Add docs for aws bedrock embedder ag 3048 (#240)

Co-authored-by: Dirk Brand <[email protected]>

Author: kausmeows

embedder/aws_bedrock.mdx

@@ -0,0 +1,80 @@
+---
+title: AWS Bedrock Embedder
+sidebarTitle: AWS Bedrock
+---
+
+The `AwsBedrockEmbedder` class is used to embed text data into vectors using the AWS Bedrock API. By default, it uses the Cohere Embed Multilingual V3 model for generating embeddings.
+
+# Setup
+
+## Set your AWS credentials
+
+```bash
+export AWS_ACCESS_KEY_ID = xxx
+export AWS_SECRET_ACCESS_KEY = xxx
+export AWS_REGION = xxx
+```
+
+<Note>
+By default, this embedder uses the `cohere.embed-multilingual-v3` model. You must enable access to this model from the AWS Bedrock model catalog before using this embedder.
+</Note>
+
+## Run PgVector
+```bash
+docker run - d \
+    - e POSTGRES_DB = ai \
+    - e POSTGRES_USER = ai \
+    - e POSTGRES_PASSWORD = ai \
+    - e PGDATA = /var/lib/postgresql/data/pgdata \
+    - v pgvolume: / var/lib/postgresql/data \
+    - p 5532: 5432 \
+    - -name pgvector \
+    agnohq/pgvector: 16
+```
+
+# Usage
+
+```python cookbook/embedders/aws_bedrock_embedder.py
+
+# Embed sentence in database
+embeddings = AwsBedrockEmbedder().get_embedding(
+    "The quick brown fox jumps over the lazy dog."
+)
+# Print the embeddings and their dimensions
+print(f"Embeddings: {embeddings[:5]}")
+print(f"Dimensions: {len(embeddings)}")
+
+# Example usage with a PDF knowledge base
+knowledge_base = PDFUrlKnowledgeBase(
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    reader=PDFUrlReader(
+        chunk_size=2048
+    ),  # Required because Cohere model has a fixed size of 2048
+    vector_db=PgVector(
+        table_name="recipes",
+        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
+        embedder=AwsBedrockEmbedder(),
+    ),
+)
+knowledge_base.load(recreate=False)
+```
+
+# Params
+
+| Parameter | Type | Default | Description |
+| ----------------------- | ----------------------------- | ----------------------------- | ------------------------------------------------------------------------------------------ |
+| `id` | `str` | `"cohere.embed-multilingual-v3"` | The model ID to use. You need to enable this model in your AWS Bedrock model catalog. |
+| `dimensions` | `int` | `1024` | The dimensionality of the embeddings generated by the model(1024 for Cohere models). |
+| `input_type` | `str` | `"search_query"` | Prepends special tokens to differentiate types. Options: 'search_document', 'search_query', 'classification', 'clustering'. |
+| `truncate` | `Optional[str]` | `None` | How to handle inputs longer than the maximum token length. Options: 'NONE', 'START', 'END'. |
+| `embedding_types` | `Optional[List[str]]` | `None` | Types of embeddings to return . Options: 'float', 'int8', 'uint8', 'binary', 'ubinary'. |
+| `aws_region` | `Optional[str]` | `None` | The AWS region to use. If not provided, falls back to AWS_REGION env variable. |
+| `aws_access_key_id` | `Optional[str]` | `None` | The AWS access key ID. If not provided, falls back to AWS_ACCESS_KEY_ID env variable. |
+| `aws_secret_access_key` | `Optional[str]` | `None` | The AWS secret access key. If not provided, falls back to AWS_SECRET_ACCESS_KEY env variable. |
+| `session` | `Optional[Session]` | `None` | A boto3 Session object to use for authentication. |
+| `request_params` | `Optional[Dict[str, Any]]` | `None` | Additional parameters to pass to the API requests. |
+| `client_params` | `Optional[Dict[str, Any]]` | `None` | Additional parameters to pass to the boto3 client. |
+| `client` | `Optional[AwsClient]` | `None` | An instance of the AWS Bedrock client to use for making API requests. |
+
+# Developer Resources
+- View [Cookbook](https://github.com/agno-agi/agno/blob/main/cookbook/agent_concepts/knowledge/embedders/aws_bedrock_embedder.py)

mint.json

@@ -377,7 +377,8 @@
             "embedder/qdrant_fastembed",
             "embedder/sentencetransformers",
             "embedder/together",
-            "embedder/voyageai"
+            "embedder/voyageai",
+            "embedder/aws_bedrock"
           ]
         },
         {