From 93faaea7bdd5481aa74147285e3bd931ddb457e3 Mon Sep 17 00:00:00 2001 From: Lance Martin <122662504+rlancemartin@users.noreply.github.com> Date: Mon, 24 Jun 2024 16:24:20 -0700 Subject: [PATCH] Update local CRAG ntbk (#795) * Update local CRAG ntbk * fmt --- examples/rag/langgraph_crag_local.ipynb | 916 +++++++++++---------- examples/tutorials/rag-agent-testing.ipynb | 2 +- 2 files changed, 480 insertions(+), 438 deletions(-) diff --git a/examples/rag/langgraph_crag_local.ipynb b/examples/rag/langgraph_crag_local.ipynb index 433067931..1c1127045 100644 --- a/examples/rag/langgraph_crag_local.ipynb +++ b/examples/rag/langgraph_crag_local.ipynb @@ -2,8 +2,8 @@ "cells": [ { "attachments": { - "d3ff129f-c0ff-4951-993c-efafc7f29dce.png": { - "image/png": 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" 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} }, "cell_type": "markdown", @@ -12,35 +12,23 @@ "source": [ "# Corrective RAG (CRAG) using local LLMs\n", "\n", - "Corrective-RAG (CRAG) is a strategy for RAG that incorporates self-reflection / self-grading on retrieved documents. \n", + "[Corrective-RAG (CRAG)](https://arxiv.org/abs/2401.15884) is a strategy for RAG that incorporates self-reflection / self-grading on retrieved documents. \n", "\n", - "In the paper [here](https://arxiv.org/pdf/2401.15884.pdf), a few steps are taken:\n", + "The paper follows this general flow:\n", + "\n", + "* If at least one document exceeds the threshold for `relevance`, then it proceeds to generation\n", + "* If all documents fall below the `relevance` threshold or if the grader is unsure, then it uses web search to supplement retrieval\n", + "* Before generation, it performs knowledge refinement of the search or retrieved documents\n", + "* This partitions the document into `knowledge strips`\n", + "* It grades each strip, and filters out irrelevant ones\n", "\n", - "* If at least one document exceeds the threshold for relevance, then it proceeds to generation\n", - "* Before generation, it performns knowledge refinement\n", - "* This partitions the document into \"knowledge strips\"\n", - "* It grades each strip, and filters our irrelevant ones\n", - "* If all documents fall below the relevance threshold or if the grader is unsure, then the framework seeks an additional datasource\n", - "* It will use web search to supplement retrieval\n", - " \n", "We will implement some of these ideas from scratch using [LangGraph](https://langchain-ai.github.io/langgraph/):\n", "\n", - "* Let's skip the knowledge refinement phase as a first pass. This can be added back as a node, if desired. \n", - "* If *any* documents are irrelevant, let's opt to supplement retrieval with web search. \n", + "* If *any* documents are irrelevant, we'll supplement retrieval with web search. \n", + "* We'll skip the knowledge refinement, but this can be added back as a node if desired. \n", "* We'll use [Tavily Search](https://python.langchain.com/v0.2/docs/integrations/tools/tavily_search/) for web search.\n", - "* Let's use query re-writing to optimize the query for web search.\n", "\n", - "![Screenshot 2024-04-01 at 12.36.05 PM.png](attachment:d3ff129f-c0ff-4951-993c-efafc7f29dce.png)\n", - "\n", - "## Running\n", - "\n", - "This notebook can be run three ways:\n", - "\n", - "(1) Mistral API\n", - "\n", - "(2) Locally \n", - "\n", - "(3) CoLab: [here](https://colab.research.google.com/drive/1U5OcwWjoXZSud30q4XOk1UlIJNjaD3kX?usp=sharing) is a link to a CoLab for this notebook. " + "![Screenshot 2024-06-24 at 3.03.16 PM.png](attachment:b77a7d3b-b28a-4dcf-9f1a-861f2f2c5f6c.png)" ] }, { @@ -48,7 +36,18 @@ "id": "6ba4302f-09d9-4d2a-a18d-a6fd23704850", "metadata": {}, "source": [ - "# Environment " + "### Environment\n", + "\n", + "We'll use [Ollama](https://ollama.ai/) to access a local LLM:\n", + "\n", + "* Download [Ollama app](https://ollama.ai/).\n", + "* Pull your model of choice, e.g.: `ollama pull llama3`\n", + "\n", + "We'll use [Tavily](https://python.langchain.com/v0.2/docs/integrations/tools/tavily_search/) for web search.\n", + "\n", + "We'll use a vectorstore with [Nomic local embeddings](https://blog.nomic.ai/posts/nomic-embed-text-v1) or, optionally, OpenAI embeddings.\n", + "\n", + "We'll use [LangSmith](https://docs.smith.langchain.com/) for tracing and evaluation." ] }, { @@ -58,92 +57,45 @@ "metadata": {}, "outputs": [], "source": [ - "! pip install --quiet langchain_community tiktoken langchainhub chromadb langchain langgraph tavily-python langchain-mistralai gpt4all" - ] - }, - { - "cell_type": "markdown", - "id": "728896ab-8aca-4cc3-a152-1491a76bc620", - "metadata": {}, - "source": [ - "### LLMs\n", - "\n", - "You can run this in two ways:\n", - "\n", - "(1) Use [Mistral API](https://auth.mistral.ai/ui/login?flow=cc5d3fa5-122b-4c87-bcd8-81e8151c6753).\n", - "\n", - "(2) Run locally, as shown below.\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", - "\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.\n", - "```\n", - "ollama pull mistral\n", - "```" + "%%capture --no-stderr\n", + "%pip install -U langchain_community tiktoken langchainhub scikit-learn langchain langgraph tavily-python nomic[local] langchain-nomic langchain_openai" ] }, { "cell_type": "code", "execution_count": null, - "id": "d0cd7ff0-534d-4743-ba6d-e12024a2fd84", - "metadata": {}, - "outputs": [], - "source": [ - "# If using Mistral API\n", - "mistral_api_key = \"\"" - ] - }, - { - "cell_type": "markdown", - "id": "83f74055-d137-466c-9555-4e2da9759ddb", - "metadata": {}, - "source": [ - "### Search\n", - " \n", - "We'll use [Tavily Search](https://python.langchain.com/docs/integrations/tools/tavily_search) for web search." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "28224481-4cb0-4bc6-bf88-2d2b383094df", + "id": "68316ba0-854b-41e1-9af5-1f9e965946e3", "metadata": {}, "outputs": [], "source": [ + "# Search\n", "import os\n", - "\n", - "os.environ[\"TAVILY_API_KEY\"] = \"\"" - ] - }, - { - "cell_type": "markdown", - "id": "55ec4c0c-65cc-4816-86df-c40b55f9c2d5", - "metadata": {}, - "source": [ - "### Tracing\n", - "\n", - "Optionally, use [LangSmith](https://docs.smith.langchain.com/) for tracing (shown at bottom)" + "os.environ[\"TAVILY_API_KEY\"] = \"xxx\"" ] }, { "cell_type": "code", "execution_count": null, - "id": "68fed362-871a-46df-8ba0-579797ff2e9c", + "id": "0be68860-dded-481e-9fc7-a5042bf92c04", "metadata": {}, "outputs": [], "source": [ + "# Embedding (optional)\n", + "os.environ[\"OPENAI_API_KEY\"] = \"xxx\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7248ab88-2b97-41eb-8dbb-4ea65525ed9a", + "metadata": {}, + "outputs": [], + "source": [ + "# Tracing and testing (optional)\n", + "os.environ[\"LANGCHAIN_API_KEY\"] = \"xxx\"\n", "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", "os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n", - "os.environ[\"LANGCHAIN_API_KEY\"] = \"\"" + "os.environ[\"LANGCHAIN_PROJECT\"] = \"corrective-rag-agent-testing\"" ] }, { @@ -151,20 +103,21 @@ "id": "c059c3a3-7f01-4d46-8289-fde4c1b4155f", "metadata": {}, "source": [ - "## Configuration\n", + "### LLM\n", "\n", - "Decide to run locally and select LLM to use with Ollama." + "You can select from [Ollama LLMs](https://ollama.com/library)." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "2f4db331-c4d0-4c7c-a9a5-0bebc8a89c6c", "metadata": {}, "outputs": [], "source": [ - "run_local = \"Yes\"\n", - "local_llm = \"mistral:latest\"" + "local_llm = \"llama3\"\n", + "model_tested = \"llama3-8b\"\n", + "metadata = f\"CRAG, {model_tested}\"" ] }, { @@ -172,51 +125,66 @@ "id": "6e2b6eed-3b3f-44b5-a34a-4ade1e94caf0", "metadata": {}, "source": [ - "## Index\n", + "### Index\n", "\n", "Let's index 3 blog posts." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "bb8b789b-475b-4e1b-9c66-03504c837830", "metadata": {}, - "outputs": [], + "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_mistralai import MistralAIEmbeddings\n", - "from langchain_nomic.embeddings import NomicEmbeddings\n", + "from langchain_community.vectorstores import SKLearnVectorStore\n", + "from langchain_nomic.embeddings import NomicEmbeddings # local\n", + "from langchain_openai import OpenAIEmbeddings # api\n", "\n", - "# Load\n", - "url = \"https://lilianweng.github.io/posts/2023-06-23-agent/\"\n", - "loader = WebBaseLoader(url)\n", - "docs = loader.load()\n", + "# List of URLs to load documents from\n", + "urls = [\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", "\n", - "# Split\n", + "# Load documents from the URLs\n", + "docs = [WebBaseLoader(url).load() for url in urls]\n", + "docs_list = [item for sublist in docs for item in sublist]\n", + "\n", + "# Initialize a text splitter with specified chunk size and overlap\n", "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n", - " chunk_size=500, chunk_overlap=100\n", + " chunk_size=250, chunk_overlap=0\n", ")\n", - "all_splits = text_splitter.split_documents(docs)\n", "\n", - "# Embed and index\n", - "if run_local == \"Yes\":\n", - " embedding = NomicEmbeddings(\n", - " model=\"nomic-embed-text-v1.5\",\n", - " inference_mode=\"local\",\n", - " )\n", - "else:\n", - " embedding = MistralAIEmbeddings(mistral_api_key=mistral_api_key)\n", + "# Split the documents into chunks\n", + "doc_splits = text_splitter.split_documents(docs_list)\n", "\n", - "# Index\n", - "vectorstore = Chroma.from_documents(\n", - " documents=all_splits,\n", - " collection_name=\"rag-chroma\",\n", + "# Embedding\n", + "'''\n", + "embedding=NomicEmbeddings(\n", + " model=\"nomic-embed-text-v1.5\",\n", + " inference_mode=\"local\",\n", + ")\n", + "'''\n", + "embedding = OpenAIEmbeddings()\n", + "\n", + "# Add the document chunks to the \"vector store\"\n", + "vectorstore = SKLearnVectorStore.from_documents(\n", + " documents=doc_splits,\n", " embedding=embedding,\n", ")\n", - "retriever = vectorstore.as_retriever()" + "retriever = vectorstore.as_retriever(k=4)" ] }, { @@ -225,12 +193,12 @@ "id": "fe7fd10a-f64a-48de-a116-6d5890def1af", "metadata": {}, "source": [ - "## LLMs" + "### Tools" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "0e75c029-6c10-47c7-871c-1f4932b25309", "metadata": {}, "outputs": [ @@ -238,7 +206,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'score': 'yes'}\n" + "{'score': '1'}\n" ] } ], @@ -251,34 +219,42 @@ "from langchain_mistralai.chat_models import ChatMistralAI\n", "\n", "# LLM\n", - "if run_local == \"Yes\":\n", - " llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n", - "else:\n", - " llm = ChatMistralAI(\n", - " model=\"mistral-medium\", temperature=0, mistral_api_key=mistral_api_key\n", - " )\n", + "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n", "\n", + "# Prompt\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", + " template=\"\"\"You are a teacher grading a quiz. You will be given: \n", + " 1/ a QUESTION\n", + " 2/ A FACT provided by the student\n", + " \n", + " You are grading RELEVANCE RECALL:\n", + " A score of 1 means that ANY of the statements in the FACT are relevant to the QUESTION. \n", + " A score of 0 means that NONE of the statements in the FACT are relevant to the QUESTION. \n", + " 1 is the highest (best) score. 0 is the lowest score you can give. \n", + " \n", + " Explain your reasoning in a step-by-step manner. Ensure your reasoning and conclusion are correct. \n", + " \n", + " Avoid simply stating the correct answer at the outset.\n", + " \n", + " Question: {question} \\n\n", + " Fact: \\n\\n {documents} \\n\\n\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", + " Provide the binary score as a JSON with a single key 'score' and no premable or explanation.\n", + " \"\"\",\n", + " input_variables=[\"question\", \"documents\"],\n", ")\n", "\n", "retrieval_grader = prompt | llm | JsonOutputParser()\n", "question = \"agent memory\"\n", - "docs = retriever.get_relevant_documents(question)\n", + "docs = retriever.invoke(question)\n", "doc_txt = docs[1].page_content\n", - "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))" + "print(retrieval_grader.invoke({\"question\": question, \"documents\": doc_txt}))" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "dad03302-bd93-43fc-949e-af51a3298cfa", "metadata": {}, "outputs": [ @@ -286,92 +262,45 @@ "name": "stdout", "output_type": "stream", "text": [ - " The given text discusses the concept of building autonomous agents using a large language model (LLM) as its core controller. The text highlights several key components of an LLM-powered agent system, including observation, retrieval, reflection, planning & reacting, and relationships between agents. It also mentions some challenges such as finite context length, long-term planning and task decomposition, and reliability of natural language interface. The text provides examples of proof-of-concept demos like AutoGPT and discusses their limitations. The architecture of the generative agent is also described, which results in emergent social behavior.\n" + "The document mentions \"memory stream\" which is a long-term memory module that records a comprehensive list of agents' experience in natural language. It also discusses short-term memory and long-term memory, with the latter providing the agent with the capability to retain and recall information over extended periods. Additionally, it mentions planning and reflection mechanisms that enable agents to behave conditioned on past experience.\n" ] } ], "source": [ "### Generate\n", "\n", - "from langchain import hub\n", "from langchain_core.output_parsers import StrOutputParser\n", "\n", "# Prompt\n", - "prompt = hub.pull(\"rlm/rag-prompt\")\n", + "prompt = PromptTemplate(\n", + " template=\"\"\"You are an assistant for question-answering tasks. \n", + " \n", + " Use the following documents to answer the question. \n", + " \n", + " If you don't know the answer, just say that you don't know. \n", + " \n", + " Use three sentences maximum and keep the answer concise:\n", + " Question: {question} \n", + " Documents: {documents} \n", + " Answer: \n", + " \"\"\",\n", + " input_variables=[\"question\", \"documents\"],\n", + ")\n", "\n", "# LLM\n", - "if run_local == \"Yes\":\n", - " llm = ChatOllama(model=local_llm, temperature=0)\n", - "else:\n", - " llm = ChatMistralAI(\n", - " model=\"mistral-medium\", temperature=0, mistral_api_key=mistral_api_key\n", - " )\n", - "\n", - "\n", - "# Post-processing\n", - "def format_docs(docs):\n", - " return \"\\n\\n\".join(doc.page_content for doc in docs)\n", - "\n", + "llm = ChatOllama(model=local_llm, temperature=0)\n", "\n", "# Chain\n", "rag_chain = prompt | llm | StrOutputParser()\n", "\n", "# Run\n", - "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n", + "generation = rag_chain.invoke({\"documents\": docs, \"question\": question})\n", "print(generation)" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "f4b61211-70b5-4471-a714-feb9cc91e860", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "' What is agent memory and how can it be effectively utilized in vector database retrieval?'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "### Question Re-writer\n", - "\n", - "# LLM\n", - "if run_local == \"Yes\":\n", - " llm = ChatOllama(model=local_llm, temperature=0)\n", - "else:\n", - " llm = ChatMistralAI(\n", - " model=\"mistral-medium\", temperature=0, mistral_api_key=mistral_api_key\n", - " )\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", - "\n", - "question_rewriter = re_write_prompt | llm | StrOutputParser()\n", - "question_rewriter.invoke({\"question\": question})" - ] - }, - { - "cell_type": "markdown", - "id": "5d7fde29-e62e-4445-80f9-122eee0a3922", - "metadata": {}, - "source": [ - "## Web Search Tool" - ] - }, - { - "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "id": "b36a2f36-bc5f-408d-a5e8-3fa203c233f6", "metadata": {}, "outputs": [], @@ -388,23 +317,34 @@ "id": "a3421cf0-9067-43fe-8681-0d3189d15dd3", "metadata": {}, "source": [ - "# Graph \n", + "### Graph \n", "\n", - "Capture the flow in as a graph.\n", - "\n", - "## Graph state" + "Here we'll explicitly define the majority of the control flow, only using an LLM to define a single branch point following grading." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "id": "10028794-2fbc-43f9-aa4c-7fe3abd69c1e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from typing import List\n", - "\n", "from typing_extensions import TypedDict\n", + "from IPython.display import Image, display\n", + "from langchain.schema import Document\n", + "from langgraph.graph import START, END, StateGraph\n", "\n", "\n", "class GraphState(TypedDict):\n", @@ -414,24 +354,15 @@ " Attributes:\n", " question: question\n", " generation: LLM generation\n", - " web_search: whether to add search\n", + " 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]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "447d1333-082d-479a-a6fa-0ac0df78bb9d", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.schema import Document\n", + " search: str\n", + " documents: List[str]\n", + " steps: List[str]\n", "\n", "\n", "def retrieve(state):\n", @@ -441,15 +372,14 @@ " Args:\n", " state (dict): The current graph state\n", "\n", - " Returns:ß\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.get_relevant_documents(question)\n", - " return {\"documents\": documents, \"question\": question}\n", + " documents = retriever.invoke(question)\n", + " steps = state[\"steps\"]\n", + " steps.append(\"retrieve_documents\")\n", + " return {\"documents\": documents, \"question\": question, \"steps\": steps}\n", "\n", "\n", "def generate(state):\n", @@ -462,13 +392,18 @@ " Returns:\n", " state (dict): New key added to state, generation, that contains LLM generation\n", " \"\"\"\n", - " print(\"---GENERATE---\")\n", + "\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", + " generation = rag_chain.invoke({\"documents\": documents, \"question\": question})\n", + " steps = state[\"steps\"]\n", + " steps.append(\"generate_answer\")\n", + " return {\n", + " \"documents\": documents,\n", + " \"question\": question,\n", + " \"generation\": generation,\n", + " \"steps\": steps,\n", + " }\n", "\n", "\n", "def grade_documents(state):\n", @@ -482,46 +417,28 @@ " state (dict): Updates documents key with only filtered relevant documents\n", " \"\"\"\n", "\n", - " print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n", " question = state[\"question\"]\n", " documents = state[\"documents\"]\n", - "\n", - " # Score each doc\n", + " steps = state[\"steps\"]\n", + " steps.append(\"grade_document_retrieval\")\n", " filtered_docs = []\n", - " web_search = \"No\"\n", + " search = \"No\"\n", " for d in documents:\n", " score = retrieval_grader.invoke(\n", - " {\"question\": question, \"document\": d.page_content}\n", + " {\"question\": question, \"documents\": d.page_content}\n", " )\n", " grade = score[\"score\"]\n", " if grade == \"yes\":\n", - " print(\"---GRADE: DOCUMENT RELEVANT---\")\n", " filtered_docs.append(d)\n", " else:\n", - " print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n", - " web_search = \"Yes\"\n", + " search = \"Yes\"\n", " continue\n", - " return {\"documents\": filtered_docs, \"question\": question, \"web_search\": web_search}\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", + " return {\n", + " \"documents\": filtered_docs,\n", + " \"question\": question,\n", + " \"search\": search,\n", + " \"steps\": steps,\n", + " }\n", "\n", "\n", "def web_search(state):\n", @@ -535,20 +452,18 @@ " state (dict): Updates documents key with appended web results\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", - " documents.append(web_results)\n", - "\n", - " return {\"documents\": documents, \"question\": question}\n", - "\n", - "\n", - "### Edges\n", + " documents = state.get(\"documents\", [])\n", + " steps = state[\"steps\"]\n", + " steps.append(\"web_search\")\n", + " web_results = web_search_tool.invoke({\"query\": question})\n", + " documents.extend(\n", + " [\n", + " Document(page_content=d[\"content\"], metadata={\"url\": d[\"url\"]})\n", + " for d in web_results\n", + " ]\n", + " )\n", + " return {\"documents\": documents, \"question\": question, \"steps\": steps}\n", "\n", "\n", "def decide_to_generate(state):\n", @@ -561,52 +476,21 @@ " 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, TRANSFORM QUERY---\"\n", - " )\n", - " return \"transform_query\"\n", + " search = state[\"search\"]\n", + " if search == \"Yes\":\n", + " return \"search\"\n", " else:\n", - " # We have relevant documents, so generate answer\n", - " print(\"---DECISION: GENERATE---\")\n", - " return \"generate\"" - ] - }, - { - "cell_type": "markdown", - "id": "6096626d-dfa5-48e0-8a24-3747b298bc67", - "metadata": {}, - "source": [ - "## Build Graph\n", + " return \"generate\"\n", "\n", - "This just follows the flow we outlined in the figure above." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "0a63776c-f9cd-46ce-b8cf-95c066dc5b06", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.graph import END, StateGraph\n", "\n", + "# Graph\n", "workflow = StateGraph(GraphState)\n", "\n", "# Define the nodes\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(\"web_search_node\", web_search) # web search\n", + "workflow.add_node(\"web_search\", web_search) # web search\n", "\n", "# Build graph\n", "workflow.set_entry_point(\"retrieve\")\n", @@ -615,174 +499,332 @@ " \"grade_documents\",\n", " decide_to_generate,\n", " {\n", - " \"transform_query\": \"transform_query\",\n", + " \"search\": \"web_search\",\n", " \"generate\": \"generate\",\n", " },\n", ")\n", - "workflow.add_edge(\"transform_query\", \"web_search_node\")\n", - "workflow.add_edge(\"web_search_node\", \"generate\")\n", + "workflow.add_edge(\"web_search\", \"generate\")\n", "workflow.add_edge(\"generate\", END)\n", "\n", - "# Compile\n", - "app = workflow.compile()" + "custom_graph = workflow.compile()\n", + "\n", + "display(Image(custom_graph.get_graph(xray=True).draw_mermaid_png()))" ] }, { "cell_type": "code", - "execution_count": 17, - "id": "3ab1d8df-a74e-4b48-a30b-e39bbfd5925a", + "execution_count": 7, + "id": "447d1333-082d-479a-a6fa-0ac0df78bb9d", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "---RETRIEVE---\n", - "\"Node 'retrieve':\"\n", - "'\\n---\\n'\n", - "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n", - "---GRADE: DOCUMENT RELEVANT---\n", - "---GRADE: DOCUMENT RELEVANT---\n", - "---GRADE: DOCUMENT RELEVANT---\n", - "---GRADE: DOCUMENT RELEVANT---\n", - "\"Node 'grade_documents':\"\n", - "'\\n---\\n'\n", - "---ASSESS GRADED DOCUMENTS---\n", - "---DECISION: GENERATE---\n", - "---GENERATE---\n", - "\"Node 'generate':\"\n", - "'\\n---\\n'\n", - "\"Node '__end__':\"\n", - "'\\n---\\n'\n", - "(' The given text discusses the concept of building autonomous agents using '\n", - " 'large language models (LLMs) as their core controllers. LLMs have the '\n", - " 'potential to be powerful general problem solvers, extending beyond '\n", - " 'generating well-written copies, stories, essays, and programs. In an '\n", - " \"LLM-powered agent system, the model functions as the agent's brain, \"\n", - " 'complemented by several key components: planning, memory, and tool use.\\n'\n", - " '\\n'\n", - " '1. Planning: The agent breaks down large tasks into smaller subgoals for '\n", - " 'efficient handling of complex tasks and can do self-criticism and '\n", - " 'self-reflection to improve results.\\n'\n", - " '2. Memory: Short-term memory is utilized for in-context learning, while '\n", - " 'long-term memory provides the capability to retain and recall information '\n", - " 'over extended periods by leveraging an external vector store and fast '\n", - " 'retrieval.\\n'\n", - " '3. Tool use: The agent learns to call external APIs for missing information, '\n", - " 'including current information, code execution capability, access to '\n", - " 'proprietary information sources, and more.\\n'\n", - " '\\n'\n", - " 'The text also discusses the types of memory in human brains, including '\n", - " 'sensory memory, short-term memory (STM), and long-term memory (LTM). Sensory '\n", - " 'memory provides the ability to retain impressions of sensory information for '\n", - " 'a few seconds, while STM stores information needed for complex cognitive '\n", - " 'tasks and lasts for 20-30 seconds. LTM can store information for remarkably '\n", - " 'long periods with an essentially unlimited storage capacity and has two '\n", - " 'subtypes: explicit/declarative memory (memory of facts and events) and '\n", - " 'implicit/procedural memory (skills and routines).\\n'\n", - " '\\n'\n", - " 'The text also includes a figure comparing different methods, including AD, '\n", - " 'ED, source policies, and RL^2, on environments that require memory and '\n", - " 'exploration.')\n" - ] + "data": { + "text/plain": [ + "{'response': 'According to the documents, there are two types of agent memory:\\n\\n* Short-term memory (STM): This is a data structure that holds information temporarily and allows the agent to process it when needed.\\n* Long-term memory (LTM): This provides the agent with the capability to retain and recall information over extended periods.\\n\\nThese types of memories allow the agent to learn, reason, and make decisions.',\n", + " 'steps': ['retrieve_documents',\n", + " 'grade_document_retrieval',\n", + " 'web_search',\n", + " 'generate_answer']}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "from pprint import pprint\n", + "import uuid\n", "\n", - "# Run\n", - "inputs = {\"question\": \"What are the types of agent memory?\"}\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", + "def predict_custom_agent_local_answer(example: dict):\n", + " config = {\"configurable\": {\"thread_id\": str(uuid.uuid4())}}\n", + " state_dict = custom_graph.invoke(\n", + " {\"question\": example[\"input\"], \"steps\": []}, config\n", + " )\n", + " return {\"response\": state_dict[\"generation\"], \"steps\": state_dict[\"steps\"]}\n", "\n", - "# Final generation\n", - "pprint(value[\"generation\"])" + "\n", + "example = {\"input\": \"What are the types of agent memory?\"}\n", + "response = predict_custom_agent_local_answer(example)\n", + "response" ] }, { "cell_type": "markdown", - "id": "03ee2be9-2368-46ea-9edd-dc064a7c7c96", + "id": "91325c88-ec77-4c79-8a77-cb2e2842bcd4", "metadata": {}, "source": [ "Trace: \n", "\n", - "https://smith.langchain.com/public/731df833-57de-4612-8fe8-07cb424bc9a6/r" + "https://smith.langchain.com/public/88e7579e-2571-4cf6-98d2-1f9ce3359967/r" + ] + }, + { + "cell_type": "markdown", + "id": "1b80d5da-f698-40d2-a2fb-4eac89e35350", + "metadata": {}, + "source": [ + "## Evaluation\n", + "\n", + "Now we've defined two different agent architectures that do roughly the same thing!\n", + "\n", + "We can evaluate them. See our [conceptual guide](https://docs.smith.langchain.com/concepts/evaluation#agents) for context on agent evaluation.\n", + "\n", + "### Response\n", + "\n", + "First, we can assess how well [our agent performs on a set of question-answer pairs](https://docs.smith.langchain.com/tutorials/Developers/agents#response-evaluation).\n", + "\n", + "We'll create a dataset and save it in LangSmith." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, + "id": "b83706ac-724b-46b1-9f08-66e6c4fac742", + "metadata": {}, + "outputs": [], + "source": [ + "from langsmith import Client\n", + "\n", + "client = Client()\n", + "\n", + "# Create a dataset\n", + "examples = [\n", + " (\n", + " \"How does the ReAct agent use self-reflection? \",\n", + " \"ReAct integrates reasoning and acting, performing actions - such tools like Wikipedia search API - and then observing / reasoning about the tool outputs.\",\n", + " ),\n", + " (\n", + " \"What are the types of biases that can arise with few-shot prompting?\",\n", + " \"The biases that can arise with few-shot prompting include (1) Majority label bias, (2) Recency bias, and (3) Common token bias.\",\n", + " ),\n", + " (\n", + " \"What are five types of adversarial attacks?\",\n", + " \"Five types of adversarial attacks are (1) Token manipulation, (2) Gradient based attack, (3) Jailbreak prompting, (4) Human red-teaming, (5) Model red-teaming.\",\n", + " ),\n", + " (\n", + " \"Who did the Chicago Bears draft first in the 2024 NFL draft”?\",\n", + " \"The Chicago Bears drafted Caleb Williams first in the 2024 NFL draft.\",\n", + " ),\n", + " (\"Who won the 2024 NBA finals?\", \"The Boston Celtics on the 2024 NBA finals\"),\n", + "]\n", + "\n", + "# Save it\n", + "dataset_name = \"Corrective RAG Agent Testing\"\n", + "if not client.has_dataset(dataset_name=dataset_name):\n", + " dataset = client.create_dataset(dataset_name=dataset_name)\n", + " inputs, outputs = zip(\n", + " *[({\"input\": text}, {\"output\": label}) for text, label in examples]\n", + " )\n", + " client.create_examples(inputs=inputs, outputs=outputs, dataset_id=dataset.id)" + ] + }, + { + "cell_type": "markdown", + "id": "a23f6bc0-2d03-488c-8f4b-747c93876788", + "metadata": {}, + "source": [ + "Now, we'll use an `LLM as a grader` to compare both agent responses to our ground truth reference answer.\n", + "\n", + "[Here](https://smith.langchain.com/hub/rlm/rag-answer-vs-reference) is the default prompt that we can use.\n", + "\n", + "We'll use `gpt-4o` as our LLM grader.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0a63776c-f9cd-46ce-b8cf-95c066dc5b06", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain import hub\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "# Grade prompt\n", + "grade_prompt_answer_accuracy = hub.pull(\"langchain-ai/rag-answer-vs-reference\")\n", + "\n", + "def answer_evaluator(run, example) -> dict:\n", + " \"\"\"\n", + " A simple evaluator for RAG answer accuracy\n", + " \"\"\"\n", + "\n", + " # Get the question, the ground truth reference answer, RAG chain answer prediction\n", + " input_question = example.inputs[\"input\"]\n", + " reference = example.outputs[\"output\"]\n", + " prediction = run.outputs[\"response\"]\n", + "\n", + " # Define an LLM grader\n", + " llm = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n", + " answer_grader = grade_prompt_answer_accuracy | llm\n", + "\n", + " # Run evaluator\n", + " score = answer_grader.invoke(\n", + " {\n", + " \"question\": input_question,\n", + " \"correct_answer\": reference,\n", + " \"student_answer\": prediction,\n", + " }\n", + " )\n", + " score = score[\"Score\"]\n", + " return {\"key\": \"answer_v_reference_score\", \"score\": score}" + ] + }, + { + "cell_type": "markdown", + "id": "960f1a01-7f8c-429f-83d0-052cea47b32b", + "metadata": {}, + "source": [ + "### Trajectory\n", + "\n", + "Second, [we can assess the list of tool calls](https://docs.smith.langchain.com/tutorials/Developers/agents#trajectory) that each agent makes relative to expected trajectories.\n", + "\n", + "This evaluates the specific reasoning traces taken by our agents!" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "id": "deb28175-27a1-4afc-9747-2983e87fc881", "metadata": {}, + "outputs": [], + "source": [ + "from langsmith.schemas import Example, Run\n", + "\n", + "# Reasoning traces that we expect the agents to take\n", + "expected_trajectory_1 = [\n", + " \"retrieve_documents\",\n", + " \"grade_document_retrieval\",\n", + " \"web_search\",\n", + " \"generate_answer\",\n", + "]\n", + "expected_trajectory_2 = [\n", + " \"retrieve_documents\",\n", + " \"grade_document_retrieval\",\n", + " \"generate_answer\",\n", + "]\n", + "\n", + "def check_trajectory_react(root_run: Run, example: Example) -> dict:\n", + " \"\"\"\n", + " Check if all expected tools are called in exact order and without any additional tool calls.\n", + " \"\"\"\n", + " messages = root_run.outputs[\"messages\"]\n", + " tool_calls = find_tool_calls_react(messages)\n", + " print(f\"Tool calls ReAct agent: {tool_calls}\")\n", + " if tool_calls == expected_trajectory_1 or tool_calls == expected_trajectory_2:\n", + " score = 1\n", + " else:\n", + " score = 0\n", + "\n", + " return {\"score\": int(score), \"key\": \"tool_calls_in_exact_order\"}\n", + "\n", + "\n", + "def check_trajectory_custom(root_run: Run, example: Example) -> dict:\n", + " \"\"\"\n", + " Check if all expected tools are called in exact order and without any additional tool calls.\n", + " \"\"\"\n", + " tool_calls = root_run.outputs[\"steps\"]\n", + " print(f\"Tool calls custom agent: {tool_calls}\")\n", + " if tool_calls == expected_trajectory_1 or tool_calls == expected_trajectory_2:\n", + " score = 1\n", + " else:\n", + " score = 0\n", + "\n", + " return {\"score\": int(score), \"key\": \"tool_calls_in_exact_order\"}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "909b097d-cda1-45ff-8210-afeb2d18b8ae", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "---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", - "\"Node 'grade_documents':\"\n", - "'\\n---\\n'\n", - "---ASSESS GRADED DOCUMENTS---\n", - "---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\n", - "---TRANSFORM QUERY---\n", - "\"Node 'transform_query':\"\n", - "'\\n---\\n'\n", - "---WEB SEARCH---\n", - "\"Node 'web_search_node':\"\n", - "'\\n---\\n'\n", - "---GENERATE---\n", - "\"Node 'generate':\"\n", - "'\\n---\\n'\n", - "\"Node '__end__':\"\n", - "'\\n---\\n'\n", - "(' AlphaCodium is a new approach to code generation by LLMs, proposed in a '\n", - " 'paper titled \"Code Generation with AlphaCodium: From Prompt Engineering to '\n", - " 'Flow Engineering.\" It\\'s described as a test-based, multi-stage flow that '\n", - " 'improves the performance of LLMs on code problems without requiring '\n", - " 'fine-tuning. The iterative process involves repeatedly running and fixing '\n", - " 'generated code against input-output tests, with two key elements being '\n", - " 'generating additional data for the process and enrichment.')\n" + "View the evaluation results for experiment: 'custom-agent-llama3-8b-answer-and-tool-use-d6006159' at:\n", + "https://smith.langchain.com/o/1fa8b1f4-fcb9-4072-9aa9-983e35ad61b8/datasets/a8b9273b-ca33-4e2f-9f69-9bbc37f6f51b/compare?selectedSessions=83c60822-ef22-43e8-ac85-4488af279c6f\n", + "\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "529952314cd34ac1bb115840536921c3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n", + "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n", + "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n", + "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n", + "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n", + "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n", + "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n", + "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n", + "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n", + "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n", + "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n", + "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n", + "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n", + "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n", + "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n" ] } ], "source": [ - "from pprint import pprint\n", + "from langsmith.evaluation import evaluate\n", "\n", - "# Run\n", - "inputs = {\"question\": \"How does the AlphaCodium paper work?\"}\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\"])" + "experiment_prefix = f\"custom-agent-{model_tested}\"\n", + "experiment_results = evaluate(\n", + " predict_custom_agent_local_answer,\n", + " data=dataset_name,\n", + " evaluators=[answer_evaluator, check_trajectory_custom],\n", + " experiment_prefix=experiment_prefix + \"-answer-and-tool-use\",\n", + " num_repetitions=3,\n", + " max_concurrency=1, # Use when running locally\n", + " metadata={\"version\": metadata},\n", + ")" ] }, { + "attachments": { + "80e86604-7734-4aeb-a200-d1413870c3cb.png": { + "image/png": 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" + } + }, "cell_type": "markdown", - "id": "598fe73e-f4e6-479f-8d9d-fc81680fff21", + "id": "47c38cd3-2c31-48c4-8281-bd9e1f7b2830", "metadata": {}, "source": [ - "Trace: \n", + "We can see the results benchmarked against `GPT-4o` and `Llama-3-70b` using `Custom` agent (as shown here) and ReAct.\n", "\n", - "https://smith.langchain.com/public/c8b75f1b-38b7-48f2-a399-7ebb969d34f6/r" + "![Screenshot 2024-06-24 at 4.14.04 PM.png](attachment:80e86604-7734-4aeb-a200-d1413870c3cb.png)\n", + "\n", + "The `local custom agent` performs well in terms of tool calling reliability: it follows the expected reasoning traces.\n", + "\n", + "However, the answer accuracy performance lags the larger models with `custom agent` implementations." ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79295798-0181-417e-abad-11dddb6ff05e", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/examples/tutorials/rag-agent-testing.ipynb b/examples/tutorials/rag-agent-testing.ipynb index 7bb3fccf4..bd0b67dca 100644 --- a/examples/tutorials/rag-agent-testing.ipynb +++ b/examples/tutorials/rag-agent-testing.ipynb @@ -44,7 +44,7 @@ "\n", "We'll use [Tavily](https://python.langchain.com/v0.2/docs/integrations/tools/tavily_search/) for web search.\n", "\n", - "We'll use [Chroma](https://python.langchain.com/v0.2/docs/integrations/vectorstores/chroma/) as our vectorstore with [OpenAI embeddings](https://python.langchain.com/v0.2/docs/integrations/text_embedding/openai/#embed-documents).\n", + "We'll use a vectorstore with [OpenAI embeddings](https://python.langchain.com/v0.2/docs/integrations/text_embedding/openai/#embed-documents).\n", "\n", "We'll use [LangSmith](https://docs.smith.langchain.com/) for tracing and evaluation." ]