github/langchain
1
0
Fork 0
mirror of https://github.com/hwchase17/langchain.git synced 2025-09-01 19:12:42 +00:00
Code Issues Packages Projects Releases Wiki Activity

docs: update tutorials (#28219)

Browse Source
This commit is contained in:
ccurme
2024-11-26 10:43:12 -05:00
committed by GitHub
parent a97c53e7c2
commit 7b9a0d9ed8
170 changed files with 4636 additions and 5949 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
libs/cli/langchain_cli/integration_template/docs/text_embedding.ipynb
View File

@@ -127,7 +127,7 @@
"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",
"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",
"\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`."
]

2
libs/cli/langchain_cli/integration_template/docs/vectorstores.ipynb
View File

@@ -292,7 +292,7 @@
"\n",
"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
"\n",
"- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
"- [Tutorials](/docs/tutorials/)\n",
"- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
"- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
]