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fix(docs): update RAG tutorials link to point to correct path (#32169)

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## **Description:** 
This PR updates the internal documentation link for the RAG tutorials to
reflect the updated path. Previously, the link pointed to the root
`/docs/tutorials/`, which was generic. It now correctly routes to the
RAG-specific tutorial page for the following text-embedding models.

1. DatabricksEmbeddings
2. IBM watsonx.ai
3. OpenAIEmbeddings
4. NomicEmbeddings
5. CohereEmbeddings
6. MistralAIEmbeddings
7. FireworksEmbeddings
8. TogetherEmbeddings
9. LindormAIEmbeddings
10. ModelScopeEmbeddings
11. ClovaXEmbeddings
12. NetmindEmbeddings
13. SambaNovaCloudEmbeddings
14. SambaStudioEmbeddings
15. ZhipuAIEmbeddings

## **Issue:** N/A
## **Dependencies:** None
## **Twitter handle:** N/A
This commit is contained in:
Kanav Bansal 2025-07-22 19:54:50 +05:30 committed by GitHub
parent a1ccabf85d
commit c14bd1fcfe
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
15 changed files with 1959 additions and 1945 deletions
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8
docs/docs/integrations/text_embedding/cohere.ipynb
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@ -54,7 +54,9 @@
"cell_type": "markdown", "cell_type": "markdown",
"id": "c84fb993", "id": "c84fb993",
"metadata": {}, "metadata": {},
"source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:" "source": [
"To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
]
}, },
{ {
"cell_type": "code", "cell_type": "code",
@ -118,7 +120,7 @@
"source": [ "source": [
"## Indexing and Retrieval\n", "## Indexing and Retrieval\n",
"\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](/docs/tutorials/).\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](/docs/tutorials/rag).\n",
"\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`." "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`."
] ]
@ -257,7 +259,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.11.4" "version": "3.9.6"
} }
}, },
"nbformat": 4, "nbformat": 4,

4
docs/docs/integrations/text_embedding/databricks.ipynb
View File

@ -125,7 +125,7 @@
"source": [ "source": [
"## Indexing and Retrieval\n", "## Indexing and Retrieval\n",
"\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](/docs/tutorials/).\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](/docs/tutorials/rag).\n",
"\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`." "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`."
] ]
@ -264,7 +264,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.5" "version": "3.9.6"
} }
}, },
"nbformat": 4, "nbformat": 4,

8
docs/docs/integrations/text_embedding/fireworks.ipynb
View File

@ -54,7 +54,9 @@
"cell_type": "markdown", "cell_type": "markdown",
"id": "c84fb993", "id": "c84fb993",
"metadata": {}, "metadata": {},
"source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:" "source": [
"To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
]
}, },
{ {
"cell_type": "code", "cell_type": "code",
@ -118,7 +120,7 @@
"source": [ "source": [
"## Indexing and Retrieval\n", "## Indexing and Retrieval\n",
"\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](/docs/tutorials/).\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](/docs/tutorials/rag).\n",
"\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`." "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`."
] ]
@ -257,7 +259,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.11.4" "version": "3.9.6"
} }
}, },
"nbformat": 4, "nbformat": 4,

8
docs/docs/integrations/text_embedding/ibm_watsonx.ipynb
View File

@ -203,7 +203,7 @@
"source": [ "source": [
"## Indexing and Retrieval\n", "## Indexing and Retrieval\n",
"\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](/docs/tutorials/).\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](/docs/tutorials/rag).\n",
"\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`." "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`."
] ]
@ -327,7 +327,7 @@
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "langchain_ibm", "display_name": "Python 3 (ipykernel)",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
@ -341,9 +341,9 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.11.12" "version": "3.9.6"
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 2 "nbformat_minor": 4
} }

4
docs/docs/integrations/text_embedding/lindorm.ipynb
View File

@ -132,7 +132,7 @@
"source": [ "source": [
"## Indexing and Retrieval\n", "## Indexing and Retrieval\n",
"\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](/docs/tutorials/).\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](/docs/tutorials/rag).\n",
"\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`." "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`."
] ]
@ -286,7 +286,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.5" "version": "3.9.6"
} }
}, },
"nbformat": 4, "nbformat": 4,

8
docs/docs/integrations/text_embedding/mistralai.ipynb
View File

@ -53,7 +53,9 @@
"cell_type": "markdown", "cell_type": "markdown",
"id": "c84fb993", "id": "c84fb993",
"metadata": {}, "metadata": {},
"source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:" "source": [
"To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
]
}, },
{ {
"cell_type": "code", "cell_type": "code",
@ -117,7 +119,7 @@
"source": [ "source": [
"## Indexing and Retrieval\n", "## Indexing and Retrieval\n",
"\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](/docs/tutorials/).\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](/docs/tutorials/rag).\n",
"\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`." "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`."
] ]
@ -256,7 +258,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.11.4" "version": "3.9.6"
} }
}, },
"nbformat": 4, "nbformat": 4,

4
docs/docs/integrations/text_embedding/modelscope_embedding.ipynb
View File

@ -128,7 +128,7 @@
"source": [ "source": [
"## Indexing and Retrieval\n", "## Indexing and Retrieval\n",
"\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](/docs/tutorials/).\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](/docs/tutorials/rag).\n",
"\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`." "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`."
] ]
@ -277,7 +277,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.16" "version": "3.9.6"
} }
}, },
"nbformat": 4, "nbformat": 4,

4
docs/docs/integrations/text_embedding/naver.ipynb
View File

@ -112,7 +112,7 @@
"source": [ "source": [
"## Indexing and Retrieval\n", "## Indexing and Retrieval\n",
"\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](/docs/tutorials/).\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](/docs/tutorials/rag).\n",
"\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`." "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`."
] ]
@ -249,7 +249,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.12.3" "version": "3.9.6"
} }
}, },
"nbformat": 4, "nbformat": 4,

96
docs/docs/integrations/text_embedding/netmind.ipynb
View File

@ -37,6 +37,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1,
"id": "36521c2a", "id": "36521c2a",
"metadata": { "metadata": {
"ExecuteTime": { "ExecuteTime": {
@ -44,15 +45,14 @@
"start_time": "2025-03-20T01:53:27.764291Z" "start_time": "2025-03-20T01:53:27.764291Z"
} }
}, },
"outputs": [],
"source": [ "source": [
"import getpass\n", "import getpass\n",
"import os\n", "import os\n",
"\n", "\n",
"if not os.getenv(\"NETMIND_API_KEY\"):\n", "if not os.getenv(\"NETMIND_API_KEY\"):\n",
" os.environ[\"NETMIND_API_KEY\"] = getpass.getpass(\"Enter your Netmind API key: \")" " os.environ[\"NETMIND_API_KEY\"] = getpass.getpass(\"Enter your Netmind API key: \")"
], ]
"outputs": [],
"execution_count": 1
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
@ -64,6 +64,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2,
"id": "39a4953b", "id": "39a4953b",
"metadata": { "metadata": {
"ExecuteTime": { "ExecuteTime": {
@ -71,12 +72,11 @@
"start_time": "2025-03-20T01:53:32.141858Z" "start_time": "2025-03-20T01:53:32.141858Z"
} }
}, },
"outputs": [],
"source": [ "source": [
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")" "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
], ]
"outputs": [],
"execution_count": 2
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
@ -90,6 +90,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3,
"id": "64853226", "id": "64853226",
"metadata": { "metadata": {
"ExecuteTime": { "ExecuteTime": {
@ -97,22 +98,21 @@
"start_time": "2025-03-20T01:53:36.171640Z" "start_time": "2025-03-20T01:53:36.171640Z"
} }
}, },
"source": [
"%pip install -qU langchain-netmind"
],
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"\r\n", "\r\n",
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m24.0\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m25.0.1\u001B[0m\r\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\r\n",
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n",
"Note: you may need to restart the kernel to use updated packages.\n" "Note: you may need to restart the kernel to use updated packages.\n"
] ]
} }
], ],
"execution_count": 3 "source": [
"%pip install -qU langchain-netmind"
]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
@ -126,6 +126,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4,
"id": "9ea7a09b", "id": "9ea7a09b",
"metadata": { "metadata": {
"ExecuteTime": { "ExecuteTime": {
@ -133,15 +134,14 @@
"start_time": "2025-03-20T01:54:30.146876Z" "start_time": "2025-03-20T01:54:30.146876Z"
} }
}, },
"outputs": [],
"source": [ "source": [
"from langchain_netmind import NetmindEmbeddings\n", "from langchain_netmind import NetmindEmbeddings\n",
"\n", "\n",
"embeddings = NetmindEmbeddings(\n", "embeddings = NetmindEmbeddings(\n",
" model=\"nvidia/NV-Embed-v2\",\n", " model=\"nvidia/NV-Embed-v2\",\n",
")" ")"
], ]
"outputs": [],
"execution_count": 4
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
@ -150,13 +150,14 @@
"source": [ "source": [
"## Indexing and Retrieval\n", "## Indexing and Retrieval\n",
"\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](/docs/tutorials/).\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](/docs/tutorials/rag).\n",
"\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`." "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", "cell_type": "code",
"execution_count": 5,
"id": "d817716b", "id": "d817716b",
"metadata": { "metadata": {
"ExecuteTime": { "ExecuteTime": {
@ -164,6 +165,18 @@
"start_time": "2025-03-20T01:54:34.500805Z" "start_time": "2025-03-20T01:54:34.500805Z"
} }
}, },
"outputs": [
{
"data": {
"text/plain": [
"'LangChain is the framework for building context-aware reasoning applications'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"# Create a vector store with a sample text\n", "# Create a vector store with a sample text\n",
"from langchain_core.vectorstores import InMemoryVectorStore\n", "from langchain_core.vectorstores import InMemoryVectorStore\n",
@ -183,21 +196,8 @@
"\n", "\n",
"# show the retrieved document's content\n", "# show the retrieved document's content\n",
"retrieved_documents[0].page_content" "retrieved_documents[0].page_content"
],
"outputs": [
{
"data": {
"text/plain": [
"'LangChain is the framework for building context-aware reasoning applications'"
] ]
}, },
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 5
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "e02b9855", "id": "e02b9855",
@ -216,6 +216,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6,
"id": "0d2befcd", "id": "0d2befcd",
"metadata": { "metadata": {
"ExecuteTime": { "ExecuteTime": {
@ -223,10 +224,6 @@
"start_time": "2025-03-20T01:54:45.196528Z" "start_time": "2025-03-20T01:54:45.196528Z"
} }
}, },
"source": [
"single_vector = embeddings.embed_query(text)\n",
"print(str(single_vector)[:100]) # Show the first 100 characters of the vector"
],
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
@ -236,7 +233,10 @@
] ]
} }
], ],
"execution_count": 6 "source": [
"single_vector = embeddings.embed_query(text)\n",
"print(str(single_vector)[:100]) # Show the first 100 characters of the vector"
]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
@ -250,6 +250,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7,
"id": "2f4d6e97", "id": "2f4d6e97",
"metadata": { "metadata": {
"ExecuteTime": { "ExecuteTime": {
@ -257,14 +258,6 @@
"start_time": "2025-03-20T01:54:52.468719Z" "start_time": "2025-03-20T01:54:52.468719Z"
} }
}, },
"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"
],
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
@ -275,7 +268,14 @@
] ]
} }
], ],
"execution_count": 7 "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", "cell_type": "markdown",
@ -291,12 +291,12 @@
] ]
}, },
{ {
"metadata": {},
"cell_type": "code", "cell_type": "code",
"outputs": [],
"execution_count": null, "execution_count": null,
"source": "", "id": "adb9e45c34733299",
"id": "adb9e45c34733299" "metadata": {},
"outputs": [],
"source": []
} }
], ],
"metadata": { "metadata": {
@ -315,7 +315,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.5" "version": "3.9.6"
} }
}, },
"nbformat": 4, "nbformat": 4,

6
docs/docs/integrations/text_embedding/nomic.ipynb
View File

@ -53,7 +53,9 @@
"cell_type": "markdown", "cell_type": "markdown",
"id": "c84fb993", "id": "c84fb993",
"metadata": {}, "metadata": {},
"source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:" "source": [
"To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
]
}, },
{ {
"cell_type": "code", "cell_type": "code",
@ -138,7 +140,7 @@
"source": [ "source": [
"## Indexing and Retrieval\n", "## Indexing and Retrieval\n",
"\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](/docs/tutorials/).\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](/docs/tutorials/rag).\n",
"\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`." "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`."
] ]

8
docs/docs/integrations/text_embedding/openai.ipynb
View File

@ -55,7 +55,9 @@
"cell_type": "markdown", "cell_type": "markdown",
"id": "c84fb993", "id": "c84fb993",
"metadata": {}, "metadata": {},
"source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:" "source": [
"To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
]
}, },
{ {
"cell_type": "code", "cell_type": "code",
@ -123,7 +125,7 @@
"source": [ "source": [
"## Indexing and Retrieval\n", "## Indexing and Retrieval\n",
"\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](/docs/tutorials/).\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](/docs/tutorials/rag).\n",
"\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`." "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`."
] ]
@ -262,7 +264,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.11.4" "version": "3.9.6"
} }
}, },
"nbformat": 4, "nbformat": 4,

4
docs/docs/integrations/text_embedding/sambanova.ipynb
View File

@ -133,7 +133,7 @@
"source": [ "source": [
"## Indexing and Retrieval\n", "## Indexing and Retrieval\n",
"\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](/docs/tutorials/).\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](/docs/tutorials/rag).\n",
"\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`." "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`."
] ]
@ -244,7 +244,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.5" "version": "3.9.6"
} }
}, },
"nbformat": 4, "nbformat": 4,

4
docs/docs/integrations/text_embedding/sambastudio.ipynb
View File

@ -141,7 +141,7 @@
"source": [ "source": [
"## Indexing and Retrieval\n", "## Indexing and Retrieval\n",
"\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](/docs/tutorials/).\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](/docs/tutorials/rag).\n",
"\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`." "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`."
] ]
@ -252,7 +252,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.5" "version": "3.9.6"
} }
}, },
"nbformat": 4, "nbformat": 4,

8
docs/docs/integrations/text_embedding/together.ipynb
View File

@ -53,7 +53,9 @@
"cell_type": "markdown", "cell_type": "markdown",
"id": "c84fb993", "id": "c84fb993",
"metadata": {}, "metadata": {},
"source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:" "source": [
"To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
]
}, },
{ {
"cell_type": "code", "cell_type": "code",
@ -128,7 +130,7 @@
"source": [ "source": [
"## Indexing and Retrieval\n", "## Indexing and Retrieval\n",
"\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](/docs/tutorials/).\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](/docs/tutorials/rag).\n",
"\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`." "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`."
] ]
@ -267,7 +269,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.11.4" "version": "3.9.6"
} }
}, },
"nbformat": 4, "nbformat": 4,

8
docs/docs/integrations/text_embedding/zhipuai.ipynb
View File

@ -54,7 +54,9 @@
"cell_type": "markdown", "cell_type": "markdown",
"id": "c84fb993", "id": "c84fb993",
"metadata": {}, "metadata": {},
"source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:" "source": [
"To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
]
}, },
{ {
"cell_type": "code", "cell_type": "code",
@ -130,7 +132,7 @@
"source": [ "source": [
"## Indexing and Retrieval\n", "## Indexing and Retrieval\n",
"\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](/docs/tutorials/).\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](/docs/tutorials/rag).\n",
"\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`." "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`."
] ]
@ -269,7 +271,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.12.3" "version": "3.9.6"
} }
}, },
"nbformat": 4, "nbformat": 4,