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docs: document FastEmbed embedder + missing org/project API endpoints (#5852)

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Kartik
2026-06-26 16:00:12 +05:30
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parent bbbfcfea07
commit d258b638ef
9 changed files with 93 additions and 230 deletions
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docs/api-reference/organization/remove-org-member.mdx
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---
title: "Remove Organization Member"
description: "Remove a member from an organization"
openapi: "delete /api/v1/orgs/organizations/{org_id}/members/"
---
docs/api-reference/organization/update-org-member.mdx
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---
title: "Update Organization Member"
description: "Update organization member role"
openapi: "put /api/v1/orgs/organizations/{org_id}/members/"
---
docs/api-reference/project/remove-project-member.mdx
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---
title: "Remove Project Member"
description: "Remove a member from a project"
openapi: "delete /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/"
---
docs/api-reference/project/update-project-member.mdx
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---
title: "Update Project Member"
description: "Update project member role"
openapi: "put /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/"
---
docs/api-reference/project/update-project.mdx
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---
title: "Update Project"
description: "Update project settings"
openapi: "patch /api/v1/orgs/organizations/{org_id}/projects/{project_id}/"
---
docs/components/embedders/models/fastembed.mdx
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---
title: "FastEmbed"
description: "Configure FastEmbed as an embedding provider in Mem0 to generate embeddings locally using ONNX-based models without a GPU."
---
You can use FastEmbed to run embedding models locally in Mem0. FastEmbed is an ONNX-based embedding library that runs efficiently on CPU without requiring a GPU or an external API key.
### Installation
```bash
pip install fastembed
```
### Usage
<CodeGroup>
```python Python
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
config = {
"embedder": {
"provider": "fastembed",
"config": {
"model": "thenlper/gte-large"
}
}
}
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about 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."}
]
m.add(messages, user_id="john")
```
</CodeGroup>
### Config
Here are the parameters available for configuring FastEmbed embedder:
| Parameter | Description | Default Value |
| --- | --- | --- |
| `model` | The name of the FastEmbed model to use | `thenlper/gte-large` |
| `embedding_dims` | Dimensions of the embedding model (auto-derived from the model if not set) | `None` |
docs/components/rerankers/models/llm.mdx
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---
title: LLM as Reranker
description: "Use any LLM as a flexible reranker in Mem0 with custom prompts and domain-specific scoring logic."
---
<Warning>
**This page has been superseded.** Please see [LLM Reranker](/components/rerankers/models/llm_reranker) for the complete and up-to-date documentation on using LLMs for reranking.
</Warning>
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.
## Supported LLM Providers
Any LLM provider supported by Mem0 can be used for reranking:
- **OpenAI**: GPT-4, GPT-3.5-turbo, etc.
- **Anthropic**: Claude models
- **Together**: Open-source models
- **Groq**: Fast inference
- **Ollama**: Local models
- And more...
## 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"
}
},
"reranker": {
"provider": "llm",
"config": {
"model": "gpt-4o-mini",
"provider": "openai",
"api_key": "your-openai-api-key", # or set OPENAI_API_KEY
"top_k": 5,
"temperature": 0.0
}
}
}
memory = Memory.from_config(config)
```
## Custom Scoring Prompt
You can provide a custom prompt for relevance scoring:
```python Python
custom_prompt = """You are a relevance scoring assistant. Rate how well this document answers the query.
Query: "{query}"
Document: "{document}"
Score from 0.0 to 1.0 where:
- 1.0: Perfect match, directly answers the query
- 0.8-0.9: Highly relevant, good match
- 0.6-0.7: Moderately relevant, partial match
- 0.4-0.5: Slightly relevant, limited useful information
- 0.0-0.3: Not relevant or no useful information
Provide only a single numerical score between 0.0 and 1.0."""
config["reranker"]["config"]["scoring_prompt"] = custom_prompt
```
## Usage Example
```python Python
import os
from mem0 import Memory
# Set API key
os.environ["OPENAI_API_KEY"] = "your-api-key"
# Initialize memory with LLM reranker
config = {
"vector_store": {"provider": "chroma"},
"llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
"reranker": {
"provider": "llm",
"config": {
"model": "gpt-4o-mini",
"provider": "openai",
"temperature": 0.0
}
}
}
memory = Memory.from_config(config)
# Add memories
messages = [
{"role": "user", "content": "I'm learning Python programming"},
{"role": "user", "content": "I find object-oriented programming challenging"},
{"role": "user", "content": "I love hiking in national parks"}
]
memory.add(messages, user_id="david")
# Search with LLM reranking
results = memory.search("What programming topics is the user studying?", filters={"user_id": "david"})
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}")
print()
```
```text Output
Memory: I'm learning Python programming
Vector Score: 0.856
Rerank Score: 0.920
Memory: I find object-oriented programming challenging
Vector Score: 0.782
Rerank Score: 0.850
```
## Domain-Specific Scoring
Create specialized scoring for your domain:
```python Python
medical_prompt = """You are a medical relevance expert. Score how relevant this medical record is to the clinical query.
Clinical Query: "{query}"
Medical Record: "{document}"
Consider:
- Clinical relevance and accuracy
- Patient safety implications
- Diagnostic value
- Treatment relevance
Score from 0.0 to 1.0. Provide only the numerical score."""
config = {
"reranker": {
"provider": "llm",
"config": {
"model": "gpt-4o-mini",
"provider": "openai",
"scoring_prompt": medical_prompt,
"temperature": 0.0
}
}
}
```
## Multiple LLM Providers
Use different LLM providers for reranking:
```python Python
# Using Anthropic Claude
anthropic_config = {
"reranker": {
"provider": "llm",
"config": {
"model": "claude-3-haiku-20240307",
"provider": "anthropic",
"temperature": 0.0
}
}
}
# Using local Ollama model
ollama_config = {
"reranker": {
"provider": "llm",
"config": {
"model": "llama2:7b",
"provider": "ollama",
"temperature": 0.0
}
}
}
```
## Configuration Parameters
| Parameter | Description | Type | Default |
|-----------|-------------|------|---------|
| `model` | LLM model to use for scoring | `str` | `"gpt-4o-mini"` |
| `provider` | LLM provider name | `str` | `"openai"` |
| `api_key` | API key for the LLM provider | `str` | `None` |
| `top_k` | Maximum documents to return | `int` | `None` |
| `temperature` | Temperature for LLM generation | `float` | `0.0` |
| `max_tokens` | Maximum tokens for LLM response | `int` | `100` |
| `scoring_prompt` | Custom prompt template | `str` | Default prompt |
## Advantages
- **Maximum Flexibility**: Custom prompts for any use case
- **Domain Expertise**: Leverage LLM knowledge for specialized domains
- **Interpretability**: Understand scoring through prompt engineering
- **Multi-criteria**: Score based on multiple relevance factors
## Considerations
- **Latency**: Higher latency than specialized rerankers
- **Cost**: LLM API costs per reranking operation
- **Consistency**: May have slight variations in scoring
- **Prompt Engineering**: Requires careful prompt design
## Best Practices
1. **Temperature**: Use 0.0 for consistent scoring
2. **Prompt Design**: Be specific about scoring criteria
3. **Token Efficiency**: Keep prompts concise to reduce costs
4. **Caching**: Cache results for repeated queries when possible
5. **Fallback**: Handle API errors gracefully
docs/docs.json
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@@ -273,7 +273,8 @@
"components/embedders/models/lmstudio",
"components/embedders/models/together",
"components/embedders/models/langchain",
"components/embedders/models/aws_bedrock"
"components/embedders/models/aws_bedrock",
"components/embedders/models/fastembed"
]
}
]
@@ -533,6 +534,8 @@
"api-reference/organization/get-org",
"api-reference/organization/get-org-members",
"api-reference/organization/add-org-member",
"api-reference/organization/update-org-member",
"api-reference/organization/remove-org-member",
"api-reference/organization/delete-org"
]
},
@@ -545,6 +548,9 @@
"api-reference/project/get-project",
"api-reference/project/get-project-members",
"api-reference/project/add-project-member",
"api-reference/project/update-project",
"api-reference/project/update-project-member",
"api-reference/project/remove-project-member",
"api-reference/project/delete-project"
]
},
@@ -624,6 +630,10 @@
]
},
"redirects": [
{
"source": "/components/rerankers/models/llm",
"destination": "/components/rerankers/models/llm_reranker"
},
{
"source": "/migration/breaking-changes",
"destination": "/"
docs/llms.txt
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@@ -367,6 +367,8 @@ All API Reference docs describe Mem0 Platform REST endpoints (requires API key).
- [Get Organization](https://docs.mem0.ai/api-reference/organization/get-org) [Platform]: Use when fetching one org.
- [Get Organization Members](https://docs.mem0.ai/api-reference/organization/get-org-members) [Platform]: Use when listing org members.
- [Add Organization Member](https://docs.mem0.ai/api-reference/organization/add-org-member) [Platform]: Use when inviting a member to an org.
- [Update Organization Member](https://docs.mem0.ai/api-reference/organization/update-org-member) [Platform]: Use when updating an org member's role.
- [Remove Organization Member](https://docs.mem0.ai/api-reference/organization/remove-org-member) [Platform]: Use when removing a member from an organization.
- [Delete Organization](https://docs.mem0.ai/api-reference/organization/delete-org) [Platform]: Use when removing an org.
### Projects
@@ -375,6 +377,9 @@ All API Reference docs describe Mem0 Platform REST endpoints (requires API key).
- [Get Project](https://docs.mem0.ai/api-reference/project/get-project) [Platform]: Use when fetching one project.
- [Get Project Members](https://docs.mem0.ai/api-reference/project/get-project-members) [Platform]: Use when listing project members.
- [Add Project Member](https://docs.mem0.ai/api-reference/project/add-project-member) [Platform]: Use when inviting a member to a project.
- [Update Project](https://docs.mem0.ai/api-reference/project/update-project) [Platform]: Use when updating project settings.
- [Update Project Member](https://docs.mem0.ai/api-reference/project/update-project-member) [Platform]: Use when updating a project member's role.
- [Remove Project Member](https://docs.mem0.ai/api-reference/project/remove-project-member) [Platform]: Use when removing a member from a project.
- [Delete Project](https://docs.mem0.ai/api-reference/project/delete-project) [Platform]: Use when removing a project.
### Webhooks
@@ -460,6 +465,7 @@ Everything below is OSS-only provider configuration. Skip this entire section wh
- [LM Studio Embeddings](https://docs.mem0.ai/components/embedders/models/lmstudio) [OSS]: Use when embeddings run through LM Studio.
- [Together Embeddings](https://docs.mem0.ai/components/embedders/models/together) [OSS]: Use when embeddings run on Together.
- [LangChain Embeddings](https://docs.mem0.ai/components/embedders/models/langchain) [OSS]: Use when embeddings are wrapped behind a LangChain adapter.
- [FastEmbed](https://docs.mem0.ai/components/embedders/models/fastembed) [OSS]: Use when embeddings run locally via FastEmbed (ONNX).
### Vector Databases [OSS]
- [Vector Database Overview](https://docs.mem0.ai/components/vectordbs/overview) [OSS]: Use when choosing a vector store.
@@ -498,7 +504,5 @@ Everything below is OSS-only provider configuration. Skip this entire section wh
- [Custom Reranker Prompts](https://docs.mem0.ai/components/rerankers/custom-prompts) [OSS]: Use when rewriting reranker prompts.
- [Cohere Reranker](https://docs.mem0.ai/components/rerankers/models/cohere) [OSS]: Use for Cohere Rerank.
- [Sentence Transformer Reranker](https://docs.mem0.ai/components/rerankers/models/sentence_transformer) [OSS]: Use for local cross-encoder rerankers.
- [Hugging Face Reranker](https://docs.mem0.ai/components/rerankers/models/huggingface) [OSS]: Use for HF-hosted reranker models.
- [LLM Reranker (prompt)](https://docs.mem0.ai/components/rerankers/models/llm) [OSS]: Use when the reranker is a prompted LLM (config guide).
- [LLM Reranker](https://docs.mem0.ai/components/rerankers/models/llm_reranker) [OSS]: Use when the reranker is a prompted LLM (implementation reference).
- [Hugging Face Reranker](https://docs.mem0.ai/components/rerankers/models/huggingface) [OSS]: Use for HF-hosted reranker models.- [LLM Reranker](https://docs.mem0.ai/components/rerankers/models/llm_reranker) [OSS]: Use when the reranker is a prompted LLM (implementation reference).
- [Zero Entropy Reranker](https://docs.mem0.ai/components/rerankers/models/zero_entropy) [OSS]: Use for the Zero Entropy reranker.