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Community: Valyu Integration docs (#30926)
PR title: docs: add Valyu integration documentation Description: This PR adds documentation and example notebooks for the Valyu integration, including retriever and tool usage. Issue: N/A Dependencies: No new dependencies. --------- Co-authored-by: ccurme <chester.curme@gmail.com>
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‎docs/docs/integrations/providers/valyu.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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"metadata": {},
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"source": [
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"# ValyuContext\n",
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"\n",
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">[Valyu](https://www.valyu.network/) allows AI applications and agents to search the internet and proprietary data sources for relevant LLM ready information.\n",
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"\n",
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"This notebook goes over how to use Valyu in LangChain.\n",
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"\n",
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"First, get an Valyu API key and add it as an environment variable. Get $10 free credit by [signing up here](https://exchange.valyu.network/).\n",
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"\n",
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"## Setup\n",
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"\n",
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"The integration lives in the `langchain-valyu` 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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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU langchain-valyu"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In order to use the package, you will also need to set the `VALYU_API_KEY` environment variable to your Valyu API key.\n",
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"\n",
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"## Context Retriever\n",
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"\n",
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"You can use the [`ValyuContextRetriever`](https://pypi.org/project/langchain-valyu/) in a standard retrieval pipeline."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_valyu import ValyuContextRetriever\n",
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"\n",
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"valyu_api_key = \"YOUR API KEY\"\n",
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"\n",
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"# Create a new instance of the ValyuContextRetriever\n",
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"valyu_retriever = ValyuContextRetriever(valyu_api_key=valyu_api_key)\n",
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"\n",
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"# Search for a query and save the results\n",
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"docs = valyu_retriever.invoke(\"What are the benefits of renewable energy?\")\n",
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"\n",
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"# Print the results\n",
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"for doc in docs:\n",
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" print(doc.page_content)\n",
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" print(doc.metadata)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Context Search Tool\n",
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"\n",
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"You can use the `ValyuSearchTool` for advanced search queries."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_valyu import ValyuSearchTool\n",
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"\n",
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"# Initialize the ValyuSearchTool\n",
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"search_tool = ValyuSearchTool(valyu_api_key=\"YOUR API KEY\")\n",
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"\n",
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"# Perform a search query\n",
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"search_results = search_tool._run(\n",
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" query=\"What are agentic search-enhanced large reasoning models?\",\n",
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" search_type=\"all\",\n",
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" max_num_results=5,\n",
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" similarity_threshold=0.4,\n",
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" query_rewrite=False,\n",
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" max_price=20.0,\n",
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")\n",
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"\n",
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"print(\"Search Results:\", search_results)"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎docs/docs/integrations/retrievers/valyu.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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"metadata": {},
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"source": [
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"# ValyuContext\n",
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"\n",
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">[Valyu](https://www.valyu.network/) allows AI applications and agents to search the internet and proprietary data sources for relevant LLM ready information.\n",
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"\n",
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"This notebook goes over how to use Valyu context tool in LangChain.\n",
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"\n",
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"First, get an Valyu API key and add it as an environment variable. Get $10 free credit by [signing up here](https://exchange.valyu.network/).\n",
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"\n",
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"## Setup\n",
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"\n",
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"The integration lives in the `langchain-valyu` 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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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU langchain-valyu"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In order to use the package, you will also need to set the `VALYU_API_KEY` environment variable to your Valyu 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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"valyu_api_key = os.environ[\"VALYU_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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"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 retriever:\n",
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"The `ValyuContextRetriever` can be configured with several parameters:\n",
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"\n",
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"- `k: int = 5` \n",
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" The number of top results to return for each query.\n",
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"\n",
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"- `search_type: str = \"all\"` \n",
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" The type of search to perform. Options may include \"all\", \"web\", \"proprietary\", etc., depending on your use case.\n",
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"\n",
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"- `similarity_threshold: float = 0.4` \n",
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" The minimum similarity score (between 0 and 1) required for a document to be considered relevant.\n",
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"\n",
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"- `query_rewrite: bool = False` \n",
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" Whether to enable automatic rewriting of the query to improve search results.\n",
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" \n",
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"- `max_price: float = 20.0`\n",
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" The maximum price (in USD) you are willing to spend per query.\n",
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"\n",
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"- `client: Optional[Valyu] = None` \n",
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" An optional custom Valyu client instance. If not provided, a new client will be created internally.\n",
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" \n",
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"- `valyu_api_key: Optional[str] = None` \n",
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" Your Valyu API key. If not provided, the retriever will look for the `VALYU_API_KEY` environment variable.\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_valyu import ValyuContextRetriever\n",
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"\n",
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"retriever = ValyuContextRetriever(\n",
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" k=5,\n",
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" search_type=\"all\",\n",
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" similarity_threshold=0.4,\n",
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" query_rewrite=False,\n",
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" max_price=20.0,\n",
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" client=None,\n",
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" valyu_api_key=os.environ[\"VALYU_API_KEY\"],\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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"metadata": {},
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"source": [
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"## Usage"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"What are the benefits of renewable energy?\"\n",
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"docs = retriever.invoke(query)\n",
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"\n",
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"for doc in docs:\n",
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" print(doc.page_content)\n",
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" print(doc.metadata)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Use within a chain\n",
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"\n",
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"We can easily combine this retriever in to a chain."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.output_parsers import StrOutputParser\n",
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"from langchain_core.runnables import RunnablePassthrough\n",
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"prompt = ChatPromptTemplate.from_template(\n",
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" \"\"\"Answer the question based only on the context provided.\n",
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"\n",
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"Context: {context}\n",
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"\n",
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"Question: {question}\"\"\"\n",
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")\n",
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"\n",
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"llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
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"\n",
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"\n",
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"def format_docs(docs):\n",
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" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
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"\n",
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"\n",
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"chain = (\n",
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" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
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" | prompt\n",
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" | llm\n",
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" | StrOutputParser()\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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"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 Valyu Context API features and configurations head to the API reference: https://docs.valyu.network/overview"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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