Doc: Feature docs changes (#2756)
This commit is contained in:
Dev Khant
@@ -45,22 +45,22 @@
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"group": "Features",
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"icon": "star",
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"pages": [
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"features/platform-overview",
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"features/advanced-retrieval",
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"features/criteria-retrieval",
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"features/contextual-add",
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"features/multimodal-support",
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"features/timestamp",
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"features/selective-memory",
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"features/custom-categories",
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"features/custom-instructions",
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"features/direct-import",
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"features/async-client",
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"features/memory-export",
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"features/webhooks",
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"features/graph-memory",
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"features/feedback-mechanism",
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"features/expiration-date"
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"platform/features/platform-overview",
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"platform/features/advanced-retrieval",
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"platform/features/criteria-retrieval",
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"platform/features/contextual-add",
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"platform/features/multimodal-support",
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"platform/features/timestamp",
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"platform/features/selective-memory",
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"platform/features/custom-categories",
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"platform/features/custom-instructions",
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"platform/features/direct-import",
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"platform/features/async-client",
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"platform/features/memory-export",
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"platform/features/webhooks",
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"platform/features/graph-memory",
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"platform/features/feedback-mechanism",
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"platform/features/expiration-date"
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]
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}
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]
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@@ -77,10 +77,10 @@
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"icon": "wrench",
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"pages": [
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"open-source/features/async-memory",
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"features/openai_compatibility",
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"features/custom-fact-extraction-prompt",
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"features/custom-update-memory-prompt",
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"open-source/multimodal-support",
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"open-source/features/openai_compatibility",
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"open-source/features/custom-fact-extraction-prompt",
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"open-source/features/custom-update-memory-prompt",
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"open-source/features/multimodal-support",
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"open-source/features/rest-api"
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]
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},
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@@ -37,13 +37,13 @@
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## Features
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[Graph Memory](https://docs.mem0.ai/features/graph-memory): Mem0's graph memory system builds relationships between entities in your data, enabling contextually relevant retrieval by analyzing connections between information points - activate it with `enable_graph=True` to enhance search results beyond direct semantic matches, ideal for applications tracking evolving relationships.
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[Advanced Retrieval](https://docs.mem0.ai/features/advanced-retrieval): Mem0 offers enhanced search capabilities through three advanced retrieval modes: keyword search (improves recall by matching specific terms), reranking (ensures most relevant results appear first using neural networks), and filtering (narrows results by specific criteria) - each can be enabled independently or in combination to optimize search precision and relevance.
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[Multimodal Support](https://docs.mem0.ai/features/multimodal-support): Mem0 extends beyond text by supporting images and documents (JPG, PNG, MDX, TXT, PDF), allowing users to integrate visual and document content through direct URLs or Base64 encoding, enhancing the memory system's ability to understand and recall information from various media types.
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[Memory Customization](https://docs.mem0.ai/features/selective-memory): Mem0 enables selective memory storage through inclusion and exclusion rules, allowing users to focus on relevant information (like specific topics) while omitting irrelevant data (such as food preferences), resulting in more efficient, accurate, and privacy-conscious AI interactions.
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[Custom Categories](https://docs.mem0.ai/features/custom-categories): Mem0 allows setting custom categories at both project level and during individual API calls, overriding default categories (like personal_details, family, sports) with more specific ones to improve memory categorization accuracy - simply provide a list of category dictionaries with descriptive definitions when adding memories.
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[Async Client](https://docs.mem0.ai/features/async-client): Mem0 provides an AsyncMemoryClient for non-blocking operations, offering the same functionality as the synchronous client (add, search, get_all, delete, etc.) but with async/await support, making it ideal for high-concurrency applications that need to perform memory operations without blocking execution.
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[Memory Export](https://docs.mem0.ai/features/memory-export): Mem0 enables exporting memories in structured formats using customizable Pydantic schemas, allowing you to transform stored memories into specific data structures by defining schemas, submitting export jobs with optional processing instructions, and retrieving the formatted data with various filtering options.
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[Graph Memory](https://docs.mem0.ai/platform/features/graph-memory): Mem0's graph memory system builds relationships between entities in your data, enabling contextually relevant retrieval by analyzing connections between information points - activate it with `enable_graph=True` to enhance search results beyond direct semantic matches, ideal for applications tracking evolving relationships.
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[Advanced Retrieval](https://docs.mem0.ai/platform/features/advanced-retrieval): Mem0 offers enhanced search capabilities through three advanced retrieval modes: keyword search (improves recall by matching specific terms), reranking (ensures most relevant results appear first using neural networks), and filtering (narrows results by specific criteria) - each can be enabled independently or in combination to optimize search precision and relevance.
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[Multimodal Support](https://docs.mem0.ai/platform/features/multimodal-support): Mem0 extends beyond text by supporting images and documents (JPG, PNG, MDX, TXT, PDF), allowing users to integrate visual and document content through direct URLs or Base64 encoding, enhancing the memory system's ability to understand and recall information from various media types.
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[Memory Customization](https://docs.mem0.ai/platform/features/selective-memory): Mem0 enables selective memory storage through inclusion and exclusion rules, allowing users to focus on relevant information (like specific topics) while omitting irrelevant data (such as food preferences), resulting in more efficient, accurate, and privacy-conscious AI interactions.
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[Custom Categories](https://docs.mem0.ai/platform/features/custom-categories): Mem0 allows setting custom categories at both project level and during individual API calls, overriding default categories (like personal_details, family, sports) with more specific ones to improve memory categorization accuracy - simply provide a list of category dictionaries with descriptive definitions when adding memories.
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[Async Client](https://docs.mem0.ai/platform/features/async-client): Mem0 provides an AsyncMemoryClient for non-blocking operations, offering the same functionality as the synchronous client (add, search, get_all, delete, etc.) but with async/await support, making it ideal for high-concurrency applications that need to perform memory operations without blocking execution.
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[Memory Export](https://docs.mem0.ai/platform/features/memory-export): Mem0 enables exporting memories in structured formats using customizable Pydantic schemas, allowing you to transform stored memories into specific data structures by defining schemas, submitting export jobs with optional processing instructions, and retrieving the formatted data with various filtering options.
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## OSS
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@@ -72,9 +72,9 @@
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### Features
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[OpenAI Compatibility](https://docs.mem0.ai/features/openai_compatibility): Mem0 offers seamless integration with OpenAI-compatible APIs, allowing developers to enhance conversational agents with structured memory by initializing with a Mem0 API key (or locally without one), supporting various LLM providers, and enabling personalized responses through user context persistence across interactions with parameters like user_id, agent_id, and custom filters.
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[Custom Fact Extraction Prompt](https://docs.mem0.ai/features/custom-fact-extraction-prompt): Mem0 enables custom fact extraction prompts to tailor information extraction for specific use cases by defining domain-specific examples and formats, allowing precise control over what information is extracted from messages - simply provide a custom prompt with few-shot examples in the config when initializing the Memory client.
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[Custom Update Memory Prompt](https://docs.mem0.ai/features/custom-update-memory-prompt): Mem0 enables customizing the update memory prompt to control how memories are modified by comparing newly retrieved facts with existing memories and determining appropriate actions (add, update, delete, or no change) based on custom logic and examples provided in the prompt configuration.
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[OpenAI Compatibility](https://docs.mem0.ai/open-source/features/openai_compatibility): Mem0 offers seamless integration with OpenAI-compatible APIs, allowing developers to enhance conversational agents with structured memory by initializing with a Mem0 API key (or locally without one), supporting various LLM providers, and enabling personalized responses through user context persistence across interactions with parameters like user_id, agent_id, and custom filters.
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[Custom Fact Extraction Prompt](https://docs.mem0.ai/open-source/features/custom-fact-extraction-prompt): Mem0 enables custom fact extraction prompts to tailor information extraction for specific use cases by defining domain-specific examples and formats, allowing precise control over what information is extracted from messages - simply provide a custom prompt with few-shot examples in the config when initializing the Memory client.
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[Custom Update Memory Prompt](https://docs.mem0.ai/open-source/features/custom-update-memory-prompt): Mem0 enables customizing the update memory prompt to control how memories are modified by comparing newly retrieved facts with existing memories and determining appropriate actions (add, update, delete, or no change) based on custom logic and examples provided in the prompt configuration.
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[REST API Server](https://docs.mem0.ai/open-source/features/rest-api): Mem0 provides a FastAPI-based REST API server that supports core operations (create/retrieve/search/update/delete memories) with OpenAPI documentation at /docs, easily deployable via Docker Compose with pre-configured databases (postgres pgvector, neo4j) - just set OPENAI_API_KEY to get started.
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[Graph Memory](https://docs.mem0.ai/open-source/graph_memory/overview): Mem0's open-source graph memory system enables building and querying relationships between entities by installing with `pip install "mem0ai[graph]"` and configuring a graph store provider (like Neo4j) - this allows for more contextual memory retrieval by combining vector and graph-based approaches to track evolving relationships between information points.
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69
docs/open-source/features/multimodal-support.mdx
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69
docs/open-source/features/multimodal-support.mdx
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---
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title: Multimodal Support
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description: Integrate images into your interactions with Mem0
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icon: "image"
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iconType: "solid"
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---
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<Snippet file="paper-release.mdx" />
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Mem0 extends its capabilities beyond text by supporting multimodal data. With this feature, users can seamlessly integrate images into their interactions—allowing Mem0 to extract relevant information.
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## How It Works
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When a user submits an image, Mem0 processes it to extract textual information and other pertinent details. These details are then added to the user's memory, enhancing the system's ability to understand and recall multimodal inputs.
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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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client = Memory()
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messages = [
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{
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"role": "user",
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"content": "Hi, my name is Alice."
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},
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{
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"role": "assistant",
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"content": "Nice to meet you, Alice! What do you like to eat?"
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},
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{
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"role": "user",
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"content": {
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"type": "image_url",
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"image_url": {
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"url": "https://www.superhealthykids.com/wp-content/uploads/2021/10/best-veggie-pizza-featured-image-square-2.jpg"
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}
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}
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},
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]
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# Calling the add method to ingest messages into the memory system
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client.add(messages, user_id="alice")
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```
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```json Output
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{
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"results": [
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{
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"memory": "Name is Alice",
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"event": "ADD",
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"id": "7ae113a3-3cb5-46e9-b6f7-486c36391847"
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},
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{
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"memory": "Likes large pizza with toppings including cherry tomatoes, black olives, green spinach, yellow bell peppers, diced ham, and sliced mushrooms",
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"event": "ADD",
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"id": "56545065-7dee-4acf-8bf2-a5b2535aabb3"
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}
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]
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}
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```
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</CodeGroup>
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Using these methods, you can seamlessly incorporate various media types into your interactions, further enhancing Mem0's multimodal capabilities.
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If you have any questions, please feel free to reach out to us using one of the following methods:
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<Snippet file="get-help.mdx" />
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