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docs: Use voyage-law-2 in the examples (#20784)
Thank you for contributing to LangChain! - [x] **PR title**: "package: description" - Where "package" is whichever of langchain, community, core, experimental, etc. is being modified. Use "docs: ..." for purely docs changes, "templates: ..." for template changes, "infra: ..." for CI changes. - Example: "community: add foobar LLM" **Description:** In VoyageAI text-embedding examples use voyage-law-2 model - [x] **Add tests and docs**: If you're adding a new integration, please include 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. - [x] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, hwchase17.
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@ -84,7 +84,13 @@
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},
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"source": [
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"## Set up the base vector store retriever\n",
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"Let's start by initializing a simple vector store retriever and storing the 2023 State of the Union speech (in chunks). We can set up the retriever to retrieve a high number (20) of docs."
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"Let's start by initializing a simple vector store retriever and storing the 2023 State of the Union speech (in chunks). We can set up the retriever to retrieve a high number (20) of docs. You can use any of the following Embeddings models: ([source](https://docs.voyageai.com/docs/embeddings)):\n",
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"\n",
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"- `voyage-large-2` (default)\n",
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"- `voyage-code-2`\n",
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"- `voyage-2`\n",
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"- `voyage-law-2`\n",
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"- `voyage-lite-02-instruct`"
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]
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},
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{
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@ -316,7 +322,7 @@
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"text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)\n",
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"texts = text_splitter.split_documents(documents)\n",
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"retriever = FAISS.from_documents(\n",
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" texts, VoyageAIEmbeddings(model=\"voyage-2\")\n",
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" texts, VoyageAIEmbeddings(model=\"voyage-law-2\")\n",
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").as_retriever(search_kwargs={\"k\": 20})\n",
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"\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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@ -27,7 +27,13 @@
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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. Please refer to the documentation for further details on the available models: https://docs.voyageai.com/embeddings/"
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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. You can use any of the following models: ([source](https://docs.voyageai.com/docs/embeddings)):\n",
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"\n",
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"- `voyage-large-2` (default)\n",
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"- `voyage-code-2`\n",
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"- `voyage-2`\n",
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"- `voyage-law-2`\n",
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"- `voyage-lite-02-instruct`"
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]
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},
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{
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@ -38,7 +44,7 @@
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"outputs": [],
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"source": [
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"embeddings = VoyageAIEmbeddings(\n",
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" voyage_api_key=\"[ Your Voyage API key ]\", model=\"voyage-2\"\n",
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" voyage_api_key=\"[ Your Voyage API key ]\", model=\"voyage-law-2\"\n",
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")"
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]
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},
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