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docs: update notebook links and add archival notices for examples (#6720)

Browse Source 
Addresses some comments in #6682

- Update links in notebooks to point to the new documentation location.
- Add archival notices indicating that the examples are no longer
updated.
- Remove some obsolete notebooks that have been moved to the new
documentation.

Please comment here if you encounter any issues
This commit is contained in:
Mason Daugherty
2026-01-26 06:00:16 +00:00
committed by GitHub
parent 2c6f99cbf0
commit fbcb8a911b
75 changed files with 1517 additions and 1339 deletions
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examples/README.md
+1 -1
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@@ -1,3 +1,3 @@
# LangGraph examples
This directory should NOT be used for documentation. All new documentation must be added to `docs/docs/` directory.
This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview). Please refer to the LangChain docs for the most up-to-date examples and usage guidelines for LangGraph.
examples/async.ipynb
-33
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@@ -1,33 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "23544406",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/async.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
examples/branching.ipynb
-33
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@@ -1,33 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "14f7ca50",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/branching.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
examples/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb
+9 -1
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@@ -5,7 +5,15 @@
"id": "10251c1c",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb"
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "c5fc63df",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
]
}
],
examples/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb
+9 -1
View File
@@ -5,7 +5,15 @@
"id": "a4351a24",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb"
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "4cc9af1e",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
]
}
],
examples/chatbots/information-gather-prompting.ipynb
+9 -1
View File
@@ -5,7 +5,15 @@
"id": "a9014f94",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbots/information-gather-prompting.ipynb"
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/chatbots/information-gather-prompting.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "f47ce992",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
]
}
],
examples/cloud_examples/langgraph_to_langgraph_cloud.ipynb
-33
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@@ -1,33 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "2b789e16",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/cloud/how-tos/langgraph_to_langgraph_cloud.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
examples/code_assistant/langgraph_code_assistant.ipynb
+9 -1
View File
@@ -5,7 +5,15 @@
"id": "1f2f13ca",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/code_assistant/langgraph_code_assistant.ipynb"
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/code_assistant/langgraph_code_assistant.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "5e4c9bfe",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
]
}
],
examples/code_assistant/langgraph_code_assistant_mistral.ipynb
+403 -16
View File
@@ -1,5 +1,13 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "1d38cbab",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
]
},
{
"attachments": {
"15d3ac32-cdf3-4800-a30c-f26d828d69c8.png": {
@@ -33,7 +41,9 @@
"id": "e501686f-323f-4b87-8f9c-8ba89133078b",
"metadata": {},
"outputs": [],
"source": ["! pip install -U langchain_community langchain-mistralai langchain langgraph"]
"source": [
"! pip install -U langchain_community langchain-mistralai langchain langgraph"
]
},
{
"cell_type": "markdown",
@@ -51,7 +61,12 @@
"id": "982e4609-86e4-4934-828f-e03d89c20393",
"metadata": {},
"outputs": [],
"source": ["import os\n\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\nmistral_api_key = os.getenv(\"MISTRAL_API_KEY\") # Ensure this is set"]
"source": [
"import os\n",
"\n",
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n",
"mistral_api_key = os.getenv(\"MISTRAL_API_KEY\") # Ensure this is set"
]
},
{
"cell_type": "markdown",
@@ -69,7 +84,12 @@
"id": "37b172d2-3a9d-49a8-898c-22ed0cb45c88",
"metadata": {},
"outputs": [],
"source": ["os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\nos.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\"\nos.environ[\"LANGCHAIN_PROJECT\"] = \"Mistral-code-gen-testing\""]
"source": [
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
"os.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\"\n",
"os.environ[\"LANGCHAIN_PROJECT\"] = \"Mistral-code-gen-testing\""
]
},
{
"cell_type": "markdown",
@@ -87,7 +107,42 @@
"id": "a188c8ca-c053-4e6d-b7af-38a3b6b371c7",
"metadata": {},
"outputs": [],
"source": ["# Select LLM\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.pydantic_v1 import BaseModel, Field\nfrom langchain_mistralai import ChatMistralAI\n\nmistral_model = \"mistral-large-latest\"\nllm = ChatMistralAI(model=mistral_model, temperature=0)\n\n# Prompt\ncode_gen_prompt_claude = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"\"\"You are a coding assistant. Ensure any code you provide can be executed with all required imports and variables \\n\n defined. Structure your answer: 1) a prefix describing the code solution, 2) the imports, 3) the functioning code block.\n \\n Here is the user question:\"\"\",\n ),\n (\"placeholder\", \"{messages}\"),\n ]\n)\n\n\n# Data model\nclass code(BaseModel):\n \"\"\"Code output\"\"\"\n\n prefix: str = Field(description=\"Description of the problem and approach\")\n imports: str = Field(description=\"Code block import statements\")\n code: str = Field(description=\"Code block not including import statements\")\n description = \"Schema for code solutions to questions about LCEL.\"\n\n\n# LLM\ncode_gen_chain = llm.with_structured_output(code, include_raw=False)"]
"source": [
"# Select LLM\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"from langchain_mistralai import ChatMistralAI\n",
"\n",
"mistral_model = \"mistral-large-latest\"\n",
"llm = ChatMistralAI(model=mistral_model, temperature=0)\n",
"\n",
"# Prompt\n",
"code_gen_prompt_claude = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\n",
" \"system\",\n",
" \"\"\"You are a coding assistant. Ensure any code you provide can be executed with all required imports and variables \\n\n",
" defined. Structure your answer: 1) a prefix describing the code solution, 2) the imports, 3) the functioning code block.\n",
" \\n Here is the user question:\"\"\",\n",
" ),\n",
" (\"placeholder\", \"{messages}\"),\n",
" ]\n",
")\n",
"\n",
"\n",
"# Data model\n",
"class code(BaseModel):\n",
" \"\"\"Code output\"\"\"\n",
"\n",
" prefix: str = Field(description=\"Description of the problem and approach\")\n",
" imports: str = Field(description=\"Code block import statements\")\n",
" code: str = Field(description=\"Code block not including import statements\")\n",
" description = \"Schema for code solutions to questions about LCEL.\"\n",
"\n",
"\n",
"# LLM\n",
"code_gen_chain = llm.with_structured_output(code, include_raw=False)"
]
},
{
"cell_type": "code",
@@ -95,7 +150,10 @@
"id": "9fc0290d-5a04-4514-8664-91f9dbf2da7b",
"metadata": {},
"outputs": [],
"source": ["question = \"Write a function for fibonacci.\"\nmessages = [(\"user\", question)]"]
"source": [
"question = \"Write a function for fibonacci.\"\n",
"messages = [(\"user\", question)]"
]
},
{
"cell_type": "code",
@@ -114,7 +172,11 @@
"output_type": "execute_result"
}
],
"source": ["# Test\nresult = code_gen_chain.invoke(messages)\nresult"]
"source": [
"# Test\n",
"result = code_gen_chain.invoke(messages)\n",
"result"
]
},
{
"cell_type": "markdown",
@@ -130,7 +192,28 @@
"id": "183d77b8-f180-4815-b39f-8ef507ec0534",
"metadata": {},
"outputs": [],
"source": ["from typing import Annotated, TypedDict\n\nfrom langgraph.graph.message import AnyMessage, add_messages\n\n\nclass GraphState(TypedDict):\n \"\"\"\n Represents the state of our graph.\n\n Attributes:\n error : Binary flag for control flow to indicate whether test error was tripped\n messages : With user question, error messages, reasoning\n generation : Code solution\n iterations : Number of tries\n \"\"\"\n\n error: str\n messages: Annotated[list[AnyMessage], add_messages]\n generation: str\n iterations: int"]
"source": [
"from typing import Annotated, TypedDict\n",
"\n",
"from langgraph.graph.message import AnyMessage, add_messages\n",
"\n",
"\n",
"class GraphState(TypedDict):\n",
" \"\"\"\n",
" Represents the state of our graph.\n",
"\n",
" Attributes:\n",
" error : Binary flag for control flow to indicate whether test error was tripped\n",
" messages : With user question, error messages, reasoning\n",
" generation : Code solution\n",
" iterations : Number of tries\n",
" \"\"\"\n",
"\n",
" error: str\n",
" messages: Annotated[list[AnyMessage], add_messages]\n",
" generation: str\n",
" iterations: int"
]
},
{
"cell_type": "markdown",
@@ -146,7 +229,163 @@
"id": "14bc89d1-3ca6-4847-a048-1803e0e4600e",
"metadata": {},
"outputs": [],
"source": ["import uuid\n\nfrom langchain_core.pydantic_v1 import BaseModel, Field\n\n### Parameters\nmax_iterations = 3\n\n\n### Nodes\ndef generate(state: GraphState):\n \"\"\"\n Generate a code solution\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): New key added to state, generation\n \"\"\"\n\n print(\"---GENERATING CODE SOLUTION---\")\n\n # State\n messages = state[\"messages\"]\n iterations = state[\"iterations\"]\n\n # Solution\n code_solution = code_gen_chain.invoke(messages)\n messages += [\n (\n \"assistant\",\n f\"Here is my attempt to solve the problem: {code_solution.prefix} \\n Imports: {code_solution.imports} \\n Code: {code_solution.code}\",\n )\n ]\n\n # Increment\n iterations = iterations + 1\n return {\"generation\": code_solution, \"messages\": messages, \"iterations\": iterations}\n\n\ndef code_check(state: GraphState):\n \"\"\"\n Check code\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): New key added to state, error\n \"\"\"\n\n print(\"---CHECKING CODE---\")\n\n # State\n messages = state[\"messages\"]\n code_solution = state[\"generation\"]\n iterations = state[\"iterations\"]\n\n # Get solution components\n imports = code_solution.imports\n code = code_solution.code\n\n # Check imports\n try:\n exec(imports)\n except Exception as e:\n print(\"---CODE IMPORT CHECK: FAILED---\")\n error_message = [\n (\n \"user\",\n f\"Your solution failed the import test. Here is the error: {e}. Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n )\n ]\n messages += error_message\n return {\n \"generation\": code_solution,\n \"messages\": messages,\n \"iterations\": iterations,\n \"error\": \"yes\",\n }\n\n # Check execution\n try:\n combined_code = f\"{imports}\\n{code}\"\n print(f\"CODE TO TEST: {combined_code}\")\n # Use a shared scope for exec\n global_scope = {}\n exec(combined_code, global_scope)\n except Exception as e:\n print(\"---CODE BLOCK CHECK: FAILED---\")\n error_message = [\n (\n \"user\",\n f\"Your solution failed the code execution test: {e}) Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n )\n ]\n messages += error_message\n return {\n \"generation\": code_solution,\n \"messages\": messages,\n \"iterations\": iterations,\n \"error\": \"yes\",\n }\n\n # No errors\n print(\"---NO CODE TEST FAILURES---\")\n return {\n \"generation\": code_solution,\n \"messages\": messages,\n \"iterations\": iterations,\n \"error\": \"no\",\n }\n\n\n### Conditional edges\n\n\ndef decide_to_finish(state: GraphState):\n \"\"\"\n Determines whether to finish.\n\n Args:\n state (dict): The current graph state\n\n Returns:\n str: Next node to call\n \"\"\"\n error = state[\"error\"]\n iterations = state[\"iterations\"]\n\n if error == \"no\" or iterations == max_iterations:\n print(\"---DECISION: FINISH---\")\n return \"end\"\n else:\n print(\"---DECISION: RE-TRY SOLUTION---\")\n return \"generate\"\n\n\n### Utilities\n\n\ndef _print_event(event: dict, _printed: set, max_length=1500):\n current_state = event.get(\"dialog_state\")\n if current_state:\n print(\"Currently in: \", current_state[-1])\n message = event.get(\"messages\")\n if message:\n if isinstance(message, list):\n message = message[-1]\n if message.id not in _printed:\n msg_repr = message.pretty_repr(html=True)\n if len(msg_repr) > max_length:\n msg_repr = msg_repr[:max_length] + \" ... (truncated)\"\n print(msg_repr)\n _printed.add(message.id)"]
"source": [
"import uuid\n",
"\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"\n",
"### Parameters\n",
"max_iterations = 3\n",
"\n",
"\n",
"### Nodes\n",
"def generate(state: GraphState):\n",
" \"\"\"\n",
" Generate a code solution\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): New key added to state, generation\n",
" \"\"\"\n",
"\n",
" print(\"---GENERATING CODE SOLUTION---\")\n",
"\n",
" # State\n",
" messages = state[\"messages\"]\n",
" iterations = state[\"iterations\"]\n",
"\n",
" # Solution\n",
" code_solution = code_gen_chain.invoke(messages)\n",
" messages += [\n",
" (\n",
" \"assistant\",\n",
" f\"Here is my attempt to solve the problem: {code_solution.prefix} \\n Imports: {code_solution.imports} \\n Code: {code_solution.code}\",\n",
" )\n",
" ]\n",
"\n",
" # Increment\n",
" iterations = iterations + 1\n",
" return {\"generation\": code_solution, \"messages\": messages, \"iterations\": iterations}\n",
"\n",
"\n",
"def code_check(state: GraphState):\n",
" \"\"\"\n",
" Check code\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): New key added to state, error\n",
" \"\"\"\n",
"\n",
" print(\"---CHECKING CODE---\")\n",
"\n",
" # State\n",
" messages = state[\"messages\"]\n",
" code_solution = state[\"generation\"]\n",
" iterations = state[\"iterations\"]\n",
"\n",
" # Get solution components\n",
" imports = code_solution.imports\n",
" code = code_solution.code\n",
"\n",
" # Check imports\n",
" try:\n",
" exec(imports)\n",
" except Exception as e:\n",
" print(\"---CODE IMPORT CHECK: FAILED---\")\n",
" error_message = [\n",
" (\n",
" \"user\",\n",
" f\"Your solution failed the import test. Here is the error: {e}. Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n",
" )\n",
" ]\n",
" messages += error_message\n",
" return {\n",
" \"generation\": code_solution,\n",
" \"messages\": messages,\n",
" \"iterations\": iterations,\n",
" \"error\": \"yes\",\n",
" }\n",
"\n",
" # Check execution\n",
" try:\n",
" combined_code = f\"{imports}\\n{code}\"\n",
" print(f\"CODE TO TEST: {combined_code}\")\n",
" # Use a shared scope for exec\n",
" global_scope = {}\n",
" exec(combined_code, global_scope)\n",
" except Exception as e:\n",
" print(\"---CODE BLOCK CHECK: FAILED---\")\n",
" error_message = [\n",
" (\n",
" \"user\",\n",
" f\"Your solution failed the code execution test: {e}) Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n",
" )\n",
" ]\n",
" messages += error_message\n",
" return {\n",
" \"generation\": code_solution,\n",
" \"messages\": messages,\n",
" \"iterations\": iterations,\n",
" \"error\": \"yes\",\n",
" }\n",
"\n",
" # No errors\n",
" print(\"---NO CODE TEST FAILURES---\")\n",
" return {\n",
" \"generation\": code_solution,\n",
" \"messages\": messages,\n",
" \"iterations\": iterations,\n",
" \"error\": \"no\",\n",
" }\n",
"\n",
"\n",
"### Conditional edges\n",
"\n",
"\n",
"def decide_to_finish(state: GraphState):\n",
" \"\"\"\n",
" Determines whether to finish.\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" str: Next node to call\n",
" \"\"\"\n",
" error = state[\"error\"]\n",
" iterations = state[\"iterations\"]\n",
"\n",
" if error == \"no\" or iterations == max_iterations:\n",
" print(\"---DECISION: FINISH---\")\n",
" return \"end\"\n",
" else:\n",
" print(\"---DECISION: RE-TRY SOLUTION---\")\n",
" return \"generate\"\n",
"\n",
"\n",
"### Utilities\n",
"\n",
"\n",
"def _print_event(event: dict, _printed: set, max_length=1500):\n",
" current_state = event.get(\"dialog_state\")\n",
" if current_state:\n",
" print(\"Currently in: \", current_state[-1])\n",
" message = event.get(\"messages\")\n",
" if message:\n",
" if isinstance(message, list):\n",
" message = message[-1]\n",
" if message.id not in _printed:\n",
" msg_repr = message.pretty_repr(html=True)\n",
" if len(msg_repr) > max_length:\n",
" msg_repr = msg_repr[:max_length] + \" ... (truncated)\"\n",
" print(msg_repr)\n",
" _printed.add(message.id)"
]
},
{
"cell_type": "code",
@@ -154,7 +393,31 @@
"id": "2dff2209-44c7-4e2c-b607-ba6675f9e45f",
"metadata": {},
"outputs": [],
"source": ["from langgraph.checkpoint.memory import InMemorySaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(GraphState)\n\n# Define the nodes\nbuilder.add_node(\"generate\", generate) # generation solution\nbuilder.add_node(\"check_code\", code_check) # check code\n\n# Build graph\nbuilder.add_edge(START, \"generate\")\nbuilder.add_edge(\"generate\", \"check_code\")\nbuilder.add_conditional_edges(\n \"check_code\",\n decide_to_finish,\n {\n \"end\": END,\n \"generate\": \"generate\",\n },\n)\n\nmemory = InMemorySaver()\ngraph = builder.compile(checkpointer=memory)"]
"source": [
"from langgraph.checkpoint.memory import InMemorySaver\n",
"from langgraph.graph import END, StateGraph, START\n",
"\n",
"builder = StateGraph(GraphState)\n",
"\n",
"# Define the nodes\n",
"builder.add_node(\"generate\", generate) # generation solution\n",
"builder.add_node(\"check_code\", code_check) # check code\n",
"\n",
"# Build graph\n",
"builder.add_edge(START, \"generate\")\n",
"builder.add_edge(\"generate\", \"check_code\")\n",
"builder.add_conditional_edges(\n",
" \"check_code\",\n",
" decide_to_finish,\n",
" {\n",
" \"end\": END,\n",
" \"generate\": \"generate\",\n",
" },\n",
")\n",
"\n",
"memory = InMemorySaver()\n",
"graph = builder.compile(checkpointer=memory)"
]
},
{
"cell_type": "code",
@@ -173,7 +436,15 @@
"output_type": "display_data"
}
],
"source": ["from IPython.display import Image, display\n\ntry:\n display(Image(graph.get_graph(xray=True).draw_mermaid_png()))\nexcept Exception:\n # This requires some extra dependencies and is optional\n pass"]
"source": [
"from IPython.display import Image, display\n",
"\n",
"try:\n",
" display(Image(graph.get_graph(xray=True).draw_mermaid_png()))\n",
"except Exception:\n",
" # This requires some extra dependencies and is optional\n",
" pass"
]
},
{
"cell_type": "code",
@@ -181,7 +452,23 @@
"id": "242aa2f0-2c31-462f-a958-ff9ae0cf7c62",
"metadata": {},
"outputs": [],
"source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n \"configurable\": {\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\nquestion = \"Write a Python program that prints 'Hello, World!' to the console.\"\nevents = graph.stream(\n {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n _print_event(event, _printed)"]
"source": [
"_printed = set()\n",
"thread_id = str(uuid.uuid4())\n",
"config = {\n",
" \"configurable\": {\n",
" # Checkpoints are accessed by thread_id\n",
" \"thread_id\": thread_id,\n",
" }\n",
"}\n",
"\n",
"question = \"Write a Python program that prints 'Hello, World!' to the console.\"\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
")\n",
"for event in events:\n",
" _print_event(event, _printed)"
]
},
{
"cell_type": "markdown",
@@ -199,7 +486,31 @@
"id": "390b2768-f395-4aea-8b0e-9d36212a31ac",
"metadata": {},
"outputs": [],
"source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n \"configurable\": {\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\nquestion = \"\"\"Create a Python program that checks if a given string is a palindrome. A palindrome is a word, phrase, number, or other sequence of characters that reads the same forward and backward (ignoring spaces, punctuation, and capitalization).\n\nRequirements:\nThe program should define a function is_palindrome(s) that takes a string s as input.\nThe function should return True if the string is a palindrome and False otherwise.\nIgnore spaces, punctuation, and case differences when checking for palindromes.\n\nGive an example of it working on an example input word.\"\"\"\n\nevents = graph.stream(\n {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n _print_event(event, _printed)"]
"source": [
"_printed = set()\n",
"thread_id = str(uuid.uuid4())\n",
"config = {\n",
" \"configurable\": {\n",
" # Checkpoints are accessed by thread_id\n",
" \"thread_id\": thread_id,\n",
" }\n",
"}\n",
"\n",
"question = \"\"\"Create a Python program that checks if a given string is a palindrome. A palindrome is a word, phrase, number, or other sequence of characters that reads the same forward and backward (ignoring spaces, punctuation, and capitalization).\n",
"\n",
"Requirements:\n",
"The program should define a function is_palindrome(s) that takes a string s as input.\n",
"The function should return True if the string is a palindrome and False otherwise.\n",
"Ignore spaces, punctuation, and case differences when checking for palindromes.\n",
"\n",
"Give an example of it working on an example input word.\"\"\"\n",
"\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
")\n",
"for event in events:\n",
" _print_event(event, _printed)"
]
},
{
"cell_type": "markdown",
@@ -217,7 +528,26 @@
"id": "0a3f946b-e2f2-44d9-905b-09f36980cf9f",
"metadata": {},
"outputs": [],
"source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n \"configurable\": {\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\nquestion = \"\"\"Write a program that prints the numbers from 1 to 100. \nBut for multiples of three, print \"Fizz\" instead of the number, and for the multiples of five, print \"Buzz\". \nFor numbers which are multiples of both three and five, print \"FizzBuzz\".\"\"\"\n\nevents = graph.stream(\n {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n _print_event(event, _printed)"]
"source": [
"_printed = set()\n",
"thread_id = str(uuid.uuid4())\n",
"config = {\n",
" \"configurable\": {\n",
" # Checkpoints are accessed by thread_id\n",
" \"thread_id\": thread_id,\n",
" }\n",
"}\n",
"\n",
"question = \"\"\"Write a program that prints the numbers from 1 to 100. \n",
"But for multiples of three, print \"Fizz\" instead of the number, and for the multiples of five, print \"Buzz\". \n",
"For numbers which are multiples of both three and five, print \"FizzBuzz\".\"\"\"\n",
"\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
")\n",
"for event in events:\n",
" _print_event(event, _printed)"
]
},
{
"cell_type": "markdown",
@@ -235,7 +565,37 @@
"id": "2bb883df-540b-46ab-9415-fe27db68456f",
"metadata": {},
"outputs": [],
"source": ["import uuid\n\n_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n \"configurable\": {\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\nquestion = \"\"\"I want to vectorize a function\n\n frame = np.zeros((out_h, out_w, 3), dtype=np.uint8)\n for i, val1 in enumerate(rows):\n for j, val2 in enumerate(cols):\n for j, val3 in enumerate(ch):\n # Assuming you want to store the pair as tuples in the matrix\n frame[i, j, k] = image[val1, val2, val3]\n\n out.write(np.array(frame))\n\nwith a simple numpy function that does something like this what is it called. Show me a test case with this working.\"\"\"\n\nevents = graph.stream(\n {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n _print_event(event, _printed)"]
"source": [
"import uuid\n",
"\n",
"_printed = set()\n",
"thread_id = str(uuid.uuid4())\n",
"config = {\n",
" \"configurable\": {\n",
" # Checkpoints are accessed by thread_id\n",
" \"thread_id\": thread_id,\n",
" }\n",
"}\n",
"\n",
"question = \"\"\"I want to vectorize a function\n",
"\n",
" frame = np.zeros((out_h, out_w, 3), dtype=np.uint8)\n",
" for i, val1 in enumerate(rows):\n",
" for j, val2 in enumerate(cols):\n",
" for j, val3 in enumerate(ch):\n",
" # Assuming you want to store the pair as tuples in the matrix\n",
" frame[i, j, k] = image[val1, val2, val3]\n",
"\n",
" out.write(np.array(frame))\n",
"\n",
"with a simple numpy function that does something like this what is it called. Show me a test case with this working.\"\"\"\n",
"\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
")\n",
"for event in events:\n",
" _print_event(event, _printed)"
]
},
{
"cell_type": "markdown",
@@ -253,7 +613,34 @@
"id": "ee05da1f-c272-405d-8a7b-552cfc3106e1",
"metadata": {},
"outputs": [],
"source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n \"configurable\": {\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\nquestion = \"\"\"Create a Python program that allows two players to play a game of Tic-Tac-Toe. The game should be played on a 3x3 grid. The program should:\n\n- Allow players to take turns to input their moves.\n- Check for invalid moves (e.g., placing a marker on an already occupied space).\n- Determine and announce the winner or if the game ends in a draw.\n\nRequirements:\n- Use a 2D list to represent the Tic-Tac-Toe board.\n- Use functions to modularize the code.\n- Validate player input.\n- Check for win conditions and draw conditions after each move.\"\"\"\n\nevents = graph.stream(\n {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n _print_event(event, _printed)"]
"source": [
"_printed = set()\n",
"thread_id = str(uuid.uuid4())\n",
"config = {\n",
" \"configurable\": {\n",
" # Checkpoints are accessed by thread_id\n",
" \"thread_id\": thread_id,\n",
" }\n",
"}\n",
"\n",
"question = \"\"\"Create a Python program that allows two players to play a game of Tic-Tac-Toe. The game should be played on a 3x3 grid. The program should:\n",
"\n",
"- Allow players to take turns to input their moves.\n",
"- Check for invalid moves (e.g., placing a marker on an already occupied space).\n",
"- Determine and announce the winner or if the game ends in a draw.\n",
"\n",
"Requirements:\n",
"- Use a 2D list to represent the Tic-Tac-Toe board.\n",
"- Use functions to modularize the code.\n",
"- Validate player input.\n",
"- Check for win conditions and draw conditions after each move.\"\"\"\n",
"\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
")\n",
"for event in events:\n",
" _print_event(event, _printed)"
]
},
{
"cell_type": "markdown",
@@ -271,7 +658,7 @@
"id": "814fc2a4-8e5b-4faa-8f52-3977226bd09a",
"metadata": {},
"outputs": [],
"source": [""]
"source": []
}
],
"metadata": {
examples/configuration.ipynb
-33
View File
@@ -1,33 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "e9a58c69",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/configuration.ipynb"
]
}
],
"metadata": {
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
examples/create-react-agent-hitl.ipynb
-33
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@@ -1,33 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "a1e6efeb",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/create-react-agent-hitl.ipynb"
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}
],
"metadata": {
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"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
examples/create-react-agent-memory.ipynb
-33
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@@ -1,33 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "1ef41a89",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/create-react-agent-memory.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
examples/create-react-agent-system-prompt.ipynb
-33
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@@ -1,33 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "9e2f7902",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/create-react-agent-system-prompt.ipynb"
]
}
],
"metadata": {
"kernelspec": {
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},
"file_extension": ".py",
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"pygments_lexer": "ipython3",
"version": "3.11.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
examples/create-react-agent.ipynb
-33
View File
@@ -1,33 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "eb07372e",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/create-react-agent.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
examples/customer-support/customer-support.ipynb
+9 -1
View File
@@ -5,7 +5,15 @@
"id": "a8232bc9",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/customer-support/customer-support.ipynb"
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/customer-support/customer-support.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "63da8671",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
]
}
],
examples/extraction/retries.ipynb
+9 -1
View File
@@ -5,7 +5,15 @@
"id": "8dbdba5b",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/extraction/retries.ipynb"
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/extraction/retries.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "1d444b7f",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
]
}
],
examples/human_in_the_loop/wait-user-input.ipynb
+9 -1
View File
@@ -5,7 +5,15 @@
"id": "3ecab357",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb"
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "3f2866bd",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
]
}
],
examples/input_output_schema.ipynb
-33
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@@ -1,33 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "fc0793cb",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/input_output_schema.ipynb"
]
}
],
"metadata": {
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"version": "3.11.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
examples/lats/lats.ipynb
+9 -1
View File
@@ -5,7 +5,15 @@
"id": "09038b53",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/lats/lats.ipynb"
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/lats/lats.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "b1669748",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
]
}
],
examples/llm-compiler/LLMCompiler.ipynb
+9 -1
View File
@@ -5,7 +5,15 @@
"id": "85205e97",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb"
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "2fdab366",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
]
}
],
examples/map-reduce.ipynb
-33
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@@ -1,33 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "42abb708",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/map-reduce.ipynb"
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}
],
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"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
examples/memory/add-summary-conversation-history.ipynb
-33
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@@ -1,33 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "298784f6",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/memory/add-summary-conversation-history.ipynb"
]
}
],
"metadata": {
"kernelspec": {
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"file_extension": ".py",
"mimetype": "text/x-python",
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"version": "3.11.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
examples/memory/delete-messages.ipynb
-33
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@@ -1,33 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "3f4370fd",
"metadata": {},
"source": [
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}
],
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"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
examples/memory/manage-conversation-history.ipynb
-33
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examples/multi_agent/hierarchical_agent_teams.ipynb
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@@ -5,7 +5,15 @@
"id": "5cc8a2ad",
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examples/multi_agent/multi-agent-collaboration.ipynb
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"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb)"
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examples/node-retries.ipynb
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{
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examples/pass-config-to-tools.ipynb
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examples/pass-run-time-values-to-tools.ipynb
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examples/pass_private_state.ipynb
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{
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examples/persistence.ipynb
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{
"cells": [
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examples/persistence_mongodb.ipynb
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examples/persistence_postgres.ipynb
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{
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examples/persistence_redis.ipynb
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examples/plan-and-execute/plan-and-execute.ipynb
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"id": "9138f92e",
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"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb"
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb)"
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],
examples/rag/langgraph_adaptive_rag.ipynb
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@@ -1,5 +1,13 @@
{
"cells": [
{
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examples/rag/langgraph_adaptive_rag_cohere.ipynb
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examples/rag/langgraph_adaptive_rag_local.ipynb
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@@ -1,5 +1,13 @@
{
"cells": [
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examples/rag/langgraph_agentic_rag.ipynb
+11 -3
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@@ -1,5 +1,13 @@
{
"cells": [
{
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"id": "47e3b43b",
"metadata": {},
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"This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
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"id": "425fb020-e864-40ce-a31f-8da40c73d14b",
@@ -200,11 +208,11 @@
"output_type": "stream",
"text": [
"********************Prompt[rlm/rag-prompt]********************\n",
"================================\u001B[1m Human Message \u001B[0m=================================\n",
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"\n",
"You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\n",
"Question: \u001B[33;1m\u001B[1;3m{question}\u001B[0m \n",
"Context: \u001B[33;1m\u001B[1;3m{context}\u001B[0m \n",
"Question: \u001b[33;1m\u001b[1;3m{question}\u001b[0m \n",
"Context: \u001b[33;1m\u001b[1;3m{context}\u001b[0m \n",
"Answer:\n"
]
}
examples/rag/langgraph_crag.ipynb
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@@ -1,5 +1,13 @@
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examples/rag/langgraph_crag_local.ipynb
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@@ -1,5 +1,13 @@
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examples/rag/langgraph_self_rag.ipynb
+8
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@@ -1,5 +1,13 @@
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examples/rag/langgraph_self_rag_local.ipynb
+8
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@@ -1,5 +1,13 @@
{
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{
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examples/rag/langgraph_self_rag_pinecone_movies.ipynb
+300 -16
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@@ -1,5 +1,13 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "403aeb6e",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
]
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@@ -54,7 +62,11 @@
"metadata": {},
"outputs": [],
"source": [
"import os\n\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\nos.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\""
"import os\n",
"\n",
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
"os.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\""
]
},
{
@@ -64,7 +76,9 @@
"metadata": {},
"outputs": [],
"source": [
"import os\n\nos.environ[\"LANGCHAIN_PROJECT\"] = \"pinecone-devconnect\""
"import os\n",
"\n",
"os.environ[\"LANGCHAIN_PROJECT\"] = \"pinecone-devconnect\""
]
},
{
@@ -84,7 +98,18 @@
"metadata": {},
"outputs": [],
"source": [
"from langchain_openai import OpenAIEmbeddings\nfrom langchain_pinecone import PineconeVectorStore\n\n# use pinecone movies database\n\n# Add to vectorDB\nvectorstore = PineconeVectorStore(\n embedding=OpenAIEmbeddings(),\n index_name=\"sample-movies\",\n text_key=\"summary\",\n)\nretriever = vectorstore.as_retriever()"
"from langchain_openai import OpenAIEmbeddings\n",
"from langchain_pinecone import PineconeVectorStore\n",
"\n",
"# use pinecone movies database\n",
"\n",
"# Add to vectorDB\n",
"vectorstore = PineconeVectorStore(\n",
" embedding=OpenAIEmbeddings(),\n",
" index_name=\"sample-movies\",\n",
" text_key=\"summary\",\n",
")\n",
"retriever = vectorstore.as_retriever()"
]
},
{
@@ -113,7 +138,11 @@
}
],
"source": [
"docs = retriever.invoke(\"James Cameron\")\nfor doc in docs:\n print(\"# \" + doc.metadata[\"title\"])\n print(doc.page_content)\n print()"
"docs = retriever.invoke(\"James Cameron\")\n",
"for doc in docs:\n",
" print(\"# \" + doc.metadata[\"title\"])\n",
" print(doc.page_content)\n",
" print()"
]
},
{
@@ -173,7 +202,12 @@
}
],
"source": [
"# Test the retrieval grader\nquestion = \"movies starring jason momoa\"\ndocs = retriever.invoke(question)\ndoc_txt = docs[0].page_content\nprint(doc_txt)\nprint(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
"# Test the retrieval grader\n",
"question = \"movies starring jason momoa\"\n",
"docs = retriever.invoke(question)\n",
"doc_txt = docs[0].page_content\n",
"print(doc_txt)\n",
"print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
]
},
{
@@ -201,7 +235,23 @@
}
],
"source": [
"### Generate\n\nfrom langchain import hub\nfrom langchain_core.output_parsers import StrOutputParser\n\n# Prompt\nprompt = hub.pull(\"rlm/rag-prompt\")\n\n# LLM\nllm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n\n# Chain\nrag_chain = prompt | llm | StrOutputParser()\n\n# Run\ngeneration = rag_chain.invoke({\"context\": docs, \"question\": question})\nprint(generation)"
"### Generate\n",
"\n",
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"\n",
"# Prompt\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"# LLM\n",
"llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
"\n",
"# Chain\n",
"rag_chain = prompt | llm | StrOutputParser()\n",
"\n",
"# Run\n",
"generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
"print(generation)"
]
},
{
@@ -329,7 +379,17 @@
}
],
"source": [
"### Question Re-writer\n\n# LLM\nllm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n\n# Prompt\nre_write_prompt = hub.pull(\"efriis/self-rag-question-rewriter\")\n\nquestion_rewriter = re_write_prompt | llm | StrOutputParser()\nprint(question)\nquestion_rewriter.invoke({\"question\": question})"
"### Question Re-writer\n",
"\n",
"# LLM\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"\n",
"# Prompt\n",
"re_write_prompt = hub.pull(\"efriis/self-rag-question-rewriter\")\n",
"\n",
"question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
"print(question)\n",
"question_rewriter.invoke({\"question\": question})"
]
},
{
@@ -351,7 +411,24 @@
"metadata": {},
"outputs": [],
"source": [
"from typing import List\n\nfrom typing_extensions import TypedDict\n\n\nclass GraphState(TypedDict):\n \"\"\"\n Represents the state of our graph.\n\n Attributes:\n question: question\n generation: LLM generation\n documents: list of documents\n \"\"\"\n\n question: str\n generation: str\n documents: List[str]"
"from typing import List\n",
"\n",
"from typing_extensions import TypedDict\n",
"\n",
"\n",
"class GraphState(TypedDict):\n",
" \"\"\"\n",
" Represents the state of our graph.\n",
"\n",
" Attributes:\n",
" question: question\n",
" generation: LLM generation\n",
" documents: list of documents\n",
" \"\"\"\n",
"\n",
" question: str\n",
" generation: str\n",
" documents: List[str]"
]
},
{
@@ -361,7 +438,95 @@
"metadata": {},
"outputs": [],
"source": [
"### Nodes\n\n\ndef retrieve(state):\n \"\"\"\n Retrieve documents\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): New key added to state, documents, that contains retrieved documents\n \"\"\"\n print(\"---RETRIEVE---\")\n question = state[\"question\"]\n\n # Retrieval\n documents = retriever.invoke(question)\n return {\"documents\": documents, \"question\": question}\n\n\ndef generate(state):\n \"\"\"\n Generate answer\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): New key added to state, generation, that contains LLM generation\n \"\"\"\n print(\"---GENERATE---\")\n question = state[\"question\"]\n documents = state[\"documents\"]\n\n # RAG generation\n generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n return {\"documents\": documents, \"question\": question, \"generation\": generation}\n\n\ndef grade_documents(state):\n \"\"\"\n Determines whether the retrieved documents are relevant to the question.\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): Updates documents key with only filtered relevant documents\n \"\"\"\n\n print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n question = state[\"question\"]\n documents = state[\"documents\"]\n\n # Score each doc\n filtered_docs = []\n for d in documents:\n score = retrieval_grader.invoke(\n {\"question\": question, \"document\": d.page_content}\n )\n grade = score.binary_score\n if grade == \"yes\":\n print(\"---GRADE: DOCUMENT RELEVANT---\")\n filtered_docs.append(d)\n else:\n print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n continue\n return {\"documents\": filtered_docs, \"question\": question}\n\n\ndef transform_query(state):\n \"\"\"\n Transform the query to produce a better question.\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): Updates question key with a re-phrased question\n \"\"\"\n\n print(\"---TRANSFORM QUERY---\")\n question = state[\"question\"]\n documents = state[\"documents\"]\n\n # Re-write question\n better_question = question_rewriter.invoke({\"question\": question})\n return {\"documents\": documents, \"question\": better_question}"
"### Nodes\n",
"\n",
"\n",
"def retrieve(state):\n",
" \"\"\"\n",
" Retrieve documents\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): New key added to state, documents, that contains retrieved documents\n",
" \"\"\"\n",
" print(\"---RETRIEVE---\")\n",
" question = state[\"question\"]\n",
"\n",
" # Retrieval\n",
" documents = retriever.invoke(question)\n",
" return {\"documents\": documents, \"question\": question}\n",
"\n",
"\n",
"def generate(state):\n",
" \"\"\"\n",
" Generate answer\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): New key added to state, generation, that contains LLM generation\n",
" \"\"\"\n",
" print(\"---GENERATE---\")\n",
" question = state[\"question\"]\n",
" documents = state[\"documents\"]\n",
"\n",
" # RAG generation\n",
" generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
" return {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
"\n",
"\n",
"def grade_documents(state):\n",
" \"\"\"\n",
" Determines whether the retrieved documents are relevant to the question.\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): Updates documents key with only filtered relevant documents\n",
" \"\"\"\n",
"\n",
" print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
" question = state[\"question\"]\n",
" documents = state[\"documents\"]\n",
"\n",
" # Score each doc\n",
" filtered_docs = []\n",
" for d in documents:\n",
" score = retrieval_grader.invoke(\n",
" {\"question\": question, \"document\": d.page_content}\n",
" )\n",
" grade = score.binary_score\n",
" if grade == \"yes\":\n",
" print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
" filtered_docs.append(d)\n",
" else:\n",
" print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
" continue\n",
" return {\"documents\": filtered_docs, \"question\": question}\n",
"\n",
"\n",
"def transform_query(state):\n",
" \"\"\"\n",
" Transform the query to produce a better question.\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): Updates question key with a re-phrased question\n",
" \"\"\"\n",
"\n",
" print(\"---TRANSFORM QUERY---\")\n",
" question = state[\"question\"]\n",
" documents = state[\"documents\"]\n",
"\n",
" # Re-write question\n",
" better_question = question_rewriter.invoke({\"question\": question})\n",
" return {\"documents\": documents, \"question\": better_question}"
]
},
{
@@ -371,7 +536,74 @@
"metadata": {},
"outputs": [],
"source": [
"### Edges\n\n\ndef decide_to_generate(state):\n \"\"\"\n Determines whether to generate an answer, or re-generate a question.\n\n Args:\n state (dict): The current graph state\n\n Returns:\n str: Binary decision for next node to call\n \"\"\"\n\n print(\"---ASSESS GRADED DOCUMENTS---\")\n state[\"question\"]\n filtered_documents = state[\"documents\"]\n\n if not filtered_documents:\n # All documents have been filtered check_relevance\n # We will re-generate a new query\n print(\n \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n )\n return \"transform_query\"\n else:\n # We have relevant documents, so generate answer\n print(\"---DECISION: GENERATE---\")\n return \"generate\"\n\n\ndef grade_generation_v_documents_and_question(state):\n \"\"\"\n Determines whether the generation is grounded in the document and answers question.\n\n Args:\n state (dict): The current graph state\n\n Returns:\n str: Decision for next node to call\n \"\"\"\n\n print(\"---CHECK HALLUCINATIONS---\")\n question = state[\"question\"]\n documents = state[\"documents\"]\n generation = state[\"generation\"]\n\n score = hallucination_grader.invoke(\n {\"documents\": documents, \"generation\": generation}\n )\n grade = score.binary_score\n\n # Check hallucination\n if grade == \"yes\":\n print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n # Check question-answering\n print(\"---GRADE GENERATION vs QUESTION---\")\n score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n grade = score.binary_score\n if grade == \"yes\":\n print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n return \"useful\"\n else:\n print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n return \"not useful\"\n else:\n pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n return \"not supported\""
"### Edges\n",
"\n",
"\n",
"def decide_to_generate(state):\n",
" \"\"\"\n",
" Determines whether to generate an answer, or re-generate a question.\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" str: Binary decision for next node to call\n",
" \"\"\"\n",
"\n",
" print(\"---ASSESS GRADED DOCUMENTS---\")\n",
" state[\"question\"]\n",
" filtered_documents = state[\"documents\"]\n",
"\n",
" if not filtered_documents:\n",
" # All documents have been filtered check_relevance\n",
" # We will re-generate a new query\n",
" print(\n",
" \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n",
" )\n",
" return \"transform_query\"\n",
" else:\n",
" # We have relevant documents, so generate answer\n",
" print(\"---DECISION: GENERATE---\")\n",
" return \"generate\"\n",
"\n",
"\n",
"def grade_generation_v_documents_and_question(state):\n",
" \"\"\"\n",
" Determines whether the generation is grounded in the document and answers question.\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" str: Decision for next node to call\n",
" \"\"\"\n",
"\n",
" print(\"---CHECK HALLUCINATIONS---\")\n",
" question = state[\"question\"]\n",
" documents = state[\"documents\"]\n",
" generation = state[\"generation\"]\n",
"\n",
" score = hallucination_grader.invoke(\n",
" {\"documents\": documents, \"generation\": generation}\n",
" )\n",
" grade = score.binary_score\n",
"\n",
" # Check hallucination\n",
" if grade == \"yes\":\n",
" print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n",
" # Check question-answering\n",
" print(\"---GRADE GENERATION vs QUESTION---\")\n",
" score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n",
" grade = score.binary_score\n",
" if grade == \"yes\":\n",
" print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n",
" return \"useful\"\n",
" else:\n",
" print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n",
" return \"not useful\"\n",
" else:\n",
" pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
" return \"not supported\""
]
},
{
@@ -390,7 +622,42 @@
"id": "0e09ca9f-e36d-4ef4-a0d5-79fdbada9fe0",
"metadata": {},
"outputs": [],
"source": ["from langgraph.graph import END, StateGraph, START\n\nworkflow = StateGraph(GraphState)\n\n# Define the nodes\nworkflow.add_node(\"retrieve\", retrieve) # retrieve\nworkflow.add_node(\"grade_documents\", grade_documents) # grade documents\nworkflow.add_node(\"generate\", generate) # generate\nworkflow.add_node(\"transform_query\", transform_query) # transform_query\n\n# Build graph\nworkflow.add_edge(START, \"retrieve\")\nworkflow.add_edge(\"retrieve\", \"grade_documents\")\nworkflow.add_conditional_edges(\n \"grade_documents\",\n decide_to_generate,\n {\n \"transform_query\": \"transform_query\",\n \"generate\": \"generate\",\n },\n)\nworkflow.add_edge(\"transform_query\", \"retrieve\")\nworkflow.add_conditional_edges(\n \"generate\",\n grade_generation_v_documents_and_question,\n {\n \"not supported\": \"generate\",\n \"useful\": END,\n \"not useful\": \"transform_query\",\n },\n)\n\n# Compile\napp = workflow.compile()"]
"source": [
"from langgraph.graph import END, StateGraph, START\n",
"\n",
"workflow = StateGraph(GraphState)\n",
"\n",
"# Define the nodes\n",
"workflow.add_node(\"retrieve\", retrieve) # retrieve\n",
"workflow.add_node(\"grade_documents\", grade_documents) # grade documents\n",
"workflow.add_node(\"generate\", generate) # generate\n",
"workflow.add_node(\"transform_query\", transform_query) # transform_query\n",
"\n",
"# Build graph\n",
"workflow.add_edge(START, \"retrieve\")\n",
"workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
"workflow.add_conditional_edges(\n",
" \"grade_documents\",\n",
" decide_to_generate,\n",
" {\n",
" \"transform_query\": \"transform_query\",\n",
" \"generate\": \"generate\",\n",
" },\n",
")\n",
"workflow.add_edge(\"transform_query\", \"retrieve\")\n",
"workflow.add_conditional_edges(\n",
" \"generate\",\n",
" grade_generation_v_documents_and_question,\n",
" {\n",
" \"not supported\": \"generate\",\n",
" \"useful\": END,\n",
" \"not useful\": \"transform_query\",\n",
" },\n",
")\n",
"\n",
"# Compile\n",
"app = workflow.compile()"
]
},
{
"cell_type": "code",
@@ -426,7 +693,18 @@
}
],
"source": [
"from pprint import pprint\n\n# Run\ninputs = {\"question\": \"Movies that star Daniel Craig\"}\nfor output in app.stream(inputs):\n for key, value in output.items():\n # Node\n pprint(f\"Node '{key}':\")\n pprint(\"\\n---\\n\")\n\n# Final generation\npprint(value[\"generation\"])"
"from pprint import pprint\n",
"\n",
"# Run\n",
"inputs = {\"question\": \"Movies that star Daniel Craig\"}\n",
"for output in app.stream(inputs):\n",
" for key, value in output.items():\n",
" # Node\n",
" pprint(f\"Node '{key}':\")\n",
" pprint(\"\\n---\\n\")\n",
"\n",
"# Final generation\n",
"pprint(value[\"generation\"])"
]
},
{
@@ -436,7 +714,15 @@
"metadata": {},
"outputs": [],
"source": [
"inputs = {\"question\": \"Which movies are about aliens?\"}\nfor output in app.stream(inputs):\n for key, value in output.items():\n # Node\n pprint(f\"Node '{key}':\")\n pprint(\"\\n---\\n\")\n\n# Final generation\npprint(value[\"generation\"])"
"inputs = {\"question\": \"Which movies are about aliens?\"}\n",
"for output in app.stream(inputs):\n",
" for key, value in output.items():\n",
" # Node\n",
" pprint(f\"Node '{key}':\")\n",
" pprint(\"\\n---\\n\")\n",
"\n",
"# Final generation\n",
"pprint(value[\"generation\"])"
]
},
{
@@ -445,9 +731,7 @@
"id": "42369ab8-322d-434a-b5dd-2266e4cb2903",
"metadata": {},
"outputs": [],
"source": [
""
]
"source": []
}
],
"metadata": {
examples/react-agent-from-scratch.ipynb
+8 -1
View File
@@ -5,7 +5,14 @@
"id": "294995c4",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/react-agent-from-scratch.ipynb"
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/react-agent-from-scratch.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
]
}
],
examples/react-agent-structured-output.ipynb
+8 -1
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@@ -5,7 +5,14 @@
"id": "40f0d107",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/react-agent-structured-output.ipynb"
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/react-agent-structured-output.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
]
}
],
examples/recursion-limit.ipynb
-33
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@@ -1,33 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "fa3f7c50",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/recursion-limit.ipynb"
]
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"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
examples/reflection/reflection.ipynb
+9 -1
View File
@@ -5,7 +5,15 @@
"id": "658773a2",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/reflection/reflection.ipynb"
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/reflection/reflection.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "1cb60657",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
]
}
],
examples/reflexion/reflexion.ipynb
+9 -1
View File
@@ -5,7 +5,15 @@
"id": "caf07859",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/reflexion/reflexion.ipynb"
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/reflexion/reflexion.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "cd1df0e0",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
]
}
],
examples/rewoo/rewoo.ipynb
+9 -1
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@@ -5,7 +5,15 @@
"id": "961f43ec",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/rewoo/rewoo.ipynb"
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/rewoo/rewoo.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "7f00c427",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
]
}
],
examples/run-id-langsmith.ipynb
+8 -1
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@@ -5,7 +5,14 @@
"id": "bbd6e9b8",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/run-id-langsmith.ipynb"
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/run-id-langsmith.md)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
]
}
],
examples/self-discover/self-discover.ipynb
+9 -1
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@@ -5,7 +5,15 @@
"id": "f6db1873",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/self-discover/self-discover.ipynb"
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/self-discover/self-discover.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "219a78f9",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
]
}
],
examples/state-model.ipynb
-33
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@@ -1,33 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "4149ffcc",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/state-model.ipynb"
]
}
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"nbformat_minor": 5
}
examples/storm/storm.ipynb
-33
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@@ -1,33 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "3e05d7f9",
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"version": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
examples/stream-multiple.ipynb
-33
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@@ -1,33 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "e663f597",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/stream-multiple.ipynb"
]
}
],
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}
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}
examples/stream-updates.ipynb
-33
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@@ -1,33 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "e6829c80",
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}
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"nbformat_minor": 5
}
examples/stream-values.ipynb
-33
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@@ -1,33 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "5ec11895",
"metadata": {},
"source": [
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]
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examples/streaming-content.ipynb
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@@ -1,33 +0,0 @@
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examples/streaming-events-from-within-tools-without-langchain.ipynb
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examples/streaming-events-from-within-tools.ipynb
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@@ -1,33 +0,0 @@
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examples/streaming-from-final-node.ipynb
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@@ -1,33 +0,0 @@
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examples/streaming-subgraphs.ipynb
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examples/streaming-tokens-without-langchain.ipynb
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@@ -1,33 +0,0 @@
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examples/subgraph-transform-state.ipynb
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examples/subgraph.ipynb
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@@ -5,7 +5,14 @@
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examples/subgraphs-manage-state.ipynb
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{
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examples/tool-calling.ipynb
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@@ -5,7 +5,14 @@
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examples/tutorials/sql-agent.ipynb
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@@ -5,7 +5,15 @@
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examples/tutorials/tnt-llm/tnt-llm.ipynb
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@@ -5,7 +5,15 @@
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examples/usaco/usaco.ipynb
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@@ -5,7 +5,15 @@
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examples/visualization.ipynb
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examples/web-navigation/web_voyager.ipynb
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@@ -5,7 +5,15 @@
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],
libs/langgraph/langgraph/types.py
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@@ -61,18 +61,22 @@ __all__ = (
Durability = Literal["sync", "async", "exit"]
"""Durability mode for the graph execution.
- `"sync"`: Changes are persisted synchronously before the next step starts.
- `"async"`: Changes are persisted asynchronously while the next step executes.
- `"exit"`: Changes are persisted only when the graph exits."""
- `'sync'`: Changes are persisted synchronously before the next step starts.
- `'async'`: Changes are persisted asynchronously while the next step executes.
- `'exit'`: Changes are persisted only when the graph exits.
"""
All = Literal["*"]
"""Special value to indicate that graph should interrupt on all nodes."""
Checkpointer = None | bool | BaseCheckpointSaver
"""Type of the checkpointer to use for a subgraph.
- True enables persistent checkpointing for this subgraph.
- False disables checkpointing, even if the parent graph has a checkpointer.
- None inherits checkpointer from the parent graph."""
- `True` enables persistent checkpointing for this subgraph.
- `False` disables checkpointing, even if the parent graph has a checkpointer.
- `None` inherits checkpointer from the parent graph.
"""
def ensure_valid_checkpointer(checkpointer: Checkpointer) -> Checkpointer: