ba59f077a2* inputs * agents * chains * custom-component * align-admonitions-in-custom * data-and-embeddings * experimental * helpers * memories * model_specs * outputs * prompts * retrievers * textsplitter * tools * utilities * vector-stores
116 lines
17 KiB
Text
116 lines
17 KiB
Text
# Embeddings
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## Amazon Bedrock Embeddings
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Used to load embedding models from [Amazon Bedrock](https://aws.amazon.com/bedrock/).
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| **Parameter** | **Type** | **Description** | **Default** |
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|-----------------------------|-------------------|------------------------------------------------------------------------------------------------------------------------------------|-------------|
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| `credentials_profile_name` | `str` | Name of the AWS credentials profile in ~/.aws/credentials or ~/.aws/config, which has access keys or role information. | |
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| `model_id` | `str` | ID of the model to call, e.g., `amazon.titan-embed-text-v1`. This is equivalent to the `modelId` property in the `list-foundation-models` API. | |
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| `endpoint_url` | `str` | URL to set a specific service endpoint other than the default AWS endpoint. | |
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| `region_name` | `str` | AWS region to use, e.g., `us-west-2`. Falls back to `AWS_DEFAULT_REGION` environment variable or region specified in ~/.aws/config if not provided. | |
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## Cohere Embeddings
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Used to load embedding models from [Cohere](https://cohere.com/).
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| **Parameter** | **Type** | **Description** | **Default** |
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|---------------------|-------------------|-------------------------------------------------------------------------------------------------------------------------------|-----------------------|
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| `cohere_api_key` | `str` | API key required to authenticate with the Cohere service. | |
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| `model` | `str` | Language model used for embedding text documents and performing queries. | `embed-english-v2.0` |
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| `truncate` | `bool` | Whether to truncate the input text to fit within the model's constraints. | `False` |
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## Azure OpenAI Embeddings
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Generate embeddings using Azure OpenAI models.
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| **Parameter** | **Type** | **Description** | **Default** |
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|---------------------|-------------------|-------------------------------------------------------------------------------------------------------------------------------|-----------------------|
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| `Azure Endpoint` | `str` | Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/` | |
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| `Deployment Name` | `str` | The name of the deployment. | |
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| `API Version` | `str` | The API version to use, options include various dates. | |
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| `API Key` | `str` | The API key to access the Azure OpenAI service. | |
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## Hugging Face API Embeddings
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Generate embeddings using Hugging Face Inference API models.
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| **Parameter** | **Type** | **Description** | **Default** |
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|---------------------|-------------------|-------------------------------------------------------------------------------------------------------------------------------|-----------------------|
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| `API Key` | `str` | API key for accessing the Hugging Face Inference API. | |
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| `API URL` | `str` | URL of the Hugging Face Inference API. | `http://localhost:8080` |
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| `Model Name` | `str` | Name of the model to use for embeddings. | `BAAI/bge-large-en-v1.5` |
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| `Cache Folder` | `str` | Folder path to cache Hugging Face models. | |
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| `Encode Kwargs` | `dict` | Additional arguments for the encoding process. | |
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| `Model Kwargs` | `dict` | Additional arguments for the model. | |
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| `Multi Process` | `bool` | Whether to use multiple processes. | `False` |
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## Hugging Face Embeddings
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Used to load embedding models from [HuggingFace](https://huggingface.co).
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| **Parameter** | **Type** | **Description** | **Default** |
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|---------------------|-------------------|-------------------------------------------------------------------------------------------------------------------------------|-----------------------|
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| `Cache Folder` | `str` | Folder path to cache HuggingFace models. | |
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| `Encode Kwargs` | `dict` | Additional arguments for the encoding process. | |
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| `Model Kwargs` | `dict` | Additional arguments for the model. | |
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| `Model Name` | `str` | Name of the HuggingFace model to use. | `sentence-transformers/all-mpnet-base-v2` |
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| `Multi Process` | `bool` | Whether to use multiple processes. | `False` |
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## OpenAI Embeddings
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Used to load embedding models from [OpenAI](https://openai.com/).
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| **Parameter** | **Type** | **Description** | **Default** |
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|-----------------------------|-------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|
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| `OpenAI API Key` | `str` | The API key to use for accessing the OpenAI API. | |
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| `Default Headers` | `Dict[str, str]` | Default headers for the HTTP requests. | |
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| `Default Query` | `NestedDict` | Default query parameters for the HTTP requests. | |
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| `Allowed Special` | `List[str]` | Special tokens allowed for processing. | `[]` |
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| `Disallowed Special` | `List[str]` | Special tokens disallowed for processing. | `["all"]` |
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| `Chunk Size` | `int` | Chunk size for processing. | `1000` |
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| `Client` | `Any` | HTTP client for making requests. | |
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| `Deployment` | `str` | Deployment name for the model. | `text-embedding-3-small` |
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| `Embedding Context Length` | `int` | Length of embedding context. | `8191` |
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| `Max Retries` | `int` | Maximum number of retries for failed requests. | `6` |
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| `Model` | `str` | Name of the model to use. | `text-embedding-3-small` |
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| `Model Kwargs` | `NestedDict` | Additional keyword arguments for the model. | |
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| `OpenAI API Base` | `str` | Base URL of the OpenAI API. | |
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| `OpenAI API Type` | `str` | Type of the OpenAI API. | |
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| `OpenAI API Version` | `str` | Version of the OpenAI API. | |
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| `OpenAI Organization` | `str` | Organization associated with the API key. | |
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| `OpenAI Proxy` | `str` | Proxy server for the requests. | |
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| `Request Timeout` | `float` | Timeout for the HTTP requests. | |
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| `Show Progress Bar` | `bool` | Whether to show a progress bar for processing. | `False` |
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| `Skip Empty` | `bool` | Whether to skip empty inputs. | `False` |
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| `TikToken Enable` | `bool` | Whether to enable TikToken. | `True` |
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| `TikToken Model Name` | `str` | Name of the TikToken model. | |
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## Ollama Embeddings
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Generate embeddings using Ollama models.
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| **Parameter** | **Type** | **Description** | **Default** |
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|---------------------|-------------------|--------------------------------------------------------------------------------------------------------------------|---------------------------|
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| `Ollama Model` | `str` | Name of the Ollama model to use. | `llama2` |
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| `Ollama Base URL` | `str` | Base URL of the Ollama API. | `http://localhost:11434` |
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| `Model Temperature` | `float` | Temperature parameter for the model. Adjusts the randomness in the generated embeddings. | |
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## VertexAI Embeddings
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Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) [Embeddings API](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings).
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| **Parameter** | **Type** | **Description** | **Default** |
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|-----------------------------|-------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|
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| `credentials` | `Credentials` | The default custom credentials to use. | |
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| `location` | `str` | The default location to use when making API calls. | `us-central1`|
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| `max_output_tokens` | `int` | Token limit determines the maximum amount of text output from one prompt. | `128` |
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| `model_name` | `str` | The name of the Vertex AI large language model. | `text-bison`|
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| `project` | `str` | The default GCP project to use when making Vertex API calls. | |
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| `request_parallelism` | `int` | The amount of parallelism allowed for requests issued to VertexAI models. | `5` |
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| `temperature` | `float` | Tunes the degree of randomness in text generations. Should be a non-negative value. | `0` |
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| `top_k` | `int` | How the model selects tokens for output, the next token is selected from the top `k` tokens. | `40` |
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| `top_p` | `float` | Tokens are selected from the most probable to least until the sum of their probabilities exceeds the top `p` value. | `0.95` |
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| `tuned_model_name` | `str` | The name of a tuned model. If provided, `model_name` is ignored. | |
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| `verbose` | `bool` | This parameter controls the level of detail in the output. When set to `True`, it prints internal states of the chain to help debug. | `False` |
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