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deployment/helm/milvus/charts/tei/README.md

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+# Text Embeddings Inference (TEI) Integration Guide
+
+This document describes how to use Text Embeddings Inference (TEI) service with Milvus Helm Chart, and how to integrate TEI with Milvus. TEI is an open-source project developed by Hugging Face, available at [https://github.com/huggingface/text-embeddings-inference](https://github.com/huggingface/text-embeddings-inference).
+
+## Overview
+
+Text Embeddings Inference (TEI) is a high-performance text embedding model inference service that converts text into vector representations. Milvus is a vector database that can store and retrieve these vectors. By combining the two, you can build powerful semantic search and retrieval systems.
+
+## Deployment Methods
+
+This guide provides two ways to use TEI:
+1. Deploy TEI service directly through the Milvus Helm Chart
+2. Use external TEI service with Milvus integration
+
+## Deploy TEI through Milvus Helm Chart
+
+### Basic Configuration
+
+```yaml
+modelId: "BAAI/bge-large-en-v1.5"  # Specify the model to use
+```
+
+This is the simplest configuration, just specify `enabled: true` and the desired `modelId`.
+
+### Complete Configuration Options
+
+```yaml
+modelId: "BAAI/bge-large-en-v1.5"    # Model ID
+extraArgs: []                        # Additional command line arguments for TEI, such as "--max-batch-tokens=16384", "--max-client-batch-size=32", "--max-concurrent-requests=128", etc.
+replicaCount: 1                      # Number of TEI replicas
+image:
+  repository: ghcr.io/huggingface/text-embeddings-inference  # Image repository
+  tag: cpu-1.6                       # Image tag (CPU version)
+  pullPolicy: IfNotPresent           # Image pull policy
+service:
+  type: ClusterIP                    # Service type
+  port: 8080                         # Service port
+  annotations: {}                    # Service annotations
+  labels: {}                         # Service labels
+resources:                           # Resource configuration
+  requests:
+    cpu: "4"                         # CPU request
+    memory: "8Gi"                    # Memory request
+  limits:
+    cpu: "8"                         # CPU limit
+    memory: "16Gi"                   # Memory limit
+persistence:                         # Persistence storage configuration
+  enabled: true                      # Enable persistence storage
+  mountPath: "/data"                 # Mount path
+  annotations: {}                    # Storage annotations
+  persistentVolumeClaim:             # PVC configuration
+    existingClaim: ""                # Use existing PVC
+    storageClass:                    # Storage class
+    accessModes: ReadWriteOnce       # Access modes
+    size: 50Gi                       # Storage size
+    subPath: ""                      # Sub path
+nodeSelector: {}                     # Node selector
+affinity: {}                         # Affinity configuration
+tolerations: []                      # Tolerations
+topologySpreadConstraints: []        # Topology spread constraints
+extraEnv: []                         # Additional environment variables
+```
+
+### Using GPU Acceleration
+
+If you have GPU resources, you can use the GPU version of the TEI image to accelerate inference:
+
+```yaml
+enabled: true
+modelId: "BAAI/bge-large-en-v1.5"
+image:
+  repository: ghcr.io/huggingface/text-embeddings-inference
+  tag: 1.6  # GPU version
+resources:
+  limits:
+    nvidia.com/gpu: 1  # Allocate 1 GPU
+```
+
+
+## Frequently Asked Questions
+
+### How to determine the embedding dimension of a model?
+
+Different models have different embedding dimensions. Here are the dimensions of some commonly used models:
+- BAAI/bge-large-en-v1.5: 1024
+- BAAI/bge-base-en-v1.5: 768
+- nomic-ai/nomic-embed-text-v1: 768
+- sentence-transformers/all-mpnet-base-v2: 768
+
+You can find this information in the model's documentation or get it through the TEI service's API.
+
+### How to test if the TEI service is working properly?
+
+After deploying the TEI service, you can use the following commands to test if the service is working properly:
+
+```bash
+# Get the TEI service endpoint
+export TEI_SERVICE=$(kubectl get svc -l component=text-embeddings-inference -o jsonpath='{.items[0].metadata.name}')
+
+# Test the embedding functionality
+kubectl run -it --rm curl --image=curlimages/curl -- curl -X POST "http://${TEI_SERVICE}:8080/embed" \
+  -H "Content-Type: application/json" \
+  -d '{"inputs":"This is a test text"}'
+```
+
+### How to use TEI-generated embeddings in Milvus?
+
+In Milvus, you can use TEI-generated embeddings for the following operations:
+
+1. When creating a collection, specify the vector dimension to match the TEI model output dimension
+2. Before inserting data, use the TEI service to convert text to vectors
+3. When searching, similarly use the TEI service to convert query text to vectors
+
+## Using Milvus Text Embedding Function
+
+Milvus provides a text embedding function feature that allows you to generate vector embeddings directly within Milvus. You can configure Milvus to use TEI as the backend for this function.
+
+### Using the Text Embedding Function in Milvus
+
+1. Specify the embedding function when creating a collection:
+
+```python
+from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
+
+# Connect to Milvus
+connections.connect(host="localhost", port="19530")
+
+# Define collection schema
+fields = [
+    FieldSchema(name="id", dtype=DataType.INT64, is_primary=True),
+    FieldSchema(name="text", dtype=DataType.VARCHAR, max_length=1000),
+    FieldSchema(name="vector", dtype=DataType.FLOAT_VECTOR, dim=768)  # Dimension should match model output
+]
+schema = CollectionSchema(fields=fields, description="Text collection with embedding function")
+
+# Create collection and specify embedding function
+collection = Collection(
+    name="text_collection",
+    schema=schema,
+    embedding_field="text",  # Specify the field to embed
+    vector_field="vector",   # Specify the field to store embedding vectors
+    embedding_config={
+        "provider": "tei",
+        "model_id": "BAAI/bge-large-en-v1.5",
+        "endpoint": "http://tei-service:8080"
+    }
+)
+```
+
+2. Automatically generate embeddings when inserting data:
+
+```python
+# Insert data, Milvus will automatically call the TEI service to generate embedding vectors
+collection.insert([
+    {"id": 1, "text": "This is a sample document about artificial intelligence."},
+    {"id": 2, "text": "Vector databases are designed to handle embeddings efficiently."}
+])
+```
+
+3. Automatically generate query embeddings when searching:
+
+```python
+# Search directly using text, Milvus will automatically call the TEI service to generate query vectors
+results = collection.search(
+    query_texts=["Tell me about AI technology"],
+    embedding_field="text",
+    limit=3
+)
+```