Merge branch 'main' into dev

Author: akshayballal95

README.md

@@ -83,15 +83,13 @@ EmbedAnything is a minimalist, yet highly performant, modular, lightning-fast, l
 
 - **No Dependency on Pytorch**: Easy to deploy on cloud, comes with low memory footprint.
 - **Highly Modular** : Choose any vectorDB adapter for RAG, with ~~1 line~~ 1 word of code
-- **Candle Backend** : Supports BERT, Jina, ColPali, Splade, ModernBERT, Reranker, Qwen
-- **ONNX Backend** : Supports BERT, Jina, ColPali, ColBERT Splade, Reranker, ModernBERT, Qwen
-- **Cloud Embedding Models:** : Supports OpenAI, Cohere, and Gemini.
-- **MultiModality** : Works with text sources like PDFs, txt, md, images, audio (.WAV), and videos (frame sampling; enable the `video` feature)
+- **Backend** : Supports Candle, ONNX and cloud models
+- **MultiModality** : Works with text sources like PDFs, txt, md, Images JPG and Audio, .WAV
 - **GPU support** : Hardware acceleration on GPU as well.
 - **Chunking** : In-built chunking methods like semantic, late-chunking
 - **Vector Streaming:** : Separate file processing, Indexing and Inferencing on different threads, reduces latency.
 - **AWS S3 Bucket:** : Directly import AWS S3 bucket files.
-- **Prebult Docker Image** : Just pull [it]( starlightsearch/embedanything-server)
+- **Prebult Docker Image** : Just pull it: starlightsearch/embedanything-server
 - **SearchAgent** : Example of how you can use index for Searchr1 reasoning.
 - **Video guide** : Quick start for frame sampling: https://embed-anything.com/guides/video/
 
@@ -144,8 +142,9 @@ We support any hugging-face models on Candle. And We also support ONNX runtime f
 **⚠️ WhichModel has been deprecated in from_pretrained_hf**
 
 ```python
-from embed_anything import EmbeddingModel, WhichModel, TextEmbedConfig
 import embed_anything
+from embed_anything import EmbeddingModel, WhichModel, TextEmbedConfig
+
 
 # Load a custom BERT model from Hugging Face
 model = EmbeddingModel.from_pretrained_hf(
@@ -190,8 +189,9 @@ for item in data:
 Sparse embeddings are useful for keyword-based retrieval and hybrid search scenarios.
 
 ```python
-from embed_anything import EmbeddingModel, TextEmbedConfig
 import embed_anything
+from embed_anything import EmbeddingModel, TextEmbedConfig
+
 
 # Load a SPLADE model for sparse embeddings
 model = EmbeddingModel.from_pretrained_hf(
@@ -217,8 +217,9 @@ ONNX models provide faster inference and lower memory usage. Use the `ONNXModel`
 ### BERT Models
 
 ```python
-from embed_anything import EmbeddingModel, WhichModel, ONNXModel, Dtype, TextEmbedConfig
 import embed_anything
+from embed_anything import EmbeddingModel, WhichModel, ONNXModel, Dtype, TextEmbedConfig
+
 
 # Option 2: Use a custom ONNX model from Hugging Face
 model = EmbeddingModel.from_pretrained_onnx(
@@ -233,6 +234,7 @@ model = EmbeddingModel.from_pretrained_onnx(
 Use cloud models for high-quality embeddings without local model deployment.
 
 ```python
+import embed_anything
 from embed_anything import EmbeddingModel, WhichModel
 import os
 
@@ -255,8 +257,8 @@ data = embed_anything.embed_file("test_files/document.pdf", embedder=model)
 Semantic chunking preserves meaning by splitting text at semantically meaningful boundaries rather than fixed sizes.
 
 ```python
-from embed_anything import EmbeddingModel, TextEmbedConfig
 import embed_anything
+from embed_anything import EmbeddingModel, TextEmbedConfig
 
 # Main embedding model for generating final embeddings
 model = EmbeddingModel.from_pretrained_hf(
@@ -291,6 +293,7 @@ for item in data:
 Late-chunking splits text into smaller units first, then combines them during embedding for better context preservation.
 
 ```python
+import embed_anything
 from embed_anything import EmbeddingModel, TextEmbedConfig, EmbedData
 
 # Load your embedding model
@@ -351,8 +354,8 @@ os.add_dll_directory("C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v12.6/b
 ### Advanced Usage with Configuration
 
 ```python
-from embed_anything import EmbeddingModel, WhichModel, TextEmbedConfig
 import embed_anything
+from embed_anything import EmbeddingModel, WhichModel, TextEmbedConfig
 
 # Load model
 model = EmbeddingModel.from_pretrained_hf(
@@ -413,6 +416,7 @@ print(f"Total chunks: {len(data)}")
 For custom or fine-tuned models, specify the Hugging Face model ID and path to the ONNX file:
 
 ```python
+import embed_anything
 from embed_anything import EmbeddingModel, WhichModel, Dtype
 
 # Load a custom ONNX model from Hugging Face

docs/blog/posts/Release Notes 0.7.md

@@ -0,0 +1,259 @@
+---
+draft: false 
+date: 2026-01-11
+authors: 
+ - sonam
+slug: release-notes-7
+title: Release Notes 0.7
+---
+# Release Notes 0.7
+
+0.7 is all about making deployment easy and integrations to data sources. Including Prebuilt Docker Image,SearchR1 example to improve context for Agents and AWS S3 bucket Integration.
+
+<!-- more -->
+
+## 📋 Summary of Changes (0.6.8 → 0.7.0)
+
+This release represents a significant milestone in making EmbedAnything easier to deploy and more powerful for production use. Here's a summary of the key improvements and additions since version 0.6.8:
+
+### 🚀 Major Features
+
+- **Prebuilt Docker Image**: Production-ready Docker image available for immediate deployment
+- **AWS S3 Integration**: Direct support for fetching and embedding files from S3 buckets
+- **SearchR1 Agent**: Advanced agent framework for interweaving retrieved results to improve context
+
+### 🐛 Bug Fixes & Stability
+
+- **Fixed Parameter Mismatching in Rerank**: Resolved issues with reranking function parameters
+- **Fixed Qwen3Embed Concurrency Panic**: Addressed panic issues when using Qwen3 embeddings concurrently
+- **Fixed File Extension Handling**: Improved error handling for files without extensions
+- **Enhanced Error Handling**: Better error messages and recovery mechanisms
+
+
+---
+
+## 🐳 Prebuilt Docker Image
+
+We're excited to announce that a prebuilt Docker image is now available! You can now pull the image and spin up the server without building from source. This makes deployment significantly easier and faster.
+
+### Quick Start with Docker
+
+#### Pull the Prebuilt Image
+
+```bash
+docker pull starlightsearch/embedanything-server:latest
+```
+
+#### Run the Container
+
+```bash
+docker run -p 8080:8080 starlightsearch/embedanything-server:latest
+```
+
+The server will start on `http://0.0.0.0:8080`.
+
+### Building from Source
+
+If you prefer to build the Docker image yourself, you can use the provided Dockerfile:
+
+```bash
+docker build -f server.Dockerfile -t embedanything-server .
+docker run -p 8080:8080 embedanything-server
+```
+
+### Server Features
+
+The Actix server provides an OpenAI-compatible API for generating embeddings. We chose Actix Server for:
+
+1. **Blazing fast**: Consistently ranks among the fastest web frameworks in benchmarks like TechEmpower
+2. **Asynchronous by default**: Built on Rust's async/await, enabling efficient I/O-bound workloads
+3. **Lightweight & modular**: Minimal core with extensible middleware, plugins, and integrations
+4. **Type-safe**: Strong type guarantees ensure fewer runtime surprises
+5. **Production-ready**: Stable, mature, and already used in industries like fintech, IoT, and SaaS platforms
+
+For benchmarks between Python and Rust servers, check out this blog: https://www.jonvet.com/blog/benchmarking-python-rust-web-servers
+
+### API Usage
+
+#### Create Embeddings
+
+**Endpoint:** `POST /v1/embeddings`
+
+**Request:**
+```json
+{
+  "model": "sentence-transformers/all-MiniLM-L12-v2",
+  "input": ["The quick brown fox jumps over the lazy dog"]
+}
+```
+
+**Response:**
+```json
+{
+  "object": "list",
+  "data": [
+    {
+      "object": "embedding",
+      "index": 0,
+      "embedding": [0.0023064255, -0.009327292, ...]
+    }
+  ],
+  "model": "sentence-transformers/all-MiniLM-L12-v2",
+  "usage": {
+    "prompt_tokens": 9,
+    "total_tokens": 9
+  }
+}
+```
+
+#### Health Check
+
+**Endpoint:** `GET /health_check`
+
+Returns a 200 OK status if the server is running.
+
+#### Example Usage with curl
+
+```bash
+# Create embeddings
+curl -X POST http://localhost:8080/v1/embeddings \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "sentence-transformers/all-MiniLM-L12-v2",
+    "input": ["Hello world", "How are you?"]
+  }'
+
+# Health check
+curl http://localhost:8080/health_check
+```
+
+#### Example Usage with Python
+
+```python
+import requests
+
+# Create embeddings
+response = requests.post(
+    "http://localhost:8080/v1/embeddings",
+    json={
+        "model": "sentence-transformers/all-MiniLM-L12-v2",
+        "input": ["The quick brown fox jumps over the lazy dog"]
+    }
+)
+
+if response.status_code == 200:
+    data = response.json()
+    print(f"Generated {len(data['data'])} embeddings")
+    print(f"First embedding dimension: {len(data['data'][0]['embedding'])}")
+else:
+    print(f"Error: {response.json()}")
+```
+
+### Error Handling
+
+The API returns OpenAI-compatible error responses:
+
+```json
+{
+  "error": {
+    "message": "Error description",
+    "type": "error_type",
+    "code": "error_code"
+  }
+}
+```
+
+For more details, see the [Actix Server Guide](/docs/guides/actix_server.md).
+
+## 🔍 SearchR1 Agent Integration
+
+We've included the SearchR1 agent, which is a powerful method of interweaving retrieved results to improve context. This agent enables more sophisticated reasoning by dynamically integrating search results into the generation process.
+
+### How SearchR1 Works
+
+SearchR1 uses a unique approach where:
+- The model conducts reasoning inside `<think>` tags
+- When knowledge gaps are identified, it calls a search engine via `<search> query </search>` tags
+- Search results are returned between `<information>` and `</information>` tags
+- The agent can search multiple times, iteratively refining its understanding
+- Once sufficient information is gathered, it provides the answer inside `<answer>` tags
+
+This interweaving of retrieved results with reasoning creates a more contextually aware and accurate response generation process. The agent actively identifies knowledge gaps and explores different perspectives of a topic before providing a final answer.
+
+### Example Usage
+
+The SearchR1 agent is available in our examples. Check out `examples/SearchAgent/` for complete implementation examples showing how to integrate SearchR1 with EmbedAnything's retrieval capabilities using LanceDB.
+
+## ☁️ Direct AWS S3 Bucket Integration
+
+We've added direct integration with AWS S3 buckets, allowing you to fetch and embed files directly from your S3 storage without manual downloads.
+
+### Features
+
+- Fetch files directly from S3 buckets
+- Support for explicit credentials or environment variables
+- Seamless integration with EmbedAnything's embedding pipeline
+- Save files locally or work with them in memory
+
+### Usage
+
+#### Using Explicit Credentials
+
+```python
+from embed_anything import S3Client, EmbeddingModel, WhichModel, TextEmbedConfig
+
+# Create S3Client with credentials
+s3_client = S3Client(
+    access_key_id="your-access-key-id",
+    secret_access_key="your-secret-access-key",
+    region="us-east-1"
+)
+
+# Fetch a file from S3
+file = s3_client.get_file_from_s3(
+    bucket_name="your-bucket-name", 
+    key="path/to/your/file.txt"
+).save_file()
+
+# Embed the file
+embedder = EmbeddingModel.from_pretrained_hf(
+    model_id="jinaai/jina-embeddings-v2-small-en"
+)
+embeddings = embedder.embed_file(
+    file, 
+    config=TextEmbedConfig(
+        chunk_size=1000, 
+        batch_size=32, 
+        splitting_strategy="sentence"
+    )
+)
+```
+
+#### Using Environment Variables
+
+```python
+from embed_anything import S3Client
+
+# Create S3Client from environment variables
+# Reads from: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION
+s3_client = S3Client.from_env()
+
+# Fetch and use files as above
+file = s3_client.get_file_from_s3(
+    bucket_name="your-bucket-name", 
+    key="path/to/your/file.pdf"
+).save_file()
+```
+
+### S3Client Methods
+
+- `get_file_from_s3(bucket_name, key)`: Fetches a file from S3 and returns an `S3File` object
+- `S3File.save_file(file_path)`: Saves the file to the local filesystem (optional path parameter)
+- `S3File.bytes`: Access file contents as bytes
+- `S3File.key`: Get the S3 key/path
+
+For a complete example, see `examples/s3_example.py`.
+
+---
+
+We're excited about these improvements and look forward to seeing how you use them in your projects! For questions or feedback, please open an issue on our [GitHub repository](https://github.com/StarlightSearch/EmbedAnything).