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Web research retriever (#8102)

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Given a user question, this will -
* Use LLM to generate a set of queries.
* Query for each.
* The URLs from search results are stored in self.urls.
* A check is performed for any new URLs that haven't been processed yet
(not in self.url_database).
* Only these new URLs are loaded, transformed, and added to the
vectorstore.
* The vectorstore is queried for relevant documents based on the
questions generated by the LLM.
* Only unique documents are returned as the final result.

This code will avoid reprocessing of URLs across multiple runs of
similar queries, which should improve the performance of the retriever.
It also keeps track of all URLs that have been processed, which could be
useful for debugging or understanding the retriever's behavior.

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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{
"cells": [
{
"cell_type": "markdown",
"id": "9c0ffe42",
"metadata": {},
"source": [
"# WebResearchRetriever\n",
"\n",
"Given a query, this retriever will: \n",
"\n",
"* Formulate a set of relate Google searches\n",
"* Search for each \n",
"* Load all the resulting URLs\n",
"* Then embed and perform similarity search with the query on the consolidate page content"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "13548212",
"metadata": {},
"outputs": [],
"source": [
"from langchain.retrievers.web_research import WebResearchRetriever"
]
},
{
"cell_type": "markdown",
"id": "90b1dcbd",
"metadata": {},
"source": [
"### Simple usage\n",
"\n",
"Specify the LLM to use for Google search query generation."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e63d1c8b",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from langchain.vectorstores import Chroma\n",
"from langchain.embeddings import OpenAIEmbeddings\n",
"from langchain.chat_models.openai import ChatOpenAI\n",
"from langchain.utilities import GoogleSearchAPIWrapper\n",
"\n",
"# Vectorstore\n",
"vectorstore = Chroma(embedding_function=OpenAIEmbeddings(),persist_directory=\"./chroma_db_oai\")\n",
"\n",
"# LLM\n",
"llm = ChatOpenAI(temperature=0)\n",
"\n",
"# Search \n",
"os.environ[\"GOOGLE_CSE_ID\"] = \"xxx\"\n",
"os.environ[\"GOOGLE_API_KEY\"] = \"xxx\"\n",
"search = GoogleSearchAPIWrapper()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "118b50aa",
"metadata": {},
"outputs": [],
"source": [
"# Initialize\n",
"web_research_retriever = WebResearchRetriever.from_llm(\n",
" vectorstore=vectorstore,\n",
" llm=llm, \n",
" search=search, \n",
")"
]
},
{
"cell_type": "markdown",
"id": "39114da4",
"metadata": {},
"source": [
"`Run with citations`\n",
"\n",
"We can use `RetrievalQAWithSourcesChain` to retrieve docs and provide citations"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "0b330acd",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Fetching pages: 100%|###################################################################################################################################| 1/1 [00:00<00:00, 3.33it/s]\n"
]
},
{
"data": {
"text/plain": [
"{'question': 'How do LLM Powered Autonomous Agents work?',\n",
" 'answer': \"LLM Powered Autonomous Agents work by using LLM (large language model) as the core controller of the agent's brain. It is complemented by several key components, including planning, memory, and tool use. The agent system is designed to be a powerful general problem solver. \\n\",\n",
" 'sources': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.chains import RetrievalQAWithSourcesChain\n",
"user_input = \"How do LLM Powered Autonomous Agents work?\"\n",
"qa_chain = RetrievalQAWithSourcesChain.from_chain_type(llm,retriever=web_research_retriever)\n",
"result = qa_chain({\"question\": user_input})\n",
"result"
]
},
{
"cell_type": "markdown",
"id": "357559fd",
"metadata": {},
"source": [
"`Run with logging`\n",
"\n",
"Here, we use `get_relevant_documents` method to return docs."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2c4e8ab3",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:langchain.retrievers.web_research:Generating questions for Google Search ...\n",
"INFO:langchain.retrievers.web_research:Questions for Google Search (raw): {'question': 'What is Task Decomposition in LLM Powered Autonomous Agents?', 'text': LineList(lines=['1. How do LLM powered autonomous agents utilize task decomposition?\\n', '2. Can you explain the concept of task decomposition in LLM powered autonomous agents?\\n', '3. What role does task decomposition play in the functioning of LLM powered autonomous agents?\\n', '4. Why is task decomposition important for LLM powered autonomous agents?\\n'])}\n",
"INFO:langchain.retrievers.web_research:Questions for Google Search: ['1. How do LLM powered autonomous agents utilize task decomposition?\\n', '2. Can you explain the concept of task decomposition in LLM powered autonomous agents?\\n', '3. What role does task decomposition play in the functioning of LLM powered autonomous agents?\\n', '4. Why is task decomposition important for LLM powered autonomous agents?\\n']\n",
"INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
"INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\" , \"What are the subgoals for achieving XYZ?'}]\n",
"INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\" , \"What are the subgoals for achieving XYZ?\" , (2)\\xa0...'}]\n",
"INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... In a LLM-powered autonomous agent system, LLM functions as the ... Task decomposition can be done (1) by LLM with simple prompting like\\xa0...'}]\n",
"INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Agent System Overview In a LLM-powered autonomous agent system, ... Task decomposition can be done (1) by LLM with simple prompting like\\xa0...'}]\n",
"INFO:langchain.retrievers.web_research:New URLs to load: []\n"
]
}
],
"source": [
"# Run\n",
"import logging\n",
"logging.basicConfig()\n",
"logging.getLogger(\"langchain.retrievers.web_research\").setLevel(logging.INFO)\n",
"user_input = \"What is Task Decomposition in LLM Powered Autonomous Agents?\"\n",
"docs = web_research_retriever.get_relevant_documents(user_input)"
]
},
{
"cell_type": "markdown",
"id": "b681a846",
"metadata": {},
"source": [
"`Generate answer using retrieved docs`\n",
"\n",
"We can use `load_qa_chain` for QA using the retrieved docs"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ceca5681",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Task decomposition in LLM-powered autonomous agents refers to the process of breaking down a complex task into smaller, more manageable subgoals. This allows the agent to efficiently handle and execute the individual steps required to complete the overall task. By decomposing the task, the agent can prioritize and organize its actions, making it easier to plan and execute the necessary steps towards achieving the desired outcome.'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.chains.question_answering import load_qa_chain\n",
"chain = load_qa_chain(llm, chain_type=\"stuff\")\n",
"output = chain({\"input_documents\": docs, \"question\": user_input},return_only_outputs=True)\n",
"output['output_text']"
]
},
{
"cell_type": "markdown",
"id": "0c0e57bb",
"metadata": {},
"source": [
"### More flexibility\n",
"\n",
"Pass an LLM chain with custom prompt and output parsing"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3d84ea47",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import re\n",
"from typing import List\n",
"from langchain.chains import LLMChain\n",
"from pydantic import BaseModel, Field\n",
"from langchain.prompts import PromptTemplate\n",
"from langchain.output_parsers.pydantic import PydanticOutputParser\n",
"\n",
"# LLMChain\n",
"search_prompt = PromptTemplate(\n",
" input_variables=[\"question\"],\n",
" template=\"\"\"You are an assistant tasked with improving Google search \n",
" results. Generate FIVE Google search queries that are similar to\n",
" this question. The output should be a numbered list of questions and each\n",
" should have a question mark at the end: {question}\"\"\",\n",
")\n",
"\n",
"class LineList(BaseModel):\n",
" \"\"\"List of questions.\"\"\"\n",
"\n",
" lines: List[str] = Field(description=\"Questions\")\n",
"\n",
"class QuestionListOutputParser(PydanticOutputParser):\n",
" \"\"\"Output parser for a list of numbered questions.\"\"\"\n",
"\n",
" def __init__(self) -> None:\n",
" super().__init__(pydantic_object=LineList)\n",
"\n",
" def parse(self, text: str) -> LineList:\n",
" lines = re.findall(r\"\\d+\\..*?\\n\", text)\n",
" return LineList(lines=lines)\n",
" \n",
"llm_chain = LLMChain(\n",
" llm=llm,\n",
" prompt=search_prompt,\n",
" output_parser=QuestionListOutputParser(),\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "851b0471",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:langchain.retrievers.web_research:Generating questions for Google Search ...\n",
"INFO:langchain.retrievers.web_research:Questions for Google Search (raw): {'question': 'What is Task Decomposition in LLM Powered Autonomous Agents?', 'text': LineList(lines=['1. How do LLM powered autonomous agents use task decomposition?\\n', '2. Why is task decomposition important for LLM powered autonomous agents?\\n', '3. Can you explain the concept of task decomposition in LLM powered autonomous agents?\\n', '4. What are the benefits of task decomposition in LLM powered autonomous agents?\\n'])}\n",
"INFO:langchain.retrievers.web_research:Questions for Google Search: ['1. How do LLM powered autonomous agents use task decomposition?\\n', '2. Why is task decomposition important for LLM powered autonomous agents?\\n', '3. Can you explain the concept of task decomposition in LLM powered autonomous agents?\\n', '4. What are the benefits of task decomposition in LLM powered autonomous agents?\\n']\n",
"INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
"INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\" , \"What are the subgoals for achieving XYZ?'}]\n",
"INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\" , \"What are the subgoals for achieving XYZ?\" , (2)\\xa0...'}]\n",
"INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\" , \"What are the subgoals for achieving XYZ?'}]\n",
"INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\" , \"What are the subgoals for achieving XYZ?'}]\n",
"INFO:langchain.retrievers.web_research:New URLs to load: ['https://lilianweng.github.io/posts/2023-06-23-agent/']\n",
"INFO:langchain.retrievers.web_research:Grabbing most relevant splits from urls ...\n",
"Fetching pages: 100%|###################################################################################################################################| 1/1 [00:00<00:00, 6.32it/s]\n"
]
}
],
"source": [
"# Initialize\n",
"web_research_retriever_llm_chain = WebResearchRetriever(\n",
" vectorstore=vectorstore,\n",
" llm_chain=llm_chain, \n",
" search=search, \n",
")\n",
"\n",
"# Run\n",
"docs = web_research_retriever_llm_chain.get_relevant_documents(user_input)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "1ee52163",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(docs)"
]
},
{
"cell_type": "markdown",
"id": "4f9530c0",
"metadata": {},
"source": [
"### Run locally\n",
"\n",
"Specify LLM and embeddings that will run locally (e.g., on your laptop)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "8cf0d155",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"llama.cpp: loading model from /Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\n",
"llama_model_load_internal: format = ggjt v3 (latest)\n",
"llama_model_load_internal: n_vocab = 32000\n",
"llama_model_load_internal: n_ctx = 4096\n",
"llama_model_load_internal: n_embd = 5120\n",
"llama_model_load_internal: n_mult = 256\n",
"llama_model_load_internal: n_head = 40\n",
"llama_model_load_internal: n_layer = 40\n",
"llama_model_load_internal: n_rot = 128\n",
"llama_model_load_internal: freq_base = 10000.0\n",
"llama_model_load_internal: freq_scale = 1\n",
"llama_model_load_internal: ftype = 2 (mostly Q4_0)\n",
"llama_model_load_internal: n_ff = 13824\n",
"llama_model_load_internal: model size = 13B\n",
"llama_model_load_internal: ggml ctx size = 0.09 MB\n",
"llama_model_load_internal: mem required = 9132.71 MB (+ 1608.00 MB per state)\n",
"llama_new_context_with_model: kv self size = 3200.00 MB\n",
"ggml_metal_init: allocating\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found model file at /Users/rlm/.cache/gpt4all/ggml-all-MiniLM-L6-v2-f16.bin\n",
"llama_new_context_with_model: max tensor size = 87.89 MB\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"ggml_metal_init: using MPS\n",
"ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/llama_cpp/ggml-metal.metal'\n",
"ggml_metal_init: loaded kernel_add 0x110fbd600\n",
"ggml_metal_init: loaded kernel_mul 0x110fbeb30\n",
"ggml_metal_init: loaded kernel_mul_row 0x110fbf350\n",
"ggml_metal_init: loaded kernel_scale 0x110fbf9e0\n",
"ggml_metal_init: loaded kernel_silu 0x110fc0150\n",
"ggml_metal_init: loaded kernel_relu 0x110fbd950\n",
"ggml_metal_init: loaded kernel_gelu 0x110fbdbb0\n",
"ggml_metal_init: loaded kernel_soft_max 0x110fc14d0\n",
"ggml_metal_init: loaded kernel_diag_mask_inf 0x110fc1980\n",
"ggml_metal_init: loaded kernel_get_rows_f16 0x110fc22a0\n",
"ggml_metal_init: loaded kernel_get_rows_q4_0 0x110fc2ad0\n",
"ggml_metal_init: loaded kernel_get_rows_q4_1 0x110fc3260\n",
"ggml_metal_init: loaded kernel_get_rows_q2_K 0x110fc3ad0\n",
"ggml_metal_init: loaded kernel_get_rows_q3_K 0x110fc41c0\n",
"ggml_metal_init: loaded kernel_get_rows_q4_K 0x110fc48c0\n",
"ggml_metal_init: loaded kernel_get_rows_q5_K 0x110fc4fa0\n",
"ggml_metal_init: loaded kernel_get_rows_q6_K 0x110fc56a0\n",
"ggml_metal_init: loaded kernel_rms_norm 0x110fc5da0\n",
"ggml_metal_init: loaded kernel_norm 0x110fc64d0\n",
"ggml_metal_init: loaded kernel_mul_mat_f16_f32 0x2a5c19990\n",
"ggml_metal_init: loaded kernel_mul_mat_q4_0_f32 0x2a5c1d4a0\n",
"ggml_metal_init: loaded kernel_mul_mat_q4_1_f32 0x2a5c19fc0\n",
"ggml_metal_init: loaded kernel_mul_mat_q2_K_f32 0x2a5c1dcc0\n",
"ggml_metal_init: loaded kernel_mul_mat_q3_K_f32 0x2a5c1e420\n",
"ggml_metal_init: loaded kernel_mul_mat_q4_K_f32 0x2a5c1edc0\n",
"ggml_metal_init: loaded kernel_mul_mat_q5_K_f32 0x2a5c1fd90\n",
"ggml_metal_init: loaded kernel_mul_mat_q6_K_f32 0x2a5c20540\n",
"ggml_metal_init: loaded kernel_rope 0x2a5c20d40\n",
"ggml_metal_init: loaded kernel_alibi_f32 0x2a5c21730\n",
"ggml_metal_init: loaded kernel_cpy_f32_f16 0x2a5c21ab0\n",
"ggml_metal_init: loaded kernel_cpy_f32_f32 0x2a5c22080\n",
"ggml_metal_init: loaded kernel_cpy_f16_f16 0x2a5c231d0\n",
"ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n",
"ggml_metal_init: hasUnifiedMemory = true\n",
"ggml_metal_init: maxTransferRate = built-in GPU\n",
"ggml_metal_add_buffer: allocated 'data ' buffer, size = 6984.06 MB, ( 6984.52 / 21845.34)\n",
"ggml_metal_add_buffer: allocated 'eval ' buffer, size = 1040.00 MB, ( 8024.52 / 21845.34)\n",
"ggml_metal_add_buffer: allocated 'kv ' buffer, size = 3202.00 MB, (11226.52 / 21845.34)\n",
"ggml_metal_add_buffer: allocated 'scr0 ' buffer, size = 597.00 MB, (11823.52 / 21845.34)\n",
"AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | \n",
"ggml_metal_add_buffer: allocated 'scr1 ' buffer, size = 512.00 MB, (12335.52 / 21845.34)\n",
"objc[33471]: Class GGMLMetalClass is implemented in both /Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/llama_cpp/libllama.dylib (0x2c7368208) and /Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x5ebf48208). One of the two will be used. Which one is undefined.\n",
"objc[33471]: Class GGMLMetalClass is implemented in both /Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/llama_cpp/libllama.dylib (0x2c7368208) and /Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libllamamodel-mainline-metal.dylib (0x5ec374208). One of the two will be used. Which one is undefined.\n"
]
}
],
"source": [
"from langchain.llms import LlamaCpp\n",
"from langchain.embeddings import GPT4AllEmbeddings\n",
"from langchain.callbacks.manager import CallbackManager\n",
"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
"\n",
"n_gpu_layers = 1 # Metal set to 1 is enough.\n",
"n_batch = 512 # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.\n",
"callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])\n",
"llama = LlamaCpp(\n",
" model_path=\"/Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\",\n",
" n_gpu_layers=n_gpu_layers,\n",
" n_batch=n_batch,\n",
" n_ctx=4096, # Context window\n",
" max_tokens=1000, # Max tokens to generate\n",
" f16_kv=True, # MUST set to True, otherwise you will run into problem after a couple of calls\n",
" callback_manager=callback_manager,\n",
" verbose=True,\n",
")\n",
"\n",
"vectorstore_llama = Chroma(embedding_function=GPT4AllEmbeddings(),persist_directory=\"./chroma_db_llama\")"
]
},
{
"cell_type": "markdown",
"id": "00f93dd4",
"metadata": {},
"source": [
"We supplied `StreamingStdOutCallbackHandler()`, so model outputs (e.g., generated questions) are streamed. \n",
"\n",
"We also have logging on, so we seem them there too."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "3e0561ca",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:langchain.retrievers.web_research:Generating questions for Google Search ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Sure, here are five Google search queries that are similar to \"What is Task Decomposition in LLM Powered Autonomous Agents?\":\n",
"\n",
"1. How does Task Decomposition work in LLM Powered Autonomous Agents? \n",
"2. What are the benefits of using Task Decomposition in LLM Powered Autonomous Agents? \n",
"3. Can you provide examples of Task Decomposition in LLM Powered Autonomous Agents? \n",
"4. How does Task Decomposition improve the performance of LLM Powered Autonomous Agents? \n",
"5. What are some common challenges or limitations of using Task Decomposition in LLM Powered Autonomous Agents, and how can they be addressed?"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"llama_print_timings: load time = 8585.01 ms\n",
"llama_print_timings: sample time = 124.24 ms / 164 runs ( 0.76 ms per token, 1320.04 tokens per second)\n",
"llama_print_timings: prompt eval time = 8584.83 ms / 101 tokens ( 85.00 ms per token, 11.76 tokens per second)\n",
"llama_print_timings: eval time = 7268.55 ms / 163 runs ( 44.59 ms per token, 22.43 tokens per second)\n",
"llama_print_timings: total time = 16236.13 ms\n",
"INFO:langchain.retrievers.web_research:Questions for Google Search (raw): {'question': 'What is Task Decomposition in LLM Powered Autonomous Agents?', 'text': LineList(lines=['1. How does Task Decomposition work in LLM Powered Autonomous Agents? \\n', '2. What are the benefits of using Task Decomposition in LLM Powered Autonomous Agents? \\n', '3. Can you provide examples of Task Decomposition in LLM Powered Autonomous Agents? \\n', '4. How does Task Decomposition improve the performance of LLM Powered Autonomous Agents? \\n'])}\n",
"INFO:langchain.retrievers.web_research:Questions for Google Search: ['1. How does Task Decomposition work in LLM Powered Autonomous Agents? \\n', '2. What are the benefits of using Task Decomposition in LLM Powered Autonomous Agents? \\n', '3. Can you provide examples of Task Decomposition in LLM Powered Autonomous Agents? \\n', '4. How does Task Decomposition improve the performance of LLM Powered Autonomous Agents? \\n']\n",
"INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
"INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\" , \"What are the subgoals for achieving XYZ?'}]\n",
"INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\" , \"What are the subgoals for achieving XYZ?\" , (2)\\xa0...'}]\n",
"INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... A complicated task usually involves many steps. An agent needs to know what they are and plan ahead. Task Decomposition#. Chain of thought (CoT;\\xa0...'}]\n",
"INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Agent System Overview In a LLM-powered autonomous agent system, ... Task decomposition can be done (1) by LLM with simple prompting like\\xa0...'}]\n",
"INFO:langchain.retrievers.web_research:New URLs to load: ['https://lilianweng.github.io/posts/2023-06-23-agent/']\n",
"INFO:langchain.retrievers.web_research:Grabbing most relevant splits from urls ...\n",
"Fetching pages: 100%|###################################################################################################################################| 1/1 [00:00<00:00, 10.49it/s]\n",
"Llama.generate: prefix-match hit\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" The content discusses Task Decomposition in LLM Powered Autonomous Agents, which involves breaking down large tasks into smaller, manageable subgoals for efficient handling of complex tasks.\n",
"SOURCES:\n",
"https://lilianweng.github.io/posts/2023-06-23-agent/"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"llama_print_timings: load time = 8585.01 ms\n",
"llama_print_timings: sample time = 52.88 ms / 72 runs ( 0.73 ms per token, 1361.55 tokens per second)\n",
"llama_print_timings: prompt eval time = 125925.13 ms / 2358 tokens ( 53.40 ms per token, 18.73 tokens per second)\n",
"llama_print_timings: eval time = 3504.16 ms / 71 runs ( 49.35 ms per token, 20.26 tokens per second)\n",
"llama_print_timings: total time = 129584.60 ms\n"
]
},
{
"data": {
"text/plain": [
"{'question': 'What is Task Decomposition in LLM Powered Autonomous Agents?',\n",
" 'answer': ' The content discusses Task Decomposition in LLM Powered Autonomous Agents, which involves breaking down large tasks into smaller, manageable subgoals for efficient handling of complex tasks.\\n',\n",
" 'sources': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.chains import RetrievalQAWithSourcesChain\n",
"# Initialize\n",
"web_research_retriever = WebResearchRetriever.from_llm(\n",
" vectorstore=vectorstore_llama,\n",
" llm=llama, \n",
" search=search, \n",
")\n",
"\n",
"# Run\n",
"user_input = \"What is Task Decomposition in LLM Powered Autonomous Agents?\"\n",
"qa_chain = RetrievalQAWithSourcesChain.from_chain_type(llama,retriever=web_research_retriever)\n",
"result = qa_chain({\"question\": user_input})\n",
"result"
]
}
],
"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": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

8
libs/langchain/langchain/document_loaders/async_html.py
Unescape Escape View File

@ -1,4 +1,3 @@
"""Web base loader class."""
import asyncio
import logging
import warnings
@ -86,7 +85,12 @@ class AsyncHtmlLoader(BaseLoader):
headers=self.session.headers,
ssl=None if self.session.verify else False,
) as response:
return await response.text()
try:
text = await response.text()
except UnicodeDecodeError:
logger.error(f"Failed to decode content from {url}")
text = ""
return text
except aiohttp.ClientConnectionError as e:
if i == retries - 1:
raise

2
libs/langchain/langchain/retrievers/__init__.py
Unescape Escape View File

@ -31,6 +31,7 @@ from langchain.retrievers.time_weighted_retriever import (
)
from langchain.retrievers.vespa_retriever import VespaRetriever
from langchain.retrievers.weaviate_hybrid_search import WeaviateHybridSearchRetriever
from langchain.retrievers.web_research import WebResearchRetriever
from langchain.retrievers.wikipedia import WikipediaRetriever
from langchain.retrievers.zep import ZepRetriever
from langchain.retrievers.zilliz import ZillizRetriever
@ -65,5 +66,6 @@ __all__ = [
"ZepRetriever",
"ZillizRetriever",
"DocArrayRetriever",
"WebResearchRetriever",
"EnsembleRetriever",
]

215
libs/langchain/langchain/retrievers/web_research.py
Unescape Escape View File

@ -0,0 +1,215 @@
import logging
import re
from typing import List, Optional
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain.chains import LLMChain
from langchain.chains.prompt_selector import ConditionalPromptSelector
from langchain.document_loaders import AsyncHtmlLoader
from langchain.document_transformers import Html2TextTransformer
from langchain.llms import LlamaCpp
from langchain.llms.base import BaseLLM
from langchain.output_parsers.pydantic import PydanticOutputParser
from langchain.prompts import BasePromptTemplate, PromptTemplate
from langchain.schema import BaseRetriever, Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.utilities import GoogleSearchAPIWrapper
from langchain.vectorstores.base import VectorStore
logger = logging.getLogger(__name__)
class SearchQueries(BaseModel):
"""Search queries to run to research for the user's goal."""
queries: List[str] = Field(
..., description="List of search queries to look up on Google"
)
DEFAULT_LLAMA_SEARCH_PROMPT = PromptTemplate(
input_variables=["question"],
template="""<<SYS>> \n You are an assistant tasked with improving Google search
results. \n <</SYS>> \n\n [INST] Generate FIVE Google search queries that
are similar to this question. The output should be a numbered list of questions
and each should have a question mark at the end: \n\n {question} [/INST]""",
)
DEFAULT_SEARCH_PROMPT = PromptTemplate(
input_variables=["question"],
template="""You are an assistant tasked with improving Google search
results. Generate FIVE Google search queries that are similar to
this question. The output should be a numbered list of questions and each
should have a question mark at the end: {question}""",
)
class LineList(BaseModel):
"""List of questions."""
lines: List[str] = Field(description="Questions")
class QuestionListOutputParser(PydanticOutputParser):
"""Output parser for a list of numbered questions."""
def __init__(self) -> None:
super().__init__(pydantic_object=LineList)
def parse(self, text: str) -> LineList:
lines = re.findall(r"\d+\..*?\n", text)
return LineList(lines=lines)
class WebResearchRetriever(BaseRetriever):
# Inputs
vectorstore: VectorStore = Field(
..., description="Vector store for storing web pages"
)
llm_chain: LLMChain
search: GoogleSearchAPIWrapper = Field(..., description="Google Search API Wrapper")
max_splits_per_doc: int = Field(100, description="Maximum splits per document")
num_search_results: int = Field(1, description="Number of pages per Google search")
text_splitter: RecursiveCharacterTextSplitter = Field(
RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=50),
description="Text splitter for splitting web pages into chunks",
)
url_database: List[str] = Field(
default_factory=list, description="List of processed URLs"
)
@classmethod
def from_llm(
cls,
vectorstore: VectorStore,
llm: BaseLLM,
search: GoogleSearchAPIWrapper,
prompt: Optional[BasePromptTemplate] = None,
max_splits_per_doc: int = 100,
num_search_results: int = 1,
text_splitter: RecursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(
chunk_size=1500, chunk_overlap=50
),
) -> "WebResearchRetriever":
"""Initialize from llm using default template.
Args:
vectorstore: Vector store for storing web pages
llm: llm for search question generation
search: GoogleSearchAPIWrapper
prompt: prompt to generating search questions
max_splits_per_doc: Maximum splits per document to keep
num_search_results: Number of pages per Google search
text_splitter: Text splitter for splitting web pages into chunks
Returns:
WebResearchRetriever
"""
if not prompt:
QUESTION_PROMPT_SELECTOR = ConditionalPromptSelector(
default_prompt=DEFAULT_SEARCH_PROMPT,
conditionals=[
(lambda llm: isinstance(llm, LlamaCpp), DEFAULT_LLAMA_SEARCH_PROMPT)
],
)
prompt = QUESTION_PROMPT_SELECTOR.get_prompt(llm)
# Use chat model prompt
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
output_parser=QuestionListOutputParser(),
)
return cls(
vectorstore=vectorstore,
llm_chain=llm_chain,
search=search,
max_splits_per_doc=max_splits_per_doc,
num_search_results=num_search_results,
text_splitter=text_splitter,
)
def search_tool(self, query: str, num_search_results: int = 1) -> List[dict]:
"""Returns num_serch_results pages per Google search."""
result = self.search.results(query, num_search_results)
return result
def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,
) -> List[Document]:
"""Search Google for documents related to the query input.
Args:
query: user query
Returns:
Relevant documents from all various urls.
"""
# Get search questions
logger.info("Generating questions for Google Search ...")
result = self.llm_chain({"question": query})
logger.info(f"Questions for Google Search (raw): {result}")
questions = getattr(result["text"], "lines", [])
logger.info(f"Questions for Google Search: {questions}")
# Get urls
logger.info("Searching for relevat urls ...")
urls_to_look = []
for query in questions:
# Google search
search_results = self.search_tool(query, self.num_search_results)
logger.info("Searching for relevat urls ...")
logger.info(f"Search results: {search_results}")
for res in search_results:
urls_to_look.append(res["link"])
# Relevant urls
urls = set(urls_to_look)
# Check for any new urls that we have not processed
new_urls = list(urls.difference(self.url_database))
logger.info(f"New URLs to load: {new_urls}")
# Load, split, and add new urls to vectorstore
if new_urls:
loader = AsyncHtmlLoader(new_urls)
html2text = Html2TextTransformer()
logger.info("Indexing new urls...")
docs = loader.load()
docs = list(html2text.transform_documents(docs))
docs = self.text_splitter.split_documents(docs)
self.vectorstore.add_documents(docs)
self.url_database.extend(new_urls)
# Search for relevant splits
# TODO: make this async
logger.info("Grabbing most relevant splits from urls...")
docs = []
for query in questions:
docs.extend(self.vectorstore.similarity_search(query))
# Get unique docs
unique_documents_dict = {
(doc.page_content, tuple(sorted(doc.metadata.items()))): doc for doc in docs
}
unique_documents = list(unique_documents_dict.values())
return unique_documents
async def _aget_relevant_documents(
self,
query: str,
*,
run_manager: AsyncCallbackManagerForRetrieverRun,
) -> List[Document]:
raise NotImplementedError
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