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[docs]: change rag reference in vector store pages (#25125)

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13 changed files with 65 additions and 733 deletions
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65
docs/docs/integrations/vectorstores/astradb.ipynb
View File

@ -455,68 +455,13 @@
"id": "734e683a",
"metadata": {},
"source": [
"## Chain usage\n",
"## Usage for retrieval-augmented generation\n",
"\n",
"The code below shows how to use the vector store as a retriever in a simple RAG chain:\n",
"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "9b3cc97b",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "08401498",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'LangGraph is used for building stateful, agentic applications. It provides a framework that facilitates the development of such applications. Its capabilities make it a preferred choice for developers in this domain.'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"\n",
"rag_chain.invoke(\"What is LangGraph used for?\")"
"- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
"- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
"- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
]
},
{

65
docs/docs/integrations/vectorstores/chroma.ipynb
View File

@ -459,68 +459,13 @@
"id": "a2b7b73c",
"metadata": {},
"source": [
"## Chain usage\n",
"## Usage for retrieval-augmented generation\n",
"\n",
"The code below shows how to use the vector store as a retriever in a simple RAG chain:\n",
"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "9aad065b",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "84a19f48",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'LangGraph is used for building stateful, agentic applications. It provides a framework that supports the development of such applications efficiently.'"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"\n",
"rag_chain.invoke(\"What is LangGraph used for?\")"
"- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
"- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
"- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
]
},
{

54
docs/docs/integrations/vectorstores/clickhouse.ipynb
View File

@ -356,57 +356,13 @@
"id": "57fade30",
"metadata": {},
"source": [
"## Chain usage\n",
"## Usage for retrieval-augmented generation\n",
"\n",
"The code below shows how to use the vector store as a retriever in a simple RAG chain:\n",
"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8a7fec6b",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ae6871dc",
"metadata": {},
"outputs": [],
"source": [
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"\n",
"rag_chain.invoke(\"What is LangGraph used for?\")"
"- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
"- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
"- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
]
},
{

54
docs/docs/integrations/vectorstores/couchbase.ipynb
View File

@ -674,57 +674,13 @@
"id": "28ab35ec",
"metadata": {},
"source": [
"## Chain usage\n",
"## Usage for retrieval-augmented generation\n",
"\n",
"The code below shows how to use the vector store as a retriever in a simple RAG chain:\n",
"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a6a849aa",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e34c9e3a",
"metadata": {},
"outputs": [],
"source": [
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"\n",
"rag_chain.invoke(\"What is LangGraph used for?\")"
"- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
"- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
"- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
]
},
{

65
docs/docs/integrations/vectorstores/elasticsearch.ipynb
View File

@ -469,68 +469,13 @@
"id": "17b509ae",
"metadata": {},
"source": [
"## Chain usage\n",
"## Usage for retrieval-augmented generation\n",
"\n",
"The code below shows how to use the vector store as a retriever in a simple RAG chain:\n",
"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "58e17804",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "01dac420",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'LanGraph is used for building stateful, agentic applications. It serves as a framework to facilitate the development of exciting new projects.'"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"\n",
"rag_chain.invoke(\"What is LanGraph used for?\")"
"- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
"- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
"- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
]
},
{

65
docs/docs/integrations/vectorstores/faiss.ipynb
View File

@ -360,68 +360,13 @@
"id": "5edd1909",
"metadata": {},
"source": [
"## Chain usage\n",
"## Usage for retrieval-augmented generation\n",
"\n",
"The code below shows how to use the vector store as a retriever in a simple RAG chain:\n",
"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "6b792eaa",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "1aca9435",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'LangGraph is used for building stateful, agentic applications. It provides a framework that facilitates the development of these types of applications.'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"\n",
"rag_chain.invoke(\"What is LangGraph used for?\")"
"- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
"- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
"- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
]
},
{

65
docs/docs/integrations/vectorstores/milvus.ipynb
View File

@ -393,68 +393,13 @@
"id": "8ac953f1",
"metadata": {},
"source": [
"## Chain usage\n",
"## Usage for retrieval-augmented generation\n",
"\n",
"The code below shows how to use the vector store as a retriever in a simple RAG chain:\n",
"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "d17118c2",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\")"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "7bbe3b95",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'LangGraph is used for building stateful, agentic applications. It provides a framework that facilitates the development of such applications effectively.'"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"\n",
"rag_chain.invoke(\"What is LangGraph used for?\")"
"- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
"- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
"- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
]
},
{

65
docs/docs/integrations/vectorstores/mongodb_atlas.ipynb
View File

@ -457,68 +457,13 @@
"id": "72312657",
"metadata": {},
"source": [
"## Chain usage\n",
"## Usage for retrieval-augmented generation\n",
"\n",
"The code below shows how to use the vector store as a retriever in a simple RAG chain:\n",
"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "a42da723",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\")"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "80c1130f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'LangGraph is used for building stateful, agentic applications. It provides a framework that facilitates the development of such applications.'"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"\n",
"rag_chain.invoke(\"What is LangGraph used for?\")"
"- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
"- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
"- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
]
},
{

65
docs/docs/integrations/vectorstores/pgvector.ipynb
View File

@ -434,68 +434,13 @@
"id": "7ecd77a0",
"metadata": {},
"source": [
"## Chain usage\n",
"## Usage for retrieval-augmented generation\n",
"\n",
"The code below shows how to use the vector store as a retriever in a simple RAG chain:\n",
"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f0b14168",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a4eba12c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'There are cats in the pond right now.'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"\n",
"rag_chain.invoke(\"Who is at the pond right now?\")"
"- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
"- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
"- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
]
},
{

65
docs/docs/integrations/vectorstores/pinecone.ipynb
View File

@ -408,68 +408,13 @@
"id": "72990cb5",
"metadata": {},
"source": [
"## Chain usage\n",
"## Usage for retrieval-augmented generation\n",
"\n",
"The code below shows how to use the vector store as a retriever in a simple RAG chain:\n",
"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "f12560cb",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "262651fc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'LangGraph is used for building stateful, agentic applications. It provides a framework that facilitates the development of these types of applications.'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"\n",
"rag_chain.invoke(\"What is LangGraph used for?\")"
"- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
"- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
"- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
]
},
{

65
docs/docs/integrations/vectorstores/qdrant.ipynb
View File

@ -713,68 +713,13 @@
"id": "6ac07288",
"metadata": {},
"source": [
"## Chain usage\n",
"## Usage for retrieval-augmented generation\n",
"\n",
"The code below shows how to use the vector store as a retriever in a simple RAG chain:\n",
"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "07bd9785",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "d97f0c91",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'LangGraph is used for building stateful, agentic applications. It provides a framework that facilitates the development of such applications.'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"\n",
"rag_chain.invoke(\"What is LangGraph used for?\")"
"- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
"- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
"- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
]
},
{

63
docs/docs/integrations/vectorstores/redis.ipynb
View File

@ -930,66 +930,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Chain usage\n",
"## Usage for retrieval-augmented generation\n",
"\n",
"The code below shows how to use the vector store as a retriever in a simple RAG chain:\n",
"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'LangGraph is used for building stateful, agentic applications. It provides a framework to facilitate the development of such applications.'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"\n",
"rag_chain.invoke(\"What is LangGraph used for?\")"
"- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
"- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
"- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
]
},
{

42
libs/cli/langchain_cli/integration_template/docs/vectorstores.ipynb
View File

@ -288,45 +288,13 @@
"id": "901c75dc",
"metadata": {},
"source": [
"## Chain usage\n",
"## Usage for retrieval-augmented generation\n",
"\n",
"The code below shows how to use the vector store as a retriever in a simple RAG chain:\n",
"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "619b5ef6",
"metadata": {},
"outputs": [],
"source": [
"from langchain_openai import ChatOpenAI\n",
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\")\n",
"\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"\n",
"rag_chain.invoke(\"thud\")"
"- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
"- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
"- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
]
},
{