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Docs to use LLMSymbolicMath and LLMBash + utilities from experimental (…
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…langchain-ai#11614)

Update docs in lieu of:

langchain-ai#11352
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@eyurtsev
eyurtsev authored and HoaNQ9 committed Feb 2, 2024
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48 changes: 19 additions & 29 deletions docs/docs_skeleton/docs/use_cases/more/code_writing/llm_bash.ipynb
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},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 1,
"metadata": {},
"outputs": [
{
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"'Hello World\\n'"
]
},
"execution_count": 9,
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.chains import LLMBashChain\n",
"from langchain_experimental.llm_bash.base import LLMBashChain\n",
"from langchain.llms import OpenAI\n",
"\n",
"llm = OpenAI(temperature=0)\n",
Expand All @@ -65,7 +65,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -98,7 +98,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 3,
"metadata": {},
"outputs": [
{
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"'Hello World\\n'"
]
},
"execution_count": 11,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
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},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 4,
"metadata": {},
"outputs": [
{
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"cd ..\n",
"```\u001b[0m\n",
"Code: \u001b[33;1m\u001b[1;3m['ls', 'cd ..']\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3mapi.html\t\t\tllm_summarization_checker.html\n",
"constitutional_chain.html\tmoderation.html\n",
"llm_bash.html\t\t\topenai_openapi.yaml\n",
"llm_checker.html\t\topenapi.html\n",
"llm_math.html\t\t\tpal.html\n",
"llm_requests.html\t\tsqlite.html\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3mcpal.ipynb llm_bash.ipynb llm_symbolic_math.ipynb\n",
"index.mdx llm_math.ipynb pal.ipynb\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'api.html\\t\\t\\tllm_summarization_checker.html\\r\\nconstitutional_chain.html\\tmoderation.html\\r\\nllm_bash.html\\t\\t\\topenai_openapi.yaml\\r\\nllm_checker.html\\t\\topenapi.html\\r\\nllm_math.html\\t\\t\\tpal.html\\r\\nllm_requests.html\\t\\tsqlite.html'"
"'cpal.ipynb llm_bash.ipynb llm_symbolic_math.ipynb\\r\\nindex.mdx llm_math.ipynb pal.ipynb'"
]
},
"execution_count": 12,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.utilities.bash import BashProcess\n",
"from langchain_experimental.llm_bash.bash import BashProcess\n",
"\n",
"\n",
"persistent_process = BashProcess(persistent=True)\n",
Expand All @@ -200,7 +196,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 5,
"metadata": {},
"outputs": [
{
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"cd ..\n",
"```\u001b[0m\n",
"Code: \u001b[33;1m\u001b[1;3m['ls', 'cd ..']\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3mexamples\t\tgetting_started.html\tindex_examples\n",
"generic\t\t\thow_to_guides.rst\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3m_category_.yml\tdata_generation.ipynb\t\t self_check\n",
"agents\t\tgraph\n",
"code_writing\tlearned_prompt_optimization.ipynb\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'examples\\t\\tgetting_started.html\\tindex_examples\\r\\ngeneric\\t\\t\\thow_to_guides.rst'"
"'_category_.yml\\tdata_generation.ipynb\\t\\t self_check\\r\\nagents\\t\\tgraph\\r\\ncode_writing\\tlearned_prompt_optimization.ipynb'"
]
},
"execution_count": 13,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
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"# Run the same command again and see that the state is maintained between calls\n",
"bash_chain.run(text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
"version": "3.11.4"
}
},
"nbformat": 4,
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32 changes: 16 additions & 16 deletions docs/docs_skeleton/docs/use_cases/more/code_writing/llm_symbolic_math.ipynb
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},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from langchain.llms import OpenAI\n",
"from langchain.chains.llm_symbolic_math.base import LLMSymbolicMathChain\n",
"from langchain_experimental.llm_symbolic_math.base import LLMSymbolicMathChain\n",
"\n",
"llm = OpenAI(temperature=0)\n",
"llm_symbolic_math = LLMSymbolicMathChain.from_llm(llm)"
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},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 4,
"metadata": {},
"outputs": [
{
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"'Answer: exp(x)*sin(x) + exp(x)*cos(x)'"
]
},
"execution_count": 23,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
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},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 5,
"metadata": {},
"outputs": [
{
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"'Answer: exp(x)*sin(x)'"
]
},
"execution_count": 18,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
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},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 6,
"metadata": {},
"outputs": [
{
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"'Answer: Eq(y(t), C2*exp(-t) + (C1 + t/2)*exp(t))'"
]
},
"execution_count": 19,
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
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},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 7,
"metadata": {},
"outputs": [
{
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"'Answer: {0, -sqrt(3)*I/3, sqrt(3)*I/3}'"
]
},
"execution_count": 21,
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
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},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 8,
"metadata": {},
"outputs": [
{
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"'Answer: (3 - sqrt(7), -sqrt(7) - 2, 1 - sqrt(7)), (sqrt(7) + 3, -2 + sqrt(7), 1 + sqrt(7))'"
]
},
"execution_count": 22,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
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],
"metadata": {
"kernelspec": {
"display_name": "venv",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "venv"
"name": "python3"
},
"language_info": {
"codemirror_mode": {
Expand All @@ -154,9 +154,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 4
}

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