Konko fix dependency

docs/extras/integrations/text_embedding/awadb.ipynb

@@ -5,9 +5,11 @@
    "id": "b14a24db",
    "metadata": {},
    "source": [
-    "# AwaEmbedding\n",
+    "# AwaDB\n",
     "\n",
-    "This notebook explains how to use AwaEmbedding, which is included in [awadb](https://github.com/awa-ai/awadb), to embedding texts in langchain."
+    ">[AwaDB](https://github.com/awa-ai/awadb) is an AI Native database for the search and storage of embedding vectors used by LLM Applications.\n",
+    "\n",
+    "This notebook explains how to use `AwaEmbeddings` in LangChain."
    ]
   },
   {
@@ -101,7 +103,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/text_embedding/bedrock.ipynb

@@ -5,7 +5,9 @@
    "id": "75e378f5-55d7-44b6-8e2e-6d7b8b171ec4",
    "metadata": {},
    "source": [
-    "# Bedrock Embeddings"
+    "# Bedrock\n",
+    "\n",
+    ">[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case.\n"
    ]
   },
   {
@@ -91,7 +93,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/text_embedding/bge_huggingface.ipynb

@@ -5,26 +5,29 @@
    "id": "719619d3",
    "metadata": {},
    "source": [
-    "# BGE Hugging Face Embeddings\n",
+    "# BGE on Hugging Face\n",
     "\n",
-    "This notebook shows how to use BGE Embeddings through Hugging Face"
+    ">[BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en) are [the best open-source embedding models](https://huggingface.co/spaces/mteb/leaderboard).\n",
+    ">BGE model is created by the [Beijing Academy of Artificial Intelligence (BAAI)](https://www.baai.ac.cn/english.html). `BAAI` is a private non-profit organization engaged in AI research and development.\n",
+    "\n",
+    "This notebook shows how to use `BGE Embeddings` through `Hugging Face`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "f7a54279",
    "metadata": {
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "# !pip install sentence_transformers"
+    "#!pip install sentence_transformers"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "9e1d5b6b",
    "metadata": {},
    "outputs": [],
@@ -43,12 +46,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "e59d1a89",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "384"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "embedding = hf.embed_query(\"hi this is harrison\")"
+    "embedding = hf.embed_query(\"hi this is harrison\")\n",
+    "len(embedding)"
    ]
   },
   {
@@ -76,7 +91,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/text_embedding/google_vertex_ai_palm.ipynb

@@ -1,13 +1,14 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Google Cloud Platform Vertex AI PaLM \n",
+    "# Google Vertex AI PaLM \n",
     "\n",
-    "Note: This is seperate from the Google PaLM integration, it exposes [Vertex AI PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) on Google Cloud. \n",
+    ">[Vertex AI PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) is a service on Google Cloud exposing the embedding models. \n",
+    "\n",
+    "Note: This integration is seperate from the Google PaLM integration.\n",
     "\n",
     "By default, Google Cloud [does not use](https://cloud.google.com/vertex-ai/docs/generative-ai/data-governance#foundation_model_development) Customer Data to train its foundation models as part of Google Cloud`s AI/ML Privacy Commitment. More details about how Google processes data can also be found in [Google's Customer Data Processing Addendum (CDPA)](https://cloud.google.com/terms/data-processing-addendum).\n",
     "\n",
@@ -96,7 +97,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

docs/extras/integrations/text_embedding/huggingfacehub.ipynb

@@ -5,13 +5,23 @@
    "id": "ed47bb62",
    "metadata": {},
    "source": [
-    "# Hugging Face Hub\n",
+    "# Hugging Face\n",
     "Let's load the Hugging Face Embedding class."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
+   "id": "16b20335-da1d-46ba-aa23-fbf3e2c6fe60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install langchain sentence_transformers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "id": "861521a9",
    "metadata": {},
    "outputs": [],
@@ -21,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 3,
    "id": "ff9be586",
    "metadata": {},
    "outputs": [],
@@ -31,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 3,
    "id": "d0a98ae9",
    "metadata": {},
    "outputs": [],
@@ -41,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "id": "5d6c682b",
    "metadata": {},
    "outputs": [],
@@ -51,7 +61,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
+   "id": "b57b8ce9-ef7d-4e63-979e-aa8763d1f9a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-0.04895168915390968, -0.03986193612217903, -0.021562768146395683]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_result[:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "id": "bb5e74c0",
    "metadata": {},
    "outputs": [],
@@ -60,19 +91,71 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aaad49f8",
+   "cell_type": "markdown",
+   "id": "92019ef1-5d30-4985-b4e6-c0d98bdfe265",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## Hugging Face Inference API\n",
+    "We can also access embedding models via the Hugging Face Inference API, which does not require us to install ``sentence_transformers`` and download models locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "66f5c6ba-1446-43e1-b012-800d17cef300",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter your HF Inference API Key:\n",
+      "\n",
+      " ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import getpass\n",
+    "\n",
+    "inference_api_key = getpass.getpass(\"Enter your HF Inference API Key:\\n\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d0623c1f-cd82-4862-9bce-3655cb9b66ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-0.038338541984558105, 0.1234646737575531, -0.028642963618040085]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.embeddings import HuggingFaceInferenceAPIEmbeddings\n",
+    "\n",
+    "embeddings = HuggingFaceInferenceAPIEmbeddings(\n",
+    "    api_key=inference_api_key,\n",
+    "    model_name=\"sentence-transformers/all-MiniLM-l6-v2\"\n",
+    ")\n",
+    "\n",
+    "query_result = embeddings.embed_query(text)\n",
+    "query_result[:3]"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "poetry-venv",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv"
   },
   "language_info": {
    "codemirror_mode": {

docs/extras/integrations/text_embedding/modelscope_hub.ipynb

@@ -1,12 +1,13 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# ModelScope\n",
     "\n",
+    ">[ModelScope](https://www.modelscope.cn/home) is big repository of the models and datasets.\n",
+    "\n",
     "Let's load the ModelScope Embedding class."
    ]
   },
@@ -67,16 +68,23 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "chatgpt",
+   "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",
-   "version": "3.9.15"
-  },
-  "orig_nbformat": 4
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/extras/integrations/text_embedding/mosaicml.ipynb

@@ -1,15 +1,14 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# MosaicML embeddings\n",
+    "# MosaicML\n",
     "\n",
-    "[MosaicML](https://docs.mosaicml.com/en/latest/inference.html) offers a managed inference service. You can either use a variety of open source models, or deploy your own.\n",
+    ">[MosaicML](https://docs.mosaicml.com/en/latest/inference.html) offers a managed inference service. You can either use a variety of open source models, or deploy your own.\n",
     "\n",
-    "This example goes over how to use LangChain to interact with MosaicML Inference for text embedding."
+    "This example goes over how to use LangChain to interact with `MosaicML` Inference for text embedding."
    ]
   },
   {
@@ -94,6 +93,11 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -103,9 +107,10 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/extras/integrations/text_embedding/nlp_cloud.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# NLP Cloud\n",
     "\n",
-    "NLP Cloud is an artificial intelligence platform that allows you to use the most advanced AI engines, and even train your own engines with your own data. \n",
+    ">[NLP Cloud](https://docs.nlpcloud.com/#introduction) is an artificial intelligence platform that allows you to use the most advanced AI engines, and even train your own engines with your own data. \n",
     "\n",
     "The [embeddings](https://docs.nlpcloud.com/#embeddings) endpoint offers the following model:\n",
     "\n",
@@ -80,7 +80,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.11.2 64-bit",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -94,7 +94,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

docs/extras/integrations/text_embedding/sagemaker-endpoint.ipynb

@@ -5,11 +5,13 @@
    "id": "1f83f273",
    "metadata": {},
    "source": [
-    "# SageMaker Endpoint Embeddings\n",
+    "# SageMaker\n",
     "\n",
-    "Let's load the SageMaker Endpoints Embeddings class. The class can be used if you host, e.g. your own Hugging Face model on SageMaker.\n",
+    "Let's load the `SageMaker Endpoints Embeddings` class. The class can be used if you host, e.g. your own Hugging Face model on SageMaker.\n",
     "\n",
-    "For instructions on how to do this, please see [here](https://www.philschmid.de/custom-inference-huggingface-sagemaker). **Note**: In order to handle batched requests, you will need to adjust the return line in the `predict_fn()` function within the custom `inference.py` script:\n",
+    "For instructions on how to do this, please see [here](https://www.philschmid.de/custom-inference-huggingface-sagemaker). \n",
+    "\n",
+    "**Note**: In order to handle batched requests, you will need to adjust the return line in the `predict_fn()` function within the custom `inference.py` script:\n",
     "\n",
     "Change from\n",
     "\n",
@@ -143,7 +145,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

docs/extras/integrations/text_embedding/self-hosted.ipynb

@@ -5,8 +5,8 @@
    "id": "eec4efda",
    "metadata": {},
    "source": [
-    "# Self Hosted Embeddings\n",
-    "Let's load the SelfHostedEmbeddings, SelfHostedHuggingFaceEmbeddings, and SelfHostedHuggingFaceInstructEmbeddings classes."
+    "# Self Hosted\n",
+    "Let's load the `SelfHostedEmbeddings`, `SelfHostedHuggingFaceEmbeddings`, and `SelfHostedHuggingFaceInstructEmbeddings` classes."
    ]
   },
   {
@@ -149,9 +149,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "fc1bfd0f",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "query_result = embeddings.embed_query(text)"
@@ -182,7 +180,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

docs/extras/integrations/text_embedding/sentence_transformers.ipynb

@@ -1,16 +1,15 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "ed47bb62",
    "metadata": {},
    "source": [
-    "# Sentence Transformers Embeddings\n",
+    "# Sentence Transformers\n",
     "\n",
-    "[SentenceTransformers](https://www.sbert.net/) embeddings are called using the `HuggingFaceEmbeddings` integration. We have also added an alias for `SentenceTransformerEmbeddings` for users who are more familiar with directly using that package.\n",
+    ">[SentenceTransformers](https://www.sbert.net/) embeddings are called using the `HuggingFaceEmbeddings` integration. We have also added an alias for `SentenceTransformerEmbeddings` for users who are more familiar with directly using that package.\n",
     "\n",
-    "SentenceTransformers is a python package that can generate text and image embeddings, originating from [Sentence-BERT](https://arxiv.org/abs/1908.10084)"
+    "`SentenceTransformers` is a python package that can generate text and image embeddings, originating from [Sentence-BERT](https://arxiv.org/abs/1908.10084)"
    ]
   },
   {
@@ -109,7 +108,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

docs/extras/integrations/text_embedding/spacy_embedding.ipynb

@@ -1,21 +1,31 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Spacy Embedding\n",
+    "# SpaCy\n",
     "\n",
-    "### Loading the Spacy embedding class to generate and query embeddings"
+    ">[spaCy](https://spacy.io/) is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython.\n",
+    " \n",
+    "\n",
+    "## Installation and Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install spacy"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Import the necessary classes"
+    "Import the necessary classes"
    ]
   },
   {
@@ -28,11 +38,12 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Initialize SpacyEmbeddings.This will load the Spacy model into memory."
+    "## Example\n",
+    "\n",
+    "Initialize SpacyEmbeddings.This will load the Spacy model into memory."
    ]
   },
   {
@@ -45,11 +56,10 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Define some example texts . These could be any documents that you want to analyze - for example, news articles, social media posts, or product reviews."
+    "Define some example texts . These could be any documents that you want to analyze - for example, news articles, social media posts, or product reviews."
    ]
   },
   {
@@ -67,11 +77,10 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Generate and print embeddings for the texts . The SpacyEmbeddings class generates an embedding for each document, which is a numerical representation of the document's content. These embeddings can be used for various natural language processing tasks, such as document similarity comparison or text classification."
+    "Generate and print embeddings for the texts . The SpacyEmbeddings class generates an embedding for each document, which is a numerical representation of the document's content. These embeddings can be used for various natural language processing tasks, such as document similarity comparison or text classification."
    ]
   },
   {
@@ -86,11 +95,10 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Generate and print an embedding for a single piece of text. You can also generate an embedding for a single piece of text, such as a search query. This can be useful for tasks like information retrieval, where you want to find documents that are similar to a given query."
+    "Generate and print an embedding for a single piece of text. You can also generate an embedding for a single piece of text, such as a search query. This can be useful for tasks like information retrieval, where you want to find documents that are similar to a given query."
    ]
   },
   {
@@ -106,11 +114,24 @@
   }
  ],
  "metadata": {
-  "language_info": {
-   "name": "python"
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "orig_nbformat": 4
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }