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Added improved docs index and overview pages and quickstart (#3603)

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Parth Sharma
2025-10-22 10:30:45 -07:00
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commit f7d7c53001
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docs/components/embedders/config.mdx
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---
title: Configurations
icon: "gear"
iconType: "solid"
---
docs/components/embedders/overview.mdx
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---
title: Overview
icon: "info"
iconType: "solid"
---
Mem0 offers support for various embedding models, allowing users to choose the one that best suits their needs.
docs/components/llms/config.mdx
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---
title: Configurations
icon: "gear"
iconType: "solid"
---
## How to define configurations?
docs/components/llms/overview.mdx
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---
title: Overview
icon: "info"
iconType: "solid"
---
Mem0 includes built-in support for various popular large language models. Memory can utilize the LLM provided by the user, ensuring efficient use for specific needs.
docs/components/rerankers/config.mdx
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---
title: Config
description: 'Configuration options for rerankers in Mem0'
icon: "gear"
iconType: "solid"
description: "Configuration options for rerankers in Mem0"
---
## Common Configuration Parameters
All rerankers share these common configuration parameters:
| Parameter | Description | Type | Default |
|-----------|-------------|------|---------|
| `provider` | Reranker provider name | `str` | Required |
| `top_k` | Maximum number of results to return after reranking | `int` | `None` |
| `api_key` | API key for the reranker service | `str` | `None` |
| Parameter | Description | Type | Default |
| ---------- | --------------------------------------------------- | ----- | -------- |
| `provider` | Reranker provider name | `str` | Required |
| `top_k` | Maximum number of results to return after reranking | `int` | `None` |
| `api_key` | API key for the reranker service | `str` | `None` |
## Provider-Specific Configuration
### Zero Entropy
| Parameter | Description | Type | Default |
|-----------|-------------|------|---------|
| `model` | Model to use: `zerank-1` or `zerank-1-small` | `str` | `"zerank-1"` |
| `api_key` | Zero Entropy API key | `str` | `None` |
| Parameter | Description | Type | Default |
| --------- | -------------------------------------------- | ----- | ------------ |
| `model` | Model to use: `zerank-1` or `zerank-1-small` | `str` | `"zerank-1"` |
| `api_key` | Zero Entropy API key | `str` | `None` |
### Cohere
| Parameter | Description | Type | Default |
|-----------|-------------|------|---------|
| `model` | Cohere rerank model | `str` | `"rerank-english-v3.0"` |
| `api_key` | Cohere API key | `str` | `None` |
| `return_documents` | Whether to return document texts in response | `bool` | `False` |
| `max_chunks_per_doc` | Maximum chunks per document | `int` | `None` |
| Parameter | Description | Type | Default |
| -------------------- | -------------------------------------------- | ------ | ----------------------- |
| `model` | Cohere rerank model | `str` | `"rerank-english-v3.0"` |
| `api_key` | Cohere API key | `str` | `None` |
| `return_documents` | Whether to return document texts in response | `bool` | `False` |
| `max_chunks_per_doc` | Maximum chunks per document | `int` | `None` |
### Sentence Transformer
| Parameter | Description | Type | Default |
|-----------|-------------|------|---------|
| `model` | HuggingFace cross-encoder model name | `str` | `"cross-encoder/ms-marco-MiniLM-L-6-v2"` |
| `device` | Device to run model on (`cpu`, `cuda`, etc.) | `str` | `None` |
| `batch_size` | Batch size for processing | `int` | `32` |
| `show_progress_bar` | Show progress during processing | `bool` | `False` |
| Parameter | Description | Type | Default |
| ------------------- | -------------------------------------------- | ------ | ---------------------------------------- |
| `model` | HuggingFace cross-encoder model name | `str` | `"cross-encoder/ms-marco-MiniLM-L-6-v2"` |
| `device` | Device to run model on (`cpu`, `cuda`, etc.) | `str` | `None` |
| `batch_size` | Batch size for processing | `int` | `32` |
| `show_progress_bar` | Show progress during processing | `bool` | `False` |
### Hugging Face
| Parameter | Description | Type | Default |
|-----------|-------------|------|---------|
| `model` | HuggingFace reranker model name | `str` | `"BAAI/bge-reranker-large"` |
| `api_key` | HuggingFace API token | `str` | `None` |
| `device` | Device to run model on (`cpu`, `cuda`, etc.) | `str` | `None` |
| Parameter | Description | Type | Default |
| --------- | -------------------------------------------- | ----- | --------------------------- |
| `model` | HuggingFace reranker model name | `str` | `"BAAI/bge-reranker-large"` |
| `api_key` | HuggingFace API token | `str` | `None` |
| `device` | Device to run model on (`cpu`, `cuda`, etc.) | `str` | `None` |
### LLM-based
| Parameter | Description | Type | Default |
|-----------|-------------|------|---------|
| `model` | LLM model to use for scoring | `str` | `"gpt-4o-mini"` |
| `provider` | LLM provider (`openai`, `anthropic`, etc.) | `str` | `"openai"` |
| `api_key` | API key for LLM provider | `str` | `None` |
| `temperature` | Temperature for LLM generation | `float` | `0.0` |
| `max_tokens` | Maximum tokens for LLM response | `int` | `100` |
| `scoring_prompt` | Custom prompt template for scoring | `str` | Default scoring prompt |
| Parameter | Description | Type | Default |
| ---------------- | ------------------------------------------ | ------- | ---------------------- |
| `model` | LLM model to use for scoring | `str` | `"gpt-4o-mini"` |
| `provider` | LLM provider (`openai`, `anthropic`, etc.) | `str` | `"openai"` |
| `api_key` | API key for LLM provider | `str` | `None` |
| `temperature` | Temperature for LLM generation | `float` | `0.0` |
| `max_tokens` | Maximum tokens for LLM response | `int` | `100` |
| `scoring_prompt` | Custom prompt template for scoring | `str` | Default scoring prompt |
### LLM Reranker
| Parameter | Description | Type | Default |
|-----------|-------------|------|---------|
| `llm.provider` | LLM provider for reranking | `str` | Required |
| `llm.config` | LLM configuration object | `dict` | Required |
| `top_n` | Number of results to return | `int` | `None` |
| Parameter | Description | Type | Default |
| -------------- | --------------------------- | ------ | -------- |
| `llm.provider` | LLM provider for reranking | `str` | Required |
| `llm.config` | LLM configuration object | `dict` | Required |
| `top_n` | Number of results to return | `int` | `None` |
## Environment Variables
@@ -93,7 +91,7 @@ config = {
"llm": {
"provider": "openai",
"config": {
"model": "gpt-4o-mini"
"model": "gpt-4.1-nano-2025-04-14"
}
},
"rerank": {
docs/components/rerankers/custom-prompts.mdx
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---
title: Custom Prompts
icon: "pencil"
iconType: "solid"
---
When using LLM rerankers, you can customize the prompts used for ranking to better suit your specific use case and domain.
@@ -54,7 +52,7 @@ config = {
"llm": {
"provider": "openai",
"config": {
"model": "gpt-4",
"model": "gpt-4.1-nano-2025-04-14",
"api_key": "your-openai-key"
}
},
@@ -71,16 +69,17 @@ memory = Memory.from_config(config)
Your custom prompt can use the following variables:
| Variable | Description |
|----------|-------------|
| `{query}` | The search query |
| `{memories}` | The list of memory entries to rank |
| `{user_id}` | The user ID (if available) |
| Variable | Description |
| ---------------- | ------------------------------------- |
| `{query}` | The search query |
| `{memories}` | The list of memory entries to rank |
| `{user_id}` | The user ID (if available) |
| `{user_context}` | Additional user context (if provided) |
## Domain-Specific Examples
### Customer Support
```python
customer_support_prompt = """
You are ranking customer support conversation memories.
@@ -100,6 +99,7 @@ Rank each memory 1-10 based on support relevance.
```
### Educational Content
```python
educational_prompt = """
Rank these learning memories for a student query.
@@ -119,6 +119,7 @@ Score each memory for educational value (1-10).
```
### Personal Assistant
```python
personal_assistant_prompt = """
Rank personal memories for relevance to the user's query.
@@ -140,6 +141,7 @@ Provide relevance scores (1-10) with brief explanations.
## Advanced Prompt Techniques
### Multi-Criteria Ranking
```python
multi_criteria_prompt = """
Evaluate memories using multiple criteria:
@@ -165,6 +167,7 @@ Format: JSON with detailed scoring
```
### Contextual Ranking
```python
contextual_prompt = """
Consider the following context when ranking memories:
@@ -214,4 +217,4 @@ for i, prompt in enumerate(prompts):
- **Too Long**: Keep prompts under token limits for your chosen LLM
- **Too Vague**: Be specific about ranking criteria
- **Inconsistent Format**: Ensure JSON output format is clearly specified
- **Missing Context**: Include relevant variables for your use case
- **Missing Context**: Include relevant variables for your use case
docs/components/rerankers/models/cohere.mdx
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---
title: Cohere
description: 'Reranking with Cohere'
icon: "building"
iconType: "solid"
description: "Reranking with Cohere"
---
Cohere provides enterprise-grade reranking models with excellent multilingual support and production-ready performance.
@@ -37,7 +35,7 @@ config = {
"llm": {
"provider": "openai",
"config": {
"model": "gpt-4o-mini"
"model": "gpt-4.1-nano-2025-04-14"
}
},
"reranker": {
@@ -124,13 +122,13 @@ config = {
## Configuration Parameters
| Parameter | Description | Type | Default |
|-----------|-------------|------|---------|
| `model` | Cohere rerank model to use | `str` | `"rerank-english-v3.0"` |
| `api_key` | Cohere API key | `str` | `None` |
| `top_k` | Maximum documents to return | `int` | `None` |
| `return_documents` | Whether to return document texts | `bool` | `False` |
| `max_chunks_per_doc` | Maximum chunks per document | `int` | `None` |
| Parameter | Description | Type | Default |
| -------------------- | -------------------------------- | ------ | ----------------------- |
| `model` | Cohere rerank model to use | `str` | `"rerank-english-v3.0"` |
| `api_key` | Cohere API key | `str` | `None` |
| `top_k` | Maximum documents to return | `int` | `None` |
| `return_documents` | Whether to return document texts | `bool` | `False` |
| `max_chunks_per_doc` | Maximum chunks per document | `int` | `None` |
## Features
@@ -144,4 +142,4 @@ config = {
1. **Model Selection**: Use `rerank-english-v3.0` for English, `rerank-multilingual-v3.0` for other languages
2. **Batch Processing**: Process multiple queries efficiently
3. **Error Handling**: Implement retry logic for production systems
4. **Monitoring**: Track reranking performance and costs
4. **Monitoring**: Track reranking performance and costs
docs/components/rerankers/models/huggingface.mdx
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---
title: Hugging Face Reranker
description: 'Access thousands of reranking models from Hugging Face Hub'
icon: "face-smile"
iconType: "solid"
---
## Overview
docs/components/rerankers/models/llm.mdx
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---
title: LLM as Reranker
description: 'Flexible reranking using LLMs'
icon: "robot"
iconType: "solid"
---
LLM-based reranker provides maximum flexibility by using any Large Language Model to score document relevance. This approach allows for custom prompts and domain-specific scoring logic.
docs/components/rerankers/models/llm_reranker.mdx
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---
title: LLM Reranker
description: 'Use any language model as a reranker with custom prompts'
icon: "robot"
iconType: "solid"
---
## Overview
docs/components/rerankers/models/sentence_transformer.mdx
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---
title: Sentence Transformer
description: 'Local reranking with HuggingFace cross-encoder models'
icon: "server"
iconType: "solid"
---
Sentence Transformer reranker provides local reranking using HuggingFace cross-encoder models, perfect for privacy-focused deployments where you want to keep data on-premises.
docs/components/rerankers/models/zero_entropy.mdx
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---
title: Zero Entropy
description: 'Neural reranking with Zero Entropy'
icon: "sparkles"
iconType: "solid"
---
[Zero Entropy](https://www.zeroentropy.dev) provides neural reranking models that significantly improve search relevance with fast performance.
docs/components/rerankers/optimization.mdx
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---
title: Performance Optimization
icon: "bolt"
iconType: "solid"
---
Optimizing reranker performance is crucial for maintaining fast search response times while improving result quality. This guide covers best practices for different reranker types.
docs/components/rerankers/overview.mdx
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---
title: Overview
icon: "arrow-up-arrow-down"
iconType: "solid"
---
Mem0 includes built-in support for various reranking providers to improve the relevance of memory search results. Rerankers post-process initial vector search results by re-scoring and re-ordering them using more sophisticated relevance models.
docs/components/vectordbs/config.mdx
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---
title: Configurations
icon: "gear"
iconType: "solid"
---
## How to define configurations?
docs/components/vectordbs/overview.mdx
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---
title: Overview
icon: "info"
iconType: "solid"
---
Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.
@@ -51,4 +49,3 @@ If you are using a customized model with different dimensions other than 1536 (f
`ValueError: shapes (0,1536) and (768,) not aligned: 1536 (dim 1) != 768 (dim 0)`
You can add `"embedding_model_dims": 768,` to the config of the vector_store to resolve this issue.