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Add set_conditional_entry_point

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Lance Martin
2024-03-26 16:59:24 -07:00
parent aefea04d31
commit cf7f6243d7
1 changed files with 38 additions and 58 deletions
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examples/rag/adaptive_rag.ipynb
+38 -58
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@@ -16,7 +16,7 @@
"\n",
"Adaptive RAG is a strategy for RAG that unites (1) [query analysis](https://blog.langchain.dev/query-construction/) with (2) [active / self-corrective RAG](https://blog.langchain.dev/agentic-rag-with-langgraph/).\n",
"\n",
"In the paper, they report query analysis to route across:\n",
"In the [paper](https://arxiv.org/abs/2403.14403), they report query analysis to route across:\n",
"\n",
"* No Retrieval\n",
"* Single-shot RAG\n",
@@ -84,7 +84,7 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 1,
"id": "b224e5ba-50ca-495a-a7fa-0f75a080e03c",
"metadata": {},
"outputs": [],
@@ -136,7 +136,7 @@
},
{
"cell_type": "code",
"execution_count": 36,
"execution_count": 3,
"id": "4dec9d98-f3dc-4b7f-abc0-9d01c754f2be",
"metadata": {},
"outputs": [
@@ -190,7 +190,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 4,
"id": "856801cb-f42a-44e7-956f-47845e3664ca",
"metadata": {},
"outputs": [
@@ -198,7 +198,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"binary_score='yes'\n"
"binary_score='no'\n"
]
}
],
@@ -235,7 +235,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 5,
"id": "2272333e-50b2-42ab-b472-e1055a3b94a8",
"metadata": {},
"outputs": [
@@ -273,7 +273,7 @@
},
{
"cell_type": "code",
"execution_count": 33,
"execution_count": 6,
"id": "f0c08d14-77a0-4eed-b882-2d636abb22a3",
"metadata": {},
"outputs": [
@@ -283,7 +283,7 @@
"GradeHallucinations(binary_score='yes')"
]
},
"execution_count": 33,
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -317,7 +317,7 @@
},
{
"cell_type": "code",
"execution_count": 34,
"execution_count": 7,
"id": "ded99680-437a-4c9d-b860-619c88949d84",
"metadata": {},
"outputs": [
@@ -327,7 +327,7 @@
"GradeAnswer(binary_score='yes')"
]
},
"execution_count": 34,
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -361,7 +361,7 @@
},
{
"cell_type": "code",
"execution_count": 35,
"execution_count": 8,
"id": "9d75f1d7-a47a-4577-bb0d-84b504b0867e",
"metadata": {},
"outputs": [
@@ -371,7 +371,7 @@
"\"What is the role of memory in an agent's functioning?\""
]
},
"execution_count": 35,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -406,7 +406,7 @@
},
{
"cell_type": "code",
"execution_count": 69,
"execution_count": 9,
"id": "01d829bb-1074-4976-b650-ead41dcb9788",
"metadata": {},
"outputs": [],
@@ -431,7 +431,7 @@
},
{
"cell_type": "code",
"execution_count": 38,
"execution_count": 10,
"id": "e723fcdb-06e6-402d-912e-899795b78408",
"metadata": {},
"outputs": [],
@@ -463,28 +463,13 @@
},
{
"cell_type": "code",
"execution_count": 65,
"execution_count": 11,
"id": "b76b5ec3-0720-443d-85b1-c0e79659ca0a",
"metadata": {},
"outputs": [],
"source": [
"from langchain.schema import Document\n",
"\n",
"def start_node(state):\n",
" \"\"\"\n",
" Starting node for graph\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): The current graph state\n",
" \"\"\"\n",
" print(\"START NODE STATE\")\n",
" print(state)\n",
" question = state[\"question\"]\n",
" return {\"question\": question}\n",
"\n",
"def retrieve(state):\n",
" \"\"\"\n",
" Retrieve documents\n",
@@ -683,7 +668,7 @@
},
{
"cell_type": "code",
"execution_count": 66,
"execution_count": 12,
"id": "67854e07-9293-4c3c-bf9a-bc9a605570ee",
"metadata": {},
"outputs": [],
@@ -695,7 +680,6 @@
"workflow = StateGraph(GraphState)\n",
"\n",
"# Define the nodes\n",
"workflow.add_node(\"start\", start_node) # start node\n",
"workflow.add_node(\"web_search\", web_search) # web search\n",
"workflow.add_node(\"retrieve\", retrieve) # retrieve\n",
"workflow.add_node(\"grade_documents\", grade_documents) # grade documents\n",
@@ -703,9 +687,7 @@
"workflow.add_node(\"transform_query\", transform_query) # transform_query\n",
"\n",
"# Build graph\n",
"workflow.set_entry_point(\"start\")\n",
"workflow.add_conditional_edges(\n",
" \"start\",\n",
"workflow.set_conditional_entry_point(\n",
" route_question,\n",
" {\n",
" \"web_search\": \"web_search\",\n",
@@ -732,7 +714,6 @@
" \"not useful\": \"transform_query\",\n",
" },\n",
")\n",
"workflow.set_finish_point(\"start\")\n",
"\n",
"# Compile\n",
"app = workflow.compile()"
@@ -740,7 +721,7 @@
},
{
"cell_type": "code",
"execution_count": 67,
"execution_count": 13,
"id": "29acc541-d726-4b75-84d1-a215845fe88a",
"metadata": {},
"outputs": [
@@ -748,14 +729,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
"START NODE STATE\n",
"{'question': 'What player at the Bears expected to draft first in the 2024 NFL draft?', 'generation': None, 'documents': None}\n",
"\"Node 'start':\"\n",
"'\\n---\\n'\n",
"---ROUTE QUESTION---\n",
"---ROUTE QUESTION TO WEB SEARCH---\n",
"\"Node '__end__':\"\n",
"'\\n---\\n'\n",
"---WEB SEARCH---\n",
"\"Node 'web_search':\"\n",
"'\\n---\\n'\n",
@@ -768,8 +743,10 @@
"---DECISION: GENERATION ADDRESSES QUESTION---\n",
"\"Node '__end__':\"\n",
"'\\n---\\n'\n",
"('Several NFL analysts expect the Bears to draft USC quarterback Caleb '\n",
" 'Williams with the No. 1 pick in the 2024 NFL draft.')\n"
"('Several NFL analysts expect the Bears to select USC quarterback Caleb '\n",
" 'Williams at No. 1 in the 2024 NFL draft. The Chicago Bears will have the No. '\n",
" '1 pick in the draft. The draft will take place in Detroit at Campus Martius '\n",
" 'Park and Hart Plaza.')\n"
]
}
],
@@ -795,12 +772,12 @@
"source": [
"Trace: \n",
"\n",
"https://smith.langchain.com/public/a33fa139-89e6-40c9-bc63-63984e9709af/r"
"https://smith.langchain.com/public/7e3aa7e5-c51f-45c2-bc66-b34f17ff2263/r"
]
},
{
"cell_type": "code",
"execution_count": 68,
"execution_count": 14,
"id": "69a985dd-03c6-45af-a67b-b15746a2cb5f",
"metadata": {},
"outputs": [
@@ -808,14 +785,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
"START NODE STATE\n",
"{'question': 'What are the types of agent memory?', 'generation': None, 'documents': None}\n",
"\"Node 'start':\"\n",
"'\\n---\\n'\n",
"---ROUTE QUESTION---\n",
"---ROUTE QUESTION TO RAG---\n",
"\"Node '__end__':\"\n",
"'\\n---\\n'\n",
"---RETRIEVE---\n",
"\"Node 'retrieve':\"\n",
"'\\n---\\n'\n",
@@ -839,10 +810,11 @@
"'\\n---\\n'\n",
"('The types of agent memory are sensory memory, short-term memory, and '\n",
" 'long-term memory. Sensory memory involves learning embedding representations '\n",
" 'for raw inputs, short-term memory is limited by the context window length of '\n",
" 'Transformer, and long-term memory is an external vector store accessible at '\n",
" 'query time. The external memory can support fast maximum inner-product '\n",
" 'search (MIPS) to alleviate the restriction of finite attention span.')\n"
" 'for raw inputs, short-term memory is in-context learning with a finite '\n",
" 'context window length, and long-term memory is an external vector store '\n",
" 'accessible via fast retrieval. The external memory can support fast maximum '\n",
" 'inner-product search (MIPS) to alleviate the restriction of finite attention '\n",
" 'span.')\n"
]
}
],
@@ -868,8 +840,16 @@
"source": [
"Trace: \n",
"\n",
"https://smith.langchain.com/public/95e8075d-fdfe-475c-b6cf-a4699f160af7/r"
"https://smith.langchain.com/public/fdf0a180-6d15-4d09-bb92-f84f2105ca51/r"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "19ac1f6f-2d84-488f-8a0e-7ee2a46b0f71",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {