"objc[31511]: Class GGMLMetalClass is implemented in both /Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x14f4e8208) and /Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libllamamodel-mainline-metal.dylib (0x14f5fc208). One of the two will be used. Which one is undefined.\n"
"objc[49534]: Class GGMLMetalClass is implemented in both /Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x131614208) and /Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libllamamodel-mainline-metal.dylib (0x131988208). One of the two will be used. Which one is undefined.\n"
"ChatPromptTemplate(input_variables=['question', 'context'], output_parser=None, partial_variables={}, messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['question', 'context'], output_parser=None, partial_variables={}, template=\"[INST]<<SYS>> 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.<</SYS>> \\nQuestion: {question} \\nContext: {context} \\nAnswer: [/INST]\", template_format='f-string', validate_template=True), additional_kwargs={})])"
"INFO:langchain.retrievers.multi_query:Generated queries: ['1. How can Task Decomposition be approached?', '2. What are the different methods for Task Decomposition?', '3. What are the various approaches to decomposing tasks?']\n"
]
},
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"import logging\n",
"\n",
@ -318,7 +300,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 9,
"id": "99fa1aec",
"metadata": {},
"outputs": [
@ -326,10 +308,10 @@
"data": {
"text/plain": [
"{'query': 'What are the approaches to Task Decomposition?',\n",
" 'result': 'There are three approaches to task decomposition:\\n\\n1. Using Language Model with simple prompting: This approach involves using a Language Model (LLM) with simple prompts like \"Steps for XYZ\" or \"What are the subgoals for achieving XYZ?\" to guide the task decomposition process.\\n\\n2. Using task-specific instructions: In this approach, task-specific instructions are provided to guide the task decomposition. For example, for the task of writing a novel, an instruction like \"Write a story outline\" can be given to help decompose the task into smaller subtasks.\\n\\n3. Human inputs: Task decomposition can also be done with the help of human inputs. This involves getting input and guidance from humans to break down a complex task into smaller, more manageable subtasks.'}"
" 'result': 'The approaches to task decomposition include:\\n\\n1. Simple prompting: This approach involves using simple prompts or questions to guide the agent in breaking down a task into smaller subgoals. For example, the agent can be prompted with \"Steps for XYZ\" or \"What are the subgoals for achieving XYZ?\" to facilitate task decomposition.\\n\\n2. Task-specific instructions: In this approach, task-specific instructions are provided to the agent to guide the decomposition process. For example, if the task is to write a novel, the agent can be instructed to \"Write a story outline\" as a step in the task decomposition.\\n\\n3. Human inputs: This approach involves incorporating human inputs in the task decomposition process. Humans can provide guidance, feedback, and assistance to the agent in breaking down complex tasks into manageable subgoals.\\n\\nThese approaches aim to enable efficient handling of complex tasks by breaking them down into smaller, more manageable subgoals.'}"
]
},
"execution_count": 10,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@ -355,97 +337,7 @@
"#### Choosing LLMs\n",
"- Browse the > 55 LLM and chat model integrations [here](https://integrations.langchain.com/).\n",
"- See further documentation on LLMs and chat models [here](/docs/modules/model_io/models/).\n",
"- Use local LLMS: The popularity of [PrivateGPT](https://github.com/imartinez/privateGPT) and [GPT4All](https://github.com/nomic-ai/gpt4all) underscore the importance of running LLMs locally.\n",
"Using `GPT4All` is as simple as [downloading the binary]((/docs/integrations/llms/gpt4all)) and then:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "02d6c9dc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found model file at /Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"objc[61331]: Class GGMLMetalClass is implemented in both /Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x2e3384208) and /Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libllamamodel-mainline-metal.dylib (0x2e37b0208). One of the two will be used. Which one is undefined.\n",
"llama.cpp: using Metal\n",
"llama.cpp: loading model from /Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\n",
"llama_model_load_internal: format = ggjt v3 (latest)\n",
"- See a guide on local LLMS [here](/docs/modules/use_cases/question_answering/how_to/local_retrieval_qa)."
]
},
{
@ -460,24 +352,17 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 10,
"id": "e4fee704",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"ggml_metal_free: deallocating\n"
]
},
{
"data": {
"text/plain": [
"'The approaches to task decomposition include using LLM with simple prompting, task-specific instructions, or human inputs. Thanks for asking!'"
"'The approaches to Task Decomposition are (1) using simple prompting by LLM, (2) using task-specific instructions, and (3) incorporating human inputs. Thanks for asking!'"
]
},
"execution_count": 13,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@ -507,8 +392,65 @@
},
{
"cell_type": "markdown",
"id": "ff40e8db",
"id": "c825e9bf-6a56-46e4-8bbb-05441f76cb96",
"metadata": {},
"source": [
"We can also store and fetch prompts from the LangChain prompt hub.\n",
"\n",
"This will work with your [LangSmith API key](https://docs.smith.langchain.com/).\n",
"\n",
"For example, see [here](https://smith.langchain.com/hub/rlm/rag-prompt) is a common prompt for RAG.\n",
"\n",
"We can load this."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a896060f-ebc4-4236-a4ad-32960601c6e8",
"metadata": {},
"outputs": [],
"source": [
"pip install langchainhub"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "aef8e734-ba54-48ae-b959-1898618f2d90",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'The approaches to task decomposition include using LLM with simple prompting, task-specific instructions, and human inputs.'"
Lance Martin
1 year ago
committed by
GitHub