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docs: format (#17143)

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Erick Friis 2024-02-06 16:32:53 -08:00 committed by GitHub
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5 changed files with 21 additions and 15 deletions
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16
docs/docs/integrations/chat/anthropic.ipynb
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@ -90,16 +90,20 @@
} }
], ],
"source": [ "source": [
"system = \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n", "system = (\n",
" \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
")\n",
"human = \"{text}\"\n", "human = \"{text}\"\n",
"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n", "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
"\n", "\n",
"chain = prompt | chat\n", "chain = prompt | chat\n",
"chain.invoke({\n", "chain.invoke(\n",
" \"input_language\": \"English\",\n", " {\n",
" \"output_language\": \"Korean\",\n", " \"input_language\": \"English\",\n",
" \"text\": \"I love Python\",\n", " \"output_language\": \"Korean\",\n",
"})" " \"text\": \"I love Python\",\n",
" }\n",
")"
] ]
}, },
{ {

4
docs/docs/integrations/chat/google_vertex_ai_palm.ipynb
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@ -424,9 +424,7 @@
"human = \"{text}\"\n", "human = \"{text}\"\n",
"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n", "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
"\n", "\n",
"chat = ChatVertexAI(\n", "chat = ChatVertexAI(model_name=\"chat-bison\", max_output_tokens=1000, temperature=0.5)\n",
" model_name=\"chat-bison\", max_output_tokens=1000, temperature=0.5\n",
")\n",
"chain = prompt | chat\n", "chain = prompt | chat\n",
"\n", "\n",
"asyncio.run(\n", "asyncio.run(\n",

4
docs/docs/use_cases/csv.ipynb
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@ -460,9 +460,7 @@
"Given a user question, write the Python code to answer it. \\\n", "Given a user question, write the Python code to answer it. \\\n",
"Return ONLY the valid Python code and nothing else. \\\n", "Return ONLY the valid Python code and nothing else. \\\n",
"Don't assume you have access to any libraries other than built-in Python ones and pandas.\"\"\"\n", "Don't assume you have access to any libraries other than built-in Python ones and pandas.\"\"\"\n",
"prompt = ChatPromptTemplate.from_messages(\n", "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", \"{question}\")])\n",
" [(\"system\", system), (\"human\", \"{question}\")]\n",
")\n",
"code_chain = prompt | llm_with_tools | parser\n", "code_chain = prompt | llm_with_tools | parser\n",
"code_chain.invoke({\"question\": \"What's the correlation between age and fare\"})" "code_chain.invoke({\"question\": \"What's the correlation between age and fare\"})"
] ]

4
docs/docs/use_cases/sql/agents.ipynb
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@ -721,7 +721,9 @@
"\n", "\n",
"If the question does not seem related to the database, just return \"I don't know\" as the answer.\"\"\"\n", "If the question does not seem related to the database, just return \"I don't know\" as the answer.\"\"\"\n",
"\n", "\n",
"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", \"{input}\"), MessagesPlaceholder(\"agent_scratchpad\")])\n", "prompt = ChatPromptTemplate.from_messages(\n",
" [(\"system\", system), (\"human\", \"{input}\"), MessagesPlaceholder(\"agent_scratchpad\")]\n",
")\n",
"agent = create_sql_agent(\n", "agent = create_sql_agent(\n",
" llm=llm,\n", " llm=llm,\n",
" db=db,\n", " db=db,\n",

8
docs/docs/use_cases/sql/query_checking.ipynb
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@ -294,11 +294,15 @@
"First draft: <<FIRST_DRAFT_QUERY>>\n", "First draft: <<FIRST_DRAFT_QUERY>>\n",
"Final answer: <<FINAL_ANSWER_QUERY>>\n", "Final answer: <<FINAL_ANSWER_QUERY>>\n",
"\"\"\"\n", "\"\"\"\n",
"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", \"{input}\")]).partial(dialect=db.dialect)\n", "prompt = ChatPromptTemplate.from_messages(\n",
" [(\"system\", system), (\"human\", \"{input}\")]\n",
").partial(dialect=db.dialect)\n",
"\n",
"\n", "\n",
"def parse_final_answer(output: str) -> str:\n", "def parse_final_answer(output: str) -> str:\n",
" return output.split(\"Final answer: \")[1]\n", " return output.split(\"Final answer: \")[1]\n",
" \n", "\n",
"\n",
"chain = create_sql_query_chain(llm, db, prompt=prompt) | parse_final_answer\n", "chain = create_sql_query_chain(llm, db, prompt=prompt) | parse_final_answer\n",
"prompt.pretty_print()" "prompt.pretty_print()"
] ]