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docs: Updating integration docs for Fireworks Embeddings (#25247)

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Providers:
* fireworks

See related issue:
* https://github.com/langchain-ai/langchain/issues/24856

Features:

```json
[
   {
      "provider": "fireworks",
      "js":  true,
      "local": false,
     "serializable": false,
   }



]


```

---------

Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com>
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Eugene Yurtsev 2024-08-13 20:04:18 -04:00 committed by GitHub
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263
docs/docs/integrations/text_embedding/fireworks.ipynb
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@ -1,19 +1,88 @@
{
"cells": [
{
"cell_type": "raw",
"id": "afaf8039",
"metadata": {},
"source": [
"---\n",
"sidebar_label: Fireworks\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "b14a24db",
"id": "9a3d6f34",
"metadata": {},
"source": [
"# FireworksEmbeddings\n",
"\n",
"This notebook explains how to use Fireworks Embeddings, which is included in the langchain_fireworks package, to embed texts in langchain. We use the default nomic-ai v1.5 model in this example."
"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://api.python.langchain.com/en/latest/embeddings/langchain_fireworks.embeddings.FireworksEmbeddings.html).\n",
"\n",
"## Overview\n",
"\n",
"### Integration details\n",
"\n",
"import { ItemTable } from \"@theme/FeatureTables\";\n",
"\n",
"<ItemTable category=\"text_embedding\" item=\"Fireworks\" />\n",
"\n",
"## Setup\n",
"\n",
"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",
"\n",
"### Credentials\n",
"\n",
"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:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "36521c2a",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"if not os.getenv(\"FIREWORKS_API_KEY\"):\n",
" os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Enter your Fireworks API key: \")"
]
},
{
"cell_type": "markdown",
"id": "c84fb993",
"metadata": {},
"source": [
"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:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "39a4953b",
"metadata": {},
"outputs": [],
"source": [
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
]
},
{
"cell_type": "markdown",
"id": "d9664366",
"metadata": {},
"source": [
"### Installation\n",
"\n",
"The LangChain Fireworks integration lives in the `langchain-fireworks` package:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0ab948fc",
"id": "64853226",
"metadata": {},
"outputs": [],
"source": [
@ -22,83 +91,163 @@
},
{
"cell_type": "markdown",
"id": "67c637ca",
"id": "45dd1724",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "5709b030",
"metadata": {},
"outputs": [],
"source": [
"from langchain_fireworks import FireworksEmbeddings"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3d81e58c",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"## Instantiation\n",
"\n",
"if \"FIREWORKS_API_KEY\" not in os.environ:\n",
" os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Fireworks API Key:\")"
]
},
{
"cell_type": "markdown",
"id": "4a2a098d",
"metadata": {},
"source": [
"# Using the Embedding Model\n",
"With `FireworksEmbeddings`, you can directly use the default model 'nomic-ai/nomic-embed-text-v1.5', or set a different one if available."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "584b9af5",
"metadata": {},
"outputs": [],
"source": [
"embedding = FireworksEmbeddings(model=\"nomic-ai/nomic-embed-text-v1.5\")"
"Now we can instantiate our model object and generate chat completions:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "be18b873",
"id": "9ea7a09b",
"metadata": {},
"outputs": [],
"source": [
"from langchain_fireworks import FireworksEmbeddings\n",
"\n",
"embeddings = FireworksEmbeddings(\n",
" model=\"nomic-ai/nomic-embed-text-v1.5\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "77d271b6",
"metadata": {},
"source": [
"## Indexing and Retrieval\n",
"\n",
"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 under the [working with external knowledge tutorials](/docs/tutorials/#working-with-external-knowledge).\n",
"\n",
"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`."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d817716b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'LangChain is the framework for building context-aware reasoning applications'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a vector store with a sample text\n",
"from langchain_core.vectorstores import InMemoryVectorStore\n",
"\n",
"text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
"\n",
"vectorstore = InMemoryVectorStore.from_texts(\n",
" [text],\n",
" embedding=embeddings,\n",
")\n",
"\n",
"# Use the vectorstore as a retriever\n",
"retriever = vectorstore.as_retriever()\n",
"\n",
"# Retrieve the most similar text\n",
"retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
"\n",
"# show the retrieved document's content\n",
"retrieved_documents[0].page_content"
]
},
{
"cell_type": "markdown",
"id": "e02b9855",
"metadata": {},
"source": [
"## Direct Usage\n",
"\n",
"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",
"\n",
"You can directly call these methods to get embeddings for your own use cases.\n",
"\n",
"### Embed single texts\n",
"\n",
"You can embed single texts or documents with `embed_query`:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0d2befcd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.01367950439453125, 0.0103607177734375, -0.157958984375, -0.003070831298828125, 0.05926513671875]\n",
"[0.0369873046875, 0.00545501708984375, -0.179931640625, -0.018707275390625, 0.0552978515625]\n"
"[0.01666259765625, 0.011688232421875, -0.1181640625, -0.10205078125, 0.05438232421875, -0.0890502929\n"
]
}
],
"source": [
"res_query = embedding.embed_query(\"The test information\")\n",
"res_document = embedding.embed_documents([\"test1\", \"another test\"])\n",
"print(res_query[:5])\n",
"print(res_document[1][:5])"
"single_vector = embeddings.embed_query(text)\n",
"print(str(single_vector)[:100]) # Show the first 100 characters of the vector"
]
},
{
"cell_type": "markdown",
"id": "1b5a7d03",
"metadata": {},
"source": [
"### Embed multiple texts\n",
"\n",
"You can embed multiple texts with `embed_documents`:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "2f4d6e97",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.016632080078125, 0.01165008544921875, -0.1181640625, -0.10186767578125, 0.05438232421875, -0.0890\n",
"[-0.02667236328125, 0.036651611328125, -0.1630859375, -0.0904541015625, -0.022430419921875, -0.09545\n"
]
}
],
"source": [
"text2 = (\n",
" \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
")\n",
"two_vectors = embeddings.embed_documents([text, text2])\n",
"for vector in two_vectors:\n",
" print(str(vector)[:100]) # Show the first 100 characters of the vector"
]
},
{
"cell_type": "markdown",
"id": "3fba556a-b53d-431c-b0c6-ffb1e2fa5a6e",
"metadata": {},
"source": [
"## API Reference\n",
"\n",
"For detailed documentation of all `FireworksEmbeddings` features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_fireworks.embeddings.FireworksEmbeddings.html)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "poetry-venv-2",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "poetry-venv-2"
"name": "python3"
},
"language_info": {
"codemirror_mode": {
@ -110,7 +259,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.11.4"
}
},
"nbformat": 4,