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0d2cea747c
This can only be reviewed by [hiding whitespaces](https://github.com/langchain-ai/langchain/pull/30302/files?diff=unified&w=1). The motivation behind this PR is to get my hands on the docs and make the LangSmith teasing short and clear. Right now I don't know how to do it, but this could be an include in the future. --------- Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
266 lines
8.1 KiB
Plaintext
266 lines
8.1 KiB
Plaintext
{
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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: Cohere\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": "9a3d6f34",
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"metadata": {},
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"source": [
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"# CohereEmbeddings\n",
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"\n",
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"This will help you get started with Cohere embedding models using LangChain. For detailed documentation on `CohereEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/cohere/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html).\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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"import { ItemTable } from \"@theme/FeatureTables\";\n",
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"\n",
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"<ItemTable category=\"text_embedding\" item=\"Cohere\" />\n",
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"\n",
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"## Setup\n",
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"\n",
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"To access Cohere embedding models you'll need to create a/an Cohere account, get an API key, and install the `langchain-cohere` integration package.\n",
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"\n",
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"### Credentials\n",
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"\n",
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"\n",
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"Head to [cohere.com](https://cohere.com) to sign up to Cohere and generate an API key. Once you’ve done this set the COHERE_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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"execution_count": 8,
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"id": "36521c2a",
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"metadata": {},
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"outputs": [],
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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(\"COHERE_API_KEY\"):\n",
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" os.environ[\"COHERE_API_KEY\"] = getpass.getpass(\"Enter your Cohere 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": "c84fb993",
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"metadata": {},
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"source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
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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": "39a4953b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
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"# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith 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": "d9664366",
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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 Cohere integration lives in the `langchain-cohere` package:"
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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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"id": "64853226",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU langchain-cohere"
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]
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},
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{
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"cell_type": "markdown",
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"id": "45dd1724",
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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:"
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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": 10,
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"id": "9ea7a09b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_cohere import CohereEmbeddings\n",
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"\n",
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"embeddings = CohereEmbeddings(\n",
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" model=\"embed-english-v3.0\",\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": "77d271b6",
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"metadata": {},
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"source": [
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"## Indexing and Retrieval\n",
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"\n",
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"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",
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"\n",
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"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`."
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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": 11,
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"id": "d817716b",
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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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"'LangChain is the framework for building context-aware reasoning applications'"
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]
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},
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"execution_count": 11,
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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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"# Create a vector store with a sample text\n",
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"from langchain_core.vectorstores import InMemoryVectorStore\n",
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"\n",
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"text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
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"\n",
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"vectorstore = InMemoryVectorStore.from_texts(\n",
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" [text],\n",
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" embedding=embeddings,\n",
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")\n",
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"\n",
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"# Use the vectorstore as a retriever\n",
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"retriever = vectorstore.as_retriever()\n",
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"\n",
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"# Retrieve the most similar text\n",
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"retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
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"\n",
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"# show the retrieved document's content\n",
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"retrieved_documents[0].page_content"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e02b9855",
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"metadata": {},
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"source": [
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"## Direct Usage\n",
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"\n",
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"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",
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"\n",
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"You can directly call these methods to get embeddings for your own use cases.\n",
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"\n",
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"### Embed single texts\n",
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"\n",
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"You can embed single texts or documents with `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": 12,
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"id": "0d2befcd",
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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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"[-0.022979736, -0.030212402, -0.08886719, -0.08569336, 0.007030487, -0.0010671616, -0.033813477, 0.0\n"
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]
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}
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],
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"source": [
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"single_vector = embeddings.embed_query(text)\n",
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"print(str(single_vector)[:100]) # Show the first 100 characters of the vector"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1b5a7d03",
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"metadata": {},
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"source": [
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"### Embed multiple texts\n",
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"\n",
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"You can embed multiple texts with `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": 13,
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"id": "2f4d6e97",
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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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"[-0.028869629, -0.030410767, -0.099121094, -0.07116699, -0.012748718, -0.0059432983, -0.04360962, 0.\n",
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"[-0.047332764, -0.049957275, -0.07458496, -0.034332275, -0.057922363, -0.0112838745, -0.06994629, 0.\n"
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]
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}
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],
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"source": [
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"text2 = (\n",
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" \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
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")\n",
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"two_vectors = embeddings.embed_documents([text, text2])\n",
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"for vector in two_vectors:\n",
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" print(str(vector)[:100]) # Show the first 100 characters of the vector"
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]
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},
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{
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"cell_type": "markdown",
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"id": "98785c12",
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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 on `CohereEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/cohere/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html).\n"
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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": "3.11.4"
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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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