From c5b9845216406e6113f5b75ec8112ca1a6f014ba Mon Sep 17 00:00:00 2001 From: Lance Martin <122662504+rlancemartin@users.noreply.github.com> Date: Wed, 25 Sep 2024 21:43:45 -0700 Subject: [PATCH] Update RAG agent w/ llama3.2 (#1852) * Update RAG agent llama3.2 * Move to doc * Rename --- .../rag/langgraph_adaptive_rag_local.ipynb | 941 +++++++++--------- .../langgraph_rag_agent_llama3_local.ipynb | 397 -------- 2 files changed, 460 insertions(+), 878 deletions(-) delete mode 100644 examples/rag/langgraph_rag_agent_llama3_local.ipynb diff --git a/docs/docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb b/docs/docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb index 6f757ad13..1768637fc 100644 --- a/docs/docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb +++ b/docs/docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb @@ -1,142 +1,144 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 11, + "id": "2631596b-8a66-4d23-a00d-d2c745c611d4", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install --quiet -U langchain langchain_community tiktoken langchain-nomic \"nomic[local]\" langchain-ollama scikit-learn langgraph tavily-python bs4" + ] + }, { "attachments": { - "3755396d-c4a8-45bd-87d4-00cb56339fe5.png": { - "image/png": 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" } }, "cell_type": "markdown", - "id": "bb89d3f0-7ade-43a8-a527-4bec45971cf6", + "id": "f79d29eb-13c4-401f-8062-dc321854c73f", "metadata": {}, "source": [ - "# Adaptive RAG using local LLMs\n", + "# Local RAG agent with LLaMA3\n", "\n", - "Adaptive RAG is a strategy for RAG that unites (1) [query analysis](https://blog.langchain.dev/query-construction/) with (2) [active / self-corrective RAG](https://blog.langchain.dev/agentic-rag-with-langgraph/).\n", + "We'll combine ideas from paper RAG papers into a RAG agent:\n", "\n", - "In the [paper](https://arxiv.org/abs/2403.14403), they report query analysis to route across:\n", + "- **Routing:** Adaptive RAG ([paper](https://arxiv.org/abs/2403.14403)). Route questions to different retrieval approaches\n", + "- **Fallback:** Corrective RAG ([paper](https://arxiv.org/pdf/2401.15884.pdf)). Fallback to web search if docs are not relevant to query\n", + "- **Self-correction:** Self-RAG ([paper](https://arxiv.org/abs/2310.11511)). Fix answers w/ hallucinations or don’t address question\n", "\n", - "* No Retrieval\n", - "* Single-shot RAG\n", - "* Iterative RAG\n", + "![langgraph_adaptive_rag.png](attachment:6cd777a6-a0b3-4feb-bd07-8e9e8a4b32a0.png)\n", "\n", - "Let's build on this using LangGraph. \n", + "## Local models\n", "\n", - "In our implementation, we will route between:\n", - "\n", - "* Web search: for questions related to recent events\n", - "* Self-corrective RAG: for questions related to our index\n", - "\n", - "![Screenshot 2024-04-01 at 1.29.15 PM.png](attachment:3755396d-c4a8-45bd-87d4-00cb56339fe5.png)" - ] - }, - { - "cell_type": "markdown", - "id": "8cece98f-a3ed-417e-8b6a-1754e8f9c42a", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "First, let's install our required packages and set our API keys" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "88debf5c-6972-415c-b8fb-f65eab203b7a", - "metadata": {}, - "outputs": [], - "source": [ - "%capture --no-stderr\n", - "%pip install -U langchain-nomic langchain_community tiktoken langchainhub chromadb langchain langgraph tavily-python nomic[local]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2369652a", - "metadata": {}, - "outputs": [], - "source": [ - "import getpass\n", - "import os\n", - "\n", - "\n", - "def _set_env(var: str):\n", - " if not os.environ.get(var):\n", - " os.environ[var] = getpass.getpass(f\"{var}: \")\n", - "\n", - "\n", - "_set_env(\"TAVILY_API_KEY\")\n", - "_set_env(\"NOMIC_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "aea269f6", - "metadata": {}, - "source": [ - "
\n", - "

Set up LangSmith for LangGraph development

\n", - "

\n", - " Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started here. \n", - "

\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "6a5d4a26-249b-4551-aa13-6c373429618e", - "metadata": {}, - "source": [ - "### LLMs\n", - "\n", - "#### Local Embeddings\n", - "\n", - "You can use `GPT4AllEmbeddings()` from Nomic, which can access use Nomic's recently released [v1](https://blog.nomic.ai/posts/nomic-embed-text-v1) and [v1.5](https://blog.nomic.ai/posts/nomic-embed-matryoshka) embeddings.\n", - "\n", - "Follow the documentation [here](https://docs.gpt4all.io/gpt4all_python_embedding.html#supported-embedding-models).\n", - "\n", - "#### Local LLM\n", - "\n", - "(1) Download [Ollama app](https://ollama.ai/).\n", - "\n", - "(2) Download a `Mistral` model from various Mistral versions [here](https://ollama.ai/library/mistral) and Mixtral versions [here](https://ollama.ai/library/mixtral) available. Also, try one of the [quantized command-R models](https://ollama.com/library/command-r).\n", + "### Embedding\n", + " \n", + "[GPT4All Embeddings](https://blog.nomic.ai/posts/nomic-embed-text-v1):\n", "\n", "```\n", - "ollama pull mistral\n", + "pip install langchain-nomic\n", + "```\n", + "\n", + "### LLM\n", + "\n", + "Use [Ollama](https://x.com/ollama/status/1839007158865899651) and [llama3.2](https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/):\n", + "\n", + "```\n", + "ollama pull llama3.2:3b-instruct-fp16 \n", "```" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "af8379bd-7eae-4ba6-b632-12e89eab9920", + "execution_count": 8, + "id": "23dbd2fd-2526-474e-bc43-cbd96207a109", "metadata": {}, "outputs": [], "source": [ - "# Ollama model name\n", - "local_llm = \"mistral\"" + "### LLM\n", + "from langchain_ollama import ChatOllama\n", + "local_llm = 'llama3.2:3b-instruct-fp16'\n", + "llm = ChatOllama(model=local_llm, temperature=0)\n", + "llm_json_mode = ChatOllama(model=local_llm, temperature=0, format='json')" ] }, { "cell_type": "markdown", - "id": "04718a0c-7a48-4243-97a2-940a0239cc12", + "id": "ab990e71", "metadata": {}, "source": [ - "## Create Index" + "### Search\n", + "\n", + "For search, we use [Tavily](https://tavily.com/), which is a search engine optimized for LLMs and RAG." ] }, { "cell_type": "code", - "execution_count": 2, - "id": "f9ff6b99-080d-4827-b2cb-f775543d76f5", + "execution_count": 4, + "id": "8a8792f5", "metadata": {}, "outputs": [], + "source": [ + "import os, getpass\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "_set_env(\"TAVILY_API_KEY\")\n", + "os.environ['TOKENIZERS_PARALLELISM'] = 'true'" + ] + }, + { + "cell_type": "markdown", + "id": "4fdeaced", + "metadata": {}, + "source": [ + "### Tracing \n", + "\n", + "Optionally, use [LangSmith](https://www.langchain.com/langsmith) for tracing. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e4ba31b0", + "metadata": {}, + "outputs": [], + "source": [ + "_set_env(\"LANGCHAIN_API_KEY\")\n", + "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", + "os.environ[\"LANGCHAIN_PROJECT\"] = \"local-llama32-rag\"" + ] + }, + { + "cell_type": "markdown", + "id": "bfaf9938", + "metadata": {}, + "source": [ + "### Vectorstore " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "86ff9905-313b-46a7-94e7-8ba75c59c708", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "USER_AGENT environment variable not set, consider setting it to identify your requests.\n" + ] + } + ], "source": [ "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import WebBaseLoader\n", - "from langchain_community.vectorstores import Chroma\n", + "from langchain_community.vectorstores import SKLearnVectorStore\n", "from langchain_nomic.embeddings import NomicEmbeddings\n", "\n", "urls = [\n", @@ -145,278 +147,273 @@ " \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n", "]\n", "\n", + "# Load documents\n", "docs = [WebBaseLoader(url).load() for url in urls]\n", "docs_list = [item for sublist in docs for item in sublist]\n", "\n", + "# Split documents\n", "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n", - " chunk_size=250, chunk_overlap=0\n", + " chunk_size=1000, chunk_overlap=200\n", ")\n", "doc_splits = text_splitter.split_documents(docs_list)\n", "\n", "# Add to vectorDB\n", - "vectorstore = Chroma.from_documents(\n", + "vectorstore = SKLearnVectorStore.from_documents(\n", " documents=doc_splits,\n", - " collection_name=\"rag-chroma\",\n", " embedding=NomicEmbeddings(model=\"nomic-embed-text-v1.5\", inference_mode=\"local\"),\n", ")\n", - "retriever = vectorstore.as_retriever()" + "\n", + "# Create retriever\n", + "retriever = vectorstore.as_retriever(k=3)" ] }, { "cell_type": "markdown", - "id": "2f3eb922-27a1-4a72-a727-85fbf5b3daf1", + "id": "d8c47fd1", "metadata": {}, "source": [ - "## LLMs\n", - "\n", - "Note: tested cmd-R on Mac M2 32GB and [latency is ~52 sec for RAG generation](https://smith.langchain.com/public/3998fe48-efc2-4d18-9069-972643d0982d/r)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7045e064-e666-4aea-9111-6e9d2007f27e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'datasource': 'vectorstore'}\n" - ] - } - ], - "source": [ - "### Router\n", - "\n", - "from langchain.prompts import PromptTemplate\n", - "from langchain_community.chat_models import ChatOllama\n", - "from langchain_core.output_parsers import JsonOutputParser\n", - "\n", - "# LLM\n", - "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n", - "\n", - "prompt = PromptTemplate(\n", - " template=\"\"\"You are an expert at routing a user question to a vectorstore or web search. \\n\n", - " Use the vectorstore for questions on LLM agents, prompt engineering, and adversarial attacks. \\n\n", - " You do not need to be stringent with the keywords in the question related to these topics. \\n\n", - " Otherwise, use web-search. Give a binary choice 'web_search' or 'vectorstore' based on the question. \\n\n", - " Return the a JSON with a single key 'datasource' and no premable or explanation. \\n\n", - " Question to route: {question}\"\"\",\n", - " input_variables=[\"question\"],\n", - ")\n", - "\n", - "question_router = prompt | llm | JsonOutputParser()\n", - "question = \"llm agent memory\"\n", - "docs = retriever.invoke(question)\n", - "doc_txt = docs[1].page_content\n", - "print(question_router.invoke({\"question\": question}))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "813cdcef-8b75-4214-a2ed-b89077b3d287", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'score': 'yes'}\n" - ] - } - ], - "source": [ - "### Retrieval Grader\n", - "\n", - "from langchain.prompts import PromptTemplate\n", - "from langchain_community.chat_models import ChatOllama\n", - "from langchain_core.output_parsers import JsonOutputParser\n", - "\n", - "# LLM\n", - "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n", - "\n", - "prompt = PromptTemplate(\n", - " template=\"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n", - " Here is the retrieved document: \\n\\n {document} \\n\\n\n", - " Here is the user question: {question} \\n\n", - " If the document contains keywords related to the user question, grade it as relevant. \\n\n", - " It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n", - " Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question. \\n\n", - " Provide the binary score as a JSON with a single key 'score' and no premable or explanation.\"\"\",\n", - " input_variables=[\"question\", \"document\"],\n", - ")\n", - "\n", - "retrieval_grader = prompt | llm | JsonOutputParser()\n", - "question = \"agent memory\"\n", - "docs = retriever.invoke(question)\n", - "doc_txt = docs[1].page_content\n", - "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))" + "### Components" ] }, { "cell_type": "code", "execution_count": 9, - "id": "aeb8b373-0289-4dec-bd4b-8b2701200301", + "id": "73e3ee8a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1. In an LLM-powered autonomous agent system, the memory component is divided into short-term and long-term memories. Short-term memory utilizes in-context learning, while long-term memory provides the capability to retain and recall information over extended periods using an external vector store.\n", - "2. The long-term memory module, also known as the memory stream, records a comprehensive list of agents' experiences in natural language.\n", - "3. The agent learns to call external APIs for extra information that is missing from the model weights, including current information, code execution capability, access to proprietary information sources and more.\n" + "{'datasource': 'websearch'} {'datasource': 'websearch'} {'datasource': 'vectorstore'}\n" + ] + } + ], + "source": [ + "### Router\n", + "import json\n", + "from langchain_core.messages import HumanMessage, SystemMessage\n", + "\n", + "# Prompt \n", + "router_instructions = \"\"\"You are an expert at routing a user question to a vectorstore or web search.\n", + "\n", + "The vectorstore contains documents related to agents, prompt engineering, and adversarial attacks.\n", + " \n", + "Use the vectorstore for questions on these topics. For all else, and especially for current events, use web-search.\n", + "\n", + "Return JSON with single key, datasource, that is 'websearch' or 'vectorstore' depending on the question.\"\"\"\n", + "\n", + "# Test router\n", + "test_web_search = llm_json_mode.invoke([SystemMessage(content=router_instructions)] + [HumanMessage(content=\"Who is favored to win the NFC Championship game in the 2024 season?\")])\n", + "test_web_search_2 = llm_json_mode.invoke([SystemMessage(content=router_instructions)] + [HumanMessage(content=\"What are the models released today for llama3.2?\")])\n", + "test_vector_store = llm_json_mode.invoke([SystemMessage(content=router_instructions)] + [HumanMessage(content=\"What are the types of agent memory?\")])\n", + "print(json.loads(test_web_search.content), json.loads(test_web_search_2.content), json.loads(test_vector_store.content))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "14275fad-89c2-49ef-847e-4fbc932eb96a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'binary_score': 'yes'}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### Retrieval Grader \n", + "\n", + "# Doc grader instructions \n", + "doc_grader_instructions = \"\"\"You are a grader assessing relevance of a retrieved document to a user question.\n", + "\n", + "If the document contains keyword(s) or semantic meaning related to the question, grade it as relevant.\"\"\"\n", + "\n", + "# Grader prompt\n", + "doc_grader_prompt = \"\"\"Here is the retrieved document: \\n\\n {document} \\n\\n Here is the user question: \\n\\n {question}. \n", + "\n", + "This carefully and objectively assess whether the document contains at least some information that is relevant to the question.\n", + "\n", + "Return JSON with single key, binary_score, that is 'yes' or 'no' score to indicate whether the document contains at least some information that is relevant to the question.\"\"\"\n", + "\n", + "# Test\n", + "question = \"What is Chain of thought prompting?\"\n", + "docs = retriever.invoke(question)\n", + "doc_txt = docs[1].page_content\n", + "doc_grader_prompt_formatted = doc_grader_prompt.format(document=doc_txt, question=question)\n", + "result = llm_json_mode.invoke([SystemMessage(content=doc_grader_instructions)] + [HumanMessage(content=doc_grader_prompt_formatted)])\n", + "json.loads(result.content)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3b381ea4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chain of Thought (CoT) prompting is a technique used in natural language processing to generate human-like responses by iteratively asking questions and refining the search space through external search queries, such as Wikipedia APIs. CoT prompting involves decomposing problems into multiple thought steps, generating multiple thoughts per step, and evaluating each state using a classifier or majority vote. The goal is to find an optimal instruction that leads to the desired output, which can be achieved by optimizing prompt parameters directly on the embedding space via gradient descent or searching over a pool of model-generated instruction candidates.\n" ] } ], "source": [ "### Generate\n", "\n", - "from langchain import hub\n", - "from langchain_community.chat_models import ChatOllama\n", - "from langchain_core.output_parsers import StrOutputParser\n", - "\n", "# Prompt\n", - "prompt = hub.pull(\"rlm/rag-prompt\")\n", + "rag_prompt = \"\"\"You are an assistant for question-answering tasks. \n", "\n", - "# LLM\n", - "llm = ChatOllama(model=local_llm, temperature=0)\n", + "Here is the context to use to answer the question:\n", "\n", + "{context} \n", + "\n", + "Think carefully about the above context. \n", + "\n", + "Now, review the user question:\n", + "\n", + "{question}\n", + "\n", + "Provide an answer to this questions using only the above context. \n", + "\n", + "Use three sentences maximum and keep the answer concise.\n", + "\n", + "Answer:\"\"\"\n", "\n", "# Post-processing\n", "def format_docs(docs):\n", " return \"\\n\\n\".join(doc.page_content for doc in docs)\n", "\n", - "\n", - "# Chain\n", - "rag_chain = prompt | llm | StrOutputParser()\n", - "\n", - "# Run\n", - "question = \"agent memory\"\n", - "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n", - "print(generation)" + "# Test\n", + "docs = retriever.invoke(question)\n", + "docs_txt = format_docs(docs)\n", + "rag_prompt_formatted = rag_prompt.format(context=docs_txt, question=question)\n", + "generation = llm.invoke([HumanMessage(content=rag_prompt_formatted)])\n", + "print(generation.content)" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "38345cff-e2d0-436e-aa09-599522a61eed", + "execution_count": 12, + "id": "c7adf546", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'score': 'yes'}" + "{'binary_score': 'yes',\n", + " 'explanation': 'The student answer provides a clear and accurate description of Chain of Thought (CoT) prompting, its components, and its goals. It also mentions various techniques used in CoT prompting, such as external search queries, prompt tuning, and automatic prompt engineering. The answer demonstrates an understanding of the concept and its applications in natural language processing.'}" ] }, - "execution_count": 7, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "### Hallucination Grader\n", + "### Hallucination Grader \n", "\n", - "# LLM\n", - "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n", + "# Hallucination grader instructions \n", + "hallucination_grader_instructions = \"\"\"\n", "\n", - "# Prompt\n", - "prompt = PromptTemplate(\n", - " template=\"\"\"You are a grader assessing whether an answer is grounded in / supported by a set of facts. \\n \n", - " Here are the facts:\n", - " \\n ------- \\n\n", - " {documents} \n", - " \\n ------- \\n\n", - " Here is the answer: {generation}\n", - " Give a binary score 'yes' or 'no' score to indicate whether the answer is grounded in / supported by a set of facts. \\n\n", - " Provide the binary score as a JSON with a single key 'score' and no preamble or explanation.\"\"\",\n", - " input_variables=[\"generation\", \"documents\"],\n", - ")\n", + "You are a teacher grading a quiz. \n", "\n", - "hallucination_grader = prompt | llm | JsonOutputParser()\n", - "hallucination_grader.invoke({\"documents\": docs, \"generation\": generation})" + "You will be given FACTS and a STUDENT ANSWER. \n", + "\n", + "Here is the grade criteria to follow:\n", + "\n", + "(1) Ensure the STUDENT ANSWER is grounded in the FACTS. \n", + "\n", + "(2) Ensure the STUDENT ANSWER does not contain \"hallucinated\" information outside the scope of the FACTS.\n", + "\n", + "Score:\n", + "\n", + "A score of yes means that the student's answer meets all of the criteria. This is the highest (best) score. \n", + "\n", + "A score of no means that the student's answer does not meet all of the criteria. This is the lowest possible score you can give.\n", + "\n", + "Explain your reasoning in a step-by-step manner to ensure your reasoning and conclusion are correct. \n", + "\n", + "Avoid simply stating the correct answer at the outset.\"\"\"\n", + "\n", + "# Grader prompt\n", + "hallucination_grader_prompt = \"\"\"FACTS: \\n\\n {documents} \\n\\n STUDENT ANSWER: {generation}. \n", + "\n", + "Return JSON with two two keys, binary_score is 'yes' or 'no' score to indicate whether the STUDENT ANSWER is grounded in the FACTS. And a key, explanation, that contains an explanation of the score.\"\"\"\n", + "\n", + "# Test using documents and generation from above \n", + "hallucination_grader_prompt_formatted = hallucination_grader_prompt.format(documents=docs_txt, generation=generation.content)\n", + "result = llm_json_mode.invoke([SystemMessage(content=hallucination_grader_instructions)] + [HumanMessage(content=hallucination_grader_prompt_formatted)])\n", + "json.loads(result.content)" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "9771caa1-5542-47c3-8354-aeeafcf51964", + "execution_count": 13, + "id": "2ecd225d-8deb-4b2e-adb3-2736619b7671", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'score': 'yes'}" + "{'binary_score': 'yes',\n", + " 'explanation': \"The student's answer helps to answer the question by providing specific details about the vision models released as part of Llama 3.2. The answer mentions two vision models (Llama 3.2 11B Vision Instruct and Llama 3.2 90B Vision Instruct) and their availability on Azure AI Model Catalog via managed compute. Additionally, the student provides context about Meta's first foray into multimodal AI and compares these models to other visual reasoning models like Claude 3 Haiku and GPT-4o mini. This extra information is not explicitly asked for in the question, but it demonstrates a thorough understanding of the topic. The answer also correctly states that these models replace the older text-only Llama 3.1 models, which meets all the criteria specified in the question.\"}" ] }, - "execution_count": 8, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "### Answer Grader\n", + "### Answer Grader \n", "\n", - "# LLM\n", - "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n", + "# Answer grader instructions \n", + "answer_grader_instructions = \"\"\"You are a teacher grading a quiz. \n", "\n", - "# Prompt\n", - "prompt = PromptTemplate(\n", - " template=\"\"\"You are a grader assessing whether an answer is useful to resolve a question. \\n \n", - " Here is the answer:\n", - " \\n ------- \\n\n", - " {generation} \n", - " \\n ------- \\n\n", - " Here is the question: {question}\n", - " Give a binary score 'yes' or 'no' to indicate whether the answer is useful to resolve a question. \\n\n", - " Provide the binary score as a JSON with a single key 'score' and no preamble or explanation.\"\"\",\n", - " input_variables=[\"generation\", \"question\"],\n", - ")\n", + "You will be given a QUESTION and a STUDENT ANSWER. \n", "\n", - "answer_grader = prompt | llm | JsonOutputParser()\n", - "answer_grader.invoke({\"question\": question, \"generation\": generation})" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "830ba5f7-9c8d-4c01-83b1-e4d51d40d48f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\" What is the function of an agent's memory in a given context?\"" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "### Question Re-writer\n", + "Here is the grade criteria to follow:\n", "\n", - "# LLM\n", - "llm = ChatOllama(model=local_llm, temperature=0)\n", + "(1) The STUDENT ANSWER helps to answer the QUESTION\n", "\n", - "# Prompt\n", - "re_write_prompt = PromptTemplate(\n", - " template=\"\"\"You a question re-writer that converts an input question to a better version that is optimized \\n \n", - " for vectorstore retrieval. Look at the initial and formulate an improved question. \\n\n", - " Here is the initial question: \\n\\n {question}. Improved question with no preamble: \\n \"\"\",\n", - " input_variables=[\"generation\", \"question\"],\n", - ")\n", + "Score:\n", "\n", - "question_rewriter = re_write_prompt | llm | StrOutputParser()\n", - "question_rewriter.invoke({\"question\": question})" + "A score of yes means that the student's answer meets all of the criteria. This is the highest (best) score. \n", + "\n", + "The student can receive a score of yes if the answer contains extra information that is not explicitly asked for in the question.\n", + "\n", + "A score of no means that the student's answer does not meet all of the criteria. This is the lowest possible score you can give.\n", + "\n", + "Explain your reasoning in a step-by-step manner to ensure your reasoning and conclusion are correct. \n", + "\n", + "Avoid simply stating the correct answer at the outset.\"\"\"\n", + "\n", + "# Grader prompt\n", + "answer_grader_prompt = \"\"\"QUESTION: \\n\\n {question} \\n\\n STUDENT ANSWER: {generation}. \n", + "\n", + "Return JSON with two two keys, binary_score is 'yes' or 'no' score to indicate whether the STUDENT ANSWER meets the criteria. And a key, explanation, that contains an explanation of the score.\"\"\"\n", + "\n", + "# Test \n", + "question = \"What are the vision models released today as part of Llama 3.2?\"\n", + "answer = \"The Llama 3.2 models released today include two vision models: Llama 3.2 11B Vision Instruct and Llama 3.2 90B Vision Instruct, which are available on Azure AI Model Catalog via managed compute. These models are part of Meta's first foray into multimodal AI and rival closed models like Anthropic's Claude 3 Haiku and OpenAI's GPT-4o mini in visual reasoning. They replace the older text-only Llama 3.1 models.\"\n", + "\n", + "# Test using question and generation from above \n", + "answer_grader_prompt_formatted = answer_grader_prompt.format(question=question, generation=answer)\n", + "result = llm_json_mode.invoke([SystemMessage(content=answer_grader_instructions)] + [HumanMessage(content=answer_grader_prompt_formatted)])\n", + "json.loads(result.content)" ] }, { "cell_type": "markdown", - "id": "686c9bb1-5069-45f9-8a7e-cba34fe07dd9", + "id": "f91fb35d", "metadata": {}, "source": [ "## Web Search Tool" @@ -424,72 +421,93 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "6c3c1c70-ff84-41e8-bf72-738ed52f2dde", + "execution_count": 14, + "id": "4bf32d8f-a640-43a7-bc8e-5f424f5f64eb", "metadata": {}, "outputs": [], "source": [ "### Search\n", - "\n", "from langchain_community.tools.tavily_search import TavilySearchResults\n", - "\n", "web_search_tool = TavilySearchResults(k=3)" ] }, { "cell_type": "markdown", - "id": "630d1751-a20b-4858-b3fd-0312de4f3ad7", + "id": "d0d1c5f5-b769-411e-b3b6-a7e9032288b2", "metadata": {}, "source": [ "# Graph \n", "\n", - "Capture the flow in as a graph.\n", + "We build the above workflow as a graph using [LangGraph](https://langchain-ai.github.io/langgraph/).\n", "\n", - "## Graph state" + "### Graph state\n", + "\n", + "The graph `state` schema contains keys that we want to:\n", + "\n", + "* Pass to each node in our graph\n", + "* Optionally, modify in each node of our graph \n", + "\n", + "See conceptual docs [here](https://langchain-ai.github.io/langgraph/concepts/low_level/#state)." ] }, { "cell_type": "code", - "execution_count": 11, - "id": "6e09087e-b2a9-437a-abee-129e426df799", + "execution_count": 15, + "id": "eaa2feff-6dd6-4de4-b41b-52f931e21eda", "metadata": {}, "outputs": [], "source": [ - "from typing import List\n", - "\n", + "import operator\n", "from typing_extensions import TypedDict\n", - "\n", + "from typing import List, Annotated\n", "\n", "class 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", + " Graph state is a dictionary that contains information we want to propagate to, and modify in, each graph node.\n", " \"\"\"\n", + " question : str # User question\n", + " generation : str # LLM generation\n", + " web_search : str # Binary decision to run web search\n", + " max_retries : int # Max number of retries for answer generation \n", + " answers : int # Number of answers generated\n", + " loop_step: Annotated[int, operator.add] \n", + " documents : List[str] # List of retrieved documents" + ] + }, + { + "cell_type": "markdown", + "id": "057a47f4-ce81-4420-90e9-bf6e26877c5c", + "metadata": {}, + "source": [ + "Each node in our graph is simply a function that:\n", "\n", - " question: str\n", - " generation: str\n", - " documents: List[str]" + "(1) Take `state` as an input\n", + "\n", + "(2) Modifies `state` \n", + "\n", + "(3) Write the modified `state` to the state schema (dict)\n", + "\n", + "See conceptual docs [here](https://langchain-ai.github.io/langgraph/concepts/low_level/#nodes).\n", + "\n", + "Each edge routes between nodes in the graph.\n", + "\n", + "See conceptual docs [here](https://langchain-ai.github.io/langgraph/concepts/low_level/#edges)." ] }, { "cell_type": "code", - "execution_count": 12, - "id": "7c5fa507-77ae-426a-a65f-f518b9525bd0", + "execution_count": 16, + "id": "a7d560a9-eb9a-4f6d-91d9-ba6f985211ae", "metadata": {}, "outputs": [], "source": [ - "### Nodes\n", - "\n", "from langchain.schema import Document\n", + "from langgraph.graph import END\n", "\n", - "\n", + "### Nodes\n", "def retrieve(state):\n", " \"\"\"\n", - " Retrieve documents\n", + " Retrieve documents from vectorstore\n", "\n", " Args:\n", " state (dict): The current graph state\n", @@ -500,14 +518,13 @@ " print(\"---RETRIEVE---\")\n", " question = state[\"question\"]\n", "\n", - " # Retrieval\n", + " # Write retrieved documents to documents key in state\n", " documents = retriever.invoke(question)\n", - " return {\"documents\": documents, \"question\": question}\n", - "\n", + " return {\"documents\": documents}\n", "\n", "def generate(state):\n", " \"\"\"\n", - " Generate answer\n", + " Generate answer using RAG on retrieved documents\n", "\n", " Args:\n", " state (dict): The current graph state\n", @@ -518,91 +535,77 @@ " print(\"---GENERATE---\")\n", " question = state[\"question\"]\n", " documents = state[\"documents\"]\n", - "\n", + " loop_step = state.get(\"loop_step\", 0)\n", + " \n", " # RAG generation\n", - " generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n", - " return {\"documents\": documents, \"question\": question, \"generation\": generation}\n", - "\n", + " docs_txt = format_docs(documents)\n", + " rag_prompt_formatted = rag_prompt.format(context=docs_txt, question=question)\n", + " generation = llm.invoke([HumanMessage(content=rag_prompt_formatted)])\n", + " return {\"generation\": generation, \"loop_step\": loop_step+1}\n", "\n", "def grade_documents(state):\n", " \"\"\"\n", - " Determines whether the retrieved documents are relevant to the question.\n", + " Determines whether the retrieved documents are relevant to the question\n", + " If any document is not relevant, we will set a flag to run web search\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", + " state (dict): Filtered out irrelevant documents and updated web_search state\n", " \"\"\"\n", "\n", " print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n", " question = state[\"question\"]\n", " documents = state[\"documents\"]\n", - "\n", + " \n", " # Score each doc\n", " filtered_docs = []\n", + " web_search = \"No\" \n", " for d in documents:\n", - " score = retrieval_grader.invoke(\n", - " {\"question\": question, \"document\": d.page_content}\n", - " )\n", - " grade = score[\"score\"]\n", - " if grade == \"yes\":\n", + " doc_grader_prompt_formatted = doc_grader_prompt.format(document=d.page_content, question=question)\n", + " result = llm_json_mode.invoke([SystemMessage(content=doc_grader_instructions)] + [HumanMessage(content=doc_grader_prompt_formatted)])\n", + " grade = json.loads(result.content)['binary_score']\n", + " # Document relevant\n", + " if grade.lower() == \"yes\":\n", " print(\"---GRADE: DOCUMENT RELEVANT---\")\n", " filtered_docs.append(d)\n", + " # Document not relevant\n", " else:\n", " print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n", + " # We do not include the document in filtered_docs\n", + " # We set a flag to indicate that we want to run web search\n", + " web_search = \"Yes\"\n", " continue\n", - " return {\"documents\": filtered_docs, \"question\": question}\n", - "\n", - "\n", - "def 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}\n", - "\n", - "\n", + " return {\"documents\": filtered_docs, \"web_search\": web_search}\n", + " \n", "def web_search(state):\n", " \"\"\"\n", - " Web search based on the re-phrased question.\n", + " Web search based based on the question\n", "\n", " Args:\n", " state (dict): The current graph state\n", "\n", " Returns:\n", - " state (dict): Updates documents key with appended web results\n", + " state (dict): Appended web results to documents\n", " \"\"\"\n", "\n", " print(\"---WEB SEARCH---\")\n", " question = state[\"question\"]\n", + " documents = state.get(\"documents\", [])\n", "\n", " # Web search\n", " docs = web_search_tool.invoke({\"query\": question})\n", " web_results = \"\\n\".join([d[\"content\"] for d in docs])\n", " web_results = Document(page_content=web_results)\n", + " documents.append(web_results)\n", + " return {\"documents\": documents}\n", "\n", - " return {\"documents\": web_results, \"question\": question}\n", - "\n", - "\n", - "### Edges ###\n", - "\n", + "### Edges\n", "\n", "def route_question(state):\n", " \"\"\"\n", - " Route question to web search or RAG.\n", + " Route question to web search or RAG \n", "\n", " Args:\n", " state (dict): The current graph state\n", @@ -612,22 +615,18 @@ " \"\"\"\n", "\n", " print(\"---ROUTE QUESTION---\")\n", - " question = state[\"question\"]\n", - " print(question)\n", - " source = question_router.invoke({\"question\": question})\n", - " print(source)\n", - " print(source[\"datasource\"])\n", - " if source[\"datasource\"] == \"web_search\":\n", + " route_question = llm_json_mode.invoke([SystemMessage(content=router_instructions)] + [HumanMessage(content=state[\"question\"])])\n", + " source = json.loads(route_question.content)['datasource']\n", + " if source == 'websearch':\n", " print(\"---ROUTE QUESTION TO WEB SEARCH---\")\n", - " return \"web_search\"\n", - " elif source[\"datasource\"] == \"vectorstore\":\n", + " return \"websearch\"\n", + " elif source == 'vectorstore':\n", " print(\"---ROUTE QUESTION TO RAG---\")\n", " return \"vectorstore\"\n", "\n", - "\n", "def decide_to_generate(state):\n", " \"\"\"\n", - " Determines whether to generate an answer, or re-generate a question.\n", + " Determines whether to generate an answer, or add web search\n", "\n", " Args:\n", " state (dict): The current graph state\n", @@ -637,25 +636,23 @@ " \"\"\"\n", "\n", " print(\"---ASSESS GRADED DOCUMENTS---\")\n", - " state[\"question\"]\n", + " question = state[\"question\"]\n", + " web_search = state[\"web_search\"]\n", " filtered_documents = state[\"documents\"]\n", "\n", - " if not filtered_documents:\n", + " if web_search == \"Yes\":\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", + " print(\"---DECISION: NOT ALL DOCUMENTS ARE RELEVANT TO QUESTION, INCLUDE WEB SEARCH---\")\n", + " return \"websearch\"\n", " else:\n", " # We have relevant documents, so generate answer\n", " print(\"---DECISION: GENERATE---\")\n", " return \"generate\"\n", "\n", - "\n", "def grade_generation_v_documents_and_question(state):\n", " \"\"\"\n", - " Determines whether the generation is grounded in the document and answers question.\n", + " Determines whether the generation is grounded in the document and answers question\n", "\n", " Args:\n", " state (dict): The current graph state\n", @@ -668,177 +665,159 @@ " question = state[\"question\"]\n", " documents = state[\"documents\"]\n", " generation = state[\"generation\"]\n", + " max_retries = state.get(\"max_retries\", 3) # Default to 3 if not provided\n", "\n", - " score = hallucination_grader.invoke(\n", - " {\"documents\": documents, \"generation\": generation}\n", - " )\n", - " grade = score[\"score\"]\n", + " hallucination_grader_prompt_formatted = hallucination_grader_prompt.format(documents=format_docs(documents), generation=generation.content)\n", + " result = llm_json_mode.invoke([SystemMessage(content=hallucination_grader_instructions)] + [HumanMessage(content=hallucination_grader_prompt_formatted)])\n", + " grade = json.loads(result.content)['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[\"score\"]\n", + " # Test using question and generation from above \n", + " answer_grader_prompt_formatted = answer_grader_prompt.format(question=question, generation=generation.content)\n", + " result = llm_json_mode.invoke([SystemMessage(content=answer_grader_instructions)] + [HumanMessage(content=answer_grader_prompt_formatted)])\n", + " grade = json.loads(result.content)['binary_score']\n", " if grade == \"yes\":\n", " print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n", " return \"useful\"\n", - " else:\n", + " elif state[\"loop_step\"] <= max_retries:\n", " print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n", " return \"not useful\"\n", + " else:\n", + " print(\"---DECISION: MAX RETRIES REACHED---\")\n", + " return \"max retries\" \n", + " elif state[\"loop_step\"] <= max_retries:\n", + " print(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n", + " return \"not supported\"\n", " else:\n", - " pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n", - " return \"not supported\"" + " print(\"---DECISION: MAX RETRIES REACHED---\")\n", + " return \"max retries\"" ] }, { "cell_type": "markdown", - "id": "ed7d8eb6-31d7-4ab5-8a88-7081b64582bb", + "id": "60b34375-20c8-440e-a860-e6abc05a950f", "metadata": {}, "source": [ - "## Build Graph" + "## Control Flow" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "450eb313-ca75-4a43-b57e-7034bd3f40bf", + "execution_count": 17, + "id": "7c5b82ff-ab09-4e86-a395-13a894b2a9fe", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "from langgraph.graph import END, StateGraph, START\n", + "from langgraph.graph import StateGraph\n", + "from IPython.display import Image, display\n", "\n", "workflow = StateGraph(GraphState)\n", "\n", "# Define the nodes\n", - "workflow.add_node(\"web_search\", web_search) # web search\n", - "workflow.add_node(\"retrieve\", retrieve) # retrieve\n", - "workflow.add_node(\"grade_documents\", grade_documents) # grade documents\n", - "workflow.add_node(\"generate\", generate) # generatae\n", - "workflow.add_node(\"transform_query\", transform_query) # transform_query\n", + "workflow.add_node(\"websearch\", web_search) # web search\n", + "workflow.add_node(\"retrieve\", retrieve) # retrieve\n", + "workflow.add_node(\"grade_documents\", grade_documents) # grade documents\n", + "workflow.add_node(\"generate\", generate) # generate\n", "\n", "# Build graph\n", - "workflow.add_conditional_edges(\n", - " START,\n", + "workflow.set_conditional_entry_point(\n", " route_question,\n", " {\n", - " \"web_search\": \"web_search\",\n", + " \"websearch\": \"websearch\",\n", " \"vectorstore\": \"retrieve\",\n", " },\n", ")\n", - "workflow.add_edge(\"web_search\", \"generate\")\n", + "workflow.add_edge(\"websearch\", \"generate\")\n", "workflow.add_edge(\"retrieve\", \"grade_documents\")\n", "workflow.add_conditional_edges(\n", " \"grade_documents\",\n", " decide_to_generate,\n", " {\n", - " \"transform_query\": \"transform_query\",\n", + " \"websearch\": \"websearch\",\n", " \"generate\": \"generate\",\n", " },\n", ")\n", - "workflow.add_edge(\"transform_query\", \"retrieve\")\n", "workflow.add_conditional_edges(\n", " \"generate\",\n", " grade_generation_v_documents_and_question,\n", " {\n", " \"not supported\": \"generate\",\n", " \"useful\": END,\n", - " \"not useful\": \"transform_query\",\n", + " \"not useful\": \"websearch\",\n", + " \"max retries\": END,\n", " },\n", ")\n", "\n", "# Compile\n", - "app = workflow.compile()" + "graph = workflow.compile()\n", + "display(Image(graph.get_graph().draw_mermaid_png()))" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "b095c1db-8bd1-4a34-937c-1a9b74ae74ff", + "execution_count": null, + "id": "469567ff-26af-4902-b5e4-3fef1c091634", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---ROUTE QUESTION---\n", - "What is the AlphaCodium paper about?\n", - "{'datasource': 'vectorstore'}\n", - "vectorstore\n", - "---ROUTE QUESTION TO RAG---\n", - "---RETRIEVE---\n", - "\"Node 'retrieve':\"\n", - "'\\n---\\n'\n", - "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n", - "---GRADE: DOCUMENT NOT RELEVANT---\n", - "---GRADE: DOCUMENT NOT RELEVANT---\n", - "---GRADE: DOCUMENT NOT RELEVANT---\n", - "---GRADE: DOCUMENT NOT RELEVANT---\n", - "---ASSESS GRADED DOCUMENTS---\n", - "---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\n", - "\"Node 'grade_documents':\"\n", - "'\\n---\\n'\n", - "---TRANSFORM QUERY---\n", - "\"Node 'transform_query':\"\n", - "'\\n---\\n'\n", - "---RETRIEVE---\n", - "\"Node 'retrieve':\"\n", - "'\\n---\\n'\n", - "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n", - "---GRADE: DOCUMENT NOT RELEVANT---\n", - "---GRADE: DOCUMENT RELEVANT---\n", - "---GRADE: DOCUMENT RELEVANT---\n", - "---GRADE: DOCUMENT NOT RELEVANT---\n", - "---ASSESS GRADED DOCUMENTS---\n", - "---DECISION: GENERATE---\n", - "\"Node 'grade_documents':\"\n", - "'\\n---\\n'\n", - "---GENERATE---\n", - "---CHECK HALLUCINATIONS---\n", - "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n", - "---GRADE GENERATION vs QUESTION---\n", - "---DECISION: GENERATION ADDRESSES QUESTION---\n", - "\"Node 'generate':\"\n", - "'\\n---\\n'\n", - "(' The \"AlphaCodium\" research paper appears to focus on the development and '\n", - " 'comparison of an autonomous agent system powered by a large language model '\n", - " '(LLM). The system is compared with several baselines, including ED, source '\n", - " 'policy, and RL^2. The LLM-powered agent demonstrates impressive performance '\n", - " 'in in-context reinforcement learning, getting close to the performance of '\n", - " 'RL^2 despite only using offline RL and learning much faster than other '\n", - " 'baselines. Additionally, the paper discusses the use of adversarial attacks '\n", - " 'on LLMs as a potential threat to their safe behavior in real-world '\n", - " 'applications.')\n" - ] - } - ], + "outputs": [], "source": [ - "from pprint import pprint\n", - "\n", - "# Run\n", - "inputs = {\"question\": \"What is the AlphaCodium paper about?\"}\n", - "for output in app.stream(inputs):\n", - " for key, value in output.items():\n", - " # Node\n", - " pprint(f\"Node '{key}':\")\n", - " # Optional: print full state at each node\n", - " # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n", - " pprint(\"\\n---\\n\")\n", - "\n", - "# Final generation\n", - "pprint(value[\"generation\"])" + "inputs = {\"question\": \"What are the types of agent memory?\", \"max_retries\": 3}\n", + "for event in graph.stream(inputs, stream_mode=\"values\"):\n", + " print(event)" ] }, { "cell_type": "markdown", - "id": "644c7293-9cb5-4236-ba08-1e63b0309cb7", + "id": "fdd44294-b172-456a-98da-7f924e112fa2", "metadata": {}, "source": [ - "Trace: \n", + "Trace:\n", "\n", - "https://smith.langchain.com/public/81813813-be53-403c-9877-afcd5786ca2e/r" + "https://smith.langchain.com/public/1e01baea-53e9-4341-a6d1-b1614a800a97/r" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb20a1a9-f55b-489b-ab9e-d063c59ddfa6", + "metadata": {}, + "outputs": [], + "source": [ + "# Test on current events\n", + "inputs = {\"question\": \"What are the models released today for llama3.2?\", \"max_retries\": 3}\n", + "for event in graph.stream(inputs, stream_mode=\"values\"):\n", + " print(event)" + ] + }, + { + "cell_type": "markdown", + "id": "60a16669-0b3e-45f9-9f96-e21a2a426a8f", + "metadata": {}, + "source": [ + "Trace:\n", + "\n", + "https://smith.langchain.com/public/acdfa49d-aa11-48fb-9d9c-13a687ff311f/r" + ] + }, + { + "cell_type": "markdown", + "id": "745d630e", + "metadata": {}, + "source": [] } ], "metadata": { @@ -857,7 +836,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.6" } }, "nbformat": 4, diff --git a/examples/rag/langgraph_rag_agent_llama3_local.ipynb b/examples/rag/langgraph_rag_agent_llama3_local.ipynb deleted file mode 100644 index 5a9df7a41..000000000 --- a/examples/rag/langgraph_rag_agent_llama3_local.ipynb +++ /dev/null @@ -1,397 +0,0 @@ -{ - "cells": [ - { - "attachments": { - "7b00797e-fb85-4474-9a9e-c505b61add81.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "0216de30-29cf-4464-9cc3-6e9a6d6c3e40", - "metadata": {}, - "source": [ - "# Local RAG agent with LLaMA3\n", - "\n", - "We'll combine ideas from paper RAG papers into a RAG agent:\n", - "\n", - "- **Routing:** Adaptive RAG ([paper](https://arxiv.org/abs/2403.14403)). Route questions to different retrieval approaches\n", - "- **Fallback:** Corrective RAG ([paper](https://arxiv.org/pdf/2401.15884.pdf)). Fallback to web search if docs are not relevant to query\n", - "- **Self-correction:** Self-RAG ([paper](https://arxiv.org/abs/2310.11511)). Fix answers w/ hallucinations or don’t address question\n", - "\n", - "![langgraph_adaptive_rag.png](attachment:7b00797e-fb85-4474-9a9e-c505b61add81.png)\n", - "\n", - "## Local models\n", - "\n", - "#### Embedding\n", - " \n", - "[GPT4All Embeddings](https://blog.nomic.ai/posts/nomic-embed-text-v1):\n", - "\n", - "```\n", - "pip install langchain-nomic\n", - "```\n", - "\n", - "### LLM\n", - "\n", - "Use [Ollama](https://ollama.ai/) and [llama3](https://ollama.ai/library/llama3):\n", - "\n", - "```\n", - "ollama pull llama3\n", - "```\n", - "\n", - "Prompt - \n", - "\n", - "https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "21e597f9", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n%pip install -U langchain-nomic langchain_community tiktoken langchainhub chromadb langchain langgraph tavily-python nomic[local] langchain-text-splitters" - ] - }, - { - "cell_type": "markdown", - "id": "5bd42c79", - "metadata": {}, - "source": [ - "### Tracing (optional)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "333cbcf4", - "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\"] = \"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2096d49c-d3dc-4329-ada7-aff56d210198", - "metadata": {}, - "outputs": [], - "source": [ - "### LLM\n\nlocal_llm = \"llama3\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "267c63e1-4c2f-439d-8d95-4c6aa01f41cf", - "metadata": {}, - "outputs": [], - "source": [ - "### Index\n\nfrom langchain_community.document_loaders import WebBaseLoader\nfrom langchain_community.vectorstores import Chroma\nfrom langchain_nomic.embeddings import NomicEmbeddings\nfrom langchain_text_splitters import RecursiveCharacterTextSplitter\n\nurls = [\n \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n]\n\ndocs = [WebBaseLoader(url).load() for url in urls]\ndocs_list = [item for sublist in docs for item in sublist]\n\ntext_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n chunk_size=250, chunk_overlap=0\n)\ndoc_splits = text_splitter.split_documents(docs_list)\n\n# Add to vectorDB\nvectorstore = Chroma.from_documents(\n documents=doc_splits,\n collection_name=\"rag-chroma\",\n embedding=NomicEmbeddings(model=\"nomic-embed-text-v1.5\", inference_mode=\"local\"),\n)\nretriever = vectorstore.as_retriever()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b008df98-8394-49da-8fb8-aefe2c90d03c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'score': 'yes'}\n" - ] - } - ], - "source": [ - "### Retrieval Grader\n\nfrom langchain_community.chat_models import ChatOllama\nfrom langchain_core.output_parsers import JsonOutputParser\nfrom langchain_core.prompts import PromptTemplate\n\n# LLM\nllm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n\nprompt = PromptTemplate(\n template=\"\"\"<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a grader assessing relevance \n of a retrieved document to a user question. If the document contains keywords related to the user question, \n grade it as relevant. It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question. \\n\n Provide the binary score as a JSON with a single key 'score' and no premable or explanation.\n <|eot_id|><|start_header_id|>user<|end_header_id|>\n Here is the retrieved document: \\n\\n {document} \\n\\n\n Here is the user question: {question} \\n <|eot_id|><|start_header_id|>assistant<|end_header_id|>\n \"\"\",\n input_variables=[\"question\", \"document\"],\n)\n\nretrieval_grader = prompt | llm | JsonOutputParser()\nquestion = \"agent memory\"\ndocs = retriever.invoke(question)\ndoc_txt = docs[1].page_content\nprint(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "1d531a81-6d4d-405e-975a-01ef1c9679fa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The context mentions that the memory component of an LLM-powered autonomous agent system includes a long-term memory module (external database) that records a comprehensive list of agents' experience in natural language, referred to as \"memory stream\". This suggests that the agent has some form of memory or recall mechanism.\n" - ] - } - ], - "source": [ - "### Generate\n\nfrom langchain_core.output_parsers import StrOutputParser\nfrom langchain_core.prompts import PromptTemplate\n\n# Prompt\nprompt = PromptTemplate(\n template=\"\"\"<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are an assistant for question-answering tasks. \n 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. \n Use three sentences maximum and keep the answer concise <|eot_id|><|start_header_id|>user<|end_header_id|>\n Question: {question} \n Context: {context} \n Answer: <|eot_id|><|start_header_id|>assistant<|end_header_id|>\"\"\",\n input_variables=[\"question\", \"document\"],\n)\n\nllm = ChatOllama(model=local_llm, temperature=0)\n\n\n# Post-processing\ndef format_docs(docs):\n return \"\\n\\n\".join(doc.page_content for doc in docs)\n\n\n# Chain\nrag_chain = prompt | llm | StrOutputParser()\n\n# Run\nquestion = \"agent memory\"\ndocs = retriever.invoke(question)\ngeneration = rag_chain.invoke({\"context\": docs, \"question\": question})\nprint(generation)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "0261a9a4-de13-4dd8-b082-95305a3e43ca", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'score': 'yes'}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "### Hallucination Grader\n\n# LLM\nllm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n\n# Prompt\nprompt = PromptTemplate(\n template=\"\"\" <|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a grader assessing whether \n an answer is grounded in / supported by a set of facts. Give a binary 'yes' or 'no' score to indicate \n whether the answer is grounded in / supported by a set of facts. Provide the binary score as a JSON with a \n single key 'score' and no preamble or explanation. <|eot_id|><|start_header_id|>user<|end_header_id|>\n Here are the facts:\n \\n ------- \\n\n {documents} \n \\n ------- \\n\n Here is the answer: {generation} <|eot_id|><|start_header_id|>assistant<|end_header_id|>\"\"\",\n input_variables=[\"generation\", \"documents\"],\n)\n\nhallucination_grader = prompt | llm | JsonOutputParser()\nhallucination_grader.invoke({\"documents\": docs, \"generation\": generation})" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "df9f6944-4fee-4971-b3a7-2b81b44ed433", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'score': 'yes'}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "### Answer Grader\n\n# LLM\nllm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n\n# Prompt\nprompt = PromptTemplate(\n template=\"\"\"<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a grader assessing whether an \n answer is useful to resolve a question. Give a binary score 'yes' or 'no' to indicate whether the answer is \n useful to resolve a question. Provide the binary score as a JSON with a single key 'score' and no preamble or explanation.\n <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the answer:\n \\n ------- \\n\n {generation} \n \\n ------- \\n\n Here is the question: {question} <|eot_id|><|start_header_id|>assistant<|end_header_id|>\"\"\",\n input_variables=[\"generation\", \"question\"],\n)\n\nanswer_grader = prompt | llm | JsonOutputParser()\nanswer_grader.invoke({\"question\": question, \"generation\": generation})" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "a9c910c1-738c-4bf7-bf9e-801862b227eb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'datasource': 'vectorstore'}\n" - ] - } - ], - "source": [ - "### Router\n\nfrom langchain_community.chat_models import ChatOllama\nfrom langchain_core.output_parsers import JsonOutputParser\nfrom langchain_core.prompts import PromptTemplate\n\n# LLM\nllm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n\nprompt = PromptTemplate(\n template=\"\"\"<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are an expert at routing a \n user question to a vectorstore or web search. Use the vectorstore for questions on LLM agents, \n prompt engineering, and adversarial attacks. You do not need to be stringent with the keywords \n in the question related to these topics. Otherwise, use web-search. Give a binary choice 'web_search' \n or 'vectorstore' based on the question. Return the a JSON with a single key 'datasource' and \n no premable or explanation. Question to route: {question} <|eot_id|><|start_header_id|>assistant<|end_header_id|>\"\"\",\n input_variables=[\"question\"],\n)\n\nquestion_router = prompt | llm | JsonOutputParser()\nquestion = \"llm agent memory\"\ndocs = retriever.get_relevant_documents(question)\ndoc_txt = docs[1].page_content\nprint(question_router.invoke({\"question\": question}))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "023ff2db-eb4e-4d44-904c-ea061abc16d9", - "metadata": {}, - "outputs": [], - "source": [ - "### Search\nfrom langchain_community.tools.tavily_search import TavilySearchResults\n\nweb_search_tool = TavilySearchResults(k=3)" - ] - }, - { - "cell_type": "markdown", - "id": "ccd59cdf-a04d-4b2e-b9cc-6a1b1e80a6c6", - "metadata": {}, - "source": [ - "We'll implement these as a control flow in LangGraph." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "07fa3d08-6a86-4705-a28b-e2721070bc5e", - "metadata": {}, - "outputs": [], - "source": [ - "from pprint import pprint\nfrom typing import List\n\nfrom langchain_core.documents import Document\nfrom typing_extensions import TypedDict\n\nfrom langgraph.graph import END, StateGraph, START\n\n### State\n\n\nclass GraphState(TypedDict):\n \"\"\"\n Represents the state of our graph.\n\n Attributes:\n question: question\n generation: LLM generation\n web_search: whether to add search\n documents: list of documents\n \"\"\"\n\n question: str\n generation: str\n web_search: str\n documents: List[str]\n\n\n### Nodes\n\n\ndef retrieve(state):\n \"\"\"\n Retrieve documents from vectorstore\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 using RAG on retrieved documents\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 If any document is not relevant, we will set a flag to run web search\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): Filtered out irrelevant documents and updated web_search state\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 web_search = \"No\"\n for d in documents:\n score = retrieval_grader.invoke(\n {\"question\": question, \"document\": d.page_content}\n )\n grade = score[\"score\"]\n # Document relevant\n if grade.lower() == \"yes\":\n print(\"---GRADE: DOCUMENT RELEVANT---\")\n filtered_docs.append(d)\n # Document not relevant\n else:\n print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n # We do not include the document in filtered_docs\n # We set a flag to indicate that we want to run web search\n web_search = \"Yes\"\n continue\n return {\"documents\": filtered_docs, \"question\": question, \"web_search\": web_search}\n\n\ndef web_search(state):\n \"\"\"\n Web search based based on the question\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): Appended web results to documents\n \"\"\"\n\n print(\"---WEB SEARCH---\")\n question = state[\"question\"]\n documents = state[\"documents\"]\n\n # Web search\n docs = web_search_tool.invoke({\"query\": question})\n web_results = \"\\n\".join([d[\"content\"] for d in docs])\n web_results = Document(page_content=web_results)\n if documents is not None:\n documents.append(web_results)\n else:\n documents = [web_results]\n return {\"documents\": documents, \"question\": question}\n\n\n### Conditional edge\n\n\ndef route_question(state):\n \"\"\"\n Route question to web search or RAG.\n\n Args:\n state (dict): The current graph state\n\n Returns:\n str: Next node to call\n \"\"\"\n\n print(\"---ROUTE QUESTION---\")\n question = state[\"question\"]\n print(question)\n source = question_router.invoke({\"question\": question})\n print(source)\n print(source[\"datasource\"])\n if source[\"datasource\"] == \"web_search\":\n print(\"---ROUTE QUESTION TO WEB SEARCH---\")\n return \"websearch\"\n elif source[\"datasource\"] == \"vectorstore\":\n print(\"---ROUTE QUESTION TO RAG---\")\n return \"vectorstore\"\n\n\ndef decide_to_generate(state):\n \"\"\"\n Determines whether to generate an answer, or add web search\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 web_search = state[\"web_search\"]\n state[\"documents\"]\n\n if web_search == \"Yes\":\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, INCLUDE WEB SEARCH---\"\n )\n return \"websearch\"\n else:\n # We have relevant documents, so generate answer\n print(\"---DECISION: GENERATE---\")\n return \"generate\"\n\n\n### Conditional edge\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[\"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[\"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\"\n\n\nworkflow = StateGraph(GraphState)\n\n# Define the nodes\nworkflow.add_node(\"websearch\", web_search) # web search\nworkflow.add_node(\"retrieve\", retrieve) # retrieve\nworkflow.add_node(\"grade_documents\", grade_documents) # grade documents\nworkflow.add_node(\"generate\", generate) # generatae" - ] - }, - { - "cell_type": "markdown", - "id": "73f21594-00d4-48a8-ae2e-4e55a010b540", - "metadata": {}, - "source": [ - "### Graph Build" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "d9a4b9e4-3ba8-47d6-958c-e5a7112ac6f4", - "metadata": {}, - "outputs": [], - "source": [ - "# Build graph\n", - "workflow.add_conditional_edges(\n", - " START,\n", - " route_question,\n", - " {\n", - " \"websearch\": \"websearch\",\n", - " \"vectorstore\": \"retrieve\",\n", - " },\n", - ")\n", - "\n", - "workflow.add_edge(\"retrieve\", \"grade_documents\")\n", - "workflow.add_conditional_edges(\n", - " \"grade_documents\",\n", - " decide_to_generate,\n", - " {\n", - " \"websearch\": \"websearch\",\n", - " \"generate\": \"generate\",\n", - " },\n", - ")\n", - "workflow.add_edge(\"websearch\", \"generate\")\n", - "workflow.add_conditional_edges(\n", - " \"generate\",\n", - " grade_generation_v_documents_and_question,\n", - " {\n", - " \"not supported\": \"generate\",\n", - " \"useful\": END,\n", - " \"not useful\": \"websearch\",\n", - " },\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "13043b0f-17c7-49d3-9ea7-8f2c0f0c8691", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---ROUTE QUESTION---\n", - "What are the types of agent memory?\n", - "{'datasource': 'vectorstore'}\n", - "vectorstore\n", - "---ROUTE QUESTION TO RAG---\n", - "---RETRIEVE---\n", - "'Finished running: retrieve:'\n", - "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n", - "---GRADE: DOCUMENT RELEVANT---\n", - "---GRADE: DOCUMENT RELEVANT---\n", - "---GRADE: DOCUMENT RELEVANT---\n", - "---GRADE: DOCUMENT RELEVANT---\n", - "---ASSESS GRADED DOCUMENTS---\n", - "---DECISION: GENERATE---\n", - "'Finished running: grade_documents:'\n", - "---GENERATE---\n", - "---CHECK HALLUCINATIONS---\n", - "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n", - "---GRADE GENERATION vs QUESTION---\n", - "---DECISION: GENERATION ADDRESSES QUESTION---\n", - "'Finished running: generate:'\n", - "('According to the provided context, there are several types of memory '\n", - " 'mentioned:\\n'\n", - " '\\n'\n", - " '1. Sensory Memory: This is the earliest stage of memory, providing the '\n", - " 'ability to retain impressions of sensory information (visual, auditory, etc) '\n", - " 'after the original stimuli have ended.\\n'\n", - " '2. Maximum Inner Product Search (MIPS): This is a long-term memory module '\n", - " \"that records a comprehensive list of agents' experience in natural \"\n", - " 'language.\\n'\n", - " '\\n'\n", - " 'These are the types of agent memory mentioned in the context.')\n" - ] - } - ], - "source": [ - "# Compile\napp = workflow.compile()\n\n# Test\n\ninputs = {\"question\": \"What are the types of agent memory?\"}\nfor output in app.stream(inputs):\n for key, value in output.items():\n pprint(f\"Finished running: {key}:\")\npprint(value[\"generation\"])" - ] - }, - { - "cell_type": "markdown", - "id": "d733ab80-7e7b-4b1a-9519-9242de647eda", - "metadata": {}, - "source": [ - "Trace: \n", - "\n", - "https://smith.langchain.com/public/8d449b67-6bc4-4ecf-9153-759cd21df24f/r" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "fbfcec3e-a09a-40b4-9c15-fead97bf4e0a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---ROUTE QUESTION---\n", - "Who are the Bears expected to draft first in the NFL draft?\n", - "{'datasource': 'web_search'}\n", - "web_search\n", - "---ROUTE QUESTION TO WEB SEARCH---\n", - "---WEB SEARCH---\n", - "'Finished running: websearch:'\n", - "---GENERATE---\n", - "---CHECK HALLUCINATIONS---\n", - "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n", - "---GRADE GENERATION vs QUESTION---\n", - "---DECISION: GENERATION ADDRESSES QUESTION---\n", - "'Finished running: generate:'\n", - "('The Bears are expected to draft USC star and 2022 Heisman Trophy winner '\n", - " 'Caleb Williams with the No. 1 overall pick.')\n" - ] - } - ], - "source": [ - "from pprint import pprint\n\n# Compile\napp = workflow.compile()\ninputs = {\"question\": \"Who are the Bears expected to draft first in the NFL draft?\"}\nfor output in app.stream(inputs):\n for key, value in output.items():\n pprint(f\"Finished running: {key}:\")\npprint(value[\"generation\"])" - ] - }, - { - "cell_type": "markdown", - "id": "2d74d784-daf2-469a-883a-697a98b48dbd", - "metadata": {}, - "source": [ - "Trace: \n", - "\n", - "https://smith.langchain.com/public/c785f9c0-f519-4a38-ad5a-febb59a2139c/r" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1da64a95-736b-4373-8fb4-6ed4bf60a647", - "metadata": {}, - "outputs": [], - "source": [ - "" - ] - } - ], - "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": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}