diff --git a/libs/ai-endpoints/docs/chat/nvidia_ai_endpoints.ipynb b/libs/ai-endpoints/docs/chat/nvidia_ai_endpoints.ipynb
index eb4cfdbc..33032175 100644
--- a/libs/ai-endpoints/docs/chat/nvidia_ai_endpoints.ipynb
+++ b/libs/ai-endpoints/docs/chat/nvidia_ai_endpoints.ipynb
@@ -41,7 +41,9 @@
"id": "e13eb331",
"metadata": {},
"outputs": [],
- "source": ["%pip install --upgrade --quiet langchain-nvidia-ai-endpoints"]
+ "source": [
+ "%pip install --upgrade --quiet langchain-nvidia-ai-endpoints"
+ ]
},
{
"cell_type": "markdown",
@@ -69,7 +71,18 @@
"id": "686c4d2f",
"metadata": {},
"outputs": [],
- "source": ["import getpass\nimport os\n\n# del os.environ['NVIDIA_API_KEY'] ## delete key and reset\nif os.environ.get(\"NVIDIA_API_KEY\", \"\").startswith(\"nvapi-\"):\n print(\"Valid NVIDIA_API_KEY already in environment. Delete to reset\")\nelse:\n nvapi_key = getpass.getpass(\"NVAPI Key (starts with nvapi-): \")\n assert nvapi_key.startswith(\"nvapi-\"), f\"{nvapi_key[:5]}... is not a valid key\"\n os.environ[\"NVIDIA_API_KEY\"] = nvapi_key"]
+ "source": [
+ "import getpass\n",
+ "import os\n",
+ "\n",
+ "# del os.environ['NVIDIA_API_KEY'] ## delete key and reset\n",
+ "if os.environ.get(\"NVIDIA_API_KEY\", \"\").startswith(\"nvapi-\"):\n",
+ " print(\"Valid NVIDIA_API_KEY already in environment. Delete to reset\")\n",
+ "else:\n",
+ " nvapi_key = getpass.getpass(\"NVAPI Key (starts with nvapi-): \")\n",
+ " assert nvapi_key.startswith(\"nvapi-\"), f\"{nvapi_key[:5]}... is not a valid key\"\n",
+ " os.environ[\"NVIDIA_API_KEY\"] = nvapi_key"
+ ]
},
{
"cell_type": "markdown",
@@ -91,7 +104,14 @@
"outputId": "e9c4cc72-8db6-414b-d8e9-95de93fc5db4"
},
"outputs": [],
- "source": ["## Core LC Chat Interface\nfrom langchain_nvidia_ai_endpoints import ChatNVIDIA\n\nllm = ChatNVIDIA(model=\"mistralai/mixtral-8x7b-instruct-v0.1\")\nresult = llm.invoke(\"Write a ballad about LangChain.\")\nprint(result.content)"]
+ "source": [
+ "## Core LC Chat Interface\n",
+ "from langchain_nvidia_ai_endpoints import ChatNVIDIA\n",
+ "\n",
+ "llm = ChatNVIDIA(model=\"mistralai/mixtral-8x7b-instruct-v0.1\")\n",
+ "result = llm.invoke(\"Write a ballad about LangChain.\")\n",
+ "print(result.content)"
+ ]
},
{
"cell_type": "markdown",
@@ -110,7 +130,12 @@
"id": "49838930",
"metadata": {},
"outputs": [],
- "source": ["from langchain_nvidia_ai_endpoints import ChatNVIDIA\n\n# connect to an embedding NIM running at localhost:8000, specifying a specific model\nllm = ChatNVIDIA(base_url=\"http://localhost:8000/v1\", model=\"meta/llama3-8b-instruct\")"]
+ "source": [
+ "from langchain_nvidia_ai_endpoints import ChatNVIDIA\n",
+ "\n",
+ "# connect to an embedding NIM running at localhost:8000, specifying a specific model\n",
+ "llm = ChatNVIDIA(base_url=\"http://localhost:8000/v1\", model=\"meta/llama3-8b-instruct\")"
+ ]
},
{
"cell_type": "markdown",
@@ -128,7 +153,11 @@
"id": "01fa5095-be72-47b0-8247-e9fac799435d",
"metadata": {},
"outputs": [],
- "source": ["print(llm.batch([\"What's 2*3?\", \"What's 2*6?\"]))\n# Or via the async API\n# await llm.abatch([\"What's 2*3?\", \"What's 2*6?\"])"]
+ "source": [
+ "print(llm.batch([\"What's 2*3?\", \"What's 2*6?\"]))\n",
+ "# Or via the async API\n",
+ "# await llm.abatch([\"What's 2*3?\", \"What's 2*6?\"])"
+ ]
},
{
"cell_type": "code",
@@ -136,7 +165,11 @@
"id": "75189ac6-e13f-414f-9064-075c77d6e754",
"metadata": {},
"outputs": [],
- "source": ["for chunk in llm.stream(\"How far can a seagull fly in one day?\"):\n # Show the token separations\n print(chunk.content, end=\"|\")"]
+ "source": [
+ "for chunk in llm.stream(\"How far can a seagull fly in one day?\"):\n",
+ " # Show the token separations\n",
+ " print(chunk.content, end=\"|\")"
+ ]
},
{
"cell_type": "code",
@@ -144,7 +177,12 @@
"id": "8a9a4122-7a10-40c0-a979-82a769ce7f6a",
"metadata": {},
"outputs": [],
- "source": ["async for chunk in llm.astream(\n \"How long does it take for monarch butterflies to migrate?\"\n):\n print(chunk.content, end=\"|\")"]
+ "source": [
+ "async for chunk in llm.astream(\n",
+ " \"How long does it take for monarch butterflies to migrate?\"\n",
+ "):\n",
+ " print(chunk.content, end=\"|\")"
+ ]
},
{
"cell_type": "markdown",
@@ -166,7 +204,10 @@
"id": "5b8a312d-38e9-4528-843e-59451bdadbac",
"metadata": {},
"outputs": [],
- "source": ["ChatNVIDIA.get_available_models()\n# llm.get_available_models()"]
+ "source": [
+ "ChatNVIDIA.get_available_models()\n",
+ "# llm.get_available_models()"
+ ]
},
{
"cell_type": "markdown",
@@ -206,7 +247,19 @@
"id": "f5f7aee8-e90c-4d5a-ac97-0dd3d45c3f4c",
"metadata": {},
"outputs": [],
- "source": ["from langchain_core.output_parsers import StrOutputParser\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_nvidia_ai_endpoints import ChatNVIDIA\n\nprompt = ChatPromptTemplate.from_messages(\n [(\"system\", \"You are a helpful AI assistant named Fred.\"), (\"user\", \"{input}\")]\n)\nchain = prompt | ChatNVIDIA(model=\"meta/llama3-8b-instruct\") | StrOutputParser()\n\nfor txt in chain.stream({\"input\": \"What's your name?\"}):\n print(txt, end=\"\")"]
+ "source": [
+ "from langchain_core.output_parsers import StrOutputParser\n",
+ "from langchain_core.prompts import ChatPromptTemplate\n",
+ "from langchain_nvidia_ai_endpoints import ChatNVIDIA\n",
+ "\n",
+ "prompt = ChatPromptTemplate.from_messages(\n",
+ " [(\"system\", \"You are a helpful AI assistant named Fred.\"), (\"user\", \"{input}\")]\n",
+ ")\n",
+ "chain = prompt | ChatNVIDIA(model=\"meta/llama3-8b-instruct\") | StrOutputParser()\n",
+ "\n",
+ "for txt in chain.stream({\"input\": \"What's your name?\"}):\n",
+ " print(txt, end=\"\")"
+ ]
},
{
"cell_type": "markdown",
@@ -224,7 +277,21 @@
"id": "49aa569b-5f33-47b3-9edc-df58313eb038",
"metadata": {},
"outputs": [],
- "source": ["prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"You are an expert coding AI. Respond only in valid python; no narration whatsoever.\",\n ),\n (\"user\", \"{input}\"),\n ]\n)\nchain = prompt | ChatNVIDIA(model=\"meta/codellama-70b\") | StrOutputParser()\n\nfor txt in chain.stream({\"input\": \"How do I solve this fizz buzz problem?\"}):\n print(txt, end=\"\")"]
+ "source": [
+ "prompt = ChatPromptTemplate.from_messages(\n",
+ " [\n",
+ " (\n",
+ " \"system\",\n",
+ " \"You are an expert coding AI. Respond only in valid python; no narration whatsoever.\",\n",
+ " ),\n",
+ " (\"user\", \"{input}\"),\n",
+ " ]\n",
+ ")\n",
+ "chain = prompt | ChatNVIDIA(model=\"meta/codellama-70b\") | StrOutputParser()\n",
+ "\n",
+ "for txt in chain.stream({\"input\": \"How do I solve this fizz buzz problem?\"}):\n",
+ " print(txt, end=\"\")"
+ ]
},
{
"cell_type": "markdown",
@@ -244,7 +311,15 @@
"id": "26625437-1695-440f-b792-b85e6add9a90",
"metadata": {},
"outputs": [],
- "source": ["import IPython\nimport requests\n\nimage_url = \"https://www.nvidia.com/content/dam/en-zz/Solutions/research/ai-playground/nvidia-picasso-3c33-p@2x.jpg\" ## Large Image\nimage_content = requests.get(image_url).content\n\nIPython.display.Image(image_content)"]
+ "source": [
+ "import IPython\n",
+ "import requests\n",
+ "\n",
+ "image_url = \"https://www.nvidia.com/content/dam/en-zz/Solutions/research/ai-playground/nvidia-picasso-3c33-p@2x.jpg\" ## Large Image\n",
+ "image_content = requests.get(image_url).content\n",
+ "\n",
+ "IPython.display.Image(image_content)"
+ ]
},
{
"cell_type": "code",
@@ -252,7 +327,11 @@
"id": "dfbbe57c-27a5-4cbb-b967-19c4e7d29fd0",
"metadata": {},
"outputs": [],
- "source": ["from langchain_nvidia_ai_endpoints import ChatNVIDIA\n\nllm = ChatNVIDIA(model=\"nvidia/neva-22b\")"]
+ "source": [
+ "from langchain_nvidia_ai_endpoints import ChatNVIDIA\n",
+ "\n",
+ "llm = ChatNVIDIA(model=\"nvidia/neva-22b\")"
+ ]
},
{
"cell_type": "markdown",
@@ -268,7 +347,20 @@
"id": "432ea2a2-4d39-43f8-a236-041294171f14",
"metadata": {},
"outputs": [],
- "source": ["from langchain_core.messages import HumanMessage\n\nllm.invoke(\n [\n HumanMessage(\n content=[\n {\"type\": \"text\", \"text\": \"Describe this image:\"},\n {\"type\": \"image_url\", \"image_url\": {\"url\": image_url}},\n ]\n )\n ]\n)"]
+ "source": [
+ "from langchain_core.messages import HumanMessage\n",
+ "\n",
+ "llm.invoke(\n",
+ " [\n",
+ " HumanMessage(\n",
+ " content=[\n",
+ " {\"type\": \"text\", \"text\": \"Describe this image:\"},\n",
+ " {\"type\": \"image_url\", \"image_url\": {\"url\": image_url}},\n",
+ " ]\n",
+ " )\n",
+ " ]\n",
+ ")"
+ ]
},
{
"cell_type": "markdown",
@@ -366,7 +458,15 @@
"id": "c58f1dd0",
"metadata": {},
"outputs": [],
- "source": ["import IPython\nimport requests\n\nimage_url = \"https://picsum.photos/seed/kitten/300/200\"\nimage_content = requests.get(image_url).content\n\nIPython.display.Image(image_content)"]
+ "source": [
+ "import IPython\n",
+ "import requests\n",
+ "\n",
+ "image_url = \"https://picsum.photos/seed/kitten/300/200\"\n",
+ "image_content = requests.get(image_url).content\n",
+ "\n",
+ "IPython.display.Image(image_content)"
+ ]
},
{
"cell_type": "code",
@@ -374,7 +474,28 @@
"id": "8c721629-42eb-4006-bf68-0296f7925ebc",
"metadata": {},
"outputs": [],
- "source": ["import base64\n\nfrom langchain_core.messages import HumanMessage\n\n## Works for simpler images. For larger images, see actual implementation\nb64_string = base64.b64encode(image_content).decode(\"utf-8\")\n\nllm.invoke(\n [\n HumanMessage(\n content=[\n {\"type\": \"text\", \"text\": \"Describe this image:\"},\n {\n \"type\": \"image_url\",\n \"image_url\": {\"url\": f\"data:image/png;base64,{b64_string}\"},\n },\n ]\n )\n ]\n)"]
+ "source": [
+ "import base64\n",
+ "\n",
+ "from langchain_core.messages import HumanMessage\n",
+ "\n",
+ "## Works for simpler images. For larger images, see actual implementation\n",
+ "b64_string = base64.b64encode(image_content).decode(\"utf-8\")\n",
+ "\n",
+ "llm.invoke(\n",
+ " [\n",
+ " HumanMessage(\n",
+ " content=[\n",
+ " {\"type\": \"text\", \"text\": \"Describe this image:\"},\n",
+ " {\n",
+ " \"type\": \"image_url\",\n",
+ " \"image_url\": {\"url\": f\"data:image/png;base64,{b64_string}\"},\n",
+ " },\n",
+ " ]\n",
+ " )\n",
+ " ]\n",
+ ")"
+ ]
},
{
"cell_type": "markdown",
@@ -392,7 +513,10 @@
"id": "00c06a9a-497b-4192-a842-b075e27401aa",
"metadata": {},
"outputs": [],
- "source": ["base64_with_mime_type = f\"data:image/png;base64,{b64_string}\"\nllm.invoke(f'What\\'s in this image?\\n![](\"{base64_with_mime_type}\")
')"]
+ "source": [
+ "base64_with_mime_type = f\"data:image/png;base64,{b64_string}\"\n",
+ "llm.invoke(f'What\\'s in this image?\\n')"
+ ]
},
{
"cell_type": "markdown",
@@ -420,7 +544,9 @@
"id": "082ccb21-91e1-4e71-a9ba-4bff1e89f105",
"metadata": {},
"outputs": [],
- "source": ["%pip install --upgrade --quiet langchain"]
+ "source": [
+ "%pip install --upgrade --quiet langchain"
+ ]
},
{
"cell_type": "code",
@@ -430,7 +556,41 @@
"id": "fd2c6bc1"
},
"outputs": [],
- "source": ["from langchain_core.chat_history import InMemoryChatMessageHistory\nfrom langchain_core.runnables.history import RunnableWithMessageHistory\n\n# store is a dictionary that maps session IDs to their corresponding chat histories.\nstore = {} # memory is maintained outside the chain\n\n\n# A function that returns the chat history for a given session ID.\ndef get_session_history(session_id: str) -> InMemoryChatMessageHistory:\n if session_id not in store:\n store[session_id] = InMemoryChatMessageHistory()\n return store[session_id]\n\n\nchat = ChatNVIDIA(\n model=\"mistralai/mixtral-8x22b-instruct-v0.1\",\n temperature=0.1,\n max_tokens=100,\n top_p=1.0,\n)\n\n# Define a RunnableConfig object, with a `configurable` key. session_id determines thread\nconfig = {\"configurable\": {\"session_id\": \"1\"}}\n\nconversation = RunnableWithMessageHistory(\n chat,\n get_session_history,\n)\n\nconversation.invoke(\n \"Hi I'm Srijan Dubey.\", # input or query\n config=config,\n)"]
+ "source": [
+ "from langchain_core.chat_history import InMemoryChatMessageHistory\n",
+ "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
+ "\n",
+ "# store is a dictionary that maps session IDs to their corresponding chat histories.\n",
+ "store = {} # memory is maintained outside the chain\n",
+ "\n",
+ "\n",
+ "# A function that returns the chat history for a given session ID.\n",
+ "def get_session_history(session_id: str) -> InMemoryChatMessageHistory:\n",
+ " if session_id not in store:\n",
+ " store[session_id] = InMemoryChatMessageHistory()\n",
+ " return store[session_id]\n",
+ "\n",
+ "\n",
+ "chat = ChatNVIDIA(\n",
+ " model=\"mistralai/mixtral-8x22b-instruct-v0.1\",\n",
+ " temperature=0.1,\n",
+ " max_tokens=100,\n",
+ " top_p=1.0,\n",
+ ")\n",
+ "\n",
+ "# Define a RunnableConfig object, with a `configurable` key. session_id determines thread\n",
+ "config = {\"configurable\": {\"session_id\": \"1\"}}\n",
+ "\n",
+ "conversation = RunnableWithMessageHistory(\n",
+ " chat,\n",
+ " get_session_history,\n",
+ ")\n",
+ "\n",
+ "conversation.invoke(\n",
+ " \"Hi I'm Srijan Dubey.\", # input or query\n",
+ " config=config,\n",
+ ")"
+ ]
},
{
"cell_type": "code",
@@ -445,7 +605,12 @@
"outputId": "79acc89d-a820-4f2c-bac2-afe99da95580"
},
"outputs": [],
- "source": ["conversation.invoke(\n \"I'm doing well! Just having a conversation with an AI.\",\n config=config,\n)"]
+ "source": [
+ "conversation.invoke(\n",
+ " \"I'm doing well! Just having a conversation with an AI.\",\n",
+ " config=config,\n",
+ ")"
+ ]
},
{
"cell_type": "code",
@@ -460,7 +625,12 @@
"outputId": "a1714513-a8fd-4d14-f974-233e39d5c4f5"
},
"outputs": [],
- "source": ["conversation.invoke(\n \"Tell me about yourself.\",\n config=config,\n)"]
+ "source": [
+ "conversation.invoke(\n",
+ " \"Tell me about yourself.\",\n",
+ " config=config,\n",
+ ")"
+ ]
},
{
"cell_type": "markdown",
@@ -488,7 +658,10 @@
"id": "e36c8911",
"metadata": {},
"outputs": [],
- "source": ["tool_models = [model for model in ChatNVIDIA.get_available_models() if model.supports_tools]\ntool_models"]
+ "source": [
+ "tool_models = [model for model in ChatNVIDIA.get_available_models() if model.supports_tools]\n",
+ "tool_models"
+ ]
},
{
"cell_type": "markdown",
@@ -504,7 +677,21 @@
"id": "bd54f174",
"metadata": {},
"outputs": [],
- "source": ["from pydantic import Field\nfrom langchain_core.tools import tool\n\n@tool\ndef get_current_weather(\n location: str = Field(..., description=\"The location to get the weather for.\")\n):\n \"\"\"Get the current weather for a location.\"\"\"\n ...\n\nllm = ChatNVIDIA(model=tool_models[0].id).bind_tools(tools=[get_current_weather])\nresponse = llm.invoke(\"What is the weather in Boston?\")\nresponse.tool_calls"]
+ "source": [
+ "from pydantic import Field\n",
+ "from langchain_core.tools import tool\n",
+ "\n",
+ "@tool\n",
+ "def get_current_weather(\n",
+ " location: str = Field(..., description=\"The location to get the weather for.\")\n",
+ "):\n",
+ " \"\"\"Get the current weather for a location.\"\"\"\n",
+ " ...\n",
+ "\n",
+ "llm = ChatNVIDIA(model=tool_models[0].id).bind_tools(tools=[get_current_weather])\n",
+ "response = llm.invoke(\"What is the weather in Boston?\")\n",
+ "response.tool_calls"
+ ]
},
{
"cell_type": "markdown",
@@ -542,7 +729,11 @@
"id": "0515f558",
"metadata": {},
"outputs": [],
- "source": ["from langchain_nvidia_ai_endpoints import ChatNVIDIA\nstructured_models = [model for model in ChatNVIDIA.get_available_models() if model.supports_structured_output]\nstructured_models"]
+ "source": [
+ "from langchain_nvidia_ai_endpoints import ChatNVIDIA\n",
+ "structured_models = [model for model in ChatNVIDIA.get_available_models() if model.supports_structured_output]\n",
+ "structured_models"
+ ]
},
{
"cell_type": "markdown",
@@ -558,7 +749,17 @@
"id": "482c37e8",
"metadata": {},
"outputs": [],
- "source": ["from pydantic import BaseModel, Field\n\nclass Person(BaseModel):\n first_name: str = Field(..., description=\"The person's first name.\")\n last_name: str = Field(..., description=\"The person's last name.\")\n\nllm = ChatNVIDIA(model=structured_models[0].id).with_structured_output(Person)\nresponse = llm.invoke(\"Who is Michael Jeffrey Jordon?\")\nresponse"]
+ "source": [
+ "from pydantic import BaseModel, Field\n",
+ "\n",
+ "class Person(BaseModel):\n",
+ " first_name: str = Field(..., description=\"The person's first name.\")\n",
+ " last_name: str = Field(..., description=\"The person's last name.\")\n",
+ "\n",
+ "llm = ChatNVIDIA(model=structured_models[0].id).with_structured_output(Person)\n",
+ "response = llm.invoke(\"Who is Michael Jeffrey Jordon?\")\n",
+ "response"
+ ]
},
{
"cell_type": "markdown",
@@ -574,7 +775,24 @@
"id": "7f802912",
"metadata": {},
"outputs": [],
- "source": ["from enum import Enum\n\nclass Choices(Enum):\n A = \"A\"\n B = \"B\"\n C = \"C\"\n\nllm = ChatNVIDIA(model=structured_models[2].id).with_structured_output(Choices)\nresponse = llm.invoke(\"\"\"\n What does 1+1 equal?\n A. -100\n B. 2\n C. doorstop\n \"\"\"\n)\nresponse"]
+ "source": [
+ "from enum import Enum\n",
+ "\n",
+ "class Choices(Enum):\n",
+ " A = \"A\"\n",
+ " B = \"B\"\n",
+ " C = \"C\"\n",
+ "\n",
+ "llm = ChatNVIDIA(model=structured_models[2].id).with_structured_output(Choices)\n",
+ "response = llm.invoke(\"\"\"\n",
+ " What does 1+1 equal?\n",
+ " A. -100\n",
+ " B. 2\n",
+ " C. doorstop\n",
+ " \"\"\"\n",
+ ")\n",
+ "response"
+ ]
},
{
"cell_type": "code",
@@ -582,7 +800,19 @@
"id": "02b7ef29",
"metadata": {},
"outputs": [],
- "source": ["model = structured_models[3].id\nllm = ChatNVIDIA(model=model).with_structured_output(Choices)\nprint(model)\nresponse = llm.invoke(\"\"\"\n What does 1+1 equal?\n A. -100\n B. 2\n C. doorstop\n \"\"\"\n)\nresponse"]
+ "source": [
+ "model = structured_models[3].id\n",
+ "llm = ChatNVIDIA(model=model).with_structured_output(Choices)\n",
+ "print(model)\n",
+ "response = llm.invoke(\"\"\"\n",
+ " What does 1+1 equal?\n",
+ " A. -100\n",
+ " B. 2\n",
+ " C. doorstop\n",
+ " \"\"\"\n",
+ ")\n",
+ "response"
+ ]
}
],
"metadata": {