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VoyageEmbeddings (#12608)
- **Description:** Integrate VoyageEmbeddings into LangChain, with tests and docs - **Issue:** N/A - **Dependencies:** N/A - **Tag maintainer:** N/A - **Twitter handle:** @Voyage_AI_ --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
Yujie Qian
GitHub
228
docs/docs/integrations/text_embedding/voyageai.ipynb
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docs/docs/integrations/text_embedding/voyageai.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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"id": "278b6c63",
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"metadata": {},
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"source": [
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"# Voyage AI\n",
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"\n",
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"Let's load the Voyage Embedding class."
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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 VoyageEmbeddings"
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]
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},
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{
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"cell_type": "markdown",
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"id": "137cfde9-b88c-409a-9394-a9e31a6bf30d",
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"metadata": {},
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"source": [
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"Voyage AI utilizes API keys to monitor usage and manage permissions. To obtain your key, create an account on our [homepage](https://www.voyageai.com). Then, create a VoyageEmbeddings model with your API key."
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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 = VoyageEmbeddings(voyage_api_key=\"[ Your Voyage 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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"id": "459dffb3-9bff-41f2-8507-642de7431b2d",
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"metadata": {},
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"source": [
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"Prepare the documents and use `embed_documents` to get their embeddings."
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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": "c85e948f-85fd-4d56-8d21-6e2f7e65cab8",
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"metadata": {},
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"outputs": [],
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"source": [
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"documents = [\n",
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" \"Caching embeddings enables the storage or temporary caching of embeddings, eliminating the necessity to recompute them each time.\",\n",
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" \"An LLMChain is a chain that composes basic LLM functionality. It consists of a PromptTemplate and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output.\",\n",
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" \"A Runnable represents a generic unit of work that can be invoked, batched, streamed, and/or transformed.\",\n",
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"]"
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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": "5a77a12d-6ac6-4ab8-b103-80ff24487019",
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"metadata": {},
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"outputs": [],
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"source": [
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"documents_embds = embeddings.embed_documents(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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"id": "2c89167c-816c-487e-8704-90908a4190bb",
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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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"[0.0562174916267395,\n",
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" 0.018221192061901093,\n",
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" 0.0025736060924828053,\n",
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" -0.009720131754875183,\n",
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" 0.04108370840549469]"
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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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"documents_embds[0][:5]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f8d796d1-4ced-44d3-81bf-282721edb6bb",
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"metadata": {},
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"source": [
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"Similarly, use `embed_query` to embed the 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": 6,
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"id": "bfb6142c",
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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"What's an LLMChain?\""
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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": 7,
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"id": "91bc875d-829b-4c3d-8e6f-fc2dda30a3bd",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_embd = embeddings.embed_query(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": 8,
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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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"[-0.0052348352037370205,\n",
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" -0.040072452276945114,\n",
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" 0.0033957737032324076,\n",
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" 0.01763271726667881,\n",
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" -0.019235141575336456]"
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]
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},
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"execution_count": 8,
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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_embd[:5]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b16ddbb2-61f0-49ec-92c3-a6f236d9517f",
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"metadata": {},
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"source": [
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"## A minimalist retrieval system"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5464cb0a-6967-4f1e-ac7c-0aab80b2795a",
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"metadata": {},
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"source": [
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"The main feature of the embeddings is that the cosine similarity between two embeddings captures the semantic relatedness of the corresponding original passages. This allows us to use the embeddings to do semantic retrieval / search."
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]
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},
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{
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"cell_type": "markdown",
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"id": "a0bd3ad2-ca68-4e75-9172-76aea28ba46e",
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"metadata": {},
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"source": [
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" We can find a few closest embeddings in the documents embeddings based on the cosine similarity, and retrieve the corresponding document using the `KNNRetriever` class from LangChain."
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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": 9,
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"id": "0a3fc579-85a9-4bd0-a944-4e32ac62e2d4",
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"metadata": {},
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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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"An LLMChain is a chain that composes basic LLM functionality. It consists of a PromptTemplate and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output.\n"
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]
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}
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],
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"source": [
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"from langchain.retrievers import KNNRetriever\n",
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"\n",
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"retriever = KNNRetriever.from_texts(documents, embeddings)\n",
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"\n",
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"# retrieve the most relevant documents\n",
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"result = retriever.get_relevant_documents(query)\n",
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"top1_retrieved_doc = result[0].page_content # return the top1 retrieved result\n",
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"\n",
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"print(top1_retrieved_doc)"
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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": "3.9.18"
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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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