit
"""
results = self.graph.query(query, params={"user_id": filters["user_id"], "limit": limit})
results = self.graph.query(query, params=params)
final_results = []
for result in results:
@@ -163,6 +166,7 @@ class MemoryGraph:
return final_results
def _retrieve_nodes_from_data(self, data, filters):
"""Extracts all the entities mentioned in the query."""
_tools = [EXTRACT_ENTITIES_TOOL]
@@ -197,23 +201,27 @@ class MemoryGraph:
return entity_type_map
def _establish_nodes_relations_from_data(self, data, filters, entity_type_map):
"""Eshtablish relations among the extracted nodes."""
"""Establish relations among the extracted nodes."""
# Compose user identification string for prompt
user_identity = f"user_id: {filters['user_id']}"
if filters.get("agent_id"):
user_identity += f", agent_id: {filters['agent_id']}"
if self.config.graph_store.custom_prompt:
system_content = EXTRACT_RELATIONS_PROMPT.replace("USER_ID", user_identity)
# Add the custom prompt line if configured
system_content = system_content.replace(
"CUSTOM_PROMPT", f"4. {self.config.graph_store.custom_prompt}"
)
messages = [
{
"role": "system",
"content": EXTRACT_RELATIONS_PROMPT.replace("USER_ID", filters["user_id"]).replace(
"CUSTOM_PROMPT", f"4. {self.config.graph_store.custom_prompt}"
),
},
{"role": "system", "content": system_content},
{"role": "user", "content": data},
]
else:
system_content = EXTRACT_RELATIONS_PROMPT.replace("USER_ID", user_identity)
messages = [
{
"role": "system",
"content": EXTRACT_RELATIONS_PROMPT.replace("USER_ID", filters["user_id"]),
},
{"role": "system", "content": system_content},
{"role": "user", "content": f"List of entities: {list(entity_type_map.keys())}. \n\nText: {data}"},
]
@@ -227,8 +235,8 @@ class MemoryGraph:
)
entities = []
if extracted_entities["tool_calls"]:
entities = extracted_entities["tool_calls"][0]["arguments"]["entities"]
if extracted_entities.get("tool_calls"):
entities = extracted_entities["tool_calls"][0].get("arguments", {}).get("entities", [])
entities = self._remove_spaces_from_entities(entities)
logger.debug(f"Extracted entities: {entities}")
@@ -237,32 +245,43 @@ class MemoryGraph:
def _search_graph_db(self, node_list, filters, limit=100):
"""Search similar nodes among and their respective incoming and outgoing relations."""
result_relations = []
agent_filter = ""
if filters.get("agent_id"):
agent_filter = "AND n.agent_id = $agent_id AND m.agent_id = $agent_id"
for node in node_list:
n_embedding = self.embedding_model.embed(node)
cypher_query = f"""
MATCH (n {self.node_label})
WHERE n.embedding IS NOT NULL AND n.user_id = $user_id
{agent_filter}
WITH n, round(2 * vector.similarity.cosine(n.embedding, $n_embedding) - 1, 4) AS similarity // denormalize for backward compatibility
WHERE similarity >= $threshold
CALL (n) {{
MATCH (n)-[r]->(m)
CALL {{
MATCH (n)-[r]->(m)
WHERE m.user_id = $user_id {agent_filter.replace("n.", "m.")}
RETURN n.name AS source, elementId(n) AS source_id, type(r) AS relationship, elementId(r) AS relation_id, m.name AS destination, elementId(m) AS destination_id
UNION
MATCH (m)-[r]->(n)
MATCH (m)-[r]->(n)
WHERE m.user_id = $user_id {agent_filter.replace("n.", "m.")}
RETURN m.name AS source, elementId(m) AS source_id, type(r) AS relationship, elementId(r) AS relation_id, n.name AS destination, elementId(n) AS destination_id
}}
WITH distinct source, source_id, relationship, relation_id, destination, destination_id, similarity //deduplicate
WITH distinct source, source_id, relationship, relation_id, destination, destination_id, similarity
RETURN source, source_id, relationship, relation_id, destination, destination_id, similarity
ORDER BY similarity DESC
LIMIT $limit
"""
params = {
"n_embedding": n_embedding,
"threshold": self.threshold,
"user_id": filters["user_id"],
"limit": limit,
}
if filters.get("agent_id"):
params["agent_id"] = filters["agent_id"]
ans = self.graph.query(cypher_query, params=params)
result_relations.extend(ans)
@@ -271,7 +290,13 @@ class MemoryGraph:
def _get_delete_entities_from_search_output(self, search_output, data, filters):
"""Get the entities to be deleted from the search output."""
search_output_string = format_entities(search_output)
system_prompt, user_prompt = get_delete_messages(search_output_string, data, filters["user_id"])
# Compose user identification string for prompt
user_identity = f"user_id: {filters['user_id']}"
if filters.get("agent_id"):
user_identity += f", agent_id: {filters['agent_id']}"
system_prompt, user_prompt = get_delete_messages(search_output_string, data, user_identity)
_tools = [DELETE_MEMORY_TOOL_GRAPH]
if self.llm_provider in ["azure_openai_structured", "openai_structured"]:
@@ -288,44 +313,59 @@ class MemoryGraph:
)
to_be_deleted = []
for item in memory_updates["tool_calls"]:
if item["name"] == "delete_graph_memory":
to_be_deleted.append(item["arguments"])
# in case if it is not in the correct format
for item in memory_updates.get("tool_calls", []):
if item.get("name") == "delete_graph_memory":
to_be_deleted.append(item.get("arguments"))
# Clean entities formatting
to_be_deleted = self._remove_spaces_from_entities(to_be_deleted)
logger.debug(f"Deleted relationships: {to_be_deleted}")
return to_be_deleted
def _delete_entities(self, to_be_deleted, user_id):
def _delete_entities(self, to_be_deleted, filters):
"""Delete the entities from the graph."""
user_id = filters["user_id"]
agent_id = filters.get("agent_id", None)
results = []
for item in to_be_deleted:
source = item["source"]
destination = item["destination"]
relationship = item["relationship"]
# Build the agent filter for the query
agent_filter = ""
params = {
"source_name": source,
"dest_name": destination,
"user_id": user_id,
}
if agent_id:
agent_filter = "AND n.agent_id = $agent_id AND m.agent_id = $agent_id"
params["agent_id"] = agent_id
# Delete the specific relationship between nodes
cypher = f"""
MATCH (n {self.node_label} {{name: $source_name, user_id: $user_id}})
-[r:{relationship}]->
(m {self.node_label} {{name: $dest_name, user_id: $user_id}})
WHERE 1=1 {agent_filter}
DELETE r
RETURN
n.name AS source,
m.name AS target,
type(r) AS relationship
"""
params = {
"source_name": source,
"dest_name": destination,
"user_id": user_id,
}
result = self.graph.query(cypher, params=params)
results.append(result)
return results
def _add_entities(self, to_be_added, user_id, entity_type_map):
def _add_entities(self, to_be_added, filters, entity_type_map):
"""Add the new entities to the graph. Merge the nodes if they already exist."""
user_id = filters["user_id"]
agent_id = filters.get("agent_id", None)
results = []
for item in to_be_added:
# entities
@@ -346,65 +386,80 @@ class MemoryGraph:
dest_embedding = self.embedding_model.embed(destination)
# search for the nodes with the closest embeddings
source_node_search_result = self._search_source_node(source_embedding, user_id, threshold=0.9)
destination_node_search_result = self._search_destination_node(dest_embedding, user_id, threshold=0.9)
source_node_search_result = self._search_source_node(source_embedding, filters, threshold=0.9)
destination_node_search_result = self._search_destination_node(dest_embedding, filters, threshold=0.9)
# TODO: Create a cypher query and common params for all the cases
if not destination_node_search_result and source_node_search_result:
cypher = f"""
MATCH (source)
WHERE elementId(source) = $source_id
SET source.mentions = coalesce(source.mentions, 0) + 1
WITH source
MERGE (destination {destination_label} {{name: $destination_name, user_id: $user_id}})
ON CREATE SET
destination.created = timestamp(),
destination.mentions = 1
{destination_extra_set}
ON MATCH SET
destination.mentions = coalesce(destination.mentions, 0) + 1
WITH source, destination
CALL db.create.setNodeVectorProperty(destination, 'embedding', $destination_embedding)
WITH source, destination
MERGE (source)-[r:{relationship}]->(destination)
ON CREATE SET
r.created = timestamp(),
r.mentions = 1
ON MATCH SET
r.mentions = coalesce(r.mentions, 0) + 1
RETURN source.name AS source, type(r) AS relationship, destination.name AS target
"""
# Build destination MERGE properties
merge_props = ["name: $destination_name", "user_id: $user_id"]
if agent_id:
merge_props.append("agent_id: $agent_id")
merge_props_str = ", ".join(merge_props)
cypher = f"""
MATCH (source)
WHERE elementId(source) = $source_id
SET source.mentions = coalesce(source.mentions, 0) + 1
WITH source
MERGE (destination {destination_label} {{{merge_props_str}}})
ON CREATE SET
destination.created = timestamp(),
destination.mentions = 1
{destination_extra_set}
ON MATCH SET
destination.mentions = coalesce(destination.mentions, 0) + 1
WITH source, destination
CALL db.create.setNodeVectorProperty(destination, 'embedding', $destination_embedding)
WITH source, destination
MERGE (source)-[r:{relationship}]->(destination)
ON CREATE SET
r.created = timestamp(),
r.mentions = 1
ON MATCH SET
r.mentions = coalesce(r.mentions, 0) + 1
RETURN source.name AS source, type(r) AS relationship, destination.name AS target
"""
params = {
"source_id": source_node_search_result[0]["elementId(source_candidate)"],
"destination_name": destination,
"destination_embedding": dest_embedding,
"user_id": user_id,
}
if agent_id:
params["agent_id"] = agent_id
elif destination_node_search_result and not source_node_search_result:
# Build source MERGE properties
merge_props = ["name: $source_name", "user_id: $user_id"]
if agent_id:
merge_props.append("agent_id: $agent_id")
merge_props_str = ", ".join(merge_props)
cypher = f"""
MATCH (destination)
WHERE elementId(destination) = $destination_id
SET destination.mentions = coalesce(destination.mentions, 0) + 1
WITH destination
MERGE (source {source_label} {{name: $source_name, user_id: $user_id}})
ON CREATE SET
source.created = timestamp(),
source.mentions = 1
{source_extra_set}
ON MATCH SET
source.mentions = coalesce(source.mentions, 0) + 1
WITH source, destination
CALL db.create.setNodeVectorProperty(source, 'embedding', $source_embedding)
WITH source, destination
MERGE (source)-[r:{relationship}]->(destination)
ON CREATE SET
r.created = timestamp(),
r.mentions = 1
ON MATCH SET
r.mentions = coalesce(r.mentions, 0) + 1
RETURN source.name AS source, type(r) AS relationship, destination.name AS target
"""
MATCH (destination)
WHERE elementId(destination) = $destination_id
SET destination.mentions = coalesce(destination.mentions, 0) + 1
WITH destination
MERGE (source {source_label} {{{merge_props_str}}})
ON CREATE SET
source.created = timestamp(),
source.mentions = 1
{source_extra_set}
ON MATCH SET
source.mentions = coalesce(source.mentions, 0) + 1
WITH source, destination
CALL db.create.setNodeVectorProperty(source, 'embedding', $source_embedding)
WITH source, destination
MERGE (source)-[r:{relationship}]->(destination)
ON CREATE SET
r.created = timestamp(),
r.mentions = 1
ON MATCH SET
r.mentions = coalesce(r.mentions, 0) + 1
RETURN source.name AS source, type(r) AS relationship, destination.name AS target
"""
params = {
"destination_id": destination_node_search_result[0]["elementId(destination_candidate)"],
@@ -412,53 +467,68 @@ class MemoryGraph:
"source_embedding": source_embedding,
"user_id": user_id,
}
if agent_id:
params["agent_id"] = agent_id
elif source_node_search_result and destination_node_search_result:
cypher = f"""
MATCH (source)
WHERE elementId(source) = $source_id
SET source.mentions = coalesce(source.mentions, 0) + 1
WITH source
MATCH (destination)
WHERE elementId(destination) = $destination_id
SET destination.mentions = coalesce(destination.mentions) + 1
MERGE (source)-[r:{relationship}]->(destination)
ON CREATE SET
r.created_at = timestamp(),
r.updated_at = timestamp(),
r.mentions = 1
ON MATCH SET r.mentions = coalesce(r.mentions, 0) + 1
RETURN source.name AS source, type(r) AS relationship, destination.name AS target
"""
MATCH (source)
WHERE elementId(source) = $source_id
SET source.mentions = coalesce(source.mentions, 0) + 1
WITH source
MATCH (destination)
WHERE elementId(destination) = $destination_id
SET destination.mentions = coalesce(destination.mentions, 0) + 1
MERGE (source)-[r:{relationship}]->(destination)
ON CREATE SET
r.created_at = timestamp(),
r.updated_at = timestamp(),
r.mentions = 1
ON MATCH SET r.mentions = coalesce(r.mentions, 0) + 1
RETURN source.name AS source, type(r) AS relationship, destination.name AS target
"""
params = {
"source_id": source_node_search_result[0]["elementId(source_candidate)"],
"destination_id": destination_node_search_result[0]["elementId(destination_candidate)"],
"user_id": user_id,
}
if agent_id:
params["agent_id"] = agent_id
else:
# Build dynamic MERGE props for both source and destination
source_props = ["name: $source_name", "user_id: $user_id"]
dest_props = ["name: $dest_name", "user_id: $user_id"]
if agent_id:
source_props.append("agent_id: $agent_id")
dest_props.append("agent_id: $agent_id")
source_props_str = ", ".join(source_props)
dest_props_str = ", ".join(dest_props)
cypher = f"""
MERGE (source {source_label} {{name: $source_name, user_id: $user_id}})
ON CREATE SET source.created = timestamp(),
source.mentions = 1
{source_extra_set}
ON MATCH SET source.mentions = coalesce(source.mentions, 0) + 1
WITH source
CALL db.create.setNodeVectorProperty(source, 'embedding', $source_embedding)
WITH source
MERGE (destination {destination_label} {{name: $dest_name, user_id: $user_id}})
ON CREATE SET destination.created = timestamp(),
destination.mentions = 1
{destination_extra_set}
ON MATCH SET destination.mentions = coalesce(destination.mentions, 0) + 1
WITH source, destination
CALL db.create.setNodeVectorProperty(destination, 'embedding', $source_embedding)
WITH source, destination
MERGE (source)-[rel:{relationship}]->(destination)
ON CREATE SET rel.created = timestamp(), rel.mentions = 1
ON MATCH SET rel.mentions = coalesce(rel.mentions, 0) + 1
RETURN source.name AS source, type(rel) AS relationship, destination.name AS target
"""
MERGE (source {source_label} {{{source_props_str}}})
ON CREATE SET source.created = timestamp(),
source.mentions = 1
{source_extra_set}
ON MATCH SET source.mentions = coalesce(source.mentions, 0) + 1
WITH source
CALL db.create.setNodeVectorProperty(source, 'embedding', $source_embedding)
WITH source
MERGE (destination {destination_label} {{{dest_props_str}}})
ON CREATE SET destination.created = timestamp(),
destination.mentions = 1
{destination_extra_set}
ON MATCH SET destination.mentions = coalesce(destination.mentions, 0) + 1
WITH source, destination
CALL db.create.setNodeVectorProperty(destination, 'embedding', $dest_embedding)
WITH source, destination
MERGE (source)-[rel:{relationship}]->(destination)
ON CREATE SET rel.created = timestamp(), rel.mentions = 1
ON MATCH SET rel.mentions = coalesce(rel.mentions, 0) + 1
RETURN source.name AS source, type(rel) AS relationship, destination.name AS target
"""
params = {
"source_name": source,
"dest_name": destination,
@@ -466,6 +536,8 @@ class MemoryGraph:
"dest_embedding": dest_embedding,
"user_id": user_id,
}
if agent_id:
params["agent_id"] = agent_id
result = self.graph.query(cypher, params=params)
results.append(result)
return results
@@ -477,11 +549,16 @@ class MemoryGraph:
item["destination"] = item["destination"].lower().replace(" ", "_")
return entity_list
def _search_source_node(self, source_embedding, user_id, threshold=0.9):
def _search_source_node(self, source_embedding, filters, threshold=0.9):
agent_filter = ""
if filters.get("agent_id"):
agent_filter = "AND source_candidate.agent_id = $agent_id"
cypher = f"""
MATCH (source_candidate {self.node_label})
WHERE source_candidate.embedding IS NOT NULL
AND source_candidate.user_id = $user_id
{agent_filter}
WITH source_candidate,
round(2 * vector.similarity.cosine(source_candidate.embedding, $source_embedding) - 1, 4) AS source_similarity // denormalize for backward compatibility
@@ -496,18 +573,26 @@ class MemoryGraph:
params = {
"source_embedding": source_embedding,
"user_id": user_id,
"user_id": filters["user_id"],
"threshold": threshold,
}
if filters.get("agent_id"):
params["agent_id"] = filters["agent_id"]
result = self.graph.query(cypher, params=params)
return result
def _search_destination_node(self, destination_embedding, user_id, threshold=0.9):
def _search_destination_node(self, destination_embedding, filters, threshold=0.9):
agent_filter = ""
if filters.get("agent_id"):
agent_filter = "AND destination_candidate.agent_id = $agent_id"
cypher = f"""
MATCH (destination_candidate {self.node_label})
WHERE destination_candidate.embedding IS NOT NULL
AND destination_candidate.user_id = $user_id
{agent_filter}
WITH destination_candidate,
round(2 * vector.similarity.cosine(destination_candidate.embedding, $destination_embedding) - 1, 4) AS destination_similarity // denormalize for backward compatibility
@@ -520,11 +605,14 @@ class MemoryGraph:
RETURN elementId(destination_candidate)
"""
params = {
"destination_embedding": destination_embedding,
"user_id": user_id,
"user_id": filters["user_id"],
"threshold": threshold,
}
if filters.get("agent_id"):
params["agent_id"] = filters["agent_id"]
result = self.graph.query(cypher, params=params)
return result

297
mem0/memory/memgraph_memory.py
View File

@@ -118,11 +118,19 @@ class MemoryGraph:
return search_results
def delete_all(self, filters):
cypher = """
MATCH (n {user_id: $user_id})
DETACH DELETE n
"""
params = {"user_id": filters["user_id"]}
"""Delete all nodes and relationships for a user or specific agent."""
if filters.get("agent_id"):
cypher = """
MATCH (n:Entity {user_id: $user_id, agent_id: $agent_id})
DETACH DELETE n
"""
params = {"user_id": filters["user_id"], "agent_id": filters["agent_id"]}
else:
cypher = """
MATCH (n:Entity {user_id: $user_id})
DETACH DELETE n
"""
params = {"user_id": filters["user_id"]}
self.graph.query(cypher, params=params)
def get_all(self, filters, limit=100):
@@ -131,20 +139,31 @@ class MemoryGraph:
Args:
filters (dict): A dictionary containing filters to be applied during the retrieval.
Supports 'user_id' (required) and 'agent_id' (optional).
limit (int): The maximum number of nodes and relationships to retrieve. Defaults to 100.
Returns:
list: A list of dictionaries, each containing:
- 'contexts': The base data store response for each memory.
- 'entities': A list of strings representing the nodes and relationships
- 'source': The source node name.
- 'relationship': The relationship type.
- 'target': The target node name.
"""
# return all nodes and relationships
query = """
MATCH (n:Entity {user_id: $user_id})-[r]->(m:Entity {user_id: $user_id})
RETURN n.name AS source, type(r) AS relationship, m.name AS target
LIMIT $limit
"""
results = self.graph.query(query, params={"user_id": filters["user_id"], "limit": limit})
# Build query based on whether agent_id is provided
if filters.get("agent_id"):
query = """
MATCH (n:Entity {user_id: $user_id, agent_id: $agent_id})-[r]->(m:Entity {user_id: $user_id, agent_id: $agent_id})
RETURN n.name AS source, type(r) AS relationship, m.name AS target
LIMIT $limit
"""
params = {"user_id": filters["user_id"], "agent_id": filters["agent_id"], "limit": limit}
else:
query = """
MATCH (n:Entity {user_id: $user_id})-[r]->(m:Entity {user_id: $user_id})
RETURN n.name AS source, type(r) AS relationship, m.name AS target
LIMIT $limit
"""
params = {"user_id": filters["user_id"], "limit": limit}
results = self.graph.query(query, params=params)
final_results = []
for result in results:
@@ -241,33 +260,65 @@ class MemoryGraph:
for node in node_list:
n_embedding = self.embedding_model.embed(node)
cypher_query = """
MATCH (n:Entity {user_id: $user_id})-[r]->(m:Entity)
WHERE n.embedding IS NOT NULL
WITH collect(n) AS nodes1, collect(m) AS nodes2, r
CALL node_similarity.cosine_pairwise("embedding", nodes1, nodes2)
YIELD node1, node2, similarity
WITH node1, node2, similarity, r
WHERE similarity >= $threshold
RETURN node1.user_id AS source, id(node1) AS source_id, type(r) AS relationship, id(r) AS relation_id, node2.user_id AS destination, id(node2) AS destination_id, similarity
UNION
MATCH (n:Entity {user_id: $user_id})<-[r]-(m:Entity)
WHERE n.embedding IS NOT NULL
WITH collect(n) AS nodes1, collect(m) AS nodes2, r
CALL node_similarity.cosine_pairwise("embedding", nodes1, nodes2)
YIELD node1, node2, similarity
WITH node1, node2, similarity, r
WHERE similarity >= $threshold
RETURN node2.name AS source, id(node2) AS source_id, type(r) AS relationship, id(r) AS relation_id, node1.name AS destination, id(node1) AS destination_id, similarity
ORDER BY similarity DESC
LIMIT $limit;
"""
params = {
"n_embedding": n_embedding,
"threshold": self.threshold,
"user_id": filters["user_id"],
"limit": limit,
}
# Build query based on whether agent_id is provided
if filters.get("agent_id"):
cypher_query = """
MATCH (n:Entity {user_id: $user_id, agent_id: $agent_id})-[r]->(m:Entity)
WHERE n.embedding IS NOT NULL
WITH collect(n) AS nodes1, collect(m) AS nodes2, r
CALL node_similarity.cosine_pairwise("embedding", nodes1, nodes2)
YIELD node1, node2, similarity
WITH node1, node2, similarity, r
WHERE similarity >= $threshold
RETURN node1.name AS source, id(node1) AS source_id, type(r) AS relationship, id(r) AS relation_id, node2.name AS destination, id(node2) AS destination_id, similarity
UNION
MATCH (n:Entity {user_id: $user_id, agent_id: $agent_id})<-[r]-(m:Entity)
WHERE n.embedding IS NOT NULL
WITH collect(n) AS nodes1, collect(m) AS nodes2, r
CALL node_similarity.cosine_pairwise("embedding", nodes1, nodes2)
YIELD node1, node2, similarity
WITH node1, node2, similarity, r
WHERE similarity >= $threshold
RETURN node2.name AS source, id(node2) AS source_id, type(r) AS relationship, id(r) AS relation_id, node1.name AS destination, id(node1) AS destination_id, similarity
ORDER BY similarity DESC
LIMIT $limit;
"""
params = {
"n_embedding": n_embedding,
"threshold": self.threshold,
"user_id": filters["user_id"],
"agent_id": filters["agent_id"],
"limit": limit,
}
else:
cypher_query = """
MATCH (n:Entity {user_id: $user_id})-[r]->(m:Entity)
WHERE n.embedding IS NOT NULL
WITH collect(n) AS nodes1, collect(m) AS nodes2, r
CALL node_similarity.cosine_pairwise("embedding", nodes1, nodes2)
YIELD node1, node2, similarity
WITH node1, node2, similarity, r
WHERE similarity >= $threshold
RETURN node1.name AS source, id(node1) AS source_id, type(r) AS relationship, id(r) AS relation_id, node2.name AS destination, id(node2) AS destination_id, similarity
UNION
MATCH (n:Entity {user_id: $user_id})<-[r]-(m:Entity)
WHERE n.embedding IS NOT NULL
WITH collect(n) AS nodes1, collect(m) AS nodes2, r
CALL node_similarity.cosine_pairwise("embedding", nodes1, nodes2)
YIELD node1, node2, similarity
WITH node1, node2, similarity, r
WHERE similarity >= $threshold
RETURN node2.name AS source, id(node2) AS source_id, type(r) AS relationship, id(r) AS relation_id, node1.name AS destination, id(node1) AS destination_id, similarity
ORDER BY similarity DESC
LIMIT $limit;
"""
params = {
"n_embedding": n_embedding,
"threshold": self.threshold,
"user_id": filters["user_id"],
"limit": limit,
}
ans = self.graph.query(cypher_query, params=params)
result_relations.extend(ans)
@@ -300,38 +351,54 @@ class MemoryGraph:
logger.debug(f"Deleted relationships: {to_be_deleted}")
return to_be_deleted
def _delete_entities(self, to_be_deleted, user_id):
def _delete_entities(self, to_be_deleted, filters):
"""Delete the entities from the graph."""
user_id = filters["user_id"]
agent_id = filters.get("agent_id", None)
results = []
for item in to_be_deleted:
source = item["source"]
destination = item["destination"]
relationship = item["relationship"]
# Build the agent filter for the query
agent_filter = ""
params = {
"source_name": source,
"dest_name": destination,
"user_id": user_id,
}
if agent_id:
agent_filter = "AND n.agent_id = $agent_id AND m.agent_id = $agent_id"
params["agent_id"] = agent_id
# Delete the specific relationship between nodes
cypher = f"""
MATCH (n:Entity {{name: $source_name, user_id: $user_id}})
-[r:{relationship}]->
(m {{name: $dest_name, user_id: $user_id}})
(m:Entity {{name: $dest_name, user_id: $user_id}})
WHERE 1=1 {agent_filter}
DELETE r
RETURN
n.name AS source,
m.name AS target,
type(r) AS relationship
"""
params = {
"source_name": source,
"dest_name": destination,
"user_id": user_id,
}
result = self.graph.query(cypher, params=params)
results.append(result)
return results
# added Entity label to all nodes for vector search to work
def _add_entities(self, to_be_added, user_id, entity_type_map):
def _add_entities(self, to_be_added, filters, entity_type_map):
"""Add the new entities to the graph. Merge the nodes if they already exist."""
user_id = filters["user_id"]
agent_id = filters.get("agent_id", None)
results = []
for item in to_be_added:
# entities
source = item["source"]
@@ -346,18 +413,21 @@ class MemoryGraph:
source_embedding = self.embedding_model.embed(source)
dest_embedding = self.embedding_model.embed(destination)
# search for the nodes with the closest embeddings; this is basically
# comparison of one embedding to all embeddings in a graph -> vector
# search with cosine similarity metric
source_node_search_result = self._search_source_node(source_embedding, user_id, threshold=0.9)
destination_node_search_result = self._search_destination_node(dest_embedding, user_id, threshold=0.9)
# search for the nodes with the closest embeddings
source_node_search_result = self._search_source_node(source_embedding, filters, threshold=0.9)
destination_node_search_result = self._search_destination_node(dest_embedding, filters, threshold=0.9)
# Prepare agent_id for node creation
agent_id_clause = ""
if agent_id:
agent_id_clause = ", agent_id: $agent_id"
# TODO: Create a cypher query and common params for all the cases
if not destination_node_search_result and source_node_search_result:
cypher = f"""
MATCH (source:Entity)
WHERE id(source) = $source_id
MERGE (destination:{destination_type}:Entity {{name: $destination_name, user_id: $user_id}})
MERGE (destination:{destination_type}:Entity {{name: $destination_name, user_id: $user_id{agent_id_clause}}})
ON CREATE SET
destination.created = timestamp(),
destination.embedding = $destination_embedding,
@@ -374,11 +444,14 @@ class MemoryGraph:
"destination_embedding": dest_embedding,
"user_id": user_id,
}
if agent_id:
params["agent_id"] = agent_id
elif destination_node_search_result and not source_node_search_result:
cypher = f"""
MATCH (destination:Entity)
WHERE id(destination) = $destination_id
MERGE (source:{source_type}:Entity {{name: $source_name, user_id: $user_id}})
MERGE (source:{source_type}:Entity {{name: $source_name, user_id: $user_id{agent_id_clause}}})
ON CREATE SET
source.created = timestamp(),
source.embedding = $source_embedding,
@@ -395,6 +468,9 @@ class MemoryGraph:
"source_embedding": source_embedding,
"user_id": user_id,
}
if agent_id:
params["agent_id"] = agent_id
elif source_node_search_result and destination_node_search_result:
cypher = f"""
MATCH (source:Entity)
@@ -412,12 +488,15 @@ class MemoryGraph:
"destination_id": destination_node_search_result[0]["id(destination_candidate)"],
"user_id": user_id,
}
if agent_id:
params["agent_id"] = agent_id
else:
cypher = f"""
MERGE (n:{source_type}:Entity {{name: $source_name, user_id: $user_id}})
MERGE (n:{source_type}:Entity {{name: $source_name, user_id: $user_id{agent_id_clause}}})
ON CREATE SET n.created = timestamp(), n.embedding = $source_embedding, n:Entity
ON MATCH SET n.embedding = $source_embedding
MERGE (m:{destination_type}:Entity {{name: $dest_name, user_id: $user_id}})
MERGE (m:{destination_type}:Entity {{name: $dest_name, user_id: $user_id{agent_id_clause}}})
ON CREATE SET m.created = timestamp(), m.embedding = $dest_embedding, m:Entity
ON MATCH SET m.embedding = $dest_embedding
MERGE (n)-[rel:{relationship}]->(m)
@@ -431,6 +510,9 @@ class MemoryGraph:
"dest_embedding": dest_embedding,
"user_id": user_id,
}
if agent_id:
params["agent_id"] = agent_id
result = self.graph.query(cypher, params=params)
results.append(result)
return results
@@ -442,37 +524,80 @@ class MemoryGraph:
item["destination"] = item["destination"].lower().replace(" ", "_")
return entity_list
def _search_source_node(self, source_embedding, user_id, threshold=0.9):
cypher = """
CALL vector_search.search("memzero", 1, $source_embedding)
YIELD distance, node, similarity
WITH node AS source_candidate, similarity
WHERE source_candidate.user_id = $user_id AND similarity >= $threshold
RETURN id(source_candidate);
"""
params = {
"source_embedding": source_embedding,
"user_id": user_id,
"threshold": threshold,
}
def _search_source_node(self, source_embedding, filters, threshold=0.9):
"""Search for source nodes with similar embeddings."""
user_id = filters["user_id"]
agent_id = filters.get("agent_id", None)
if agent_id:
cypher = """
CALL vector_search.search("memzero", 1, $source_embedding)
YIELD distance, node, similarity
WITH node AS source_candidate, similarity
WHERE source_candidate.user_id = $user_id
AND source_candidate.agent_id = $agent_id
AND similarity >= $threshold
RETURN id(source_candidate);
"""
params = {
"source_embedding": source_embedding,
"user_id": user_id,
"agent_id": agent_id,
"threshold": threshold,
}
else:
cypher = """
CALL vector_search.search("memzero", 1, $source_embedding)
YIELD distance, node, similarity
WITH node AS source_candidate, similarity
WHERE source_candidate.user_id = $user_id
AND similarity >= $threshold
RETURN id(source_candidate);
"""
params = {
"source_embedding": source_embedding,
"user_id": user_id,
"threshold": threshold,
}
result = self.graph.query(cypher, params=params)
return result
def _search_destination_node(self, destination_embedding, user_id, threshold=0.9):
cypher = """
CALL vector_search.search("memzero", 1, $destination_embedding)
YIELD distance, node, similarity
WITH node AS destination_candidate, similarity
WHERE node.user_id = $user_id AND similarity >= $threshold
RETURN id(destination_candidate);
"""
params = {
"destination_embedding": destination_embedding,
"user_id": user_id,
"threshold": threshold,
}
def _search_destination_node(self, destination_embedding, filters, threshold=0.9):
"""Search for destination nodes with similar embeddings."""
user_id = filters["user_id"]
agent_id = filters.get("agent_id", None)
if agent_id:
cypher = """
CALL vector_search.search("memzero", 1, $destination_embedding)
YIELD distance, node, similarity
WITH node AS destination_candidate, similarity
WHERE node.user_id = $user_id
AND node.agent_id = $agent_id
AND similarity >= $threshold
RETURN id(destination_candidate);
"""
params = {
"destination_embedding": destination_embedding,
"user_id": user_id,
"agent_id": agent_id,
"threshold": threshold,
}
else:
cypher = """
CALL vector_search.search("memzero", 1, $destination_embedding)
YIELD distance, node, similarity
WITH node AS destination_candidate, similarity
WHERE node.user_id = $user_id
AND similarity >= $threshold
RETURN id(destination_candidate);
"""
params = {
"destination_embedding": destination_embedding,
"user_id": user_id,
"threshold": threshold,
}
result = self.graph.query(cypher, params=params)
return result

2
pyproject.toml
View File

@@ -43,6 +43,8 @@ llms = [
"ollama>=0.1.0",
"vertexai>=0.1.0",
"google-generativeai>=0.3.0",
"google-genai>=1.0.0",
]
extras = [
"boto3>=1.34.0",