diff --git a/3p-integrations/langchain/README.md b/3p-integrations/langchain/README.md index 6f3209408..960d67c2f 100644 --- a/3p-integrations/langchain/README.md +++ b/3p-integrations/langchain/README.md @@ -1,42 +1,50 @@ -# LangChain <> Llama3 Cookbooks +# LangChain + Llama 3 Cookbooks -### `Agents` +## Understanding Workflows and Agents -LLM agents use [planning, memory, and tools](https://lilianweng.github.io/posts/2023-06-23-agent/) to accomplish tasks. Here, we show how to build agents capable of [tool-calling](https://python.langchain.com/docs/integrations/chat/) using [LangGraph](https://python.langchain.com/docs/langgraph) with Llama 3. +Agentic systems can be implemented as either "workflows" or "agents": -Agents can empower Llama 3 with important new capabilities. In particular, we will show how to give Llama 3 the ability to perform web search, as well as multi-modality: image generation (text-to-image), image analysis (image-to-text), and voice (text-to-speech) tools! +* **Workflows**: Systems where LLMs and tools are orchestrated through predefined code paths +* **Agents**: Systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks -Tool-calling agents with LangGraph use two nodes: (1) a node with an LLM decides which tool to invoke based upon the user question. It outputs the tool name and arguments to use. (2) the tool name and arguments are passed to a tool node, which calls the tool itself with the specified arguments and returns the result back to the LLM. +Learn more about this distinction in the [LangGraph documentation](https://langchain-ai.github.io/langgraph/tutorials/workflows/). -![Screenshot 2024-05-30 at 10 48 58 AM](https://github.com/rlancemartin/llama-recipes/assets/122662504/a2c2ec40-2c7b-486e-9290-33b6da26c304) +[LangGraph](https://langchain-ai.github.io/langgraph/concepts/high_level/) is a powerful library for building both workflows and agents, offering benefits such as: +- Persistence +- Streaming +- Debugging support +- Deployment capabilities -Our first notebook, `langgraph-tool-calling-agent`, shows how to build our agent mentioned above using LangGraph. +These notebooks demonstrate how to build effective agents and workflows with LangGraph using Llama models. -See this [video overview](https://www.youtube.com/watch?v=j2OAeeujQ9M) for more detail on the design of this agent. +![LangGraph Overview](https://github.com/rlancemartin/llama-recipes/assets/122662504/a2c2ec40-2c7b-486e-9290-33b6da26c304) ---- +## Notebooks in this Collection -### `RAG Agent` +### 1. LangGraph Tool Calling Agent -Our second notebook, `langgraph-rag-agent`, shows how to apply LangGraph to build a custom Llama 3 powered RAG agent that uses ideas from 3 papers: +In [`langgraph_tool_calling_agent.ipynb`](./langgraph_tool_calling_agent.ipynb), we demonstrate how to build an agent using LangGraph with tool calling capabilities. This implementation shows how to create flexible, dynamic agents that can select and use tools to complete tasks. -* Corrective-RAG (CRAG) [paper](https://arxiv.org/pdf/2401.15884.pdf) uses self-grading on retrieved documents and web-search fallback if documents are not relevant. -* Self-RAG [paper](https://arxiv.org/abs/2310.11511) adds self-grading on generations for hallucinations and for ability to answer the question. -* Adaptive RAG [paper](https://arxiv.org/abs/2403.14403) routes queries between different RAG approaches based on their complexity. +Watch the [video overview](https://www.youtube.com/watch?v=j2OAeeujQ9M) for a detailed explanation of this agent's design. -We implement each approach as a control flow in LangGraph: -- **Planning:** The sequence of RAG steps (e.g., retrieval, grading, and generation) that we want the agent to take. -- **Memory:** All the RAG-related information (input question, retrieved documents, etc) that we want to pass between steps. -- **Tool use:** All the tools needed for RAG (e.g., decide web search or vectorstore retrieval based on the question). +### 2. LangGraph RAG Workflow -We will build from CRAG (blue, below) to Self-RAG (green) and finally to Adaptive RAG (red): +In [`langgraph_rag_workflow.ipynb`](./langgraph_rag_workflow.ipynb), we show how to build a custom Llama-powered RAG workflow that incorporates ideas from three research papers: -![langgraph_rag_agent_](https://github.com/rlancemartin/llama-recipes/assets/122662504/ec4aa1cd-3c7e-4cd1-a1e7-7deddc4033a8) +* **Corrective-RAG (CRAG)** [paper](https://arxiv.org/pdf/2401.15884.pdf): Uses self-grading on retrieved documents and web-search fallback when documents aren't relevant +* **Self-RAG** [paper](https://arxiv.org/abs/2310.11511): Adds self-grading on generations to detect hallucinations and evaluate answer quality +* **Adaptive RAG** [paper](https://arxiv.org/abs/2403.14403): Routes queries between different RAG approaches based on query complexity ---- - -### `Local LangGraph RAG Agent` +We implement these approaches as control flows in LangGraph with three key components: -Our third notebook, `langgraph-rag-agent-local`, shows how to apply LangGraph to build advanced RAG agents using Llama 3 that run locally and reliably. +- **Planning:** The sequence of RAG steps (retrieval, grading, generation) +- **Memory:** RAG-related information (questions, retrieved documents) passed between steps +- **Tool use:** Tools for RAG operations (deciding between web search or vectorstore retrieval) -See this [video overview](https://www.youtube.com/watch?v=sgnrL7yo1TE) for more detail on the design of this agent. +The workflow progressively builds from CRAG (blue) to Self-RAG (green) to Adaptive RAG (red): + +![RAG Workflow Evolution](https://github.com/rlancemartin/llama-recipes/assets/122662504/ec4aa1cd-3c7e-4cd1-a1e7-7deddc4033a8) + +This implementation demonstrates how workflows can constrain control flow, enabling effective operation even with low-capacity local LLMs. + +Watch the [video overview](https://www.youtube.com/watch?v=sgnrL7yo1TE) for a detailed explanation of this workflow's design. diff --git a/3p-integrations/langchain/langgraph_rag_agent.ipynb b/3p-integrations/langchain/langgraph_rag_agent.ipynb deleted file mode 100644 index 0ad8a956e..000000000 --- a/3p-integrations/langchain/langgraph_rag_agent.ipynb +++ /dev/null @@ -1,643 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "6912ab05-f66a-40a9-a4a5-4deb80d2e0d9", - "metadata": {}, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "79e9c850-d829-4266-a2b6-4e69ad24e30e", - "metadata": {}, - "outputs": [], - "source": [ - "! pip install -U langchain_groq langchain langgraph langchain_community sentence_transformers tavily-python tiktoken langchainhub chromadb" - ] - }, - { - "attachments": { - "dccfae03-f250-494e-82d6-f229eafb0ea6.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "783f3dba-888c-4b74-a29e-b5d7a2386712", - "metadata": {}, - "source": [ - "# LangGraph RAG agent with Llama 3\n", - "\n", - "Previously, we showed how to build simple agents with LangGraph and Llama 3.\n", - "\n", - "Now, we'll pick a more advanced use-case: advanced RAG.\n", - "\n", - "## Ideas\n", - "\n", - "We'll combine ideas from three 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", - "![Screenshot 2024-05-03 at 10.50.02 AM.png](attachment:dccfae03-f250-494e-82d6-f229eafb0ea6.png)\n", - "\n", - "Note that this will incorporate [a few general ideas for agents](https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/):\n", - "\n", - "- **Reflection**: The self-correction mechanism is a form of reflection, where the LangGraph agent reflects on its retrieval and generations\n", - "- **Planning**: The control flow laid out in the graph is a form of planning \n", - "- **Tool use**: Specific nodes in the control flow (e.g., web search) will use tools\n", - "\n", - "## Models\n", - "\n", - "### LLM\n", - "\n", - "We can use one of the providers that (1) offer Llama 3 and (2) [provide structure outputs](https://python.langchain.com/docs/how_to/structured_output/).\n", - "\n", - "Here, we use [Groq](https://groq.com/).\n", - "\n", - "### Tracing\n", - "\n", - "```\n", - "### Tracing (optional)\n", - "os.environ['LANGCHAIN_TRACING_V2'] = 'true'\n", - "os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'\n", - "os.environ['LANGCHAIN_API_KEY'] = 'LANGCHAIN_API_KEY'\n", - "```\n", - "\n", - "### Search\n", - "\n", - "Uses [Tavily](https://tavily.com/#api)m for web search." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a688f62-a11e-4f44-8e66-0bf2a533dedf", - "metadata": {}, - "outputs": [], - "source": [ - "### LLMs\n", - "import os\n", - "\n", - "os.environ['LANGCHAIN_TRACING_V2'] = 'true'\n", - "os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'\n", - "os.environ['LANGCHAIN_API_KEY'] = 'LANGCHAIN_API_KEY'\n", - "\n", - "os.environ['TAVILY_API_KEY'] = 'YOUR_TAVILY_API_KEY'\n", - "os.environ['GROQ_API_KEY'] = 'YOUR_GROQ_API_KEY'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "27a89322-0e88-4886-bcb4-3ac9bc1db316", - "metadata": {}, - "outputs": [], - "source": [ - "### Build Index\n", - "\n", - "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.embeddings import HuggingFaceEmbeddings\n", - "\n", - "# Docs to index\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", - "# Load\n", - "docs = [WebBaseLoader(url).load() for url in urls]\n", - "docs_list = [item for sublist in docs for item in sublist]\n", - "\n", - "# Split\n", - "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n", - " chunk_size=500, chunk_overlap=0\n", - ")\n", - "doc_splits = text_splitter.split_documents(docs_list)\n", - "\n", - "# Add to vectorstore\n", - "vectorstore = Chroma.from_documents(\n", - " documents=doc_splits,\n", - " collection_name=\"rag-chroma\",\n", - " embedding=HuggingFaceEmbeddings(),\n", - ")\n", - "retriever = vectorstore.as_retriever()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1041afc4-6680-4be0-950a-1c5b80b6b895", - "metadata": {}, - "outputs": [], - "source": [ - "### Router\n", - "\n", - "from typing import Literal\n", - "\n", - "from langchain_core.prompts import ChatPromptTemplate\n", - "from langchain_core.pydantic_v1 import BaseModel, Field\n", - "from langchain_groq import ChatGroq\n", - "\n", - "# Data model\n", - "class RouteQuery(BaseModel):\n", - " \"\"\"Route a user query to the most relevant datasource.\"\"\"\n", - "\n", - " datasource: Literal[\"vectorstore\", \"web_search\"] = Field(\n", - " ...,\n", - " description=\"Given a user question choose to route it to web search or a vectorstore.\",\n", - " )\n", - "\n", - "# LLM with function call \n", - "llm = ChatGroq(temperature=0, model=\"llama3-70b-8192\")\n", - "structured_llm_router = llm.with_structured_output(RouteQuery)\n", - "\n", - "# Prompt \n", - "system = \"\"\"You are an expert at routing a user question to a vectorstore or web search.\n", - "The vectorstore contains documents related to agents, prompt engineering, and adversarial attacks.\n", - "Use the vectorstore for questions on these topics. Otherwise, use web-search.\"\"\"\n", - "route_prompt = ChatPromptTemplate.from_messages(\n", - " [\n", - " (\"system\", system),\n", - " (\"human\", \"{question}\"),\n", - " ]\n", - ")\n", - "\n", - "question_router = route_prompt | structured_llm_router\n", - "print(question_router.invoke({\"question\": \"Who will the Bears draft first in the NFL draft?\"}))\n", - "print(question_router.invoke({\"question\": \"What are the types of agent memory?\"}))### Index" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3bd753fb-9330-4ffa-8721-f133dc8a86aa", - "metadata": {}, - "outputs": [], - "source": [ - "### Retrieval Grader \n", - "\n", - "# Data model\n", - "class GradeDocuments(BaseModel):\n", - " \"\"\"Binary score for relevance check on retrieved documents.\"\"\"\n", - "\n", - " score: str = Field(description=\"Documents are relevant to the question, 'yes' or 'no'\")\n", - "\n", - "# LLM with function call \n", - "llm = ChatGroq(temperature=0, model=\"llama3-70b-8192\")\n", - "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n", - "\n", - "# Prompt \n", - "system = \"\"\"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 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", - "grade_prompt = ChatPromptTemplate.from_messages(\n", - " [\n", - " (\"system\", system),\n", - " (\"human\", \"Retrieved document: \\n\\n {document} \\n\\n User question: {question}\"),\n", - " ]\n", - ")\n", - "\n", - "retrieval_grader = grade_prompt | structured_llm_grader\n", - "question = \"agent memory\"\n", - "docs = retriever.get_relevant_documents(question)\n", - "doc_txt = docs[1].page_content\n", - "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2d1fc9af-3426-491a-9d1c-3ccb3b7aba1a", - "metadata": {}, - "outputs": [], - "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", - "\n", - "# LLM\n", - "llm = ChatGroq(temperature=0, model=\"llama3-70b-8192\")\n", - "\n", - "# Post-processing\n", - "def format_docs(docs):\n", - " return \"\\n\\n\".join(doc.page_content for doc in docs)\n", - "\n", - "# Chain\n", - "rag_chain = prompt | llm | StrOutputParser()\n", - "\n", - "# Run\n", - "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n", - "print(generation)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c0e522e7-4347-4b80-9972-8c9ed582995e", - "metadata": {}, - "outputs": [], - "source": [ - "### Hallucination Grader \n", - "\n", - "# Data model\n", - "class GradeHallucinations(BaseModel):\n", - " \"\"\"Binary score for hallucination present in generation answer.\"\"\"\n", - "\n", - " score: str = Field(description=\"Answer is grounded in the facts, 'yes' or 'no'\")\n", - "\n", - "# LLM with function call \n", - "llm = ChatGroq(temperature=0, model=\"llama3-70b-8192\")\n", - "structured_llm_grader = llm.with_structured_output(GradeHallucinations)\n", - "\n", - "# Prompt \n", - "system = \"\"\"You are a grader assessing whether an LLM generation is grounded in / supported by a set of retrieved facts. \\n \n", - " Give a binary score 'yes' or 'no'. 'Yes' means that the answer is grounded in / supported by the set of facts.\"\"\"\n", - "hallucination_prompt = ChatPromptTemplate.from_messages(\n", - " [\n", - " (\"system\", system),\n", - " (\"human\", \"Set of facts: \\n\\n {documents} \\n\\n LLM generation: {generation}\"),\n", - " ]\n", - ")\n", - "\n", - "hallucination_grader = hallucination_prompt | structured_llm_grader\n", - "hallucination_grader.invoke({\"documents\": docs, \"generation\": generation})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "daf65df4-72a2-4805-92a4-0025cb7db5ac", - "metadata": {}, - "outputs": [], - "source": [ - "### Answer Grader \n", - "\n", - "# Data model\n", - "class GradeAnswer(BaseModel):\n", - " \"\"\"Binary score to assess answer addresses question.\"\"\"\n", - "\n", - " score: str = Field(description=\"Answer addresses the question, 'yes' or 'no'\")\n", - "\n", - "# LLM with function call \n", - "llm = ChatGroq(temperature=0, model=\"llama3-70b-8192\")\n", - "structured_llm_grader = llm.with_structured_output(GradeAnswer)\n", - "\n", - "# Prompt \n", - "system = \"\"\"You are a grader assessing whether an answer addresses / resolves a question \\n \n", - " Give a binary score 'yes' or 'no'. Yes' means that the answer resolves the question.\"\"\"\n", - "answer_prompt = ChatPromptTemplate.from_messages(\n", - " [\n", - " (\"system\", system),\n", - " (\"human\", \"User question: \\n\\n {question} \\n\\n LLM generation: {generation}\"),\n", - " ]\n", - ")\n", - "\n", - "answer_grader = answer_prompt | structured_llm_grader\n", - "answer_grader.invoke({\"question\": question,\"generation\": generation})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "06a0af45-8b57-4ac6-a034-b368309f02cf", - "metadata": {}, - "outputs": [], - "source": [ - "### Search\n", - "\n", - "from langchain_community.tools.tavily_search import TavilySearchResults\n", - "web_search_tool = TavilySearchResults(k=3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2b1c0474-4cd1-4802-b49b-dcbb70842411", - "metadata": {}, - "outputs": [], - "source": [ - "from typing_extensions import TypedDict\n", - "from typing import List\n", - "\n", - "### State\n", - "\n", - "class 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", - " question : str\n", - " generation : str\n", - " web_search : str\n", - " documents : List[str]\n", - "\n", - "from langchain.schema import Document\n", - "\n", - "### Nodes\n", - "\n", - "def 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", - "def 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", - "def 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({\"question\": question, \"document\": d.page_content})\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", - "def 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", - "### Conditional edge\n", - "\n", - "def 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", - " source = question_router.invoke({\"question\": question}) \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", - "def 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", - " question = state[\"question\"]\n", - " web_search = state[\"web_search\"]\n", - " filtered_documents = 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(\"---DECISION: ALL DOCUMENTS ARE NOT 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", - "### Conditional edge\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", - "\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({\"documents\": documents, \"generation\": generation})\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", - "from langgraph.graph import END, StateGraph\n", - "workflow = StateGraph(GraphState)\n", - "\n", - "# Define the nodes\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) # generatae" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "26a0ad7a-87d3-4e32-9608-ab9e4408ad76", - "metadata": {}, - "outputs": [], - "source": [ - "# Build graph\n", - "workflow.set_conditional_entry_point(\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": null, - "id": "1a2af1cb-4bba-4baf-9345-d21ce0e65503", - "metadata": {}, - "outputs": [], - "source": [ - "# Compile\n", - "app = workflow.compile()\n", - "\n", - "# Test\n", - "from pprint import pprint\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", - " pprint(f\"Finished running: {key}:\")\n", - "pprint(value[\"generation\"])" - ] - }, - { - "cell_type": "markdown", - "id": "4411adb6-a98d-41b4-ac00-5f643e008389", - "metadata": {}, - "source": [ - "Trace: \n", - "\n", - "https://smith.langchain.com/public/2babc6ec-a243-40d0-844b-5e6b40f70fc9/r" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0642aaaa-4657-45df-92f2-6a279d696497", - "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": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/3p-integrations/langchain/langgraph_rag_agent_local.ipynb b/3p-integrations/langchain/langgraph_rag_workflow.ipynb similarity index 78% rename from 3p-integrations/langchain/langgraph_rag_agent_local.ipynb rename to 3p-integrations/langchain/langgraph_rag_workflow.ipynb index cffa7bd2c..fd80e6ba8 100644 --- a/3p-integrations/langchain/langgraph_rag_agent_local.ipynb +++ b/3p-integrations/langchain/langgraph_rag_workflow.ipynb @@ -5,17 +5,18 @@ "id": "1f53f753-12c6-4fac-b910-6e96677d8a49", "metadata": {}, "source": [ - "\"Open" + "\"Open" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "6b9ab14a-fd80-4ca2-afc5-efe1c39532bf", "metadata": {}, "outputs": [], "source": [ - "! pip install -U langchain_community tiktoken langchainhub chromadb langchain langgraph tavily-python sentence-transformers" + "%%capture --no-stderr\n", + "%pip install --quiet -U langchain langchain_community tiktoken langchain-nomic \"nomic[local]\" langchain-ollama scikit-learn langgraph tavily-python bs4 langchain_groq" ] }, { @@ -28,105 +29,153 @@ "id": "0216de30-29cf-4464-9cc3-6e9a6d6c3e40", "metadata": {}, "source": [ - "# Local LangGraph RAG agent with Llama 3\n", - "\n", - "Previously, we showed how to build simple agents with LangGraph and Llama 3.\n", - "\n", - "Now, we'll pick a more advanced use-case: advanced RAG, with the requirement that it runs locally.\n", + "# RAG workflow with LLaMA 3\n", "\n", - "## Ideas\n", - "\n", - "We'll combine ideas from three RAG papers into a RAG agent:\n", + "Workflows are systems where LLMs and tools are orchestrated through predefined code paths. Here, we'll use a few common workflow patterns that feature ideas from three RAG papers: \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", + "- **Evaluator-Optimizer:** Perform self-grading, as shown in corrective RAG ([paper](https://arxiv.org/pdf/2401.15884.pdf)) and Self-RAG ([paper](https://arxiv.org/abs/2310.11511)).\n", "\n", "![langgraph_adaptive_rag.png](attachment:7b00797e-fb85-4474-9a9e-c505b61add81.png)\n", "\n", - "Note that this will incorporate [a few general ideas for agents](https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/):\n", - "\n", - "- **Reflection**: The self-correction mechanism is a form of reflection, where the LangGraph agent reflects on its retrieval and generations\n", - "- **Planning**: The control flow laid out in the graph is a form of planning \n", - "- **Tool use**: Specific nodes in the control flow (e.g., web search) will use tools\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", + "An important benefit of workflows in LangGraph is that they are quite reliable. Unlike agents, which perform tool calling in a loop, [workflows are systems where LLMs and tools are orchestrated through predefined code paths](https://langchain-ai.github.io/langgraph/tutorials/workflows/). Here, we show that a fairly complex series of steps can even be performed with low capacity local LLMs using workflows! See our [tutorial](https://langchain-ai.github.io/langgraph/tutorials/workflows/) on common agent and workflow patterns. \n", "\n", "### LLM\n", "\n", - "Use [Ollama](https://ollama.ai/) and [llama3](https://ollama.ai/library/llama3):\n", + "We'll use [Ollama](https://ollama.ai/) to pull and run a desired Llama model (e.g., [llama3.2](https://ollama.com/library/llama3.2)) locally:\n", "\n", "```\n", - "ollama pull llama3\n", + "ollama pull llama3.2\n", "```\n", "\n", - "Prompt - \n", + "You can also use other LLMs that host Llama models such as Groq!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "83ab7ef6", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import getpass\n", "\n", - "https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/\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(\"GROQ_API_KEY\")\n", "\n", - "### Tracing\n", + "# LLM \n", + "from langchain_groq import ChatGroq\n", + "llm = ChatGroq(temperature=0, model=\"llama-3.3-70b-versatile\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a871b98a", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_ollama import ChatOllama\n", + "\n", + "# Local LLM\n", + "local_llm = \"llama3.2\"\n", + "llm = ChatOllama(model=local_llm, temperature=0)" + ] + }, + { + "cell_type": "markdown", + "id": "4779760a", + "metadata": {}, + "source": [ + "### Embedding\n", + " \n", + "We'll use [Nomic](https://blog.nomic.ai/posts/nomic-embed-text-v1) for embeddings that can run locally." + ] + }, + { + "cell_type": "markdown", + "id": "333bcc9f", + "metadata": {}, + "source": [ "\n", - "```\n", - "### Tracing (optional)\n", - "os.environ['LANGCHAIN_TRACING_V2'] = 'true'\n", - "os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'\n", - "os.environ['LANGCHAIN_API_KEY'] = 'LANGCHAIN_API_KEY'\n", - "```\n", "\n", "### Search\n", "\n", - "Uses [Tavily](https://tavily.com/#api)" + "We'll use [Tavily](https://tavily.com/#api), a search engine optimized for LLMs and RAG." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "5d2242c5-0dbb-4f21-9771-92f6d679b1a6", "metadata": {}, "outputs": [], "source": [ "import os\n", + "import getpass\n", "\n", - "os.environ['LANGCHAIN_TRACING_V2'] = 'true'\n", - "os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'\n", - "os.environ['LANGCHAIN_API_KEY'] = 'LANGCHAIN_API_KEY'\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", "\n", - "os.environ['TAVILY_API_KEY'] = 'TAVILY_API_KEY'" + "_set_env(\"TAVILY_API_KEY\")\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"" + ] + }, + { + "cell_type": "markdown", + "id": "3d508832", + "metadata": {}, + "source": [ + "### Tracing\n", + "\n", + "Optionally, use [LangSmith](https://www.langchain.com/langsmith) for tracing." ] }, { "cell_type": "code", - "execution_count": null, - "id": "2096d49c-d3dc-4329-ada7-aff56d210198", + "execution_count": 33, + "id": "a89f7f2b", "metadata": {}, "outputs": [], "source": [ - "### LLM\n", - "\n", - "local_llm = 'llama3'" + "_set_env(\"LANGSMITH_API_KEY\")\n", + "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", + "os.environ[\"LANGCHAIN_PROJECT\"] = \"local-llama-rag\"" + ] + }, + { + "cell_type": "markdown", + "id": "2096d49c-d3dc-4329-ada7-aff56d210198", + "metadata": {}, + "source": [ + "### Vectorstore" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "267c63e1-4c2f-439d-8d95-4c6aa01f41cf", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "USER_AGENT environment variable not set, consider setting it to identify your requests.\n", + "Embedding texts: 100%|██████████| 47/47 [00:04<00:00, 9.65inputs/s]\n" + ] + } + ], "source": [ - "### Index\n", - "\n", "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.embeddings import HuggingFaceEmbeddings\n", + "from langchain_community.vectorstores import SKLearnVectorStore\n", + "from langchain_nomic.embeddings import NomicEmbeddings\n", "\n", "urls = [\n", " \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n", @@ -134,212 +183,472 @@ " \"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=HuggingFaceEmbeddings(),\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": "8c7d4de0", + "metadata": {}, + "source": [ + "### Components" ] }, { "cell_type": "code", - "execution_count": null, - "id": "b008df98-8394-49da-8fb8-aefe2c90d03c", + "execution_count": 6, + "id": "79088aba", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "RouteQuery(datasource='web_search')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "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 \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", - " Provide the binary score as a JSON with a single key 'score' and no premable or explanation.\n", - " \n", - " Here is the retrieved document: \n", - " {document}\n", - " \n", - " Here is the user question: \n", - " {question}\n", - " \"\"\",\n", - " input_variables=[\"question\", \"document\"],\n", - ")\n", + "from typing import Literal\n", + "from pydantic import BaseModel, Field\n", + "from langchain_core.messages import HumanMessage, SystemMessage\n", + "\n", + "### Router\n", + "\n", + "# Schema\n", + "class RouteQuery(BaseModel):\n", + " \"\"\"Route a user query to the most relevant datasource.\"\"\"\n", + "\n", + " datasource: Literal[\"vectorstore\", \"web_search\"] = Field(\n", + " ...,\n", + " description=\"Given a user question choose to route it to web search or a vectorstore.\",\n", + " )\n", "\n", - "retrieval_grader = prompt | llm | JsonOutputParser()\n", - "question = \"agent memory\"\n", + "# LLM with structured output \n", + "structured_llm_router = llm.with_structured_output(RouteQuery)\n", + "\n", + "# Prompt\n", + "router_instructions = \"\"\"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", + "test_web_search = structured_llm_router.invoke([SystemMessage(content=router_instructions),\n", + " HumanMessage(content=\"Who is favored to be the first pick in the 2025 NFL Draft?\")])\n", + "\n", + "test_web_search" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "1d531a81-6d4d-405e-975a-01ef1c9679fa", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding texts: 100%|██████████| 1/1 [00:00<00:00, 3.36inputs/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "GradeDocuments(score='yes')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### Retrieval Grader\n", + "\n", + "# Schema\n", + "class GradeDocuments(BaseModel):\n", + " \"\"\"Binary score for relevance check on retrieved documents.\"\"\"\n", + "\n", + " score: str = Field(description=\"Documents are relevant to the question, 'yes' or 'no'\")\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 a score that is 'yes' or 'no' 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", - "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))" + "doc_grader_prompt_formatted = doc_grader_prompt.format(\n", + " document=doc_txt, question=question\n", + ")\n", + "\n", + "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n", + "\n", + "result = structured_llm_grader.invoke([SystemMessage(content=doc_grader_instructions),\n", + " HumanMessage(content=doc_grader_prompt_formatted)])\n", + "result" ] }, { "cell_type": "code", - "execution_count": null, - "id": "1d531a81-6d4d-405e-975a-01ef1c9679fa", + "execution_count": 8, + "id": "faf6373e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding texts: 100%|██████████| 1/1 [00:00<00:00, 17.85inputs/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chain-of-thought (CoT) prompting generates a sequence of short sentences to describe reasoning logics step by step, known as reasoning chains or rationales, to eventually lead to the final answer. This approach is beneficial for complicated reasoning tasks, particularly when using large models. There are two main types of CoT prompting: few-shot CoT, which uses manually written reasoning chains, and zero-shot CoT, which uses natural language statements to encourage the model to generate reasoning chains.\n" + ] + } + ], "source": [ "### Generate\n", "\n", - "from langchain.prompts import PromptTemplate\n", - "from langchain import hub\n", - "from langchain_core.output_parsers import StrOutputParser\n", - "\n", "# Prompt\n", - "prompt = PromptTemplate(\n", - " template=\"\"\"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:\n", - " Question: {question} \n", - " Context: {context} \n", - " Answer: \n", - " \"\"\",\n", - " input_variables=[\"question\", \"document\"],\n", - ")\n", + "rag_prompt = \"\"\"You are an assistant for question-answering tasks. \n", + "\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", - "llm = ChatOllama(model=local_llm, temperature=0)\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", - "# Chain\n", - "rag_chain = prompt | llm | StrOutputParser()\n", - "\n", - "# Run\n", - "question = \"agent memory\"\n", + "# Test\n", "docs = retriever.invoke(question)\n", - "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n", - "print(generation)" + "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": null, - "id": "0261a9a4-de13-4dd8-b082-95305a3e43ca", + "id": "da9984a0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "GradeHallucinations(score='yes', explanation='The student answer is grounded in the facts as it accurately describes Chain-of-thought (CoT) prompting and its types. The information provided in the student answer is supported by the facts, and there is no hallucinated information outside the scope of the facts.')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "### Hallucination Grader \n", "\n", - "# LLM\n", - "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n", + "# Data model\n", + "class GradeHallucinations(BaseModel):\n", + " \"\"\"Binary score for hallucination present in generation answer.\"\"\"\n", "\n", - "# Prompt\n", - "prompt = PromptTemplate(\n", - " template=\"\"\"You are a grader assessing whether \n", - " an answer is grounded in / supported by a set of facts. Give a binary score '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.\n", - " \n", - " Here are the facts:\n", - " {documents} \n", - "\n", - " Here is the answer: \n", - " {generation}\n", - " \"\"\",\n", - " input_variables=[\"generation\", \"documents\"],\n", - ")\n", + " score: str = Field(description=\"Answer is grounded in the facts, 'yes' or 'no'\")\n", + " explanation: str = Field(description=\"Explanation of the score\")\n", + "\n", + "# LLM with structured output \n", + "hallucination_grader = llm.with_structured_output(GradeHallucinations)\n", + "\n", + "# Hallucination grader instructions\n", + "hallucination_grader_instructions = \"\"\"\n", "\n", - "hallucination_grader = prompt | llm | JsonOutputParser()\n", - "hallucination_grader.invoke({\"documents\": docs, \"generation\": generation})" + "You are a teacher grading a quiz. \n", + "\n", + "Reflect on the provided FACTS and STUDENT ANSWER. \n", + " \n", + "(1) Consider whether the STUDENT ANSWER is grounded in the FACTS. \n", + "\n", + "(2) Consider whether the STUDENT ANSWER contains \"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", + "Give a binary score 'yes' or 'no'. 'Yes' means that the answer is grounded in / supported by the set of facts.\n", + "\n", + "Also, provide an explanation of the score.\"\"\"\n", + "\n", + "# Hallucination grader prompt\n", + "hallucination_grader_prompt = \"\"\" \\n\\n {documents} \\n\\n {generation} \"\"\"\n", + "\n", + "# Run \n", + "result = hallucination_grader.invoke([SystemMessage(content=hallucination_grader_prompt.format(documents=format_docs(docs), \n", + " generation=generation.content)),\n", + " HumanMessage(content=hallucination_grader_instructions)])\n", + "result" ] }, { "cell_type": "code", - "execution_count": null, - "id": "df9f6944-4fee-4971-b3a7-2b81b44ed433", + "execution_count": 10, + "id": "9f581669", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "GradeAnswer(score='yes')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "### Answer Grader \n", "\n", - "# LLM\n", - "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n", + "# Data model\n", + "class GradeAnswer(BaseModel):\n", + " \"\"\"Binary score to assess answer addresses question.\"\"\"\n", "\n", - "# Prompt\n", - "prompt = PromptTemplate(\n", - " template=\"\"\"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", - " \n", - " Here is the answer:\n", - " {generation} \n", - "\n", - " Here is the question: {question}\n", - " \"\"\",\n", - " input_variables=[\"generation\", \"question\"],\n", + " score: str = Field(description=\"Answer addresses the question, 'yes' or 'no'\")\n", + "\n", + "# LLM with structured output \n", + "answer_grader = llm.with_structured_output(GradeAnswer)\n", + "\n", + "# Hallucination grader instructions\n", + "answer_grader_instructions = \"\"\"You are a teacher grading a quiz. \n", + "\n", + "You will be given a QUESTION and a STUDENT ANSWER. \n", + "\n", + "Here is the grade criteria to follow:\n", + "\n", + "(1) The STUDENT ANSWER helps to answer the QUESTION\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", + "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", + "Give a binary score 'yes' or 'no'. 'Yes' means that the answer is grounded in / supported by the set of facts.\n", + "\n", + "Also, provide an explanation of the score.\"\"\"\n", + "\n", + "answer_grader_prompt = \"\"\"User question: \\n\\n {question} \\n\\n LLM generation: {generation}\"\"\"\n", + "\n", + "# Run \n", + "result = answer_grader.invoke([SystemMessage(content=answer_grader_prompt.format(question=question, \n", + " generation=generation.content)),\n", + " HumanMessage(content=answer_grader_instructions)])\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "df9f6944-4fee-4971-b3a7-2b81b44ed433", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GradeHallucinations(score='yes')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### Hallucination Grader\n", + "\n", + "# Data model\n", + "class GradeHallucinations(BaseModel):\n", + " \"\"\"Binary score for hallucination present in generation answer.\"\"\"\n", + "\n", + " score: str = Field(description=\"Answer is grounded in the facts, 'yes' or 'no'\")\n", + "\n", + "# LLM with function call \n", + "hallucination_grader = llm.with_structured_output(GradeHallucinations)\n", + "\n", + "# Hallucination grader instructions\n", + "hallucination_grader_instructions = \"\"\"\n", + "\n", + "You are a teacher grading a quiz. \n", + "\n", + "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", + "# Test using documents and generation from above\n", + "hallucination_grader_prompt_formatted = hallucination_grader_prompt.format(\n", + " documents=docs_txt, generation=generation.content\n", + ")\n", + "result = hallucination_grader.invoke(\n", + " [SystemMessage(content=hallucination_grader_instructions),\n", + " HumanMessage(content=hallucination_grader_prompt_formatted)]\n", ")\n", "\n", - "answer_grader = prompt | llm | JsonOutputParser()\n", - "answer_grader.invoke({\"question\": question,\"generation\": generation})" + "result" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "a9c910c1-738c-4bf7-bf9e-801862b227eb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "GradeAnswer(score='yes')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "### Router\n", + "### Answer Grader\n", + "\n", + "# Data model\n", + "class GradeAnswer(BaseModel):\n", + " \"\"\"Binary score to assess answer addresses question.\"\"\"\n", + "\n", + " score: str = Field(description=\"Answer addresses the question, 'yes' or 'no'\")\n", + "\n", + "# LLM with function call \n", + "answer_grader = llm.with_structured_output(GradeAnswer)\n", + "\n", + "# Answer grader instructions\n", + "answer_grader_instructions = \"\"\"You are a teacher grading a quiz. \n", + "\n", + "You will be given a QUESTION and a STUDENT ANSWER. \n", + "\n", + "Here is the grade criteria to follow:\n", + "\n", + "(1) The STUDENT ANSWER helps to answer the QUESTION\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", + "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", - "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 \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. \n", - " \n", - " Question to route: \n", - " {question}\"\"\",\n", - " input_variables=[\"question\"],\n", + "# Grader prompt\n", + "answer_grader_prompt = \"\"\"QUESTION: \\n\\n {question} \\n\\n STUDENT ANSWER: {generation}.\"\"\"\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(\n", + " question=question, generation=answer\n", + ")\n", + "result = answer_grader.invoke(\n", + " [SystemMessage(content=answer_grader_instructions),\n", + " HumanMessage(content=answer_grader_prompt_formatted)]\n", ")\n", "\n", - "question_router = prompt | llm | JsonOutputParser()\n", - "question = \"llm agent memory\"\n", - "docs = retriever.get_relevant_documents(question)\n", - "doc_txt = docs[1].page_content\n", - "print(question_router.invoke({\"question\": question}))" + "result" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "023ff2db-eb4e-4d44-904c-ea061abc16d9", "metadata": {}, "outputs": [], "source": [ "### Search\n", - "\n", "from langchain_community.tools.tavily_search import TavilySearchResults\n", + "\n", "web_search_tool = TavilySearchResults(k=3)" ] }, @@ -348,40 +657,79 @@ "id": "ccd59cdf-a04d-4b2e-b9cc-6a1b1e80a6c6", "metadata": {}, "source": [ - "We'll implement these as a control flow in LangGraph." + "### Graph\n", + "\n", + "We build the above workflow as a graph using LangGraph.\n", + "\n", + "See an overview of LangGraph [here](https://langchain-ai.github.io/langgraph/concepts/high_level/).\n", + "\n", + "### Graph state\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": null, - "id": "07fa3d08-6a86-4705-a28b-e2721070bc5e", + "execution_count": 15, + "id": "aee776ca", "metadata": {}, "outputs": [], "source": [ + "import operator\n", "from typing_extensions import TypedDict\n", - "from typing import List\n", - "\n", - "### State\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", - " web_search: whether to add search\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\n", - " generation : str\n", - " web_search : str\n", - " documents : List[str]\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\n" + ] + }, + { + "cell_type": "markdown", + "id": "e87353d4", + "metadata": {}, + "source": [ + "Each node in our graph is simply a function that:\n", + "\n", + "(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).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "d90bb524", + "metadata": {}, + "outputs": [], + "source": [ "from langchain.schema import Document\n", + "from langgraph.graph import END\n", "\n", - "### Nodes\n", "\n", + "### Nodes\n", "def retrieve(state):\n", " \"\"\"\n", " Retrieve documents from vectorstore\n", @@ -395,9 +743,10 @@ " 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", + " return {\"documents\": documents}\n", + "\n", "\n", "def generate(state):\n", " \"\"\"\n", @@ -412,10 +761,14 @@ " 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", + " 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", "\n", "def grade_documents(state):\n", " \"\"\"\n", @@ -432,13 +785,19 @@ " 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({\"question\": question, \"document\": d.page_content})\n", - " grade = score['score']\n", + " doc_grader_prompt_formatted = doc_grader_prompt.format(\n", + " document=d.page_content, question=question\n", + " )\n", + " result = structured_llm_grader.invoke(\n", + " [SystemMessage(content=doc_grader_instructions),\n", + " HumanMessage(content=doc_grader_prompt_formatted)]\n", + " )\n", + " grade = result.score\n", " # Document relevant\n", " if grade.lower() == \"yes\":\n", " print(\"---GRADE: DOCUMENT RELEVANT---\")\n", @@ -450,8 +809,9 @@ " # 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", + " return {\"documents\": filtered_docs, \"web_search\": web_search}\n", + "\n", + "\n", "def web_search(state):\n", " \"\"\"\n", " Web search based based on the question\n", @@ -465,23 +825,20 @@ "\n", " print(\"---WEB SEARCH---\")\n", " question = state[\"question\"]\n", - " documents = state[\"documents\"]\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", - " if documents is not None:\n", - " documents.append(web_results)\n", - " else:\n", - " documents = [web_results]\n", - " return {\"documents\": documents, \"question\": question}\n", + " documents.append(web_results)\n", + " return {\"documents\": documents}\n", "\n", - "### Conditional edge\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", @@ -491,18 +848,19 @@ " \"\"\"\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 = structured_llm_router.invoke(\n", + " [SystemMessage(content=router_instructions),\n", + " HumanMessage(content=state[\"question\"])]\n", + " )\n", + " source = route_question.datasource\n", + " if source == \"websearch\":\n", " print(\"---ROUTE QUESTION TO WEB SEARCH---\")\n", " return \"websearch\"\n", - " elif source['datasource'] == 'vectorstore':\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 add web search\n", @@ -515,25 +873,24 @@ " \"\"\"\n", "\n", " print(\"---ASSESS GRADED DOCUMENTS---\")\n", - " question = state[\"question\"]\n", " web_search = state[\"web_search\"]\n", - " filtered_documents = 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(\"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, INCLUDE WEB SEARCH---\")\n", + " print(\n", + " \"---DECISION: NOT ALL DOCUMENTS ARE 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", - "### Conditional edge\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", @@ -546,52 +903,89 @@ " 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({\"documents\": documents, \"generation\": generation})\n", - " grade = score['score']\n", + " hallucination_grader_prompt_formatted = hallucination_grader_prompt.format(\n", + " documents=format_docs(documents), generation=generation.content\n", + " )\n", + " result = hallucination_grader.invoke(\n", + " [SystemMessage(content=hallucination_grader_instructions),\n", + " HumanMessage(content=hallucination_grader_prompt_formatted)]\n", + " )\n", + " grade = result.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(\n", + " question=question, generation=generation.content\n", + " )\n", + " result = answer_grader.invoke(\n", + " [SystemMessage(content=answer_grader_instructions),\n", + " HumanMessage(content=answer_grader_prompt_formatted)]\n", + " )\n", + " grade = result.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", - " pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\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", - "\n", - "from langgraph.graph import END, StateGraph\n", - "workflow = StateGraph(GraphState)\n", - "\n", - "# Define the nodes\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) # generatae" + " else:\n", + " print(\"---DECISION: MAX RETRIES REACHED---\")\n", + " return \"max retries\"" ] }, { "cell_type": "markdown", - "id": "73f21594-00d4-48a8-ae2e-4e55a010b540", + "id": "7626c0b3", "metadata": {}, "source": [ - "### Graph Build" + "### Control flow\n", + "\n", + "We can create a workflow that connects the nodes and edges.\n", + "\n", + "See our [tutorial](https://langchain-ai.github.io/langgraph/tutorials/workflows/) on common agent and workflow patterns." ] }, { "cell_type": "code", - "execution_count": null, - "id": "d9a4b9e4-3ba8-47d6-958c-e5a7112ac6f4", + "execution_count": 23, + "id": "57ceb8f6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "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(\"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.set_conditional_entry_point(\n", " route_question,\n", @@ -600,7 +994,7 @@ " \"vectorstore\": \"retrieve\",\n", " },\n", ")\n", - "\n", + "workflow.add_edge(\"websearch\", \"generate\")\n", "workflow.add_edge(\"retrieve\", \"grade_documents\")\n", "workflow.add_conditional_edges(\n", " \"grade_documents\",\n", @@ -610,7 +1004,6 @@ " \"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", @@ -618,72 +1011,64 @@ " \"not supported\": \"generate\",\n", " \"useful\": END,\n", " \"not useful\": \"websearch\",\n", + " \"max retries\": END,\n", " },\n", - ")" + ")\n", + "\n", + "# Compile\n", + "graph = workflow.compile()\n", + "display(Image(graph.get_graph().draw_mermaid_png()))" ] }, { "cell_type": "code", "execution_count": null, - "id": "13043b0f-17c7-49d3-9ea7-8f2c0f0c8691", + "id": "9bcfd638", "metadata": {}, "outputs": [], "source": [ - "# Compile\n", - "app = workflow.compile()\n", - "\n", - "# Test\n", - "from pprint import pprint\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", - " pprint(f\"Finished running: {key}:\")\n", - "pprint(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" + "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": "code", - "execution_count": null, - "id": "fbfcec3e-a09a-40b4-9c15-fead97bf4e0a", + "execution_count": 29, + "id": "0d92ac92", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'There are at least two types of agent memory mentioned in the context: short-term memory (e.g., \"messages\" list) and long-term memory (e.g., storing information across interactions). Additionally, there is a concept of \"hot path\" memory updates. Agent memory allows AI models to retain information across interactions rather than treating every query as a new conversation.'" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Compile\n", - "app = workflow.compile()\n", - "\n", - "# Test\n", - "from pprint import pprint\n", - "inputs = {\"question\": \"Who are the Bears expected to draft first in the NFL draft?\"}\n", - "for output in app.stream(inputs):\n", - " for key, value in output.items():\n", - " pprint(f\"Finished running: {key}:\")\n", - "pprint(value[\"generation\"])" + "event['generation'].content" ] }, { "cell_type": "markdown", - "id": "f9051bea-13fa-4eb3-b671-16f59755931d", + "id": "dc3f2b30", "metadata": {}, "source": [ - "Trace: \n", + "### LangSmith Trace\n", "\n", - "https://smith.langchain.com/public/c785f9c0-f519-4a38-ad5a-febb59a2139c/r" + "For more detailed information and visualization of the workflow execution, you can view this trace in LangSmith:\n", + "\n", + "https://smith.langchain.com/public/72865345-ce90-4393-9feb-449b6285e4f6/r" ] }, { "cell_type": "code", "execution_count": null, - "id": "a1059b3e-d197-47fc-b55f-82a3406016f3", + "id": "d9a4b9e4-3ba8-47d6-958c-e5a7112ac6f4", "metadata": {}, "outputs": [], "source": [] @@ -691,7 +1076,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "test", "language": "python", "name": "python3" }, @@ -705,7 +1090,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.13.1" } }, "nbformat": 4, diff --git a/3p-integrations/langchain/langgraph_tool_calling_agent.ipynb b/3p-integrations/langchain/langgraph_tool_calling_agent.ipynb index 8a15be2e3..e5bdc77ac 100644 --- a/3p-integrations/langchain/langgraph_tool_calling_agent.ipynb +++ b/3p-integrations/langchain/langgraph_tool_calling_agent.ipynb @@ -5,7 +5,7 @@ "id": "8ac4ba3b-c438-4f2e-8f52-39846beb5642", "metadata": {}, "source": [ - "\"Open" + "\"Open" ] }, { @@ -19,73 +19,49 @@ ] }, { - "attachments": { - "e5e59030-655b-401d-962c-2ef75410b177.png": { - "image/png": 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" - } - }, "cell_type": "markdown", "id": "3a7cc5f2-0fd0-4f73-b5a2-c4d45335c356", "metadata": {}, "source": [ "# LangGraph Tool Calling Agent with Llama3\n", "\n", - "LLM-powered agents combine planning, memory, and tool-use (see [here](https://lilianweng.github.io/posts/2023-06-23-agent/), [here](https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/)). \n", + "Agents are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks. In this notebook, we'll build an agent using Llama3 and LangGraph. [LangGraph](https://langchain-ai.github.io/langgraph/concepts/high_level/) is a library that can be used to build agents:\n", "\n", - "LangGraph is a library that can be used to build agents:\n", - " \n", "1) It allows us to define `nodes` for our assistant (which decides whether to call a tool) and our actions (tool calls).\n", "2) It allows us to define specific `edges` that connect these nodes (e.g., based upon whether a tool call is decided).\n", "3) It enables `cycles`, where we can call our assistant in a loop until a stopping condition.\n", "\n", - "![Screenshot 2024-05-30 at 10.53.54 AM.png](attachment:e5e59030-655b-401d-962c-2ef75410b177.png)\n", + "See our [tutorial](https://langchain-ai.github.io/langgraph/tutorials/workflows/) on common agent and workflow patterns.\n", "\n", - "We'll augment a tool-calling version of Llama 3 with various multi-model capabilities using an agent. \n", + "### Tools\n", "\n", - "### Environment\n", + "We'll define a number of tools that our agent can use: \n", "\n", - "We'll use [Tavily](https://tavily.com/#api) for web search.\n", + "* We'll use [Tavily](https://tavily.com/#api), a search engine optimized for LLMs and RAG.\n", + "* We'll use [Replicate](https://replicate.com/), which offers free to try API key and for various multi-modal capabilities.\n", "\n", - "We'll use [Replicate](https://replicate.com/), which offers free to try API key and for various multi-modal capabilities.\n", + "### LLM\n", "\n", - "We can review LangChain LLM integrations that support tool calling [here](https://python.langchain.com/docs/integrations/chat/).\n", - "\n", - "Groq is included. [Here](https://github.com/groq/groq-api-cookbook/blob/main/tutorials/llama3-stock-market-function-calling/llama3-stock-market-function-calling.ipynb) is a notebook by Groq on function calling with Llama 3 and LangChain." + "We'll use [Groq](https://groq.com/inference/) via the LangChain integration [here](https://python.langchain.com/docs/integrations/chat/groq/)." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "d39c2a04-d7e7-42f4-9265-780a14f591c0", "metadata": {}, "outputs": [], "source": [ "import os\n", - "from getpass import getpass\n", - "TAVILY_API_KEY = getpass()\n", - "os.environ[\"TAVILY_API_KEY\"] = TAVILY_API_KEY" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d8bba6bb-d3f2-4502-83ff-a5cf61d859c8", - "metadata": {}, - "outputs": [], - "source": [ - "REPLICATE_API_TOKEN = getpass()\n", - "os.environ[\"REPLICATE_API_TOKEN\"] = REPLICATE_API_TOKEN" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bd3c1fe4-d6ce-484a-9b3c-e80491f03066", - "metadata": {}, - "outputs": [], - "source": [ - "GROQ_API_KEY = getpass()\n", - "os.environ[\"GROQ_API_KEY\"] = GROQ_API_KEY" + "import 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", + "_set_env(\"REPLICATE_API_TOKEN\")\n", + "_set_env(\"GROQ_API_KEY\")" ] }, { @@ -103,20 +79,9 @@ "metadata": {}, "outputs": [], "source": [ - "os.environ['LANGCHAIN_TRACING_V2'] = 'true'\n", - "os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'\n", - "os.environ['LANGCHAIN_API_KEY'] = " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bb7387b2-0094-480b-9b59-3788a22ed06e", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.environ[\"LANGCHAIN_PROJECT\"] = \"llama3-tool-use-agent\"" + "_set_env(\"LANGSMITH_API_KEY\")\n", + "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", + "os.environ[\"LANGCHAIN_PROJECT\"] = \"llama-agent\"" ] }, { @@ -124,39 +89,45 @@ "id": "9f0193d8-123e-4e1c-94e4-b7fb04d0f2f2", "metadata": {}, "source": [ - "### Define tools\n", - "\n", - "These are the same tools that we used in the [tool-calling-agent notebook](tool-calling-agent.ipynb)." + "markdown### Define tools" ] }, { "cell_type": "code", - "execution_count": null, - "id": "586d8bba-2451-4136-b322-f47bcd4b9841", + "execution_count": 1, + "id": "0d31f350", "metadata": {}, "outputs": [], "source": [ "import replicate\n", "\n", "from langchain_core.tools import tool\n", - "from langgraph.prebuilt import ToolNode\n", - "from langchain_community.tools.tavily_search import TavilySearchResults\n", + "from langchain_community.tools import TavilySearchResults\n", + "\n", + "web_search = TavilySearchResults(max_results=2)\n", "\n", "@tool\n", "def magic_function(input: int) -> int:\n", - " \"\"\"Applies a magic function to an input.\"\"\"\n", + " \"\"\"Applies a magic function to an input.\n", + " \n", + " Args:\n", + " input (int): The number to apply the magic function to\n", + " \n", + " Returns:\n", + " int: The input number plus 2\n", + " \"\"\"\n", " return input + 2\n", "\n", "@tool\n", - "def web_search(input: str) -> str:\n", - " \"\"\"Runs web search.\"\"\"\n", - " web_search_tool = TavilySearchResults()\n", - " docs = web_search_tool.invoke({\"query\": input})\n", - " return docs\n", - "\n", - "@tool\n", "def text2image(text: str) -> str:\n", - " \"\"\"generate an image based on a text.\"\"\"\n", + " \"\"\"generate an image based on a text.\n", + " \n", + " Args:\n", + " text (str): The text to generate an image from\n", + " \n", + " Returns:\n", + " str: The URL of the generated image\n", + " \"\"\"\n", " output = replicate.run(\n", " \"stability-ai/sdxl:7762fd07cf82c948538e41f63f77d685e02b063e37e496e96eefd46c929f9bdc\",\n", " input={\n", @@ -177,7 +148,16 @@ "\n", "@tool\n", "def image2text(image_url: str, prompt: str) -> str:\n", - " \"\"\"generate text for image_url based on prompt.\"\"\"\n", + " \"\"\"generate text for image_url based on prompt.\n", + "\n", + " Args:\n", + " image_url (str): The URL of the image to generate text for\n", + " prompt (str): The prompt to generate text for\n", + " \n", + " Returns:\n", + " str: The text generated for the image\n", + " \"\"\"\n", + "\n", " input = {\n", " \"image\": image_url,\n", " \"prompt\": prompt\n", @@ -192,7 +172,15 @@ "\n", "@tool\n", "def text2speech(text: str) -> int:\n", - " \"\"\"convert text to a speech.\"\"\"\n", + " \"\"\"convert text to a speech.\n", + "\n", + " Args:\n", + " text (str): The text to convert to speech\n", + " \n", + " Returns:\n", + " int: The URL of the generated speech\n", + " \"\"\"\n", + "\n", " output = replicate.run(\n", " \"cjwbw/seamless_communication:668a4fec05a887143e5fe8d45df25ec4c794dd43169b9a11562309b2d45873b0\",\n", " input={\n", @@ -206,39 +194,6 @@ " )\n", " return output['audio_output']\n", "\n", - "def create_tool_node_with_fallback(tools: list) -> dict:\n", - " return ToolNode(tools).with_fallbacks(\n", - " [RunnableLambda(handle_tool_error)], exception_key=\"error\"\n", - " )\n", - "\n", - "def _print_event(event: dict, _printed: set, max_length=1500):\n", - " current_state = event.get(\"dialog_state\")\n", - " if current_state:\n", - " print(f\"Currently in: \", current_state[-1])\n", - " message = event.get(\"messages\")\n", - " if message:\n", - " if isinstance(message, list):\n", - " message = message[-1]\n", - " if message.id not in _printed:\n", - " msg_repr = message.pretty_repr(html=True)\n", - " if len(msg_repr) > max_length:\n", - " msg_repr = msg_repr[:max_length] + \" ... (truncated)\"\n", - " print(msg_repr)\n", - " _printed.add(message.id)\n", - "\n", - "def handle_tool_error(state) -> dict:\n", - " error = state.get(\"error\")\n", - " tool_calls = state[\"messages\"][-1].tool_calls\n", - " return {\n", - " \"messages\": [\n", - " ToolMessage(\n", - " content=f\"Error: {repr(error)}\\n please fix your mistakes.\",\n", - " tool_call_id=tc[\"id\"],\n", - " )\n", - " for tc in tool_calls\n", - " ]\n", - " }\n", - "\n", "# List of tools\n", "tools = [\n", " magic_function,\n", @@ -251,321 +206,299 @@ }, { "cell_type": "markdown", - "id": "2579e847-33f2-4d88-8579-78d9f4affbb3", + "id": "5bb2d50b", "metadata": {}, "source": [ - "### State\n", - "\n", - "This list of messages is passed to each node of our agent.\n", - "\n", - "This will serve as short-term memory that persists during the lifetime of our agent. \n", - "\n", - "See [this overview](https://github.com/langchain-ai/langgraph) of LangGraph for more detail." + "### Connect tools to LLM" ] }, { "cell_type": "code", - "execution_count": null, - "id": "f41abc45-bf1a-45a8-a6d3-d90c1cbbd3dd", + "execution_count": 2, + "id": "c8359ea0", "metadata": {}, "outputs": [], "source": [ - "from typing import Annotated\n", - "from typing_extensions import TypedDict\n", - "from langgraph.graph.message import AnyMessage, add_messages\n", - "\n", - "class State(TypedDict):\n", - " messages: Annotated[list[AnyMessage], add_messages]" - ] - }, - { - "cell_type": "markdown", - "id": "1c06614c-e0d3-40a1-9b65-0f5f825cb4ca", - "metadata": {}, - "source": [ - "### Assistant \n", - "\n", - "This is Llama 3, with tool-calling, using [Groq](https://python.langchain.com/v0.1/docs/integrations/chat/groq/).\n", - "\n", - "We bind the available tools to Llama 3. \n", - "\n", - "And we further specify the available tools in our assistant prompt." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "29fe9a47-857f-4554-8539-8777734e3faa", - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", "from langchain_groq import ChatGroq\n", - "from langchain_core.prompts import ChatPromptTemplate\n", - "\n", - "from langchain_community.tools.tavily_search import TavilySearchResults\n", - "from langchain_core.prompts import ChatPromptTemplate\n", - "from langchain_core.runnables import Runnable, RunnableConfig\n", - "\n", - "# Assistant\n", - "class Assistant:\n", - " \n", - " def __init__(self, runnable: Runnable):\n", - " self.runnable = runnable\n", - "\n", - " def __call__(self, state: State, config: RunnableConfig):\n", - " while True:\n", - " # Get any user-provided configs \n", - " image_url = config['configurable'].get(\"image_url\", None)\n", - " # Append to state\n", - " state = {**state, \"image_url\": image_url}\n", - " # Invoke the tool-calling LLM\n", - " result = self.runnable.invoke(state)\n", - " # If it is a tool call -> response is valid\n", - " # If it has meaningful text -> response is valid\n", - " # Otherwise, we re-prompt it b/c response is not meaningful\n", - " if not result.tool_calls and (\n", - " not result.content\n", - " or isinstance(result.content, list)\n", - " and not result.content[0].get(\"text\")\n", - " ):\n", - " messages = state[\"messages\"] + [(\"user\", \"Respond with a real output.\")]\n", - " state = {**state, \"messages\": messages}\n", - " else:\n", - " break\n", - " return {\"messages\": result}\n", - "\n", - "# Prompt \n", - "primary_assistant_prompt = ChatPromptTemplate.from_messages(\n", - " [\n", - " (\n", - " \"system\",\n", - " \"You are a helpful assistant for with five tools: (1) web search, \"\n", - " \"(2) a custom, magic_function, (3) text to image, (4) image to text \"\n", - " \"(5) text to speech. Use these provided tools in response to the user question. \"\n", - " \"Your image url is: {image_url} \"\n", - " \"Current time: {time}.\",\n", - " ),\n", - " (\"placeholder\", \"{messages}\"),\n", - " ]\n", - ").partial(time=datetime.now())\n", "\n", "# LLM chain\n", - "llm = ChatGroq(temperature=0, model=\"llama3-70b-8192\")\n", - "assistant_runnable = primary_assistant_prompt | llm.bind_tools(tools)" + "llm = ChatGroq(temperature=0, model=\"llama-3.3-70b-versatile\")\n", + "tools_by_name = {tool.name: tool for tool in tools}\n", + "llm_with_tools = llm.bind_tools(tools)" ] }, { "cell_type": "markdown", - "id": "96514473-c092-4195-bf76-9c61089b5072", + "id": "5c14780d", "metadata": {}, "source": [ - "### Graph\n", - "\n", - "Here, we lay out the graph." + "### Build agent" ] }, { "cell_type": "code", - "execution_count": null, - "id": "dacf444f-be0f-41bd-b9bd-5ac776fbd5f8", + "execution_count": 3, + "id": "54b52b37", "metadata": {}, "outputs": [], "source": [ - "from langgraph.checkpoint.sqlite import SqliteSaver\n", - "from langgraph.graph import END, StateGraph\n", - "from langgraph.prebuilt import ToolNode, tools_condition\n", - "from langchain_core.runnables import RunnableLambda\n", - "\n", - "# Graph\n", - "builder = StateGraph(State)\n", - "\n", - "# Define nodes: these do the work\n", - "builder.add_node(\"assistant\", Assistant(assistant_runnable))\n", - "builder.add_node(\"tools\", create_tool_node_with_fallback(tools))\n", - "\n", - "# Define edges: these determine how the control flow moves\n", - "builder.set_entry_point(\"assistant\")\n", - "builder.add_conditional_edges(\n", - " \"assistant\",\n", - " # If the latest message (result) from assistant is a tool call -> tools_condition routes to tools\n", - " # If the latest message (result) from assistant is a not a tool call -> tools_condition routes to END\n", - " tools_condition, \n", - " # \"tools\" calls one of our tools. END causes the graph to terminate (and respond to the user)\n", - " {\"tools\": \"tools\", END: END},\n", - ")\n", - "builder.add_edge(\"tools\", \"assistant\")\n", + "SYSTEM_PROMPT = \"\"\"You are a helpful assistant with access to five tools: \n", "\n", - "# The checkpointer lets the graph persist its state\n", - "memory = SqliteSaver.from_conn_string(\":memory:\")\n", - "graph = builder.compile(checkpointer=memory)" - ] - }, - { - "cell_type": "markdown", - "id": "d404bfe1-fc7a-49f1-9e7a-4bd77f830c6d", - "metadata": {}, - "source": [ - "We can visualize it." + "(1) web search any current events \n", + "(2) a custom magic_function\n", + "(3) text to image \n", + "(4) image to text \n", + "(5) text to speech \n", + "\n", + "Use these provided tools in response to the user question. \n", + "You may be provided with an image url: {image_url} \n", + "\"You may be provided with a time: {time}.\"\"\"" ] }, { "cell_type": "code", - "execution_count": null, - "id": "983ab01b-bd31-47b1-bfd8-15cb0e555156", - "metadata": {}, - "outputs": [], + "execution_count": 4, + "id": "f1ee5a84", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "from datetime import datetime\n", + "from typing import Literal\n", "from IPython.display import Image, display\n", "\n", - "try:\n", - " display(Image(graph.get_graph(xray=True).draw_mermaid_png()))\n", - "except:\n", - " pass" - ] - }, - { - "cell_type": "markdown", - "id": "d6388175-6b8f-483c-ab31-948258a8fb7e", - "metadata": {}, - "source": [ - "### Test\n", + "from langchain_core.messages import SystemMessage, ToolMessage, HumanMessage\n", + "from langchain_core.runnables import RunnableConfig\n", "\n", - "Now, we can test each tool!\n", + "from langgraph.graph import MessagesState, START, END, StateGraph\n", "\n", - "See the traces to audit specifically what is happening." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bbc9a513-56cf-45fc-a276-d68c54620203", - "metadata": {}, - "outputs": [], - "source": [ - "questions = [\"What is magic_function(3)\",\n", - " \"What is the weather in SF?\",\n", - " \"Generate an image based upon this text: 'a yellow lab puppy running free with wild flowers in the mountain behind'\",\n", - " \"Tell me a story about this image\",\n", - " \"Convert this text to speech: The image features a small white dog running down a dirt path, surrounded by a beautiful landscape. The dog is happily smiling as it runs, and the path is lined with colorful flowers, creating a vibrant and lively atmosphere. The scene appears to be set in a mountainous area, adding to the picturesque nature of the image.\"\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e3baef29-84c5-491d-8ff7-107d64f23435", - "metadata": {}, - "outputs": [], - "source": [ - "import uuid \n", - "_printed = set()\n", - "image_url = None\n", - "thread_id = str(uuid.uuid4())\n", + "# Nodes\n", + "def llm_call(state: MessagesState, config: RunnableConfig):\n", + " \"\"\"LLM decides whether to call a tool or not\"\"\"\n", "\n", - "config = {\n", - " \"configurable\": {\n", - " \"image_url\": image_url,\n", - " # Checkpoints are accessed by thread_id\n", - " \"thread_id\": thread_id,\n", + " return {\n", + " \"messages\": [\n", + " llm_with_tools.invoke(\n", + " [\n", + " SystemMessage(\n", + " content=SYSTEM_PROMPT.format(\n", + " image_url=config[\"configurable\"].get(\"image_url\", None),\n", + " time=datetime.now()\n", + " )\n", + " )\n", + " ]\n", + " + state[\"messages\"]\n", + " )\n", + " ]\n", " }\n", - "}\n", "\n", - "events = graph.stream(\n", - " {\"messages\": (\"user\", questions[0])}, config, stream_mode=\"values\"\n", + "def tool_node(state: dict):\n", + " \"\"\"Performs the tool call\"\"\"\n", + "\n", + " result = []\n", + " for tool_call in state[\"messages\"][-1].tool_calls:\n", + " tool = tools_by_name[tool_call[\"name\"]]\n", + " observation = tool.invoke({\"args\": tool_call[\"args\"], \"id\": tool_call[\"id\"], \"type\": \"tool_call\"}) \n", + " result.append(ToolMessage(content=observation, tool_call_id=tool_call[\"id\"]))\n", + " return {\"messages\": result}\n", + "\n", + "# Conditional edge function to route to the tool node or end based upon whether the LLM made a tool call\n", + "def should_continue(state: MessagesState) -> Literal[\"environment\", END]:\n", + " \"\"\"Decide if we should continue the loop or stop based upon whether the LLM made a tool call\"\"\"\n", + "\n", + " messages = state[\"messages\"]\n", + " last_message = messages[-1]\n", + " # If the LLM makes a tool call, then perform an action\n", + " if last_message.tool_calls:\n", + " return \"Action\"\n", + " # Otherwise, we stop (reply to the user)\n", + " return END\n", + "\n", + "# Build workflow\n", + "agent_builder = StateGraph(MessagesState)\n", + "\n", + "# Add nodes\n", + "agent_builder.add_node(\"llm_call\", llm_call)\n", + "agent_builder.add_node(\"environment\", tool_node)\n", + "\n", + "# Add edges to connect nodes\n", + "agent_builder.add_edge(START, \"llm_call\")\n", + "agent_builder.add_conditional_edges(\n", + " \"llm_call\",\n", + " should_continue,\n", + " {\n", + " # Name returned by should_continue : Name of next node to visit\n", + " \"Action\": \"environment\",\n", + " END: END,\n", + " },\n", ")\n", - "for event in events:\n", - " _print_event(event, _printed)" - ] - }, - { - "cell_type": "markdown", - "id": "d25fdf4f-feac-41c6-828c-24494d4bc7c9", - "metadata": {}, - "source": [ - "Trace: \n", + "agent_builder.add_edge(\"environment\", \"llm_call\")\n", + "\n", + "# Compile the agent\n", + "agent = agent_builder.compile()\n", "\n", - "https://smith.langchain.com/public/e4f4055f-eb68-482a-8843-cecc67ea76d3/r" + "# Show the agent\n", + "display(Image(agent.get_graph(xray=True).draw_mermaid_png()))" ] }, { "cell_type": "code", - "execution_count": null, - "id": "5542c838-44f8-45d4-a866-72a056ece638", - "metadata": {}, - "outputs": [], + "execution_count": 56, + "id": "0c491b9c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "What is magic_function(3)?\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " magic_function (call_v5a3)\n", + " Call ID: call_v5a3\n", + " Args:\n", + " input: 3\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "\n", + "content='5' name='magic_function' tool_call_id='call_v5a3'\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The result of magic_function(3) is 5.\n" + ] + } + ], "source": [ - "_printed = set()\n", - "image_url = None\n", - "thread_id = str(uuid.uuid4())\n", - "\n", "config = {\n", " \"configurable\": {\n", - " \"image_url\": image_url,\n", - " # Checkpoints are accessed by thread_id\n", - " \"thread_id\": thread_id,\n", + " \"image_url\": None,\n", " }\n", "}\n", "\n", - "events = graph.stream(\n", - " {\"messages\": (\"user\", questions[1])}, config, stream_mode=\"values\"\n", - ")\n", - "for event in events:\n", - " _print_event(event, _printed)" - ] - }, - { - "cell_type": "markdown", - "id": "8391f5fa-aa60-4784-a732-a5e098d11624", - "metadata": {}, - "source": [ - "Trace: \n", - "\n", - "https://smith.langchain.com/public/1a46bdba-448b-4b23-a78b-650d28d5ee7f/r" + "messages = [HumanMessage(content=\"What is magic_function(3)?\")]\n", + "messages = agent.invoke({\"messages\": messages}, config=config)\n", + "for m in messages[\"messages\"]:\n", + " m.pretty_print()" ] }, { "cell_type": "code", - "execution_count": null, - "id": "ed58a028-fad7-4ba4-8117-b69a5d0b6489", - "metadata": {}, - "outputs": [], + "execution_count": 57, + "id": "ce972b11", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "What is the weather in SF?\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " tavily_search_results_json (call_hm9p)\n", + " Call ID: call_hm9p\n", + " Args:\n", + " query: San Francisco weather\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "\n", + "content='[{\"title\": \"Weather in San Francisco\", \"url\": \"https://www.weatherapi.com/\", \"content\": \"{\\'location\\': {\\'name\\': \\'San Francisco\\', \\'region\\': \\'California\\', \\'country\\': \\'United States of America\\', \\'lat\\': 37.775, \\'lon\\': -122.4183, \\'tz_id\\': \\'America/Los_Angeles\\', \\'localtime_epoch\\': 1741747880, \\'localtime\\': \\'2025-03-11 19:51\\'}, \\'current\\': {\\'last_updated_epoch\\': 1741747500, \\'last_updated\\': \\'2025-03-11 19:45\\', \\'temp_c\\': 13.3, \\'temp_f\\': 55.9, \\'is_day\\': 0, \\'condition\\': {\\'text\\': \\'Partly cloudy\\', \\'icon\\': \\'//cdn.weatherapi.com/weather/64x64/night/116.png\\', \\'code\\': 1003}, \\'wind_mph\\': 7.6, \\'wind_kph\\': 12.2, \\'wind_degree\\': 230, \\'wind_dir\\': \\'SW\\', \\'pressure_mb\\': 1010.0, \\'pressure_in\\': 29.83, \\'precip_mm\\': 0.0, \\'precip_in\\': 0.0, \\'humidity\\': 72, \\'cloud\\': 75, \\'feelslike_c\\': 12.3, \\'feelslike_f\\': 54.1, \\'windchill_c\\': 9.0, \\'windchill_f\\': 48.2, \\'heatindex_c\\': 11.0, \\'heatindex_f\\': 51.7, \\'dewpoint_c\\': 9.4, \\'dewpoint_f\\': 49.0, \\'vis_km\\': 16.0, \\'vis_miles\\': 9.0, \\'uv\\': 0.0, \\'gust_mph\\': 10.8, \\'gust_kph\\': 17.4}}\", \"score\": 0.9166437}, {\"title\": \"Weather in San Francisco in March 2025 (California)\", \"url\": \"https://world-weather.info/forecast/usa/san_francisco/march-2025/\", \"content\": \"Weather in San Francisco in March 2025 (California) - Detailed Weather Forecast for a Month Weather in San Francisco Weather in San Francisco in March 2025 San Francisco Weather Forecast for March 2025 is based on long term prognosis and previous years\\' statistical data. 1 +54°+52° 2 +54°+50° 3 +54°+50° 4 +54°+48° 5 +61°+46° +59°+50° +59°+50° +61°+50° +61°+52° +61°+52° +63°+52° +63°+52° +61°+52° +61°+52° +63°+54° +61°+52° +63°+50° +61°+52° +61°+52° +59°+52° +61°+52° +59°+50° +57°+50° +57°+50° +59°+50° +59°+50° +61°+52° +63°+52° +63°+54° +63°+52° +63°+54° Extended weather forecast in San Francisco HourlyWeek10-Day14-Day30-DayYear Weather in Washington, D.C.+32° Sacramento+55° Pleasanton+52° Redwood City+55° San Leandro+55° San Mateo+54° San Rafael+55° San Ramon+52° South San Francisco+54° Vallejo+54° Palo Alto+55° Pacifica+55° Berkeley+57° Castro Valley+54° Concord+54° Daly City+54° Lagunitas+55° world\\'s temperature today day day Temperature units\", \"score\": 0.9163571}]' name='tavily_search_results_json' tool_call_id='call_hm9p' artifact={'query': 'San Francisco weather', 'follow_up_questions': None, 'answer': None, 'images': [], 'results': [{'title': 'Weather in San Francisco', 'url': 'https://www.weatherapi.com/', 'content': \"{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.775, 'lon': -122.4183, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1741747880, 'localtime': '2025-03-11 19:51'}, 'current': {'last_updated_epoch': 1741747500, 'last_updated': '2025-03-11 19:45', 'temp_c': 13.3, 'temp_f': 55.9, 'is_day': 0, 'condition': {'text': 'Partly cloudy', 'icon': '//cdn.weatherapi.com/weather/64x64/night/116.png', 'code': 1003}, 'wind_mph': 7.6, 'wind_kph': 12.2, 'wind_degree': 230, 'wind_dir': 'SW', 'pressure_mb': 1010.0, 'pressure_in': 29.83, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 72, 'cloud': 75, 'feelslike_c': 12.3, 'feelslike_f': 54.1, 'windchill_c': 9.0, 'windchill_f': 48.2, 'heatindex_c': 11.0, 'heatindex_f': 51.7, 'dewpoint_c': 9.4, 'dewpoint_f': 49.0, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 0.0, 'gust_mph': 10.8, 'gust_kph': 17.4}}\", 'score': 0.9166437, 'raw_content': None}, {'url': 'https://world-weather.info/forecast/usa/san_francisco/march-2025/', 'title': 'Weather in San Francisco in March 2025 (California)', 'content': \"Weather in San Francisco in March 2025 (California) - Detailed Weather Forecast for a Month Weather in San Francisco Weather in San Francisco in March 2025 San Francisco Weather Forecast for March 2025 is based on long term prognosis and previous years' statistical data. 1 +54°+52° 2 +54°+50° 3 +54°+50° 4 +54°+48° 5 +61°+46° +59°+50° +59°+50° +61°+50° +61°+52° +61°+52° +63°+52° +63°+52° +61°+52° +61°+52° +63°+54° +61°+52° +63°+50° +61°+52° +61°+52° +59°+52° +61°+52° +59°+50° +57°+50° +57°+50° +59°+50° +59°+50° +61°+52° +63°+52° +63°+54° +63°+52° +63°+54° Extended weather forecast in San Francisco HourlyWeek10-Day14-Day30-DayYear Weather in Washington, D.C.+32° Sacramento+55° Pleasanton+52° Redwood City+55° San Leandro+55° San Mateo+54° San Rafael+55° San Ramon+52° South San Francisco+54° Vallejo+54° Palo Alto+55° Pacifica+55° Berkeley+57° Castro Valley+54° Concord+54° Daly City+54° Lagunitas+55° world's temperature today day day Temperature units\", 'score': 0.9163571, 'raw_content': None}], 'response_time': 2.19}\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The current weather in San Francisco is partly cloudy with a temperature of 55.9°F (13.3°C) and a humidity of 72%. The wind is blowing at 7.6 mph (12.2 kph) from the southwest.\n" + ] + } + ], "source": [ - "_printed = set()\n", - "image_url = None\n", - "thread_id = str(uuid.uuid4())\n", - "\n", - "config = {\n", - " \"configurable\": {\n", - " \"image_url\": image_url,\n", - " # Checkpoints are accessed by thread_id\n", - " \"thread_id\": thread_id,\n", - " }\n", - "}\n", - "\n", - "events = graph.stream(\n", - " {\"messages\": (\"user\", questions[2])}, config, stream_mode=\"values\"\n", - ")\n", - "for event in events:\n", - " _print_event(event, _printed)" + "messages = [HumanMessage(content=\"What is the weather in SF?\")]\n", + "messages = agent.invoke({\"messages\": messages}, config=config)\n", + "for m in messages[\"messages\"]:\n", + " m.pretty_print()" ] }, { - "cell_type": "markdown", - "id": "03af241a-83a7-4f65-a628-fac0971468b6", - "metadata": {}, + "cell_type": "code", + "execution_count": 58, + "id": "0a89a52c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[]\n", + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "Generate an image based upon this text: 'a yellow lab puppy running free with wild flowers in the mountain behind'\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " text2image (call_4j6w)\n", + " Call ID: call_4j6w\n", + " Args:\n", + " text: a yellow lab puppy running free with wild flowers in the mountain behind\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "\n", + "content='https://replicate.delivery/xezq/TvyRIXJfiS2mbSeQJzM7FCdXlLMcaj6fuQeXOImOCFq7LqeiC/out-0.png' name='text2image' tool_call_id='call_4j6w'\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " image2text (call_dyn7)\n", + " Call ID: call_dyn7\n", + " Args:\n", + " image_url: https://replicate.delivery/xezq/TvyRIXJfiS2mbSeQJzM7FCdXlLMcaj6fuQeXOImOCFq7LqeiC/out-0.png\n", + " prompt: a yellow lab puppy running free with wild flowers in the mountain behind\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "\n", + "content='The image features a yellow lab puppy running through a field of wildflowers, with a mountain in the background. The puppy appears to be enjoying the open space and the beautiful surroundings, as it runs through the colorful flowers. The scene captures the essence of nature and the joy of a young dog exploring its environment.' name='image2text' tool_call_id='call_dyn7'\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " text2speech (call_4y4k)\n", + " Call ID: call_4y4k\n", + " Args:\n", + " text: The image features a yellow lab puppy running through a field of wildflowers, with a mountain in the background. The puppy appears to be enjoying the open space and the beautiful surroundings, as it runs through the colorful flowers. The scene captures the essence of nature and the joy of a young dog exploring its environment.\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "\n", + "content='https://replicate.delivery/xezq/EuB2wKTdGY7eWKmrlenSkmMUeBwhcZdI0bel5KkV1jOWTqeiC/out.wav' name='text2speech' tool_call_id='call_4y4k'\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The image has been generated and the text has been converted to speech. The speech can be accessed at the provided URL.\n" + ] + } + ], "source": [ - "Trace: \n", - "\n", - "https://smith.langchain.com/public/cc9ca4f1-05c8-4dea-a85b-c852f22c14ae/r" + "messages = [HumanMessage(content=\"Generate an image based upon this text: 'a yellow lab puppy running free with wild flowers in the mountain behind'\")]\n", + "messages = agent.invoke({\"messages\": messages}, config=config)\n", + "for m in messages[\"messages\"]:\n", + " m.pretty_print()" ] }, { "cell_type": "code", - "execution_count": null, - "id": "e8283c47-145e-4243-9f5c-4203bfde62d3", - "metadata": {}, - "outputs": [], + "execution_count": 61, + "id": "05210617", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import requests\n", "from PIL import Image\n", @@ -580,103 +513,106 @@ " plt.axis('off')\n", " plt.show()\n", "\n", - "image_url = event['messages'][-2].content\n", - "display_image(image_url)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19fd5aaa-c882-4de3-b94f-ee290947f944", - "metadata": {}, - "outputs": [], - "source": [ - "import uuid \n", - "\n", - "_printed = set()\n", - "thread_id = str(uuid.uuid4())\n", - "\n", - "config = {\n", - " \"configurable\": {\n", - " \"image_url\": image_url,\n", - " # Checkpoints are accessed by thread_id\n", - " \"thread_id\": thread_id,\n", - " }\n", - "}\n", - "\n", - "events = graph.stream(\n", - " {\"messages\": (\"user\", questions[3])}, config, stream_mode=\"values\"\n", - ")\n", - "for event in events:\n", - " _print_event(event, _printed)" - ] - }, - { - "cell_type": "markdown", - "id": "dd67d65a-717c-4da1-bbd6-1a796bfc077e", - "metadata": {}, - "source": [ - "Trace: \n", - "\n", - "https://smith.langchain.com/public/89f45ee4-effc-4cca-b3e6-f12cf5c29168/r" + "display_image(\"https://replicate.delivery/xezq/TvyRIXJfiS2mbSeQJzM7FCdXlLMcaj6fuQeXOImOCFq7LqeiC/out-0.png\")" ] }, { "cell_type": "code", - "execution_count": null, - "id": "73e3e12e-d725-4e66-aba1-ddc2d270f468", - "metadata": {}, - "outputs": [], + "execution_count": 60, + "id": "4c800426", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "Tell me a story about this image\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " image2text (call_spz8)\n", + " Call ID: call_spz8\n", + " Args:\n", + " image_url: https://replicate.delivery/xezq/TvyRIXJfiS2mbSeQJzM7FCdXlLMcaj6fuQeXOImOCFq7LqeiC/out-0.png\n", + " prompt: Tell me a story about this image\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "\n", + "content=\"In the image, a small white dog is happily running through a field of wildflowers, with its tongue out, enjoying the beautiful scenery. The dog appears to be in a playful mood, as it runs through the colorful flowers, which are scattered all around the field. The scene is set in a mountainous area, adding to the picturesque atmosphere. The dog's joyful expression and the vibrant flowers create a lively and cheerful scene, capturing the essence of nature and the simple pleasures of life.\" name='image2text' tool_call_id='call_spz8'\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " text2speech (call_rc69)\n", + " Call ID: call_rc69\n", + " Args:\n", + " text: In the image, a small white dog is happily running through a field of wildflowers, with its tongue out, enjoying the beautiful scenery. The dog appears to be in a playful mood, as it runs through the colorful flowers, which are scattered all around the field. The scene is set in a mountainous area, adding to the picturesque atmosphere. The dog's joyful expression and the vibrant flowers create a lively and cheerful scene, capturing the essence of nature and the simple pleasures of life.\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "\n", + "content='https://replicate.delivery/xezq/Y2IIteG2q3UCPi1RLgPeI8fE6D6H6qxogFsgjvVVGXsWVVvoA/out.wav' name='text2speech' tool_call_id='call_rc69'\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The story about the image is about a small white dog happily running through a field of wildflowers, with its tongue out, enjoying the beautiful scenery. The dog appears to be in a playful mood, as it runs through the colorful flowers, which are scattered all around the field. The scene is set in a mountainous area, adding to the picturesque atmosphere. The dog's joyful expression and the vibrant flowers create a lively and cheerful scene, capturing the essence of nature and the simple pleasures of life.\n" + ] + } + ], "source": [ - "_printed = set()\n", - "image_url = None\n", - "thread_id = str(uuid.uuid4())\n", - "\n", "config = {\n", " \"configurable\": {\n", - " \"image_url\": image_url,\n", - " # Checkpoints are accessed by thread_id\n", - " \"thread_id\": thread_id,\n", + " \"image_url\": \"https://replicate.delivery/xezq/TvyRIXJfiS2mbSeQJzM7FCdXlLMcaj6fuQeXOImOCFq7LqeiC/out-0.png\",\n", " }\n", "}\n", "\n", - "events = graph.stream(\n", - " {\"messages\": (\"user\", questions[4])}, config, stream_mode=\"values\"\n", - ")\n", - "for event in events:\n", - " _print_event(event, _printed)" + "messages = [HumanMessage(content=\"Tell me a story about this image\")]\n", + "messages = agent.invoke({\"messages\": messages}, config=config)\n", + "for m in messages[\"messages\"]:\n", + " m.pretty_print()" ] }, { "cell_type": "code", - "execution_count": null, - "id": "bb7536d7-8883-4e8b-8584-251e1c3b92f2", - "metadata": {}, - "outputs": [], + "execution_count": 62, + "id": "382b5efc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from IPython.display import Audio\n", "\n", "def play_audio(output_url):\n", " return Audio(url=output_url, autoplay=False)\n", "\n", - "audio_url = event['messages'][-2].content\n", - "play_audio(audio_url)" + "play_audio(\"https://replicate.delivery/xezq/Y2IIteG2q3UCPi1RLgPeI8fE6D6H6qxogFsgjvVVGXsWVVvoA/out.wav\")" ] }, { - "cell_type": "markdown", - "id": "25ec5e4c-37ff-495f-992f-c3b3935b8565", + "cell_type": "code", + "execution_count": null, + "id": "58127b9e", "metadata": {}, - "source": [ - "Trace: \n", - "\n", - "https://smith.langchain.com/public/b504a513-3123-4bfd-8796-3364968559b2/r" - ] + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "test", "language": "python", "name": "python3" }, @@ -690,7 +626,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.13.1" } }, "nbformat": 4,