migrate to langchain-aws

packages/jupyter-ai-magics/jupyter_ai_magics/__init__.py

@@ -3,7 +3,6 @@
 # expose embedding model providers on the package root
 from .embedding_providers import (
     BaseEmbeddingsProvider,
-    BedrockEmbeddingsProvider,
     GPT4AllEmbeddingsProvider,
     HfHubEmbeddingsProvider,
     OllamaEmbeddingsProvider,
@@ -20,13 +19,10 @@
 from .providers import (
     AI21Provider,
     BaseProvider,
-    BedrockChatProvider,
-    BedrockProvider,
     GPT4AllProvider,
     HfHubProvider,
     OllamaProvider,
     QianfanProvider,
-    SmEndpointProvider,
     TogetherAIProvider,
 )
 

packages/jupyter-ai-magics/jupyter_ai_magics/embedding_providers.py

@@ -2,14 +2,12 @@
 
 from jupyter_ai_magics.providers import (
     AuthStrategy,
-    AwsAuthStrategy,
     EnvAuthStrategy,
     Field,
     MultiEnvAuthStrategy,
 )
 from langchain.pydantic_v1 import BaseModel, Extra
 from langchain_community.embeddings import (
-    BedrockEmbeddings,
     GPT4AllEmbeddings,
     HuggingFaceHubEmbeddings,
     OllamaEmbeddings,
@@ -93,16 +91,6 @@ class HfHubEmbeddingsProvider(BaseEmbeddingsProvider, HuggingFaceHubEmbeddings):
     registry = True
 
 
-# See model ID list here: https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html
-class BedrockEmbeddingsProvider(BaseEmbeddingsProvider, BedrockEmbeddings):
-    id = "bedrock"
-    name = "Bedrock"
-    models = ["amazon.titan-embed-text-v1", "amazon.titan-embed-text-v2:0"]
-    model_id_key = "model_id"
-    pypi_package_deps = ["boto3"]
-    auth_strategy = AwsAuthStrategy()
-
-
 class GPT4AllEmbeddingsProvider(BaseEmbeddingsProvider, GPT4AllEmbeddings):
     def __init__(self, **kwargs):
         from gpt4all import GPT4All

packages/jupyter-ai-magics/jupyter_ai_magics/partner_providers/aws.py

@@ -0,0 +1,167 @@
+from typing import Any, Coroutine, Dict
+import copy
+import json
+from jsonpath_ng import parse
+
+from langchain_aws import Bedrock, ChatBedrock, SagemakerEndpoint, BedrockEmbeddings
+from langchain_aws.llms.sagemaker_endpoint import LLMContentHandler
+from langchain_core.outputs import LLMResult
+
+from ..providers import BaseProvider, AwsAuthStrategy, TextField, MultilineTextField
+from ..embedding_providers import BaseEmbeddingsProvider
+
+# See model ID list here: https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html
+class BedrockProvider(BaseProvider, Bedrock):
+    id = "bedrock"
+    name = "Amazon Bedrock"
+    models = [
+        "amazon.titan-text-express-v1",
+        "amazon.titan-text-lite-v1",
+        "ai21.j2-ultra-v1",
+        "ai21.j2-mid-v1",
+        "cohere.command-light-text-v14",
+        "cohere.command-text-v14",
+        "cohere.command-r-v1:0",
+        "cohere.command-r-plus-v1:0",
+        "meta.llama2-13b-chat-v1",
+        "meta.llama2-70b-chat-v1",
+        "meta.llama3-8b-instruct-v1:0",
+        "meta.llama3-70b-instruct-v1:0",
+        "meta.llama3-1-8b-instruct-v1:0",
+        "meta.llama3-1-70b-instruct-v1:0",
+        "mistral.mistral-7b-instruct-v0:2",
+        "mistral.mixtral-8x7b-instruct-v0:1",
+        "mistral.mistral-large-2402-v1:0",
+    ]
+    model_id_key = "model_id"
+    pypi_package_deps = ["langchain-aws"]
+    auth_strategy = AwsAuthStrategy()
+    fields = [
+        TextField(
+            key="credentials_profile_name",
+            label="AWS profile (optional)",
+            format="text",
+        ),
+        TextField(key="region_name", label="Region name (optional)", format="text"),
+    ]
+
+    async def _acall(self, *args, **kwargs) -> Coroutine[Any, Any, str]:
+        return await self._call_in_executor(*args, **kwargs)
+
+
+# See model ID list here: https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html
+class BedrockChatProvider(BaseProvider, ChatBedrock):
+    id = "bedrock-chat"
+    name = "Amazon Bedrock Chat"
+    models = [
+        "anthropic.claude-v2",
+        "anthropic.claude-v2:1",
+        "anthropic.claude-instant-v1",
+        "anthropic.claude-3-sonnet-20240229-v1:0",
+        "anthropic.claude-3-haiku-20240307-v1:0",
+        "anthropic.claude-3-opus-20240229-v1:0",
+        "anthropic.claude-3-5-sonnet-20240620-v1:0",
+    ]
+    model_id_key = "model_id"
+    pypi_package_deps = ["langchain-aws"]
+    auth_strategy = AwsAuthStrategy()
+    fields = [
+        TextField(
+            key="credentials_profile_name",
+            label="AWS profile (optional)",
+            format="text",
+        ),
+        TextField(key="region_name", label="Region name (optional)", format="text"),
+    ]
+
+    async def _acall(self, *args, **kwargs) -> Coroutine[Any, Any, str]:
+        return await self._call_in_executor(*args, **kwargs)
+
+    async def _agenerate(self, *args, **kwargs) -> Coroutine[Any, Any, LLMResult]:
+        return await self._generate_in_executor(*args, **kwargs)
+
+    @property
+    def allows_concurrency(self):
+        return not "anthropic" in self.model_id
+
+
+# See model ID list here: https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html
+class BedrockEmbeddingsProvider(BaseEmbeddingsProvider, BedrockEmbeddings):
+    id = "bedrock"
+    name = "Bedrock"
+    models = ["amazon.titan-embed-text-v1", "amazon.titan-embed-text-v2:0"]
+    model_id_key = "model_id"
+    pypi_package_deps = ["langchain-aws"]
+    auth_strategy = AwsAuthStrategy()
+
+
+class JsonContentHandler(LLMContentHandler):
+    content_type = "application/json"
+    accepts = "application/json"
+
+    def __init__(self, request_schema, response_path):
+        self.request_schema = json.loads(request_schema)
+        self.response_path = response_path
+        self.response_parser = parse(response_path)
+
+    def replace_values(self, old_val, new_val, d: Dict[str, Any]):
+        """Replaces values of a dictionary recursively."""
+        for key, val in d.items():
+            if val == old_val:
+                d[key] = new_val
+            if isinstance(val, dict):
+                self.replace_values(old_val, new_val, val)
+
+        return d
+
+    def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
+        request_obj = copy.deepcopy(self.request_schema)
+        self.replace_values("<prompt>", prompt, request_obj)
+        request = json.dumps(request_obj).encode("utf-8")
+        return request
+
+    def transform_output(self, output: bytes) -> str:
+        response_json = json.loads(output.read().decode("utf-8"))
+        matches = self.response_parser.find(response_json)
+        return matches[0].value
+
+
+class SmEndpointProvider(BaseProvider, SagemakerEndpoint):
+    id = "sagemaker-endpoint"
+    name = "SageMaker endpoint"
+    models = ["*"]
+    model_id_key = "endpoint_name"
+    model_id_label = "Endpoint name"
+    # This all needs to be on one line of markdown, for use in a table
+    help = (
+        "Specify an endpoint name as the model ID. "
+        "In addition, you must specify a region name, request schema, and response path. "
+        "For more information, see the documentation about [SageMaker endpoints deployment](https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints-deployment.html) "
+        "and about [using magic commands with SageMaker endpoints](https://jupyter-ai.readthedocs.io/en/latest/users/index.html#using-magic-commands-with-sagemaker-endpoints)."
+    )
+
+    pypi_package_deps = ["langchain-aws"]
+    auth_strategy = AwsAuthStrategy()
+    registry = True
+    fields = [
+        TextField(key="region_name", label="Region name (required)", format="text"),
+        MultilineTextField(
+            key="request_schema", label="Request schema (required)", format="json"
+        ),
+        TextField(
+            key="response_path", label="Response path (required)", format="jsonpath"
+        ),
+    ]
+
+    def __init__(self, *args, **kwargs):
+        request_schema = kwargs.pop("request_schema")
+        response_path = kwargs.pop("response_path")
+        content_handler = JsonContentHandler(
+            request_schema=request_schema, response_path=response_path
+        )
+
+        super().__init__(*args, **kwargs, content_handler=content_handler)
+
+    async def _acall(self, *args, **kwargs) -> Coroutine[Any, Any, str]:
+        return await self._call_in_executor(*args, **kwargs)
+