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LLMRails Embedding Integration This PR provides integration with LLMRails. Implemented here are: langchain/embeddings/llm_rails.py docs/extras/integrations/text_embedding/llm_rails.ipynb Hi @hwchase17 after adding our vectorstore integration to langchain with confirmation of you and @baskaryan, now we want to add our embedding integration --------- Co-authored-by: Anar Aliyev <aaliyev@mgmt.cloudnet.services> Co-authored-by: Bagatur <baskaryan@gmail.com>
134 lines
2.9 KiB
Plaintext
134 lines
2.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "278b6c63",
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"metadata": {},
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"source": [
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"# LLMRails\n",
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"\n",
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"Let's load the LLMRails Embeddings class.\n",
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"\n",
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"To use LLMRails embedding you need to pass api key by argument or set it in environment with `LLM_RAILS_API_KEY` key.\n",
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"To gey API Key you need to sign up in https://console.llmrails.com/signup and then go to https://console.llmrails.com/api-keys and copy key from there after creating one key in platform."
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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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"id": "0be1af71",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import LLMRailsEmbeddings"
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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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"id": "2c66e5da",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = LLMRailsEmbeddings(model='embedding-english-v1') # or embedding-multi-v1"
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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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"id": "01370375",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"This is a test document.\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "a42e4035",
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"metadata": {},
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"source": [
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"To generate embeddings, you can either query an invidivual text, or you can query a list of texts."
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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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"id": "91bc875d-829b-4c3d-8e6f-fc2dda30a3bd",
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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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"[-0.09996652603149414,\n",
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" 0.015568195842206478,\n",
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" 0.17670190334320068,\n",
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" 0.16521021723747253,\n",
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" 0.21193109452724457]"
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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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"query_result = embeddings.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": "code",
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"execution_count": 6,
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"id": "a4b0d49e-0c73-44b6-aed5-5b426564e085",
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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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"[-0.04242777079343796,\n",
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" 0.016536075621843338,\n",
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" 0.10052520781755447,\n",
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" 0.18272875249385834,\n",
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" 0.2079043835401535]"
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]
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},
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"execution_count": 6,
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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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"doc_result = embeddings.embed_documents([text])\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": "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": "3.11.5"
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},
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"vscode": {
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"interpreter": {
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"hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
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}
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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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