Embedding Models
Embeddings models are used to convert text into numerical vectors. These vectors can be used for various tasks such as similarity search, clustering, and classification.
This component generates embeddings using Azure OpenAI models.
| Name | Type | Description |
|---|
| Azure Endpoint | String | Your Azure endpoint, including the resource. Example: https://example-resource.azure.openai.com/ |
| Deployment Name | String | The name of the deployment |
| API Version | String | The API version to use, options include various dates |
| API Key | String | The API key to access the Azure OpenAI service |
| Name | Type | Description |
|---|
| embeddings | Embeddings | An instance for generating embeddings using Azure OpenAI |
This component generates embeddings using NVIDIA models.
| Name | Type | Description |
|---|
| model | String | The NVIDIA model to use for embeddings (e.g., nvidia/nv-embed-v1) |
| base_url | String | Base URL for the NVIDIA API (default: https://integrate.api.nvidia.com/v1) |
| nvidia_api_key | SecretString | API key for authenticating with NVIDIA's service |
| temperature | Float | Model temperature for embedding generation (default: 0.1) |
| Name | Type | Description |
|---|
| embeddings | Embeddings | NVIDIAEmbeddings instance for generating embeddings |
This component generates embeddings using Ollama models.
| Name | Type | Description |
|---|
| Ollama Model | String | Name of the Ollama model to use (default: llama2) |
| Ollama Base URL | String | Base URL of the Ollama API (default: http://localhost:11434) |
| Model Temperature | Float | Temperature parameter for the model. Adjusts the randomness in the generated embeddings |
| Name | Type | Description |
|---|
| embeddings | Embeddings | An instance for generating embeddings using Ollama |
This component is used to load embedding models from OpenAI.
| Name | Type | Description |
|---|
| OpenAI API Key | String | The API key to use for accessing the OpenAI API |
| Default Headers | Dict | Default headers for the HTTP requests |
| Default Query | NestedDict | Default query parameters for the HTTP requests |
| Allowed Special | List | Special tokens allowed for processing (default: []) |
| Disallowed Special | List | Special tokens disallowed for processing (default: ["all"]) |
| Chunk Size | Integer | Chunk size for processing (default: 1000) |
| Client | Any | HTTP client for making requests |
| Deployment | String | Deployment name for the model (default: text-embedding-3-small) |
| Embedding Context Length | Integer | Length of embedding context (default: 8191) |
| Max Retries | Integer | Maximum number of retries for failed requests (default: 6) |
| Model | String | Name of the model to use (default: text-embedding-3-small) |
| Model Kwargs | NestedDict | Additional keyword arguments for the model |
| OpenAI API Base | String | Base URL of the OpenAI API |
| OpenAI API Type | String | Type of the OpenAI API |
| OpenAI API Version | String | Version of the OpenAI API |
| OpenAI Organization | String | Organization associated with the API key |
| OpenAI Proxy | String | Proxy server for the requests |
| Request Timeout | Float | Timeout for the HTTP requests |
| Show Progress Bar | Boolean | Whether to show a progress bar for processing (default: False) |
| Skip Empty | Boolean | Whether to skip empty inputs (default: False) |
| TikToken Enable | Boolean | Whether to enable TikToken (default: True) |
| TikToken Model Name | String | Name of the TikToken model |
| Name | Type | Description |
|---|
| embeddings | Embeddings | An instance for generating embeddings using OpenAI |
This component is a wrapper around Google Vertex AI Embeddings API.
| Name | Type | Description |
|---|
| credentials | Credentials | The default custom credentials to use |
| location | String | The default location to use when making API calls (default: us-central1) |
| max_output_tokens | Integer | Token limit determines the maximum amount of text output from one prompt (default: 128) |
| model_name | String | The name of the Vertex AI large language model (default: text-bison) |
| project | String | The default GCP project to use when making Vertex API calls |
| request_parallelism | Integer | The amount of parallelism allowed for requests issued to VertexAI models (default: 5) |
| temperature | Float | Tunes the degree of randomness in text generations. Should be a non-negative value (default: 0) |
| top_k | Integer | How the model selects tokens for output, the next token is selected from the top k tokens (default: 40) |
| top_p | Float | Tokens are selected from the most probable to least until the sum of their probabilities exceeds the top p value (default: 0.95) |
| tuned_model_name | String | The name of a tuned model. If provided, model_name is ignored |
| verbose | Boolean | This parameter controls the level of detail in the output. When set to True, it prints internal states of the chain to help debug (default: False) |
| Name | Type | Description |
|---|
| embeddings | Embeddings | An instance for generating embeddings using VertexAI |
This component computes selected forms of similarity between two embedding vectors.
| Name | Type | Description |
|---|
| embedding_vectors | Embedding Vectors | A list containing exactly two data objects with embedding vectors to compare. |
| similarity_metric | Similarity Metric | Select the similarity metric to use. Options: "Cosine Similarity", "Euclidean Distance", "Manhattan Distance". |
| Name | Type | Description |
|---|
| similarity_data | Similarity Data | Data object containing the computed similarity score and additional information. |
Text Embedder
This component generates embeddings for a given message using a specified embedding model.
| Name | Type | Description |
|---|
| embedding_model | Embedding Model | The embedding model to use for generating embeddings. |
| message | Message | The message for which to generate embeddings. |
| Name | Type | Description |
|---|
| embeddings | Embedding Data | Data object containing the original text and its embedding vector. |
Previous Vector Stores
