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ibm: Add support for Embedding Models (#20647)
--------- Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Mateusz Szewczyk
GitHub
@@ -37,3 +37,13 @@ See a [usage example](/docs/integrations/llms/ibm_watsonx).
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```python
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from langchain_ibm import WatsonxLLM
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```
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## Embedding Models
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### WatsonxEmbeddings
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See a [usage example](/docs/integrations/text_embedding/ibm_watsonx).
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```python
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from langchain_ibm import WatsonxEmbeddings
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```
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243
docs/docs/integrations/text_embedding/ibm_watsonx.ipynb
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243
docs/docs/integrations/text_embedding/ibm_watsonx.ipynb
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@@ -0,0 +1,243 @@
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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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"# IBM watsonx.ai\n",
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"\n",
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">WatsonxEmbeddings is a wrapper for IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) foundation models.\n",
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"\n",
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"This example shows how to communicate with `watsonx.ai` models using `LangChain`."
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]
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},
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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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"## Setting up\n",
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"\n",
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"Install the package `langchain-ibm`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install -qU langchain-ibm"
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]
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},
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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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"This cell defines the WML credentials required to work with watsonx Embeddings.\n",
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"\n",
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"**Action:** Provide the IBM Cloud user API key. For details, see\n",
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"[documentation](https://cloud.ibm.com/docs/account?topic=account-userapikey&interface=ui)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"from getpass import getpass\n",
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"\n",
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"watsonx_api_key = getpass()\n",
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"os.environ[\"WATSONX_APIKEY\"] = watsonx_api_key"
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]
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},
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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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"Additionaly you are able to pass additional secrets as an 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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"WATSONX_URL\"] = \"your service instance url\"\n",
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"os.environ[\"WATSONX_TOKEN\"] = \"your token for accessing the CPD cluster\"\n",
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"os.environ[\"WATSONX_PASSWORD\"] = \"your password for accessing the CPD cluster\"\n",
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"os.environ[\"WATSONX_USERNAME\"] = \"your username for accessing the CPD cluster\"\n",
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"os.environ[\"WATSONX_INSTANCE_ID\"] = \"your instance_id for accessing the CPD cluster\""
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]
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},
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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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"## Load the model\n",
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"\n",
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"You might need to adjust model `parameters` for different models."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames\n",
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"\n",
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"embed_params = {\n",
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" EmbedTextParamsMetaNames.TRUNCATE_INPUT_TOKENS: 3,\n",
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" EmbedTextParamsMetaNames.RETURN_OPTIONS: {\"input_text\": True},\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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"metadata": {},
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"source": [
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"Initialize the `WatsonxEmbeddings` class with previously set parameters.\n",
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"\n",
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"\n",
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"**Note**: \n",
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"\n",
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"- To provide context for the API call, you must add `project_id` or `space_id`. For more information see [documentation](https://www.ibm.com/docs/en/watsonx-as-a-service?topic=projects).\n",
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"- Depending on the region of your provisioned service instance, use one of the urls described [here](https://ibm.github.io/watsonx-ai-python-sdk/setup_cloud.html#authentication).\n",
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"\n",
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"In this example, we’ll use the `project_id` and Dallas url.\n",
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"\n",
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"\n",
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"You need to specify `model_id` that will be used for inferencing."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_ibm import WatsonxEmbeddings\n",
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"\n",
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"watsonx_embedding = WatsonxEmbeddings(\n",
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" model_id=\"ibm/slate-125m-english-rtrvr\",\n",
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" url=\"https://us-south.ml.cloud.ibm.com\",\n",
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" project_id=\"PASTE YOUR PROJECT_ID HERE\",\n",
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" params=embed_params,\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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"metadata": {},
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"source": [
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"Alternatively you can use Cloud Pak for Data credentials. For details, see [documentation](https://ibm.github.io/watsonx-ai-python-sdk/setup_cpd.html). "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"watsonx_embedding = WatsonxEmbeddings(\n",
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" model_id=\"ibm/slate-125m-english-rtrvr\",\n",
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" url=\"PASTE YOUR URL HERE\",\n",
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" username=\"PASTE YOUR USERNAME HERE\",\n",
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" password=\"PASTE YOUR PASSWORD HERE\",\n",
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" instance_id=\"openshift\",\n",
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" version=\"5.0\",\n",
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" project_id=\"PASTE YOUR PROJECT_ID HERE\",\n",
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" params=embed_params,\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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"metadata": {},
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"source": [
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"## Usage\n",
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"\n",
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"### Embed query"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[0.0094472, -0.024981909, -0.026013248, -0.040483925, -0.057804465]"
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]
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},
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"execution_count": 4,
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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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"source": [
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"text = \"This is a test document.\"\n",
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"\n",
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"query_result = watsonx_embedding.embed_query(text)\n",
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"query_result[:5]"
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]
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},
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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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"### Embed documents"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[0.009447193, -0.024981918, -0.026013244, -0.040483937, -0.057804447]"
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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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"source": [
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"texts = [\"This is a content of the document\", \"This is another document\"]\n",
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"\n",
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"doc_result = watsonx_embedding.embed_documents(texts)\n",
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"doc_result[0][:5]"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "langchain",
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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": "3.10.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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