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community: add netmind integration (#30149)
Co-authored-by: yanrujing <rujing.yan@protagonist-ai.com> Co-authored-by: ccurme <chester.curme@gmail.com>
This commit is contained in:
小豆豆学长
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docs/docs/integrations/chat/netmind.ipynb
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docs/docs/integrations/chat/netmind.ipynb
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"cells": [
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{
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"cell_type": "raw",
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"id": "afaf8039",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: Netmind\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e49f1e0d",
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"metadata": {},
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"source": [
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"# ChatNetmind\n",
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"\n",
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"This will help you getting started with Netmind [chat models](https://www.netmind.ai/). For detailed documentation of all ChatNetmind features and configurations head to the [API reference](https://github.com/protagolabs/langchain-netmind).\n",
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"\n",
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"- See https://www.netmind.ai/ for an example.\n",
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"\n",
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"## Overview\n",
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"### Integration details\n",
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"\n",
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"| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/) | Package downloads | Package latest |\n",
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"|:---------------------------------------------------------------------------------------------| :--- |:-----:|:------------:|:--------------------------------------------------------------:| :---: | :---: |\n",
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"| [ChatNetmind](https://python.langchain.com/api_reference/) | [langchain-netmind](https://python.langchain.com/api_reference/) | ✅ | ❌ | ❌ |  |  |\n",
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"\n",
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"### Model features\n",
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"| [Tool calling](../../how_to/tool_calling.ipynb) | [Structured output](../../how_to/structured_output.ipynb) | JSON mode | [Image input](../../how_to/multimodal_inputs.ipynb) | Audio input | Video input | [Token-level streaming](../../how_to/chat_streaming.ipynb) | Native async | [Token usage](../../how_to/chat_token_usage_tracking.ipynb) | [Logprobs](../../how_to/logprobs.ipynb) |\n",
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"|:-----------------------------------------------:|:---------------------------------------------------------:|:---------:|:---------------------------------------------------:|:-----------:|:-----------:|:----------------------------------------------------------:|:------------:|:-----------------------------------------------------------:|:---------------------------------------:|\n",
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"| ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | \n",
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"\n",
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"## Setup\n",
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"\n",
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"To access Netmind models you'll need to create a/an Netmind account, get an API key, and install the `langchain-netmind` integration package.\n",
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"\n",
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"### Credentials\n",
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"\n",
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"Head to https://www.netmind.ai/ to sign up to Netmind and generate an API key. Once you've done this set the NETMIND_API_KEY environment variable:"
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]
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},
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{
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"cell_type": "code",
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"id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-20T02:00:30.732333Z",
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"start_time": "2025-03-20T02:00:28.384208Z"
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}
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},
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"if not os.getenv(\"NETMIND_API_KEY\"):\n",
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" os.environ[\"NETMIND_API_KEY\"] = getpass.getpass(\"Enter your Netmind API key: \")"
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],
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"outputs": [],
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"execution_count": 1
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},
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{
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"cell_type": "markdown",
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"id": "72ee0c4b-9764-423a-9dbf-95129e185210",
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"metadata": {},
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"source": [
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"If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
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]
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},
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{
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"cell_type": "code",
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"id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-20T02:00:33.421446Z",
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"start_time": "2025-03-20T02:00:33.419081Z"
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}
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},
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"source": [
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"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
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],
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"outputs": [],
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"execution_count": 2
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},
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{
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"cell_type": "markdown",
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"id": "0730d6a1-c893-4840-9817-5e5251676d5d",
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"metadata": {},
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"source": [
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"### Installation\n",
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"\n",
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"The LangChain Netmind integration lives in the `langchain-netmind` package:"
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]
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},
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{
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"cell_type": "code",
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"id": "652d6238-1f87-422a-b135-f5abbb8652fc",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-20T02:00:35.923300Z",
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"start_time": "2025-03-20T02:00:34.505928Z"
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}
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},
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"source": [
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"%pip install -qU langchain-netmind"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\r\n",
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"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m24.0\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m25.0.1\u001B[0m\r\n",
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"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"execution_count": 3
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},
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{
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"cell_type": "markdown",
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"id": "a38cde65-254d-4219-a441-068766c0d4b5",
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"metadata": {},
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"source": [
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"## Instantiation\n",
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"\n",
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"Now we can instantiate our model object and generate chat completions:\n"
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]
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},
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{
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"cell_type": "code",
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"id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-20T02:01:08.007764Z",
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"start_time": "2025-03-20T02:01:07.391951Z"
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}
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},
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"source": [
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"from langchain_netmind import ChatNetmind\n",
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"\n",
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"llm = ChatNetmind(\n",
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" model=\"deepseek-ai/DeepSeek-V3\",\n",
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" temperature=0,\n",
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" max_tokens=None,\n",
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" timeout=None,\n",
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" max_retries=2,\n",
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" # other params...\n",
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")"
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],
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"outputs": [],
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"execution_count": 4
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},
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{
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"cell_type": "markdown",
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"id": "2b4f3e15",
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"metadata": {},
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"source": "## Invocation\n"
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},
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{
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"cell_type": "code",
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"id": "62e0dbc3",
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"metadata": {
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"tags": [],
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"ExecuteTime": {
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"end_time": "2025-03-20T02:01:19.011273Z",
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"start_time": "2025-03-20T02:01:10.295510Z"
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}
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},
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"source": [
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"messages = [\n",
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" (\n",
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" \"system\",\n",
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" \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
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" ),\n",
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" (\"human\", \"I love programming.\"),\n",
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"]\n",
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"ai_msg = llm.invoke(messages)\n",
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"ai_msg"
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],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content=\"J'adore programmer.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 31, 'total_tokens': 44, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'deepseek-ai/DeepSeek-V3', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-ca6c2010-844d-4bf6-baac-6e248491b000-0', usage_metadata={'input_tokens': 31, 'output_tokens': 13, 'total_tokens': 44, 'input_token_details': {}, 'output_token_details': {}})"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": 5
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},
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{
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"cell_type": "code",
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"id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-20T02:01:20.240190Z",
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"start_time": "2025-03-20T02:01:20.238242Z"
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}
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},
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"source": [
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"print(ai_msg.content)"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"J'adore programmer.\n"
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]
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}
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],
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"execution_count": 6
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},
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{
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"cell_type": "markdown",
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"id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
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"metadata": {},
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"source": [
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"## Chaining\n",
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"\n",
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"We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:\n"
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]
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},
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{
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"cell_type": "code",
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"id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-20T02:01:27.456393Z",
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"start_time": "2025-03-20T02:01:23.993410Z"
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}
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},
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"source": [
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"\n",
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"prompt = ChatPromptTemplate(\n",
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" [\n",
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" (\n",
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" \"system\",\n",
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" \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
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" ),\n",
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" (\"human\", \"{input}\"),\n",
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" ]\n",
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")\n",
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"\n",
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"chain = prompt | llm\n",
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"chain.invoke(\n",
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" {\n",
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" \"input_language\": \"English\",\n",
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" \"output_language\": \"German\",\n",
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" \"input\": \"I love programming.\",\n",
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" }\n",
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")"
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],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='Ich liebe es zu programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 26, 'total_tokens': 40, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'deepseek-ai/DeepSeek-V3', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-d63adcc6-53ba-4caa-9a79-78d640b39274-0', usage_metadata={'input_tokens': 26, 'output_tokens': 14, 'total_tokens': 40, 'input_token_details': {}, 'output_token_details': {}})"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": 7
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},
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{
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"cell_type": "markdown",
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"id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
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"metadata": {},
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"source": ""
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},
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{
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"cell_type": "markdown",
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"id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
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"metadata": {},
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"source": [
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"## API reference\n",
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"\n",
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"For detailed documentation of all ChatNetmind features and configurations head to the API reference: \n",
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"* [API reference](https://python.langchain.com/api_reference/) \n",
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"* [langchain-netmind](https://github.com/protagolabs/langchain-netmind) \n",
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"* [pypi](https://pypi.org/project/langchain-netmind/)"
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]
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": "",
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"id": "30f8be8c940bfbf3"
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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73
docs/docs/integrations/providers/netmind.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Netmind\n",
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"\n",
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"[Netmind AI](https://www.netmind.ai/) Build AI Faster, Smarter, and More Affordably.\n",
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"Train, Fine-tune, Run Inference, and Scale with our Global GPU Network—Your all-in-one AI Engine.\n",
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"\n",
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||||||
|
"This example goes over how to use LangChain to interact with Netmind AI models.\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metadata": {},
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"source": [
|
||||||
|
"## Installation and Setup\n",
|
||||||
|
"\n",
|
||||||
|
"```bash\n",
|
||||||
|
"pip install langchain-netmind\n",
|
||||||
|
"```\n",
|
||||||
|
"\n",
|
||||||
|
"Get an Netmind api key and set it as an environment variable (`NETMIND_API_KEY`). \n",
|
||||||
|
"Head to https://www.netmind.ai/ to sign up to Netmind and generate an API key. \n",
|
||||||
|
"\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metadata": {},
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"source": [
|
||||||
|
"## Chat Models\n",
|
||||||
|
"\n",
|
||||||
|
"For more on Netmind chat models, visit the guide [here](/docs/integrations/chat/netmind)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metadata": {},
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"source": [
|
||||||
|
"## Embedding Model\n",
|
||||||
|
"\n",
|
||||||
|
"For more on Netmind embedding models, visit the [guide](/docs/integrations/text_embedding/netmind)\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"colab": {
|
||||||
|
"provenance": []
|
||||||
|
},
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3 (ipykernel)",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.10.11"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 1
|
||||||
|
}
|
||||||
323
docs/docs/integrations/text_embedding/netmind.ipynb
Normal file
323
docs/docs/integrations/text_embedding/netmind.ipynb
Normal file
@ -0,0 +1,323 @@
|
|||||||
|
{
|
||||||
|
"cells": [
|
||||||
|
{
|
||||||
|
"cell_type": "raw",
|
||||||
|
"id": "afaf8039",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"---\n",
|
||||||
|
"sidebar_label: Netmind\n",
|
||||||
|
"---"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "9a3d6f34",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"# NetmindEmbeddings\n",
|
||||||
|
"\n",
|
||||||
|
"This will help you get started with Netmind embedding models using LangChain. For detailed documentation on `NetmindEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/).\n",
|
||||||
|
"\n",
|
||||||
|
"## Overview\n",
|
||||||
|
"### Integration details\n",
|
||||||
|
"\n",
|
||||||
|
"| Provider | Package |\n",
|
||||||
|
"|:--------:|:-------:|\n",
|
||||||
|
"| [Netmind](/docs/integrations/providers/netmind/) | [langchain-netmind](https://python.langchain.com/api_reference/) |\n",
|
||||||
|
"\n",
|
||||||
|
"## Setup\n",
|
||||||
|
"\n",
|
||||||
|
"To access Netmind embedding models you'll need to create a/an Netmind account, get an API key, and install the `langchain-netmind` integration package.\n",
|
||||||
|
"\n",
|
||||||
|
"### Credentials\n",
|
||||||
|
"\n",
|
||||||
|
"Head to https://www.netmind.ai/ to sign up to Netmind and generate an API key. Once you've done this set the NETMIND_API_KEY environment variable:"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"id": "36521c2a",
|
||||||
|
"metadata": {
|
||||||
|
"ExecuteTime": {
|
||||||
|
"end_time": "2025-03-20T01:53:29.982962Z",
|
||||||
|
"start_time": "2025-03-20T01:53:27.764291Z"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"import getpass\n",
|
||||||
|
"import os\n",
|
||||||
|
"\n",
|
||||||
|
"if not os.getenv(\"NETMIND_API_KEY\"):\n",
|
||||||
|
" os.environ[\"NETMIND_API_KEY\"] = getpass.getpass(\"Enter your Netmind API key: \")"
|
||||||
|
],
|
||||||
|
"outputs": [],
|
||||||
|
"execution_count": 1
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "c84fb993",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"id": "39a4953b",
|
||||||
|
"metadata": {
|
||||||
|
"ExecuteTime": {
|
||||||
|
"end_time": "2025-03-20T01:53:32.143687Z",
|
||||||
|
"start_time": "2025-03-20T01:53:32.141858Z"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
|
||||||
|
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
|
||||||
|
],
|
||||||
|
"outputs": [],
|
||||||
|
"execution_count": 2
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "d9664366",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"### Installation\n",
|
||||||
|
"\n",
|
||||||
|
"The LangChain Netmind integration lives in the `langchain-netmind` package:"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"id": "64853226",
|
||||||
|
"metadata": {
|
||||||
|
"ExecuteTime": {
|
||||||
|
"end_time": "2025-03-20T01:53:38.639440Z",
|
||||||
|
"start_time": "2025-03-20T01:53:36.171640Z"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"%pip install -qU langchain-netmind"
|
||||||
|
],
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"\r\n",
|
||||||
|
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m24.0\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m25.0.1\u001B[0m\r\n",
|
||||||
|
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n",
|
||||||
|
"Note: you may need to restart the kernel to use updated packages.\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"execution_count": 3
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "45dd1724",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Instantiation\n",
|
||||||
|
"\n",
|
||||||
|
"Now we can instantiate our model object:\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"id": "9ea7a09b",
|
||||||
|
"metadata": {
|
||||||
|
"ExecuteTime": {
|
||||||
|
"end_time": "2025-03-20T01:54:31.005334Z",
|
||||||
|
"start_time": "2025-03-20T01:54:30.146876Z"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"from langchain_netmind import NetmindEmbeddings\n",
|
||||||
|
"\n",
|
||||||
|
"embeddings = NetmindEmbeddings(\n",
|
||||||
|
" model=\"nvidia/NV-Embed-v2\",\n",
|
||||||
|
")"
|
||||||
|
],
|
||||||
|
"outputs": [],
|
||||||
|
"execution_count": 4
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "77d271b6",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Indexing and Retrieval\n",
|
||||||
|
"\n",
|
||||||
|
"Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
|
||||||
|
"\n",
|
||||||
|
"Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"id": "d817716b",
|
||||||
|
"metadata": {
|
||||||
|
"ExecuteTime": {
|
||||||
|
"end_time": "2025-03-20T01:54:40.963137Z",
|
||||||
|
"start_time": "2025-03-20T01:54:34.500805Z"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"# Create a vector store with a sample text\n",
|
||||||
|
"from langchain_core.vectorstores import InMemoryVectorStore\n",
|
||||||
|
"\n",
|
||||||
|
"text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
|
||||||
|
"\n",
|
||||||
|
"vectorstore = InMemoryVectorStore.from_texts(\n",
|
||||||
|
" [text],\n",
|
||||||
|
" embedding=embeddings,\n",
|
||||||
|
")\n",
|
||||||
|
"\n",
|
||||||
|
"# Use the vectorstore as a retriever\n",
|
||||||
|
"retriever = vectorstore.as_retriever()\n",
|
||||||
|
"\n",
|
||||||
|
"# Retrieve the most similar text\n",
|
||||||
|
"retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
|
||||||
|
"\n",
|
||||||
|
"# show the retrieved document's content\n",
|
||||||
|
"retrieved_documents[0].page_content"
|
||||||
|
],
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"'LangChain is the framework for building context-aware reasoning applications'"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 5,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"execution_count": 5
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "e02b9855",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Direct Usage\n",
|
||||||
|
"\n",
|
||||||
|
"Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
|
||||||
|
"\n",
|
||||||
|
"You can directly call these methods to get embeddings for your own use cases.\n",
|
||||||
|
"\n",
|
||||||
|
"### Embed single texts\n",
|
||||||
|
"\n",
|
||||||
|
"You can embed single texts or documents with `embed_query`:"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"id": "0d2befcd",
|
||||||
|
"metadata": {
|
||||||
|
"ExecuteTime": {
|
||||||
|
"end_time": "2025-03-20T01:54:49.540750Z",
|
||||||
|
"start_time": "2025-03-20T01:54:45.196528Z"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"single_vector = embeddings.embed_query(text)\n",
|
||||||
|
"print(str(single_vector)[:100]) # Show the first 100 characters of the vector"
|
||||||
|
],
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"[-0.0051240199245512486, -0.01726294495165348, 0.011966848745942116, -0.0018107350915670395, 0.01146\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"execution_count": 6
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "1b5a7d03",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"### Embed multiple texts\n",
|
||||||
|
"\n",
|
||||||
|
"You can embed multiple texts with `embed_documents`:"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"id": "2f4d6e97",
|
||||||
|
"metadata": {
|
||||||
|
"ExecuteTime": {
|
||||||
|
"end_time": "2025-03-20T01:54:57.089847Z",
|
||||||
|
"start_time": "2025-03-20T01:54:52.468719Z"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"text2 = (\n",
|
||||||
|
" \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
|
||||||
|
")\n",
|
||||||
|
"two_vectors = embeddings.embed_documents([text, text2])\n",
|
||||||
|
"for vector in two_vectors:\n",
|
||||||
|
" print(str(vector)[:100]) # Show the first 100 characters of the vector"
|
||||||
|
],
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"[-0.0051240199245512486, -0.01726294495165348, 0.011966848745942116, -0.0018107350915670395, 0.01146\n",
|
||||||
|
"[0.022523142397403717, -0.002223758026957512, -0.008578270673751831, -0.006029821466654539, 0.008752\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"execution_count": 7
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "98785c12",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## API Reference\n",
|
||||||
|
"\n",
|
||||||
|
"For detailed documentation on `NetmindEmbeddings` features and configuration options, please refer to the: \n",
|
||||||
|
"* [API reference](https://python.langchain.com/api_reference/) \n",
|
||||||
|
"* [langchain-netmind](https://github.com/protagolabs/langchain-netmind) \n",
|
||||||
|
"* [pypi](https://pypi.org/project/langchain-netmind/)\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metadata": {},
|
||||||
|
"cell_type": "code",
|
||||||
|
"outputs": [],
|
||||||
|
"execution_count": null,
|
||||||
|
"source": "",
|
||||||
|
"id": "adb9e45c34733299"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3 (ipykernel)",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.10.5"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
@ -517,6 +517,9 @@ packages:
|
|||||||
repo: OpenGradient/og-langchain
|
repo: OpenGradient/og-langchain
|
||||||
downloads: 274
|
downloads: 274
|
||||||
downloads_updated_at: '2025-03-22T21:59:15.663971+00:00'
|
downloads_updated_at: '2025-03-22T21:59:15.663971+00:00'
|
||||||
|
- name: langchain-netmind
|
||||||
|
path: .
|
||||||
|
repo: protagolabs/langchain-netmind
|
||||||
- name: langchain-agentql
|
- name: langchain-agentql
|
||||||
path: langchain
|
path: langchain
|
||||||
repo: tinyfish-io/agentql-integrations
|
repo: tinyfish-io/agentql-integrations
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user