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268 lines
7.4 KiB
Plaintext
268 lines
7.4 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: Fireworks\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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"# FireworksEmbeddings\n",
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"\n",
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"This will help you get started with Fireworks embedding models using LangChain. For detailed documentation on `FireworksEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/fireworks/embeddings/langchain_fireworks.embeddings.FireworksEmbeddings.html).\n",
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"\n",
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"## Overview\n",
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"\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=\"Fireworks\" />\n",
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"\n",
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"## Setup\n",
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"\n",
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"To access Fireworks embedding models you'll need to create a Fireworks account, get an API key, and install the `langchain-fireworks` integration package.\n",
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"\n",
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"### Credentials\n",
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"\n",
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"Head to [fireworks.ai](https://fireworks.ai/) to sign up to Fireworks and generate an API key. Once you’ve done this set the FIREWORKS_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": 1,
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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(\"FIREWORKS_API_KEY\"):\n",
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" os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Enter your Fireworks 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": [
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"If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
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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": "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 Fireworks integration lives in the `langchain-fireworks` 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-fireworks"
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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": 4,
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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_fireworks import FireworksEmbeddings\n",
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"\n",
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"embeddings = FireworksEmbeddings(\n",
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" model=\"nomic-ai/nomic-embed-text-v1.5\",\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": 5,
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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": 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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"# 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": 6,
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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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"[0.01666259765625, 0.011688232421875, -0.1181640625, -0.10205078125, 0.05438232421875, -0.0890502929\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": 7,
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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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"[0.016632080078125, 0.01165008544921875, -0.1181640625, -0.10186767578125, 0.05438232421875, -0.0890\n",
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"[-0.02667236328125, 0.036651611328125, -0.1630859375, -0.0904541015625, -0.022430419921875, -0.09545\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": "3fba556a-b53d-431c-b0c6-ffb1e2fa5a6e",
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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 of all `FireworksEmbeddings` features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/embeddings/langchain_fireworks.embeddings.FireworksEmbeddings.html)."
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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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