Merge pull request #105 from langchain-ai/mattf/add-langchain_nvidia

allow `import langchain_nvidia`

libs/ai-endpoints/langchain_nvidia.py

@@ -0,0 +1,72 @@
+"""
+**LangChain NVIDIA AI Endpoints**
+
+This comprehensive module integrates NVIDIA's state-of-the-art NIM endpoints,
+featuring advanced models for conversational AI and semantic embeddings,
+into the LangChain framework. It provides robust classes for seamless interaction
+with AI models, particularly tailored for enriching conversational experiences
+and enhancing semantic understanding in various applications.
+
+**Features:**
+
+1. **`ChatNVIDIA`:** This class serves as the primary interface for interacting
+   with chat models. Users can effortlessly utilize advanced models like 'Nemotron'
+   to engage in rich, context-aware conversations, applicable across diverse
+   domains from customer support to interactive storytelling.
+
+2. **`NVIDIAEmbeddings`:** The class offers capabilities to generate sophisticated
+   embeddings using AI models. These embeddings are instrumental for tasks like
+   semantic analysis, text similarity assessments, and contextual understanding,
+   significantly enhancing the depth of NLP applications.
+
+3. **`NVIDIARerank`:** This class provides an interface for reranking search results
+    using AI models. Users can leverage this functionality to enhance search
+    relevance and improve user experience in information retrieval systems.
+
+4. **`NVIDIA`:** This class enables users to interact with large language models
+    through a completions, or prompting, interface. Users can generate text
+    completions, summaries, and other language model outputs using this class.
+    This class is particularly useful for code generation tasks.
+
+**Installation:**
+
+Install this module easily using pip:
+
+```python
+pip install langchain-nvidia-ai-endpoints
+```
+
+After setting up the environment, interact with NIM endpoints -
+
+## Utilizing chat models:
+
+```python
+from langchain_nvidia import ChatNVIDIA
+
+llm = ChatNVIDIA(model="nvidia/llama-3.1-nemotron-51b-instruct")
+response = llm.invoke("Tell me about the LangChain integration.")
+```
+
+## Generating semantic embeddings:
+
+Create embeddings useful in various NLP tasks:
+
+```python
+from langchain_nvidia import NVIDIAEmbeddings
+
+embedder = NVIDIAEmbeddings(model="nvidia/nv-embedqa-e5-v5")
+embedding = embedder.embed_query("Exploring AI capabilities.")
+```
+
+## Code completion using large language models:
+
+```python
+from langchain_nvidia import NVIDIA
+
+llm = NVIDIA(model="meta/codellama-70b")
+completion = llm.invoke("def hello_world():")
+```
+"""  # noqa: E501
+
+from langchain_nvidia_ai_endpoints import *  # noqa: F403
+from langchain_nvidia_ai_endpoints import __all__  # noqa: F401

libs/ai-endpoints/langchain_nvidia_ai_endpoints/__init__.py

@@ -1,4 +1,6 @@
 """
+**NOTE: You can `import langchain_nvidia` instead.**
+
 **LangChain NVIDIA AI Foundation Model Playground Integration**
 
 This comprehensive module integrates NVIDIA's state-of-the-art AI Foundation Models, featuring advanced models for conversational AI and semantic embeddings, into the LangChain framework. It provides robust classes for seamless interaction with NVIDIA's AI models, particularly tailored for enriching conversational experiences and enhancing semantic understanding in various applications.

libs/ai-endpoints/pyproject.toml

@@ -6,6 +6,10 @@ authors = []
 readme = "README.md"
 repository = "https://github.com/langchain-ai/langchain-nvidia"
 license = "MIT"
+packages = [
+  { include = "langchain_nvidia.py" },
+  { include = "langchain_nvidia_ai_endpoints" },
+]
 
 [tool.poetry.urls]
 "Source Code" = "https://github.com/langchain-ai/langchain-nvidia/tree/main/libs/ai-endpoints"

libs/ai-endpoints/tests/unit_tests/test_imports.py

@@ -1,4 +1,5 @@
-from langchain_nvidia_ai_endpoints import __all__
+from langchain_nvidia import __all__ as short_all
+from langchain_nvidia_ai_endpoints import __all__ as long_all
 
 EXPECTED_ALL = [
     "ChatNVIDIA",
@@ -11,4 +12,5 @@
 
 
 def test_all_imports() -> None:
-    assert sorted(EXPECTED_ALL) == sorted(__all__)
+    assert sorted(EXPECTED_ALL) == sorted(short_all)
+    assert sorted(EXPECTED_ALL) == sorted(long_all)