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docs: Replace unsupported models with structured output-supported models (#3982)

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Ykoh
2025-06-09 14:17:05 -04:00
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parent c42cd57a32
commit 0035ab9825
10 changed files with 144 additions and 47 deletions
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docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb
+1 -3
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@@ -580,9 +580,7 @@
" ]\n",
")\n",
"\n",
"evaluator = prompt | ChatOpenAI(model=\"gpt-4-turbo-preview\").with_structured_output(\n",
" RedTeamingResult, method=\"function_calling\"\n",
")\n",
"evaluator = prompt | ChatOpenAI(model=\"gpt-4o\").with_structured_output(RedTeamingResult)\n",
"\n",
"\n",
"def did_resist(run, example):\n",
docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb
+9 -9
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@@ -471,7 +471,7 @@
"\n",
"_get_pass(\"TAVILY_API_KEY\")\n",
"\n",
"calculate = get_math_tool(ChatOpenAI(model=\"gpt-4-turbo-preview\"))\n",
"calculate = get_math_tool(ChatOpenAI(model=\"gpt-4o\"))\n",
"search = TavilySearchResults(\n",
" max_results=1,\n",
" description='tavily_search_results_json(query=\"the search query\") - a search engine.',\n",
@@ -540,11 +540,11 @@
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001b[1m System Message \u001b[0m================================\n",
"================================\u001B[1m System Message \u001B[0m================================\n",
"\n",
"Given a user query, create a plan to solve it with the utmost parallelizability. Each plan should comprise an action from the following \u001b[33;1m\u001b[1;3m{num_tools}\u001b[0m types:\n",
"\u001b[33;1m\u001b[1;3m{tool_descriptions}\u001b[0m\n",
"\u001b[33;1m\u001b[1;3m{num_tools}\u001b[0m. join(): Collects and combines results from prior actions.\n",
"Given a user query, create a plan to solve it with the utmost parallelizability. Each plan should comprise an action from the following \u001B[33;1m\u001B[1;3m{num_tools}\u001B[0m types:\n",
"\u001B[33;1m\u001B[1;3m{tool_descriptions}\u001B[0m\n",
"\u001B[33;1m\u001B[1;3m{num_tools}\u001B[0m. join(): Collects and combines results from prior actions.\n",
"\n",
" - An LLM agent is called upon invoking join() to either finalize the user query or wait until the plans are executed.\n",
" - join should always be the last action in the plan, and will be called in two scenarios:\n",
@@ -561,11 +561,11 @@
" - Only use the provided action types. If a query cannot be addressed using these, invoke the join action for the next steps.\n",
" - Never introduce new actions other than the ones provided.\n",
"\n",
"=============================\u001b[1m Messages Placeholder \u001b[0m=============================\n",
"=============================\u001B[1m Messages Placeholder \u001B[0m=============================\n",
"\n",
"\u001b[33;1m\u001b[1;3m{messages}\u001b[0m\n",
"\u001B[33;1m\u001B[1;3m{messages}\u001B[0m\n",
"\n",
"================================\u001b[1m System Message \u001b[0m================================\n",
"================================\u001B[1m System Message \u001B[0m================================\n",
"\n",
"Remember, ONLY respond with the task list in the correct format! E.g.:\n",
"idx. tool(arg_name=args)\n",
@@ -1030,7 +1030,7 @@
"joiner_prompt = hub.pull(\"wfh/llm-compiler-joiner\").partial(\n",
" examples=\"\"\n",
") # You can optionally add examples\n",
"llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")\n",
"llm = ChatOpenAI(model=\"gpt-4o\")\n",
"\n",
"runnable = joiner_prompt | llm.with_structured_output(\n",
" JoinOutputs, method=\"function_calling\"\n",
docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb
-1
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@@ -135,7 +135,6 @@
"metadata": {},
"outputs": [],
"source": [
"from langchain import hub\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"from langgraph.prebuilt import create_react_agent\n",
docs/docs/tutorials/rag/langgraph_crag.ipynb
+1 -1
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@@ -185,7 +185,7 @@
"\n",
"\n",
"# LLM with function call\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
"structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
"\n",
"# Prompt\n",
examples/rag/langgraph_adaptive_rag.ipynb
+4 -4
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@@ -184,7 +184,7 @@
"\n",
"\n",
"# LLM with function call\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
"structured_llm_router = llm.with_structured_output(RouteQuery)\n",
"\n",
"# Prompt\n",
@@ -235,7 +235,7 @@
"\n",
"\n",
"# LLM with function call\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
"structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
"\n",
"# Prompt\n",
@@ -328,7 +328,7 @@
"\n",
"\n",
"# LLM with function call\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
"structured_llm_grader = llm.with_structured_output(GradeHallucinations)\n",
"\n",
"# Prompt\n",
@@ -376,7 +376,7 @@
"\n",
"\n",
"# LLM with function call\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
"structured_llm_grader = llm.with_structured_output(GradeAnswer)\n",
"\n",
"# Prompt\n",
examples/rag/langgraph_agentic_rag.ipynb
+4 -4
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@@ -200,11 +200,11 @@
"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"
]
}
@@ -244,7 +244,7 @@
" binary_score: str = Field(description=\"Relevance score 'yes' or 'no'\")\n",
"\n",
" # LLM\n",
" model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
" model = ChatOpenAI(temperature=0, model=\"gpt-4o\", streaming=True)\n",
"\n",
" # LLM with tool and validation\n",
" llm_with_tool = model.with_structured_output(grade)\n",
examples/rag/langgraph_crag.ipynb
+1 -1
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@@ -171,7 +171,7 @@
"\n",
"\n",
"# LLM with function call\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
"structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
"\n",
"# Prompt\n",
examples/rag/langgraph_self_rag.ipynb
+3 -3
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@@ -191,7 +191,7 @@
"\n",
"\n",
"# LLM with function call\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
"structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
"\n",
"# Prompt\n",
@@ -284,7 +284,7 @@
"\n",
"\n",
"# LLM with function call\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
"structured_llm_grader = llm.with_structured_output(GradeHallucinations)\n",
"\n",
"# Prompt\n",
@@ -332,7 +332,7 @@
"\n",
"\n",
"# LLM with function call\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
"structured_llm_grader = llm.with_structured_output(GradeAnswer)\n",
"\n",
"# Prompt\n",
examples/rag/langgraph_self_rag_pinecone_movies.ipynb
+117 -17
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@@ -33,7 +33,9 @@
"id": "a384cc48-0425-4e8f-aafc-cfb8e56025c9",
"metadata": {},
"outputs": [],
"source": ["%pip install -qU langchain-pinecone langchain-openai langchainhub langgraph"]
"source": [
"%pip install -qU langchain-pinecone langchain-openai langchainhub langgraph"
]
},
{
"cell_type": "markdown",
@@ -51,7 +53,9 @@
"id": "ccc3dae5-1df6-48ca-af8a-50f0e6128876",
"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>\""]
"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>\""
]
},
{
"cell_type": "code",
@@ -59,7 +63,9 @@
"id": "88637820",
"metadata": {},
"outputs": [],
"source": ["import os\n\nos.environ[\"LANGCHAIN_PROJECT\"] = \"pinecone-devconnect\""]
"source": [
"import os\n\nos.environ[\"LANGCHAIN_PROJECT\"] = \"pinecone-devconnect\""
]
},
{
"cell_type": "markdown",
@@ -77,7 +83,9 @@
"id": "565a6d44-2c9f-4fff-b1ec-eea05df9350d",
"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()"]
"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()"
]
},
{
"cell_type": "code",
@@ -104,7 +112,9 @@
]
}
],
"source": ["docs = retriever.invoke(\"James Cameron\")\nfor doc in docs:\n print(\"# \" + doc.metadata[\"title\"])\n print(doc.page_content)\n print()"]
"source": [
"docs = retriever.invoke(\"James Cameron\")\nfor doc in docs:\n print(\"# \" + doc.metadata[\"title\"])\n print(doc.page_content)\n print()"
]
},
{
"cell_type": "markdown",
@@ -120,7 +130,32 @@
"id": "1fafad21-60cc-483e-92a3-6a7edb1838e3",
"metadata": {},
"outputs": [],
"source": ["### Retrieval Grader\n\nfrom langchain import hub\nfrom langchain_core.pydantic_v1 import BaseModel, Field\nfrom langchain_openai import ChatOpenAI\n\n\n# Data model\nclass GradeDocuments(BaseModel):\n \"\"\"Binary score for relevance check on retrieved documents.\"\"\"\n\n binary_score: str = Field(\n description=\"Documents are relevant to the question, 'yes' or 'no'\"\n )\n\n\n# https://smith.langchain.com/hub/efriis/self-rag-retrieval-grader\ngrade_prompt = hub.pull(\"efriis/self-rag-retrieval-grader\")\n\n# LLM with function call\nllm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\nstructured_llm_grader = llm.with_structured_output(GradeDocuments)\n\nretrieval_grader = grade_prompt | structured_llm_grader"]
"source": [
"### Retrieval Grader\n",
"\n",
"from langchain import hub\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"\n",
"# Data model\n",
"class GradeDocuments(BaseModel):\n",
" \"\"\"Binary score for relevance check on retrieved documents.\"\"\"\n",
"\n",
" binary_score: str = Field(\n",
" description=\"Documents are relevant to the question, 'yes' or 'no'\"\n",
" )\n",
"\n",
"\n",
"# https://smith.langchain.com/hub/efriis/self-rag-retrieval-grader\n",
"grade_prompt = hub.pull(\"efriis/self-rag-retrieval-grader\")\n",
"\n",
"# LLM with function call\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
"structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
"\n",
"retrieval_grader = grade_prompt | structured_llm_grader"
]
},
{
"cell_type": "code",
@@ -137,7 +172,9 @@
]
}
],
"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}))"]
"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}))"
]
},
{
"cell_type": "markdown",
@@ -163,7 +200,9 @@
]
}
],
"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)"]
"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)"
]
},
{
"cell_type": "code",
@@ -189,7 +228,30 @@
"output_type": "execute_result"
}
],
"source": ["### Hallucination Grader\n\n\n# Data model\nclass GradeHallucinations(BaseModel):\n \"\"\"Binary score for hallucination present in generation answer.\"\"\"\n\n binary_score: str = Field(\n description=\"Answer is grounded in the facts, 'yes' or 'no'\"\n )\n\n\n# LLM with function call\nllm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\nstructured_llm_grader = llm.with_structured_output(GradeHallucinations)\n\n# https://smith.langchain.com/hub/efriis/self-rag-hallucination-grader\nhallucination_prompt = hub.pull(\"efriis/self-rag-hallucination-grader\")\n\nhallucination_grader = hallucination_prompt | structured_llm_grader\nprint(generation)\nhallucination_grader.invoke({\"documents\": docs, \"generation\": generation})"]
"source": [
"### Hallucination Grader\n",
"\n",
"\n",
"# Data model\n",
"class GradeHallucinations(BaseModel):\n",
" \"\"\"Binary score for hallucination present in generation answer.\"\"\"\n",
"\n",
" binary_score: str = Field(\n",
" description=\"Answer is grounded in the facts, 'yes' or 'no'\"\n",
" )\n",
"\n",
"\n",
"# LLM with function call\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
"structured_llm_grader = llm.with_structured_output(GradeHallucinations)\n",
"\n",
"# https://smith.langchain.com/hub/efriis/self-rag-hallucination-grader\n",
"hallucination_prompt = hub.pull(\"efriis/self-rag-hallucination-grader\")\n",
"\n",
"hallucination_grader = hallucination_prompt | structured_llm_grader\n",
"print(generation)\n",
"hallucination_grader.invoke({\"documents\": docs, \"generation\": generation})"
]
},
{
"cell_type": "code",
@@ -216,7 +278,31 @@
"output_type": "execute_result"
}
],
"source": ["### Answer Grader\n\n\n# Data model\nclass GradeAnswer(BaseModel):\n \"\"\"Binary score to assess answer addresses question.\"\"\"\n\n binary_score: str = Field(\n description=\"Answer addresses the question, 'yes' or 'no'\"\n )\n\n\n# LLM with function call\nllm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\nstructured_llm_grader = llm.with_structured_output(GradeAnswer)\n\n# Prompt\nanswer_prompt = hub.pull(\"efriis/self-rag-answer-grader\")\n\nanswer_grader = answer_prompt | structured_llm_grader\nprint(question)\nprint(generation)\nanswer_grader.invoke({\"question\": question, \"generation\": generation})"]
"source": [
"### Answer Grader\n",
"\n",
"\n",
"# Data model\n",
"class GradeAnswer(BaseModel):\n",
" \"\"\"Binary score to assess answer addresses question.\"\"\"\n",
"\n",
" binary_score: str = Field(\n",
" description=\"Answer addresses the question, 'yes' or 'no'\"\n",
" )\n",
"\n",
"\n",
"# LLM with function call\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
"structured_llm_grader = llm.with_structured_output(GradeAnswer)\n",
"\n",
"# Prompt\n",
"answer_prompt = hub.pull(\"efriis/self-rag-answer-grader\")\n",
"\n",
"answer_grader = answer_prompt | structured_llm_grader\n",
"print(question)\n",
"print(generation)\n",
"answer_grader.invoke({\"question\": question, \"generation\": generation})"
]
},
{
"cell_type": "code",
@@ -242,7 +328,9 @@
"output_type": "execute_result"
}
],
"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})"]
"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})"
]
},
{
"cell_type": "markdown",
@@ -262,7 +350,9 @@
"id": "f1617e9e-66a8-4c1a-a1fe-cc936284c085",
"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]"]
"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]"
]
},
{
"cell_type": "code",
@@ -270,7 +360,9 @@
"id": "add509d8-6682-4127-8d95-13dd37d79702",
"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}"]
"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}"
]
},
{
"cell_type": "code",
@@ -278,7 +370,9 @@
"id": "09fc91b4",
"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\""]
"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\""
]
},
{
"cell_type": "markdown",
@@ -331,7 +425,9 @@
]
}
],
"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\"])"]
"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\"])"
]
},
{
"cell_type": "code",
@@ -339,7 +435,9 @@
"id": "4138bc51-8c84-4b8a-8d24-f7f470721f6f",
"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\"])"]
"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\"])"
]
},
{
"cell_type": "code",
@@ -347,7 +445,9 @@
"id": "42369ab8-322d-434a-b5dd-2266e4cb2903",
"metadata": {},
"outputs": [],
"source": [""]
"source": [
""
]
}
],
"metadata": {
libs/cli/examples/graphs/storm.py
+4 -4
View File
@@ -22,10 +22,10 @@ from langgraph.graph import END, StateGraph
from pydantic import BaseModel, Field
from typing_extensions import TypedDict
fast_llm = ChatOpenAI(model="gpt-3.5-turbo")
fast_llm = ChatOpenAI(model="gpt-4o-mini")
# Uncomment for a Fireworks model
# fast_llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", max_tokens=32_000)
long_context_llm = ChatOpenAI(model="gpt-4-turbo-preview")
long_context_llm = ChatOpenAI(model="gpt-4o")
direct_gen_outline_prompt = ChatPromptTemplate.from_messages(
@@ -144,7 +144,7 @@ gen_perspectives_prompt = ChatPromptTemplate.from_messages(
)
gen_perspectives_chain = gen_perspectives_prompt | ChatOpenAI(
model="gpt-3.5-turbo"
model="gpt-4o-mini"
).with_structured_output(Perspectives)
@@ -270,7 +270,7 @@ gen_queries_prompt = ChatPromptTemplate.from_messages(
]
)
gen_queries_chain = gen_queries_prompt | ChatOpenAI(
model="gpt-3.5-turbo"
model="gpt-4o-mini"
).with_structured_output(Queries, include_raw=True)