[docs] Welcome page thumbnail and reranker fix (#3660)
This commit is contained in:
Parth Sharma
@@ -0,0 +1,398 @@
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---
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title: Graph Memory
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description: "Layer relationships onto Mem0 search so agents remember who did what, when, and with whom."
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---
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Graph Memory extends Mem0 by persisting nodes and edges alongside embeddings, so recalls stitch together people, places, and events instead of just keywords.
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<Info icon="sparkles">
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**You’ll use this when…**
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- Conversation history mixes multiple actors and objects that vectors alone blur together
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- Compliance or auditing demands a graph of who said what and when
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- Agent teams need shared context without duplicating every memory in each run
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</Info>
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## How Graph Memory Maps Context
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Mem0 extracts entities and relationships from every memory write, stores embeddings in your vector database, and mirrors relationships in a graph backend. On retrieval, vector search narrows candidates, then the graph supplies context and re-ranks results.
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```mermaid
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graph LR
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A[Conversation] --> B(Extraction LLM)
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B --> C[Vector Store]
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B --> D[Graph Store]
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E[Query] --> C
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C --> F[Candidate Memories]
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F --> D
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D --> G[Contextual Recall]
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```
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<Info icon="lightbulb">
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Graph Memory complements your vector store. Keep both healthy to avoid blind spots.
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</Info>
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## How It Works
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<Steps>
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<Step title="Extract people, places, and facts">
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Mem0’s extraction LLM identifies entities, relationships, and timestamps from the conversation payload you send to `memory.add`.
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</Step>
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<Step title="Store vectors and edges together">
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Embeddings land in your configured vector database while nodes and edges flow into a Bolt-compatible graph backend (Neo4j, Memgraph, Neptune, or Kuzu).
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</Step>
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<Step title="Blend graph context at search time">
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`memory.search` first performs vector similarity, then follows connected nodes to boost (or filter) answers before optionally handing results to a reranker.
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</Step>
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</Steps>
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## Quickstart (Neo4j Aura)
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<Info icon="clock">
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**Time to implement:** ~10 minutes · **Prerequisites:** Python 3.10+, Node.js 18+, Neo4j Aura DB (free tier)
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</Info>
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Provision a free [Neo4j Aura](https://neo4j.com/product/auradb/) instance, copy the Bolt URI, username, and password, then follow the language tab that matches your stack.
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<Tabs>
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<Tab title="Python">
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<Steps>
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<Step title="Install Mem0 with graph extras">
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```bash
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pip install "mem0ai[graph]"
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```
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</Step>
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<Step title="Export Neo4j credentials">
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```bash
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export NEO4J_URL="neo4j+s://<your-instance>.databases.neo4j.io"
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export NEO4J_USERNAME="neo4j"
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export NEO4J_PASSWORD="your-password"
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```
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</Step>
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<Step title="Add and recall a relationship">
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```python
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import os
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from mem0 import Memory
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config = {
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"graph_store": {
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"provider": "neo4j",
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"config": {
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"url": os.environ["NEO4J_URL"],
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"username": os.environ["NEO4J_USERNAME"],
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"password": os.environ["NEO4J_PASSWORD"],
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"database": "neo4j",
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}
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}
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}
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memory = Memory.from_config(config)
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conversation = [
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{"role": "user", "content": "Alice met Bob at GraphConf 2025 in San Francisco."},
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{"role": "assistant", "content": "Great! Logging that connection."},
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]
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memory.add(conversation, user_id="demo-user")
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results = memory.search(
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"Who did Alice meet at GraphConf?",
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user_id="demo-user",
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limit=3,
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rerank=True,
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)
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for hit in results["results"]:
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print(hit["memory"])
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```
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</Step>
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</Steps>
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</Tab>
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<Tab title="TypeScript">
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<Steps>
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<Step title="Install the OSS SDK">
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```bash
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npm install mem0ai
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```
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</Step>
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<Step title="Load Neo4j credentials">
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```bash
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export NEO4J_URL="neo4j+s://<your-instance>.databases.neo4j.io"
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export NEO4J_USERNAME="neo4j"
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export NEO4J_PASSWORD="your-password"
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```
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</Step>
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<Step title="Enable graph memory and query it">
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```typescript
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import { Memory } from "mem0ai/oss";
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const config = {
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enableGraph: true,
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graphStore: {
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provider: "neo4j",
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config: {
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url: process.env.NEO4J_URL!,
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username: process.env.NEO4J_USERNAME!,
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password: process.env.NEO4J_PASSWORD!,
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database: "neo4j",
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},
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},
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};
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const memory = new Memory(config);
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const conversation = [
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{ role: "user", content: "Alice met Bob at GraphConf 2025 in San Francisco." },
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{ role: "assistant", content: "Great! Logging that connection." },
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];
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await memory.add(conversation, { userId: "demo-user" });
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const results = await memory.search(
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"Who did Alice meet at GraphConf?",
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{ userId: "demo-user", limit: 3, rerank: true }
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);
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results.results.forEach((hit) => {
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console.log(hit.memory);
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});
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```
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</Step>
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</Steps>
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</Tab>
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</Tabs>
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<Info icon="check">
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Expect to see **Alice met Bob at GraphConf 2025** in the output. In Neo4j Browser run `MATCH (p:Person)-[r]->(q:Person) RETURN p,r,q LIMIT 5;` to confirm the edge exists.
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</Info>
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## Operate Graph Memory Day-to-Day
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<AccordionGroup>
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<Accordion title="Refine extraction prompts">
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Guide which relationships become nodes and edges.
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<CodeGroup>
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```python Python
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import os
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from mem0 import Memory
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config = {
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"graph_store": {
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"provider": "neo4j",
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"config": {
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"url": os.environ["NEO4J_URL"],
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"username": os.environ["NEO4J_USERNAME"],
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"password": os.environ["NEO4J_PASSWORD"],
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},
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"custom_prompt": "Please only capture people, organisations, and project links.",
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}
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}
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memory = Memory.from_config(config_dict=config)
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```
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```typescript TypeScript
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import { Memory } from "mem0ai/oss";
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const config = {
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enableGraph: true,
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graphStore: {
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provider: "neo4j",
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config: {
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url: process.env.NEO4J_URL!,
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username: process.env.NEO4J_USERNAME!,
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password: process.env.NEO4J_PASSWORD!,
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},
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customPrompt: "Please only capture people, organisations, and project links.",
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}
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};
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const memory = new Memory(config);
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```
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</CodeGroup>
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</Accordion>
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<Accordion title="Raise the confidence threshold">
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Keep noisy edges out of the graph by demanding higher extraction confidence.
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```python
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config["graph_store"]["config"]["threshold"] = 0.75
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```
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</Accordion>
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<Accordion title="Toggle graph writes per request">
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Disable graph writes or reads when you only want vector behaviour.
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```python
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memory.add(messages, user_id="demo-user", enable_graph=False)
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results = memory.search("marketing partners", user_id="demo-user", enable_graph=False)
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```
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</Accordion>
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<Accordion title="Organize multi-agent graphs">
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Separate or share context across agents and sessions with `user_id`, `agent_id`, and `run_id`.
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<CodeGroup>
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```typescript TypeScript
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memory.add("I prefer Italian cuisine", { userId: "bob", agentId: "food-assistant" });
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memory.add("I'm allergic to peanuts", { userId: "bob", agentId: "health-assistant" });
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memory.add("I live in Seattle", { userId: "bob" });
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const food = await memory.search("What food do I like?", { userId: "bob", agentId: "food-assistant" });
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const allergies = await memory.search("What are my allergies?", { userId: "bob", agentId: "health-assistant" });
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const location = await memory.search("Where do I live?", { userId: "bob" });
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```
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</CodeGroup>
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</Accordion>
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</AccordionGroup>
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<Note>
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Monitor graph growth, especially on free tiers, by periodically cleaning dormant nodes: `MATCH (n) WHERE n.lastSeen < date() - duration('P90D') DETACH DELETE n`.
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</Note>
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## Troubleshooting
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<AccordionGroup>
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<Accordion title="Neo4j connection refused">
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Confirm Bolt connectivity is enabled, credentials match Aura, and your IP is allow-listed. Retry after confirming the URI format is `neo4j+s://...`.
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</Accordion>
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<Accordion title="Neptune Analytics rejects requests">
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Ensure the graph identifier matches the vector dimension used by your embedder and that the IAM role allows `neptune-graph:*DataViaQuery` actions.
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</Accordion>
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<Accordion title="Graph store outage fallback">
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Catch the provider error and retry with `enable_graph=False` so vector-only search keeps serving responses while the graph backend recovers.
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</Accordion>
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</AccordionGroup>
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## Decision Points
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- Select the graph store that fits your deployment (managed Aura vs. self-hosted Neo4j vs. AWS Neptune vs. local Kuzu).
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- Decide when to enable graph writes per request; routine conversations may stay vector-only to save latency.
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- Set a policy for pruning stale relationships so your graph stays fast and affordable.
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## Provider setup
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Choose your backend and expand the matching panel for configuration details and links.
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<AccordionGroup>
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<Accordion title="Neo4j Aura or self-hosted">
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Install the APOC plugin for self-hosted deployments, then configure Mem0:
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```typescript
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import { Memory } from "mem0ai/oss";
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const config = {
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enableGraph: true,
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graphStore: {
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provider: "neo4j",
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config: {
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url: "neo4j+s://<HOST>",
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username: "neo4j",
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password: "<PASSWORD>",
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}
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}
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};
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const memory = new Memory(config);
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```
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Additional docs: [Neo4j Aura Quickstart](https://neo4j.com/docs/aura/), [APOC installation](https://neo4j.com/docs/apoc/current/installation/).
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</Accordion>
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<Accordion title="Memgraph (Docker)">
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Run Memgraph Mage locally with schema introspection enabled:
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```bash
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docker run -p 7687:7687 memgraph/memgraph-mage:latest --schema-info-enabled=True
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```
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Then point Mem0 at the instance:
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```python
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from mem0 import Memory
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config = {
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"graph_store": {
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"provider": "memgraph",
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"config": {
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"url": "bolt://localhost:7687",
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"username": "memgraph",
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"password": "your-password",
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},
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},
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}
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m = Memory.from_config(config_dict=config)
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```
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Learn more: [Memgraph Docs](https://memgraph.com/docs).
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</Accordion>
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<Accordion title="Amazon Neptune Analytics">
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Match vector dimensions between Neptune and your embedder, enable public connectivity (if needed), and grant IAM permissions:
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```python
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from mem0 import Memory
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config = {
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"graph_store": {
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"provider": "neptune",
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"config": {
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"endpoint": "neptune-graph://<GRAPH_ID>",
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},
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},
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}
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m = Memory.from_config(config_dict=config)
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```
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Reference: [Neptune Analytics Guide](https://docs.aws.amazon.com/neptune/latest/analytics/).
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</Accordion>
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<Accordion title="Amazon Neptune DB (with external vectors)">
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Create a Neptune cluster, enable the public endpoint if you operate outside the VPC, and point Mem0 at the host:
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```python
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from mem0 import Memory
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config = {
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"graph_store": {
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"provider": "neptunedb",
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"config": {
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"collection_name": "<VECTOR_COLLECTION_NAME>",
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"endpoint": "neptune-graph://<HOST_ENDPOINT>",
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},
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},
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}
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m = Memory.from_config(config_dict=config)
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```
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Reference: [Accessing Data in Neptune DB](https://docs.aws.amazon.com/neptune/latest/userguide/).
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</Accordion>
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<Accordion title="Kuzu (embedded)">
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Kuzu runs in-process, so supply a path (or `:memory:`) for the database file:
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```python
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config = {
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"graph_store": {
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"provider": "kuzu",
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"config": {
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"db": "/tmp/mem0-example.kuzu"
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}
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}
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}
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```
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Kuzu will clear its state when using `:memory:` once the process exits. See the [Kuzu documentation](https://kuzudb.com/docs/) for advanced settings.
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</Accordion>
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</AccordionGroup>
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<CardGroup cols={2}>
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<Card
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title="Enhanced Metadata Filtering"
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description="Blend field-level filters with graph context to zero in on the right memories."
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icon="funnel"
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href="/open-source/features/metadata-filtering"
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/>
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<Card
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title="Reranker-Enhanced Search"
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description="Layer rerankers on top of vectors and graphs for the cleanest results."
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icon="sparkles"
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href="/open-source/features/reranker-search"
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/>
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</CardGroup>
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@@ -48,7 +48,7 @@ Choose your preferred approach:
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## Next Steps
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- Explore [specific features](./async-memory) in detail
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- Learn about [graph memory](../graph_memory/overview) capabilities
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- Learn about [graph memory](./graph-memory) capabilities
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- Set up [vector databases](/components/vectordbs/overview) and [LLM integrations](/components/llms/overview)
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- Check out our [examples](/examples) for practical implementations
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- Join our [Discord community](https://mem0.dev/DiD) for support
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@@ -3,8 +3,8 @@ title: Reranker-Enhanced Search
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description: 'Improve search relevance with reranking models in Mem0 1.0.0 '
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---
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<Info>
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Reranker-enhanced search is available in **Mem0 1.0.0 ** and later versions. This feature significantly improves search relevance by using specialized reranking models to reorder search results.
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<Info icon="sparkles">
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**Mem0 1.0.0+** supports reranker-enhanced search, letting specialized models reorder vector hits so you deliver the most relevant memories.
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</Info>
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## Overview
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@@ -40,6 +40,14 @@ m = Memory.from_config(config)
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### Supported Providers
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Mem0 supports multiple reranking providers. See the complete documentation for each:
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- **[Cohere](../../components/rerankers/models/cohere)**: Enterprise-grade with multilingual support
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- **[Sentence Transformer](../../components/rerankers/models/sentence_transformer)**: Local HuggingFace models
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- **[Hugging Face](../../components/rerankers/models/huggingface)**: Custom models from HuggingFace
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- **[LLM Reranker](../../components/rerankers/models/llm_reranker)**: Use any LLM for flexible scoring
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- **[Zero Entropy](../../components/rerankers/models/zero_entropy)**: State-of-the-art neural reranking
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#### Cohere Reranker
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```python
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@@ -123,6 +131,10 @@ for result in results["results"]:
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print(f"Score: {result['score']}")
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```
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||||
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<Info icon="check">
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||||
Expect each result to include both the base vector score and an updated rerank score so you can compare quality improvements.
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</Info>
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### Controlling Reranking
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||||
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||||
```python
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@@ -414,3 +426,18 @@ results = m.search("query", user_id="alice") # Automatically reranked
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<Info>
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||||
Reranker-enhanced search significantly improves result relevance. Start with a local model and upgrade to API-based solutions as your needs grow.
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</Info>
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<CardGroup cols={2}>
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<Card
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||||
title="Configure Rerankers"
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description="Review provider fields, defaults, and environment variables."
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icon="settings"
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href="/components/rerankers/config"
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/>
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<Card
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title="Build a Custom LLM Reranker"
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description="Combine reranking with tailored prompts and scoring logic."
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||||
icon="sparkles"
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||||
href="/components/rerankers/models/llm_reranker"
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||||
/>
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</CardGroup>
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@@ -1,128 +1,6 @@
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||||
---
|
||||
title: Reranking
|
||||
description: 'Improve memory search relevance with advanced reranking capabilities'
|
||||
description: 'Redirect to the canonical reranker-enhanced search guide.'
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||||
---
|
||||
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||||
## Overview
|
||||
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||||
Reranking is an advanced feature that improves the relevance of memory search results by re-ordering them based on more sophisticated relevance scoring. After initial vector similarity search, rerankers use specialized models to provide more accurate relevance scores.
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||||
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||||
<Note>
|
||||
Reranking operates as a post-processing step after the initial vector search. It takes the top results from vector similarity search and re-scores them using more advanced models or custom logic.
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||||
</Note>
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||||
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||||
## How It Works
|
||||
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||||
1. **Vector Search**: Initial semantic similarity search retrieves candidate memories
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||||
2. **Reranking**: Selected reranker re-scores candidates using advanced models
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||||
3. **Final Results**: Re-ordered results with both vector and rerank scores
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||||
|
||||
## Quick Start
|
||||
|
||||
Enable reranking by adding a `rerank` section to your memory configuration:
|
||||
|
||||
```python Python
|
||||
from mem0 import Memory
|
||||
|
||||
config = {
|
||||
"vector_store": {
|
||||
"provider": "chroma",
|
||||
"config": {
|
||||
"collection_name": "my_memories",
|
||||
"path": "./chroma_db"
|
||||
}
|
||||
},
|
||||
"llm": {
|
||||
"provider": "openai",
|
||||
"config": {
|
||||
"model": "gpt-4o-mini"
|
||||
}
|
||||
},
|
||||
"rerank": {
|
||||
"provider": "zero_entropy",
|
||||
"config": {
|
||||
"model": "zerank-1",
|
||||
"top_k": 5
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
memory = Memory.from_config(config)
|
||||
|
||||
# Add memories
|
||||
messages = [
|
||||
{"role": "user", "content": "I love Italian pasta, especially carbonara"},
|
||||
{"role": "assistant", "content": "Carbonara is a classic Roman dish!"}
|
||||
]
|
||||
|
||||
memory.add(messages, user_id="alice")
|
||||
|
||||
# Search with reranking - results automatically include rerank scores
|
||||
results = memory.search("What Italian dishes does the user like?", user_id="alice")
|
||||
|
||||
for result in results['results']:
|
||||
print(f"Memory: {result['memory']}")
|
||||
print(f"Vector Score: {result['score']:.3f}")
|
||||
print(f"Rerank Score: {result['rerank_score']:.3f}")
|
||||
```
|
||||
|
||||
## Supported Providers
|
||||
|
||||
Mem0 supports multiple reranking providers:
|
||||
|
||||
- **[Zero Entropy](../../components/rerankers/models/zero_entropy)**: State-of-the-art neural reranking
|
||||
- **[Cohere](../../components/rerankers/models/cohere)**: Enterprise-grade with multilingual support
|
||||
- **[Sentence Transformer](../../components/rerankers/models/sentence_transformer)**: Local HuggingFace models
|
||||
- **[LLM-based](../../components/rerankers/models/llm)**: Custom scoring using any LLM
|
||||
|
||||
## When to Use Reranking
|
||||
|
||||
Reranking is particularly effective for:
|
||||
|
||||
- **Improved Relevance**: When vector search alone doesn't provide sufficiently relevant results
|
||||
- **Domain-Specific Queries**: Specialized terminology or context that benefits from advanced models
|
||||
- **Customer Support**: Finding the most relevant help articles and documentation
|
||||
- **Knowledge Management**: Better search results in internal knowledge bases
|
||||
- **Personal AI Assistants**: More accurate memory recall for user queries
|
||||
|
||||
## Configuration Options
|
||||
|
||||
Each reranker has specific configuration options. See the [Rerankers Documentation](../../components/rerankers/overview) for detailed configuration parameters.
|
||||
|
||||
### Basic Configuration
|
||||
|
||||
```python Python
|
||||
"rerank": {
|
||||
"provider": "zero_entropy", # or "cohere", "sentence_transformer", "llm"
|
||||
"config": {
|
||||
"top_k": 5, # Limit results after reranking
|
||||
"api_key": "your-key" # Provider-specific API key
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Controlling Reranking
|
||||
|
||||
You can enable or disable reranking per search:
|
||||
|
||||
```python Python
|
||||
# Search with reranking (default when configured)
|
||||
results = memory.search("query", user_id="alice", rerank=True)
|
||||
|
||||
# Search without reranking
|
||||
results = memory.search("query", user_id="alice", rerank=False)
|
||||
```
|
||||
|
||||
## Performance Considerations
|
||||
|
||||
- **Latency**: Reranking adds processing time but significantly improves relevance
|
||||
- **Cost**: API-based rerankers (Zero Entropy, Cohere, LLM) have per-request costs
|
||||
- **Local Options**: Sentence Transformer reranker runs locally with no API costs
|
||||
- **Quality vs Speed**: Balance based on your application's requirements
|
||||
|
||||
## Next Steps
|
||||
|
||||
- Explore specific [reranker providers](../../components/rerankers/overview) and their capabilities
|
||||
- Learn about [configuration options](../../components/rerankers/config) for fine-tuning
|
||||
- Check out [Vector Stores](../../components/vectordbs/overview) for different storage backends
|
||||
- See [Async Memory](./async-memory) for non-blocking reranking operations
|
||||
<Redirect href="/open-source/features/reranker-search" />
|
||||
|
||||
Reference in New Issue
Block a user