x

Favor langchain_openai over langchain_community for evaluation logic.

---------

Co-authored-by: ccurme <chester.curme@gmail.com>

.github/workflows/_release_note_experiments.yml

@@ -1,59 +0,0 @@
-name: release note experiments
-run-name: Release note for ${{ inputs.working-directory }} by @${{ github.actor }}
-on:
-  workflow_dispatch:
-    inputs:
-      working-directory:
-        required: true
-        type: string
-        default: 'libs/langchain'
-
-env:
-  PYTHON_VERSION: "3.11"
-  POETRY_VERSION: "1.7.1"
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-
-    outputs:
-      pkg-name: ${{ steps.check-version.outputs.pkg-name }}
-      version: ${{ steps.check-version.outputs.version }}
-
-    steps:
-      - uses: actions/checkout@v4
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
-        with:
-          python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: release
-      - name: Check Version
-        id: check-version
-        shell: bash
-        working-directory: ${{ inputs.working-directory }}
-        run: |
-          echo pkg-name="$(poetry version | cut -d ' ' -f 1)" >> $GITHUB_OUTPUT
-          echo version="$(poetry version --short)" >> $GITHUB_OUTPUT
-  release-notes:
-    needs:
-      - build
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
-        with:
-          python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: release
-      - name: Generate Release Notes
-        env:
-          TAG_NAME: ${{ needs.build.outputs.pkg-name }}-v${{ needs.build.outputs.version }}
-          RELEASE_NAME: ${{ needs.build.outputs.pkg-name }}==${{ needs.build.outputs.version }}
-        run: |
-          echo "TAG_NAME=${TAG_NAME}"
-          echo "RELEASE_NAME=${RELEASE_NAME}"

cookbook/Multi_modal_RAG.ipynb

@@ -604,7 +604,7 @@
    "source": [
     "# Check retrieval\n",
     "query = \"Give me company names that are interesting investments based on EV / NTM and NTM rev growth. Consider EV / NTM multiples vs historical?\"\n",
-    "docs = retriever_multi_vector_img.get_relevant_documents(query, limit=6)\n",
+    "docs = retriever_multi_vector_img.invoke(query, limit=6)\n",
     "\n",
     "# We get 4 docs\n",
     "len(docs)"
@@ -630,7 +630,7 @@
    "source": [
     "# Check retrieval\n",
     "query = \"What are the EV / NTM and NTM rev growth for MongoDB, Cloudflare, and Datadog?\"\n",
-    "docs = retriever_multi_vector_img.get_relevant_documents(query, limit=6)\n",
+    "docs = retriever_multi_vector_img.invoke(query, limit=6)\n",
     "\n",
     "# We get 4 docs\n",
     "len(docs)"

cookbook/Multi_modal_RAG_google.ipynb

@@ -604,7 +604,7 @@
    ],
    "source": [
     "query = \"What are the EV / NTM and NTM rev growth for MongoDB, Cloudflare, and Datadog?\"\n",
-    "docs = retriever_multi_vector_img.get_relevant_documents(query, limit=1)\n",
+    "docs = retriever_multi_vector_img.invoke(query, limit=1)\n",
     "\n",
     "# We get 2 docs\n",
     "len(docs)"

cookbook/Semi_Structured_RAG.ipynb

@@ -75,7 +75,7 @@
     "\n",
     "Apply to the [`LLaMA2`](https://arxiv.org/pdf/2307.09288.pdf) paper. \n",
     "\n",
-    "We use the Unstructured [`partition_pdf`](https://unstructured-io.github.io/unstructured/bricks/partition.html#partition-pdf), which segments a PDF document by using a layout model. \n",
+    "We use the Unstructured [`partition_pdf`](https://unstructured-io.github.io/unstructured/core/partition.html#partition-pdf), which segments a PDF document by using a layout model. \n",
     "\n",
     "This layout model makes it possible to extract elements, such as tables, from pdfs. \n",
     "\n",

cookbook/Semi_structured_and_multi_modal_RAG.ipynb

@@ -562,9 +562,7 @@
    ],
    "source": [
     "# We can retrieve this table\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What are results for LLaMA across across domains / subjects?\"\n",
-    ")[1]"
+    "retriever.invoke(\"What are results for LLaMA across across domains / subjects?\")[1]"
    ]
   },
   {
@@ -614,9 +612,7 @@
     }
    ],
    "source": [
-    "retriever.get_relevant_documents(\"Images / figures with playful and creative examples\")[\n",
-    "    1\n",
-    "]"
+    "retriever.invoke(\"Images / figures with playful and creative examples\")[1]"
    ]
   },
   {

cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb

@@ -501,9 +501,7 @@
     }
    ],
    "source": [
-    "retriever.get_relevant_documents(\"Images / figures with playful and creative examples\")[\n",
-    "    0\n",
-    "]"
+    "retriever.invoke(\"Images / figures with playful and creative examples\")[0]"
    ]
   },
   {

cookbook/advanced_rag_eval.ipynb

@@ -342,7 +342,7 @@
     "# Testing on retrieval\n",
     "query = \"What percentage of CPI is dedicated to Housing, and how does it compare to the combined percentage of Medical Care, Apparel, and Other Goods and Services?\"\n",
     "suffix_for_images = \" Include any pie charts, graphs, or tables.\"\n",
-    "docs = retriever_multi_vector_img.get_relevant_documents(query + suffix_for_images)"
+    "docs = retriever_multi_vector_img.invoke(query + suffix_for_images)"
    ]
   },
   {

cookbook/custom_agent_with_plugin_retrieval.ipynb

@@ -169,7 +169,7 @@
     "\n",
     "def get_tools(query):\n",
     "    # Get documents, which contain the Plugins to use\n",
-    "    docs = retriever.get_relevant_documents(query)\n",
+    "    docs = retriever.invoke(query)\n",
     "    # Get the toolkits, one for each plugin\n",
     "    tool_kits = [toolkits_dict[d.metadata[\"plugin_name\"]] for d in docs]\n",
     "    # Get the tools: a separate NLAChain for each endpoint\n",

cookbook/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb

@@ -193,7 +193,7 @@
     "\n",
     "def get_tools(query):\n",
     "    # Get documents, which contain the Plugins to use\n",
-    "    docs = retriever.get_relevant_documents(query)\n",
+    "    docs = retriever.invoke(query)\n",
     "    # Get the toolkits, one for each plugin\n",
     "    tool_kits = [toolkits_dict[d.metadata[\"plugin_name\"]] for d in docs]\n",
     "    # Get the tools: a separate NLAChain for each endpoint\n",

cookbook/custom_agent_with_tool_retrieval.ipynb

@@ -142,7 +142,7 @@
     "\n",
     "\n",
     "def get_tools(query):\n",
-    "    docs = retriever.get_relevant_documents(query)\n",
+    "    docs = retriever.invoke(query)\n",
     "    return [ALL_TOOLS[d.metadata[\"index\"]] for d in docs]"
    ]
   },

cookbook/langgraph_crag.ipynb

@@ -206,7 +206,7 @@
     "    print(\"---RETRIEVE---\")\n",
     "    state_dict = state[\"keys\"]\n",
     "    question = state_dict[\"question\"]\n",
-    "    documents = retriever.get_relevant_documents(question)\n",
+    "    documents = retriever.invoke(question)\n",
     "    return {\"keys\": {\"documents\": documents, \"question\": question}}\n",
     "\n",
     "\n",

cookbook/langgraph_self_rag.ipynb

@@ -213,7 +213,7 @@
     "    print(\"---RETRIEVE---\")\n",
     "    state_dict = state[\"keys\"]\n",
     "    question = state_dict[\"question\"]\n",
-    "    documents = retriever.get_relevant_documents(question)\n",
+    "    documents = retriever.invoke(question)\n",
     "    return {\"keys\": {\"documents\": documents, \"question\": question}}\n",
     "\n",
     "\n",

cookbook/multi_modal_RAG_chroma.ipynb

@@ -435,7 +435,7 @@
     "    display(HTML(image_html))\n",
     "\n",
     "\n",
-    "docs = retriever.get_relevant_documents(\"Woman with children\", k=10)\n",
+    "docs = retriever.invoke(\"Woman with children\", k=10)\n",
     "for doc in docs:\n",
     "    if is_base64(doc.page_content):\n",
     "        plt_img_base64(doc.page_content)\n",

cookbook/multi_modal_RAG_vdms.ipynb

@@ -443,7 +443,7 @@
     "\n",
     "\n",
     "query = \"Woman with children\"\n",
-    "docs = retriever.get_relevant_documents(query, k=10)\n",
+    "docs = retriever.invoke(query, k=10)\n",
     "\n",
     "for doc in docs:\n",
     "    if is_base64(doc.page_content):\n",

cookbook/rag_semantic_chunking_azureaidocintelligence.ipynb

@@ -168,7 +168,7 @@
     "\n",
     "retriever = vector_store.as_retriever(search_type=\"similarity\", search_kwargs={\"k\": 3})\n",
     "\n",
-    "retrieved_docs = retriever.get_relevant_documents(\"<your question>\")\n",
+    "retrieved_docs = retriever.invoke(\"<your question>\")\n",
     "\n",
     "print(retrieved_docs[0].page_content)\n",
     "\n",

cookbook/self_query_hotel_search.ipynb

@@ -1227,7 +1227,7 @@
     }
    ],
    "source": [
-    "results = retriever.get_relevant_documents(\n",
+    "results = retriever.invoke(\n",
     "    \"I want to stay somewhere highly rated along the coast. I want a room with a patio and a fireplace.\"\n",
     ")\n",
     "for res in results:\n",

docs/.local_build.sh

@@ -19,6 +19,9 @@ poetry run python scripts/copy_templates.py
 wget -q https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md -O docs/langserve.md
 wget -q https://raw.githubusercontent.com/langchain-ai/langgraph/main/README.md -O docs/langgraph.md
 
-yarn
 
-poetry run quarto preview docs
+poetry run quarto render docs
+poetry run python scripts/generate_api_reference_links.py  --docs_dir docs
+
+yarn
+yarn start

docs/docs/get_started/quickstart.mdx

@@ -194,7 +194,7 @@ Prompt templates convert raw user input to better input to the LLM.
 ```python
 from langchain_core.prompts import ChatPromptTemplate
 prompt = ChatPromptTemplate.from_messages([
-    ("system", "You are world class technical documentation writer."),
+    ("system", "You are a world class technical documentation writer."),
     ("user", "{input}")
 ])
 ```

docs/docs/guides/productionization/safety/hugging_face_prompt_injection.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "This notebook shows how to prevent prompt injection attacks using the text classification model from `HuggingFace`.\n",
     "\n",
-    "By default, it uses a *[laiyer/deberta-v3-base-prompt-injection](https://huggingface.co/laiyer/deberta-v3-base-prompt-injection)* model trained to identify prompt injections. \n",
+    "By default, it uses a *[protectai/deberta-v3-base-prompt-injection-v2](https://huggingface.co/protectai/deberta-v3-base-prompt-injection-v2)* model trained to identify prompt injections. \n",
     "\n",
     "In this notebook, we will use the ONNX version of the model to speed up the inference. "
    ]
@@ -49,11 +49,15 @@
     "from optimum.onnxruntime import ORTModelForSequenceClassification\n",
     "from transformers import AutoTokenizer, pipeline\n",
     "\n",
-    "# Using https://huggingface.co/laiyer/deberta-v3-base-prompt-injection\n",
-    "model_path = \"laiyer/deberta-v3-base-prompt-injection\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
-    "tokenizer.model_input_names = [\"input_ids\", \"attention_mask\"]  # Hack to run the model\n",
-    "model = ORTModelForSequenceClassification.from_pretrained(model_path, subfolder=\"onnx\")\n",
+    "# Using https://huggingface.co/protectai/deberta-v3-base-prompt-injection-v2\n",
+    "model_path = \"laiyer/deberta-v3-base-prompt-injection-v2\"\n",
+    "revision = None  # We recommend specifiying the revision to avoid breaking changes or supply chain attacks\n",
+    "tokenizer = AutoTokenizer.from_pretrained(\n",
+    "    model_path, revision=revision, model_input_names=[\"input_ids\", \"attention_mask\"]\n",
+    ")\n",
+    "model = ORTModelForSequenceClassification.from_pretrained(\n",
+    "    model_path, revision=revision, subfolder=\"onnx\"\n",
+    ")\n",
     "\n",
     "classifier = pipeline(\n",
     "    \"text-classification\",\n",

docs/docs/integrations/chat/maritalk.ipynb

@@ -184,7 +184,7 @@
     "\n",
     "query = \"Qual o tempo máximo para realização da prova?\"\n",
     "\n",
-    "docs = retriever.get_relevant_documents(query)\n",
+    "docs = retriever.invoke(query)\n",
     "\n",
     "chain.invoke(\n",
     "    {\"input_documents\": docs, \"query\": query}\n",

docs/docs/integrations/document_loaders/docugami.ipynb

@@ -630,7 +630,7 @@
    ],
    "source": [
     "# Query retriever, should return parents (using MMR since that was set as search_type above)\n",
-    "retrieved_parent_docs = retriever.get_relevant_documents(\n",
+    "retrieved_parent_docs = retriever.invoke(\n",
     "    \"what signs does Birch Street allow on their property?\"\n",
     ")\n",
     "for chunk in retrieved_parent_docs:\n",

docs/docs/integrations/document_loaders/figma.ipynb

@@ -97,7 +97,7 @@
     "    # delete the gpt-4 model_name to use the default gpt-3.5 turbo for faster results\n",
     "    gpt_4 = ChatOpenAI(temperature=0.02, model_name=\"gpt-4\")\n",
     "    # Use the retriever's 'get_relevant_documents' method if needed to filter down longer docs\n",
-    "    relevant_nodes = figma_doc_retriever.get_relevant_documents(human_input)\n",
+    "    relevant_nodes = figma_doc_retriever.invoke(human_input)\n",
     "    conversation = [system_message_prompt, human_message_prompt]\n",
     "    chat_prompt = ChatPromptTemplate.from_messages(conversation)\n",
     "    response = gpt_4(\n",

docs/docs/integrations/document_loaders/google_drive.ipynb

@@ -50,7 +50,7 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_community.document_loaders import GoogleDriveLoader"
+    "from langchain_google_community import GoogleDriveLoader"
    ]
   },
   {
@@ -339,7 +339,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.document_loaders import GoogleDriveLoader\n",
+    "from langchain_google_community import GoogleDriveLoader\n",
     "\n",
     "loader = GoogleDriveLoader(\n",
     "    folder_id=folder_id,\n",

docs/docs/integrations/document_loaders/spreedly.ipynb

@@ -99,7 +99,7 @@
    ],
    "source": [
     "# Test the retriever\n",
-    "spreedly_doc_retriever.get_relevant_documents(\"CRC\")"
+    "spreedly_doc_retriever.invoke(\"CRC\")"
    ]
   },
   {

docs/docs/integrations/document_transformers/cross_encoder_reranker.ipynb

@@ -82,7 +82,7 @@
     ")\n",
     "\n",
     "query = \"What is the plan for the economy?\"\n",
-    "docs = retriever.get_relevant_documents(query)\n",
+    "docs = retriever.invoke(query)\n",
     "pretty_print_docs(docs)"
    ]
   },
@@ -162,9 +162,7 @@
     "    base_compressor=compressor, base_retriever=retriever\n",
     ")\n",
     "\n",
-    "compressed_docs = compression_retriever.get_relevant_documents(\n",
-    "    \"What is the plan for the economy?\"\n",
-    ")\n",
+    "compressed_docs = compression_retriever.invoke(\"What is the plan for the economy?\")\n",
     "pretty_print_docs(compressed_docs)"
    ]
   },

docs/docs/integrations/document_transformers/openvino_rerank.ipynb

@@ -350,7 +350,7 @@
     "retriever = FAISS.from_documents(texts, embedding).as_retriever(search_kwargs={\"k\": 20})\n",
     "\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = retriever.get_relevant_documents(query)\n",
+    "docs = retriever.invoke(query)\n",
     "pretty_print_docs(docs)"
    ]
   },
@@ -388,7 +388,7 @@
     "    base_compressor=ov_compressor, base_retriever=retriever\n",
     ")\n",
     "\n",
-    "compressed_docs = compression_retriever.get_relevant_documents(\n",
+    "compressed_docs = compression_retriever.invoke(\n",
     "    \"What did the president say about Ketanji Jackson Brown\"\n",
     ")\n",
     "print([doc.metadata[\"id\"] for doc in compressed_docs])"

docs/docs/integrations/document_transformers/voyageai-reranker.ipynb

@@ -320,7 +320,7 @@
     ").as_retriever(search_kwargs={\"k\": 20})\n",
     "\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = retriever.get_relevant_documents(query)\n",
+    "docs = retriever.invoke(query)\n",
     "pretty_print_docs(docs)"
    ]
   },
@@ -382,7 +382,7 @@
     "    base_compressor=compressor, base_retriever=retriever\n",
     ")\n",
     "\n",
-    "compressed_docs = compression_retriever.get_relevant_documents(\n",
+    "compressed_docs = compression_retriever.invoke(\n",
     "    \"What did the president say about Ketanji Jackson Brown\"\n",
     ")\n",
     "pretty_print_docs(compressed_docs)"

docs/docs/integrations/graphs/apache_age.ipynb

@@ -0,0 +1,689 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c94240f5",
+   "metadata": {},
+   "source": [
+    "# Apache AGE\n",
+    "\n",
+    ">[Apache AGE](https://age.apache.org/) is a PostgreSQL extension that provides graph database functionality. AGE is an acronym for A Graph Extension, and is inspired by Bitnine’s fork of PostgreSQL 10, AgensGraph, which is a multi-model database. The goal of the project is to create single storage that can handle both relational and graph model data so that users can use standard ANSI SQL along with openCypher, the Graph query language. The data elements `Apache AGE` stores are nodes, edges connecting them, and attributes of nodes and edges.\n",
+    "\n",
+    ">This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the `Cypher` query language.\n",
+    "\n",
+    ">[Cypher](https://en.wikipedia.org/wiki/Cypher_(query_language)) is a declarative graph query language that allows for expressive and efficient data querying in a property graph.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbc0ee68",
+   "metadata": {},
+   "source": [
+    "## Settin up\n",
+    "\n",
+    "You will need to have a running `Postgre` instance with the AGE extension installed. One option for testing is to run a docker container using the official AGE docker image.\n",
+    "You can run a local docker container by running the executing the following script:\n",
+    "\n",
+    "```\n",
+    "docker run \\\n",
+    "    --name age  \\\n",
+    "    -p 5432:5432 \\\n",
+    "    -e POSTGRES_USER=postgresUser \\\n",
+    "    -e POSTGRES_PASSWORD=postgresPW \\\n",
+    "    -e POSTGRES_DB=postgresDB \\\n",
+    "    -d \\\n",
+    "    apache/age\n",
+    "```\n",
+    "\n",
+    "Additional instructions on running in docker can be found [here](https://hub.docker.com/r/apache/age)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "62812aad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import GraphCypherQAChain\n",
+    "from langchain_community.graphs.age_graph import AGEGraph\n",
+    "from langchain_openai import ChatOpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0928915d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conf = {\n",
+    "    \"database\": \"postgresDB\",\n",
+    "    \"user\": \"postgresUser\",\n",
+    "    \"password\": \"postgresPW\",\n",
+    "    \"host\": \"localhost\",\n",
+    "    \"port\": 5432,\n",
+    "}\n",
+    "\n",
+    "graph = AGEGraph(graph_name=\"age_test\", conf=conf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "995ea9b9",
+   "metadata": {},
+   "source": [
+    "## Seeding the database\n",
+    "\n",
+    "Assuming your database is empty, you can populate it using Cypher query language. The following Cypher statement is idempotent, which means the database information will be the same if you run it one or multiple times."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "fedd26b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "graph.query(\n",
+    "    \"\"\"\n",
+    "MERGE (m:Movie {name:\"Top Gun\"})\n",
+    "WITH m\n",
+    "UNWIND [\"Tom Cruise\", \"Val Kilmer\", \"Anthony Edwards\", \"Meg Ryan\"] AS actor\n",
+    "MERGE (a:Actor {name:actor})\n",
+    "MERGE (a)-[:ACTED_IN]->(m)\n",
+    "\"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58c1a8ea",
+   "metadata": {},
+   "source": [
+    "## Refresh graph schema information\n",
+    "If the schema of database changes, you can refresh the schema information needed to generate Cypher statements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4e3de44f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph.refresh_schema()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1fe76ccd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "        Node properties are the following:\n",
+      "        [{'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Actor'}, {'properties': [{'property': 'property_a', 'type': 'STRING'}], 'labels': 'LabelA'}, {'properties': [], 'labels': 'LabelB'}, {'properties': [], 'labels': 'LabelC'}, {'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Movie'}]\n",
+      "        Relationship properties are the following:\n",
+      "        [{'properties': [], 'type': 'ACTED_IN'}, {'properties': [{'property': 'rel_prop', 'type': 'STRING'}], 'type': 'REL_TYPE'}]\n",
+      "        The relationships are the following:\n",
+      "        ['(:`Actor`)-[:`ACTED_IN`]->(:`Movie`)', '(:`LabelA`)-[:`REL_TYPE`]->(:`LabelB`)', '(:`LabelA`)-[:`REL_TYPE`]->(:`LabelC`)']\n",
+      "        \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(graph.schema)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68a3c677",
+   "metadata": {},
+   "source": [
+    "## Querying the graph\n",
+    "\n",
+    "We can now use the graph cypher QA chain to ask question of the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7476ce98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = GraphCypherQAChain.from_llm(\n",
+    "    ChatOpenAI(temperature=0), graph=graph, verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef8ee27b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generated Cypher:\n",
+      "\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie)\n",
+      "WHERE m.name = 'Top Gun'\n",
+      "RETURN a.name\u001b[0m\n",
+      "Full Context:\n",
+      "\u001b[32;1m\u001b[1;3m[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}]\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'query': 'Who played in Top Gun?',\n",
+       " 'result': 'Tom Cruise, Val Kilmer, Anthony Edwards, Meg Ryan played in Top Gun.'}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke(\"Who played in Top Gun?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d28c4df",
+   "metadata": {},
+   "source": [
+    "## Limit the number of results\n",
+    "You can limit the number of results from the Cypher QA Chain using the `top_k` parameter.\n",
+    "The default is 10."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "df230946",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = GraphCypherQAChain.from_llm(\n",
+    "    ChatOpenAI(temperature=0), graph=graph, verbose=True, top_k=2\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3f1600ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n",
+      "Generated Cypher:\n",
+      "\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})\n",
+      "RETURN a.name\u001b[0m\n",
+      "Full Context:\n",
+      "\u001b[32;1m\u001b[1;3m[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}]\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'query': 'Who played in Top Gun?',\n",
+       " 'result': 'Tom Cruise, Val Kilmer played in Top Gun.'}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke(\"Who played in Top Gun?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88c16206",
+   "metadata": {},
+   "source": [
+    "## Return intermediate results\n",
+    "You can return intermediate steps from the Cypher QA Chain using the `return_intermediate_steps` parameter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "e412f36b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = GraphCypherQAChain.from_llm(\n",
+    "    ChatOpenAI(temperature=0), graph=graph, verbose=True, return_intermediate_steps=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "4f4699dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n",
+      "Generated Cypher:\n",
+      "\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie)\n",
+      "WHERE m.name = 'Top Gun'\n",
+      "RETURN a.name\u001b[0m\n",
+      "Full Context:\n",
+      "\u001b[32;1m\u001b[1;3m[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}]\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "Intermediate steps: [{'query': \"MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)\\nWHERE m.name = 'Top Gun'\\nRETURN a.name\"}, {'context': [{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}]}]\n",
+      "Final answer: Tom Cruise, Val Kilmer, Anthony Edwards, Meg Ryan played in Top Gun.\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = chain(\"Who played in Top Gun?\")\n",
+    "print(f\"Intermediate steps: {result['intermediate_steps']}\")\n",
+    "print(f\"Final answer: {result['result']}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6e1b054",
+   "metadata": {},
+   "source": [
+    "## Return direct results\n",
+    "You can return direct results from the Cypher QA Chain using the `return_direct` parameter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2d3acf10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = GraphCypherQAChain.from_llm(\n",
+    "    ChatOpenAI(temperature=0), graph=graph, verbose=True, return_direct=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b0a9d143",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n",
+      "Generated Cypher:\n",
+      "\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})\n",
+      "RETURN a.name\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'query': 'Who played in Top Gun?',\n",
+       " 'result': [{'name': 'Tom Cruise'},\n",
+       "  {'name': 'Val Kilmer'},\n",
+       "  {'name': 'Anthony Edwards'},\n",
+       "  {'name': 'Meg Ryan'}]}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke(\"Who played in Top Gun?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f01dfb72-24ec-4ae7-883a-ee6646889b59",
+   "metadata": {},
+   "source": [
+    "## Add examples in the Cypher generation prompt\n",
+    "You can define the Cypher statement you want the LLM to generate for particular questions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "59baeb88-adfa-4c26-8334-fcbff3a98efb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts.prompt import PromptTemplate\n",
+    "\n",
+    "CYPHER_GENERATION_TEMPLATE = \"\"\"Task:Generate Cypher statement to query a graph database.\n",
+    "Instructions:\n",
+    "Use only the provided relationship types and properties in the schema.\n",
+    "Do not use any other relationship types or properties that are not provided.\n",
+    "Schema:\n",
+    "{schema}\n",
+    "Note: Do not include any explanations or apologies in your responses.\n",
+    "Do not respond to any questions that might ask anything else than for you to construct a Cypher statement.\n",
+    "Do not include any text except the generated Cypher statement.\n",
+    "Examples: Here are a few examples of generated Cypher statements for particular questions:\n",
+    "# How many people played in Top Gun?\n",
+    "MATCH (m:Movie {{title:\"Top Gun\"}})<-[:ACTED_IN]-()\n",
+    "RETURN count(*) AS numberOfActors\n",
+    "\n",
+    "The question is:\n",
+    "{question}\"\"\"\n",
+    "\n",
+    "CYPHER_GENERATION_PROMPT = PromptTemplate(\n",
+    "    input_variables=[\"schema\", \"question\"], template=CYPHER_GENERATION_TEMPLATE\n",
+    ")\n",
+    "\n",
+    "chain = GraphCypherQAChain.from_llm(\n",
+    "    ChatOpenAI(temperature=0),\n",
+    "    graph=graph,\n",
+    "    verbose=True,\n",
+    "    cypher_prompt=CYPHER_GENERATION_PROMPT,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "47c64027-cf42-493a-9c76-2d10ba753728",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generated Cypher:\n",
+      "\u001b[32;1m\u001b[1;3mMATCH (:Movie {name:\"Top Gun\"})<-[:ACTED_IN]-(:Actor)\n",
+      "RETURN count(*) AS numberOfActors\u001b[0m\n",
+      "Full Context:\n",
+      "\u001b[32;1m\u001b[1;3m[{'numberofactors': 4}]\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'query': 'How many people played in Top Gun?',\n",
+       " 'result': \"I don't know the answer.\"}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke(\"How many people played in Top Gun?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e721cad-aa87-4526-9231-2dfc0e365939",
+   "metadata": {},
+   "source": [
+    "## Use separate LLMs for Cypher and answer generation\n",
+    "You can use the `cypher_llm` and `qa_llm` parameters to define different llms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6f9becc2-f579-45bf-9b50-2ce02bde92da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = GraphCypherQAChain.from_llm(\n",
+    "    graph=graph,\n",
+    "    cypher_llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo\"),\n",
+    "    qa_llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-16k\"),\n",
+    "    verbose=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ff18e3e3-3402-4683-aec4-a19898f23ca1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generated Cypher:\n",
+      "\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie)\n",
+      "WHERE m.name = 'Top Gun'\n",
+      "RETURN a.name\u001b[0m\n",
+      "Full Context:\n",
+      "\u001b[32;1m\u001b[1;3m[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}]\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'query': 'Who played in Top Gun?',\n",
+       " 'result': 'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke(\"Who played in Top Gun?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eefea16b-508f-4552-8942-9d5063ed7d37",
+   "metadata": {},
+   "source": [
+    "## Ignore specified node and relationship types\n",
+    "\n",
+    "You can use `include_types` or `exclude_types` to ignore parts of the graph schema when generating Cypher statements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a20fa21e-fb85-41c4-aac0-53fb25e34604",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = GraphCypherQAChain.from_llm(\n",
+    "    graph=graph,\n",
+    "    cypher_llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo\"),\n",
+    "    qa_llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-16k\"),\n",
+    "    verbose=True,\n",
+    "    exclude_types=[\"Movie\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "3ad7f6b8-543e-46e4-a3b2-40fa3e66e895",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Node properties are the following:\n",
+      "Actor {name: STRING},LabelA {property_a: STRING},LabelB {},LabelC {}\n",
+      "Relationship properties are the following:\n",
+      "ACTED_IN {},REL_TYPE {rel_prop: STRING}\n",
+      "The relationships are the following:\n",
+      "(:LabelA)-[:REL_TYPE]->(:LabelB),(:LabelA)-[:REL_TYPE]->(:LabelC)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Inspect graph schema\n",
+    "print(chain.graph_schema)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0202e88-d700-40ed-aef9-0c969c7bf951",
+   "metadata": {},
+   "source": [
+    "## Validate generated Cypher statements\n",
+    "You can use the `validate_cypher` parameter to validate and correct relationship directions in generated Cypher statements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "53665d03-7afd-433c-bdd5-750127bfb152",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = GraphCypherQAChain.from_llm(\n",
+    "    llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo\"),\n",
+    "    graph=graph,\n",
+    "    verbose=True,\n",
+    "    validate_cypher=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "19e1a591-9c10-4d7b-aa36-a5e1b778a97b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n",
+      "Generated Cypher:\n",
+      "\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie)\n",
+      "WHERE m.name = 'Top Gun'\n",
+      "RETURN a.name\u001b[0m\n",
+      "Full Context:\n",
+      "\u001b[32;1m\u001b[1;3m[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}]\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'query': 'Who played in Top Gun?',\n",
+       " 'result': 'Tom Cruise, Val Kilmer, Anthony Edwards, Meg Ryan played in Top Gun.'}"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke(\"Who played in Top Gun?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/graphs/memgraph.ipynb

@@ -19,26 +19,22 @@
     "\n",
     "To complete this tutorial, you will need [Docker](https://www.docker.com/get-started/) and [Python 3.x](https://www.python.org/) installed.\n",
     "\n",
-    "Ensure you have a running `Memgraph` instance. You can download and run it in a local Docker container by executing the following script:\n",
+    "Ensure you have a running Memgraph instance. To quickly run Memgraph Platform (Memgraph database + MAGE library + Memgraph Lab) for the first time, do the following:\n",
+    "\n",
+    "On Linux/MacOS:\n",
     "```\n",
-    "docker run \\\n",
-    "    -it \\\n",
-    "    -p 7687:7687 \\\n",
-    "    -p 7444:7444 \\\n",
-    "    -p 3000:3000 \\\n",
-    "    -e MEMGRAPH=\"--bolt-server-name-for-init=Neo4j/\" \\\n",
-    "    -v mg_lib:/var/lib/memgraph memgraph/memgraph-platform\n",
+    "curl https://install.memgraph.com | sh\n",
     "```\n",
     "\n",
-    "You will need to wait a few seconds for the database to start. If the process is completed successfully, you should see something like this:\n",
+    "On Windows:\n",
     "```\n",
-    "mgconsole X.X\n",
-    "Connected to 'memgraph://127.0.0.1:7687'\n",
-    "Type :help for shell usage\n",
-    "Quit the shell by typing Ctrl-D(eof) or :quit\n",
-    "memgraph>\n",
+    "iwr https://windows.memgraph.com | iex\n",
     "```\n",
     "\n",
+    "Both commands run a script that downloads a Docker Compose file to your system, builds and starts `memgraph-mage` and `memgraph-lab` Docker services in two separate containers. \n",
+    "\n",
+    "Read more about the installation process on [Memgraph documentation](https://memgraph.com/docs/getting-started/install-memgraph).\n",
+    "\n",
     "Now you can start playing with `Memgraph`!"
    ]
   },
@@ -89,7 +85,7 @@
    "id": "95ba37a4",
    "metadata": {},
    "source": [
-    "We're utilizing the Python library [GQLAlchemy](https://github.com/memgraph/gqlalchemy) to establish a connection between our Memgraph database and Python script. To execute queries, we can set up a Memgraph instance as follows:"
+    "We're utilizing the Python library [GQLAlchemy](https://github.com/memgraph/gqlalchemy) to establish a connection between our Memgraph database and Python script. You can establish the connection to a running Memgraph instance with the Neo4j driver as well, since it's compatible with Memgraph. To execute queries with GQLAlchemy, we can set up a Memgraph instance as follows:"
    ]
   },
   {

docs/docs/integrations/platforms/google.mdx

@@ -4,6 +4,9 @@ All functionality related to [Google Cloud Platform](https://cloud.google.com/)
 
 ## LLMs
 
+We recommend individual developers to start with Gemini API (`langchain-google-genai`) and move to Vertex AI (`langchain-google-vertexai`) when they need access to commercial support and higher rate limits. If you’re already Cloud-friendly or Cloud-native, then you can get started in Vertex AI straight away.
+Please, find more information [here](https://ai.google.dev/gemini-api/docs/migrate-to-cloud).
+
 ### Google Generative AI
 
 Access GoogleAI `Gemini` models such as `gemini-pro` and `gemini-pro-vision` through the `GoogleGenerativeAI` class.
@@ -620,7 +623,7 @@ docai_wh_retriever = GoogleDocumentAIWarehouseRetriever(
     project_number=...
 )
 query = ...
-documents = docai_wh_retriever.get_relevant_documents(
+documents = docai_wh_retriever.invoke(
     query, user_ldap=...
 )
 ```

docs/docs/integrations/providers/cohere.mdx

@@ -83,7 +83,7 @@ from langchain.retrievers import CohereRagRetriever
 from langchain_core.documents import Document
 
 rag = CohereRagRetriever(llm=ChatCohere())
-print(rag.get_relevant_documents("What is cohere ai?"))
+print(rag.invoke("What is cohere ai?"))
 ```
 
 Usage of the Cohere [RAG Retriever](/docs/integrations/retrievers/cohere)

docs/docs/integrations/providers/metal.mdx

@@ -22,5 +22,5 @@ from metal_sdk.metal import Metal
 metal = Metal("API_KEY", "CLIENT_ID", "INDEX_ID");
 retriever = MetalRetriever(metal, params={"limit": 2})
 
-docs = retriever.get_relevant_documents("search term")
+docs = retriever.invoke("search term")
 ```

docs/docs/integrations/providers/ragatouille.ipynb

@@ -199,7 +199,7 @@
     "    base_compressor=RAG.as_langchain_document_compressor(), base_retriever=retriever\n",
     ")\n",
     "\n",
-    "compressed_docs = compression_retriever.get_relevant_documents(\n",
+    "compressed_docs = compression_retriever.invoke(\n",
     "    \"What animation studio did Miyazaki found\"\n",
     ")"
    ]

docs/docs/integrations/providers/vectara/vectara_chat.ipynb

@@ -154,9 +154,7 @@
     "openai_api_key = os.environ[\"OPENAI_API_KEY\"]\n",
     "llm = OpenAI(openai_api_key=openai_api_key, temperature=0)\n",
     "retriever = vectara.as_retriever()\n",
-    "d = retriever.get_relevant_documents(\n",
-    "    \"What did the president say about Ketanji Brown Jackson\", k=2\n",
-    ")\n",
+    "d = retriever.invoke(\"What did the president say about Ketanji Brown Jackson\", k=2)\n",
     "print(d)"
    ]
   },

docs/docs/integrations/retrievers/amazon_kendra_retriever.ipynb

@@ -69,7 +69,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "retriever.get_relevant_documents(\"what is langchain\")"
+    "retriever.invoke(\"what is langchain\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/arcee.ipynb

@@ -83,7 +83,7 @@
    "outputs": [],
    "source": [
     "query = \"Can AI-driven music therapy contribute to the rehabilitation of patients with disorders of consciousness?\"\n",
-    "documents = retriever.get_relevant_documents(query=query)"
+    "documents = retriever.invoke(query)"
    ]
   },
   {
@@ -108,7 +108,7 @@
     "]\n",
     "\n",
     "# Retrieve documents with filters and size params\n",
-    "documents = retriever.get_relevant_documents(query=query, size=5, filters=filters)"
+    "documents = retriever.invoke(query, size=5, filters=filters)"
    ]
   }
  ],

docs/docs/integrations/retrievers/arxiv.ipynb

@@ -97,7 +97,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "docs = retriever.get_relevant_documents(query=\"1605.08386\")"
+    "docs = retriever.invoke(\"1605.08386\")"
    ]
   },
   {
@@ -162,7 +162,7 @@
    },
    "outputs": [
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       " ········\n"

docs/docs/integrations/retrievers/azure_ai_search.ipynb

@@ -117,7 +117,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "retriever.get_relevant_documents(\"what is langchain?\")"
+    "retriever.invoke(\"what is langchain?\")"
    ]
   },
   {
@@ -263,7 +263,7 @@
     }
    ],
    "source": [
-    "retriever.get_relevant_documents(\"What is Azure OpenAI?\")"
+    "retriever.invoke(\"What is Azure OpenAI?\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/bedrock.ipynb

@@ -58,7 +58,7 @@
    "source": [
     "query = \"What did the president say about Ketanji Brown?\"\n",
     "\n",
-    "retriever.get_relevant_documents(query=query)"
+    "retriever.invoke(query)"
    ]
   },
   {

docs/docs/integrations/retrievers/bm25.ipynb

@@ -103,7 +103,7 @@
    },
    "outputs": [],
    "source": [
-    "result = retriever.get_relevant_documents(\"foo\")"
+    "result = retriever.invoke(\"foo\")"
    ]
   },
   {

docs/docs/integrations/retrievers/breebs.ipynb

@@ -64,7 +64,7 @@
    "source": [
     "breeb_key = \"Parivoyage\"\n",
     "retriever = BreebsRetriever(breeb_key)\n",
-    "documents = retriever.get_relevant_documents(\n",
+    "documents = retriever.invoke(\n",
     "    \"What are some unique, lesser-known spots to explore in Paris?\"\n",
     ")\n",
     "print(documents)"

docs/docs/integrations/retrievers/chaindesk.ipynb

@@ -83,7 +83,7 @@
     }
    ],
    "source": [
-    "retriever.get_relevant_documents(\"What is Daftpage?\")"
+    "retriever.invoke(\"What is Daftpage?\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/chatgpt-plugin.ipynb

@@ -150,7 +150,7 @@
     }
    ],
    "source": [
-    "retriever.get_relevant_documents(\"alice's phone number\")"
+    "retriever.invoke(\"alice's phone number\")"
    ]
   },
   {

docs/docs/integrations/retrievers/cohere-reranker.ipynb

@@ -314,7 +314,7 @@
     ")\n",
     "\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = retriever.get_relevant_documents(query)\n",
+    "docs = retriever.invoke(query)\n",
     "pretty_print_docs(docs)"
    ]
   },
@@ -344,7 +344,7 @@
     "    base_compressor=compressor, base_retriever=retriever\n",
     ")\n",
     "\n",
-    "compressed_docs = compression_retriever.get_relevant_documents(\n",
+    "compressed_docs = compression_retriever.invoke(\n",
     "    \"What did the president say about Ketanji Jackson Brown\"\n",
     ")\n",
     "pretty_print_docs(compressed_docs)"

docs/docs/integrations/retrievers/cohere.ipynb

@@ -118,7 +118,7 @@
     }
    ],
    "source": [
-    "_pretty_print(rag.get_relevant_documents(\"What is cohere ai?\"))"
+    "_pretty_print(rag.invoke(\"What is cohere ai?\"))"
    ]
   },
   {
@@ -172,7 +172,7 @@
     }
    ],
    "source": [
-    "_pretty_print(await rag.aget_relevant_documents(\"What is cohere ai?\"))  # async version"
+    "_pretty_print(await rag.ainvoke(\"What is cohere ai?\"))  # async version"
    ]
   },
   {
@@ -198,7 +198,7 @@
     }
    ],
    "source": [
-    "docs = rag.get_relevant_documents(\n",
+    "docs = rag.invoke(\n",
     "    \"Does langchain support cohere RAG?\",\n",
     "    source_documents=[\n",
     "        Document(page_content=\"Langchain supports cohere RAG!\"),\n",

docs/docs/integrations/retrievers/docarray_retriever.ipynb

@@ -143,7 +143,7 @@
     ")\n",
     "\n",
     "# find the relevant document\n",
-    "doc = retriever.get_relevant_documents(\"some query\")\n",
+    "doc = retriever.invoke(\"some query\")\n",
     "print(doc)"
    ]
   },
@@ -216,7 +216,7 @@
     ")\n",
     "\n",
     "# find the relevant document\n",
-    "doc = retriever.get_relevant_documents(\"some query\")\n",
+    "doc = retriever.invoke(\"some query\")\n",
     "print(doc)"
    ]
   },
@@ -313,7 +313,7 @@
     ")\n",
     "\n",
     "# find the relevant document\n",
-    "doc = retriever.get_relevant_documents(\"some query\")\n",
+    "doc = retriever.invoke(\"some query\")\n",
     "print(doc)"
    ]
   },
@@ -388,7 +388,7 @@
     ")\n",
     "\n",
     "# find the relevant document\n",
-    "doc = retriever.get_relevant_documents(\"some query\")\n",
+    "doc = retriever.invoke(\"some query\")\n",
     "print(doc)"
    ]
   },
@@ -481,7 +481,7 @@
     ")\n",
     "\n",
     "# find the relevant document\n",
-    "doc = retriever.get_relevant_documents(\"some query\")\n",
+    "doc = retriever.invoke(\"some query\")\n",
     "print(doc)"
    ]
   },
@@ -658,7 +658,7 @@
     ")\n",
     "\n",
     "# find the relevant document\n",
-    "doc = retriever.get_relevant_documents(\"movie about dreams\")\n",
+    "doc = retriever.invoke(\"movie about dreams\")\n",
     "print(doc)"
    ]
   },
@@ -700,7 +700,7 @@
     ")\n",
     "\n",
     "# find relevant documents\n",
-    "docs = retriever.get_relevant_documents(\"space travel\")\n",
+    "docs = retriever.invoke(\"space travel\")\n",
     "print(docs)"
    ]
   },
@@ -743,7 +743,7 @@
     ")\n",
     "\n",
     "# find relevant documents\n",
-    "docs = retriever.get_relevant_documents(\"action movies\")\n",
+    "docs = retriever.invoke(\"action movies\")\n",
     "print(docs)"
    ]
   },

docs/docs/integrations/retrievers/dria_index.ipynb

@@ -158,7 +158,7 @@
    "outputs": [],
    "source": [
     "query = \"Find information about Dria.\"\n",
-    "result = retriever.get_relevant_documents(query)\n",
+    "result = retriever.invoke(query)\n",
     "for doc in result:\n",
     "    print(doc)"
    ]

docs/docs/integrations/retrievers/elastic_search_bm25.ipynb

@@ -130,7 +130,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = retriever.get_relevant_documents(\"foo\")"
+    "result = retriever.invoke(\"foo\")"
    ]
   },
   {

docs/docs/integrations/retrievers/elasticsearch_retriever.ipynb

@@ -263,7 +263,7 @@
     "    url=es_url,\n",
     ")\n",
     "\n",
-    "vector_retriever.get_relevant_documents(\"foo\")"
+    "vector_retriever.invoke(\"foo\")"
    ]
   },
   {
@@ -313,7 +313,7 @@
     "    url=es_url,\n",
     ")\n",
     "\n",
-    "bm25_retriever.get_relevant_documents(\"foo\")"
+    "bm25_retriever.invoke(\"foo\")"
    ]
   },
   {
@@ -371,7 +371,7 @@
     "    url=es_url,\n",
     ")\n",
     "\n",
-    "hybrid_retriever.get_relevant_documents(\"foo\")"
+    "hybrid_retriever.invoke(\"foo\")"
    ]
   },
   {
@@ -424,7 +424,7 @@
     "    url=es_url,\n",
     ")\n",
     "\n",
-    "fuzzy_retriever.get_relevant_documents(\"fox\")  # note the character tolernace"
+    "fuzzy_retriever.invoke(\"fox\")  # note the character tolernace"
    ]
   },
   {
@@ -483,7 +483,7 @@
     "    url=es_url,\n",
     ")\n",
     "\n",
-    "filtering_retriever.get_relevant_documents(\"foo\")"
+    "filtering_retriever.invoke(\"foo\")"
    ]
   },
   {
@@ -541,7 +541,7 @@
     "    url=es_url,\n",
     ")\n",
     "\n",
-    "custom_mapped_retriever.get_relevant_documents(\"foo\")"
+    "custom_mapped_retriever.invoke(\"foo\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/embedchain.ipynb

@@ -194,9 +194,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = retriever.get_relevant_documents(\n",
-    "    \"How many companies does Elon Musk run and name those?\"\n",
-    ")"
+    "result = retriever.invoke(\"How many companies does Elon Musk run and name those?\")"
    ]
   },
   {

docs/docs/integrations/retrievers/flashrank-reranker.ipynb

@@ -328,7 +328,7 @@
     "retriever = FAISS.from_documents(texts, embedding).as_retriever(search_kwargs={\"k\": 20})\n",
     "\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = retriever.get_relevant_documents(query)\n",
+    "docs = retriever.invoke(query)\n",
     "pretty_print_docs(docs)"
    ]
   },
@@ -375,7 +375,7 @@
     "    base_compressor=compressor, base_retriever=retriever\n",
     ")\n",
     "\n",
-    "compressed_docs = compression_retriever.get_relevant_documents(\n",
+    "compressed_docs = compression_retriever.invoke(\n",
     "    \"What did the president say about Ketanji Jackson Brown\"\n",
     ")\n",
     "print([doc.metadata[\"id\"] for doc in compressed_docs])"

docs/docs/integrations/retrievers/fleet_context.ipynb

@@ -131,7 +131,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "vecstore_retriever.get_relevant_documents(\"How does the multi vector retriever work\")"
+    "vecstore_retriever.invoke(\"How does the multi vector retriever work\")"
    ]
   },
   {
@@ -176,7 +176,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "parent_retriever.get_relevant_documents(\"How does the multi vector retriever work\")"
+    "parent_retriever.invoke(\"How does the multi vector retriever work\")"
    ]
   },
   {

docs/docs/integrations/retrievers/google_drive.ipynb

@@ -114,7 +114,7 @@
    },
    "outputs": [],
    "source": [
-    "retriever.get_relevant_documents(\"machine learning\")"
+    "retriever.invoke(\"machine learning\")"
    ]
   },
   {
@@ -149,7 +149,7 @@
     "    template=\"gdrive-query\",  # Search everywhere\n",
     "    num_results=2,  # But take only 2 documents\n",
     ")\n",
-    "for doc in retriever.get_relevant_documents(\"machine learning\"):\n",
+    "for doc in retriever.invoke(\"machine learning\"):\n",
     "    print(\"---\")\n",
     "    print(doc.page_content.strip()[:60] + \"...\")"
    ]
@@ -187,7 +187,7 @@
     "    includeItemsFromAllDrives=False,\n",
     "    supportsAllDrives=False,\n",
     ")\n",
-    "for doc in retriever.get_relevant_documents(\"machine learning\"):\n",
+    "for doc in retriever.invoke(\"machine learning\"):\n",
     "    print(f\"{doc.metadata['name']}:\")\n",
     "    print(\"---\")\n",
     "    print(doc.page_content.strip()[:60] + \"...\")"
@@ -222,7 +222,7 @@
     "    includeItemsFromAllDrives=False,\n",
     "    supportsAllDrives=False,\n",
     ")\n",
-    "retriever.get_relevant_documents(\"machine learning\")"
+    "retriever.invoke(\"machine learning\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/google_vertex_ai_search.ipynb

@@ -198,7 +198,7 @@
    "source": [
     "query = \"What are Alphabet's Other Bets?\"\n",
     "\n",
-    "result = retriever.get_relevant_documents(query)\n",
+    "result = retriever.invoke(query)\n",
     "for doc in result:\n",
     "    print(doc)"
    ]
@@ -225,7 +225,7 @@
     "    get_extractive_answers=True,\n",
     ")\n",
     "\n",
-    "result = retriever.get_relevant_documents(query)\n",
+    "result = retriever.invoke(query)\n",
     "for doc in result:\n",
     "    print(doc)"
    ]
@@ -251,7 +251,7 @@
     "    engine_data_type=1,\n",
     ")\n",
     "\n",
-    "result = retriever.get_relevant_documents(query)\n",
+    "result = retriever.invoke(query)\n",
     "for doc in result:\n",
     "    print(doc)"
    ]
@@ -279,7 +279,7 @@
     "    engine_data_type=2,\n",
     ")\n",
     "\n",
-    "result = retriever.get_relevant_documents(query)\n",
+    "result = retriever.invoke(query)\n",
     "for doc in result:\n",
     "    print(doc)"
    ]
@@ -305,7 +305,7 @@
     "    engine_data_type=3,\n",
     ")\n",
     "\n",
-    "result = retriever.get_relevant_documents(query)\n",
+    "result = retriever.invoke(query)\n",
     "for doc in result:\n",
     "    print(doc)"
    ]
@@ -329,7 +329,7 @@
     "    project_id=PROJECT_ID, location_id=LOCATION_ID, data_store_id=DATA_STORE_ID\n",
     ")\n",
     "\n",
-    "result = retriever.get_relevant_documents(query)\n",
+    "result = retriever.invoke(query)\n",
     "for doc in result:\n",
     "    print(doc)"
    ]

docs/docs/integrations/retrievers/kay.ipynb

@@ -92,7 +92,7 @@
     "retriever = KayAiRetriever.create(\n",
     "    dataset_id=\"company\", data_types=[\"10-K\", \"10-Q\", \"PressRelease\"], num_contexts=3\n",
     ")\n",
-    "docs = retriever.get_relevant_documents(\n",
+    "docs = retriever.invoke(\n",
     "    \"What were the biggest strategy changes and partnerships made by Roku in 2023??\"\n",
     ")"
    ]

docs/docs/integrations/retrievers/knn.ipynb

@@ -62,7 +62,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = retriever.get_relevant_documents(\"foo\")"
+    "result = retriever.invoke(\"foo\")"
    ]
   },
   {

docs/docs/integrations/retrievers/llmlingua.ipynb

@@ -296,7 +296,7 @@
     "retriever = FAISS.from_documents(texts, embedding).as_retriever(search_kwargs={\"k\": 20})\n",
     "\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = retriever.get_relevant_documents(query)\n",
+    "docs = retriever.invoke(query)\n",
     "pretty_print_docs(docs)"
    ]
   },
@@ -350,7 +350,7 @@
     "    base_compressor=compressor, base_retriever=retriever\n",
     ")\n",
     "\n",
-    "compressed_docs = compression_retriever.get_relevant_documents(\n",
+    "compressed_docs = compression_retriever.invoke(\n",
     "    \"What did the president say about Ketanji Jackson Brown\"\n",
     ")\n",
     "pretty_print_docs(compressed_docs)"

docs/docs/integrations/retrievers/metal.ipynb

@@ -123,7 +123,7 @@
     }
    ],
    "source": [
-    "retriever.get_relevant_documents(\"foo1\")"
+    "retriever.invoke(\"foo1\")"
    ]
   },
   {

docs/docs/integrations/retrievers/outline.ipynb

@@ -109,7 +109,7 @@
     }
    ],
    "source": [
-    "retriever.get_relevant_documents(query=\"LangChain\", doc_content_chars_max=100)"
+    "retriever.invoke(\"LangChain\", doc_content_chars_max=100)"
    ]
   },
   {

docs/docs/integrations/retrievers/pinecone_hybrid_search.ipynb

@@ -295,7 +295,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = retriever.get_relevant_documents(\"foo\")"
+    "result = retriever.invoke(\"foo\")"
    ]
   },
   {

docs/docs/integrations/retrievers/pubmed.ipynb

@@ -53,7 +53,7 @@
     }
    ],
    "source": [
-    "retriever.get_relevant_documents(\"chatgpt\")"
+    "retriever.invoke(\"chatgpt\")"
    ]
   },
   {

docs/docs/integrations/retrievers/qdrant-sparse.ipynb

@@ -227,7 +227,7 @@
     }
    ],
    "source": [
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"Life and ethical dilemmas of AI\",\n",
     ")"
    ]

docs/docs/integrations/retrievers/re_phrase.ipynb

@@ -100,7 +100,7 @@
     }
    ],
    "source": [
-    "docs = retriever_from_llm.get_relevant_documents(\n",
+    "docs = retriever_from_llm.invoke(\n",
     "    \"Hi I'm Lance. What are the approaches to Task Decomposition?\"\n",
     ")"
    ]
@@ -120,7 +120,7 @@
     }
    ],
    "source": [
-    "docs = retriever_from_llm.get_relevant_documents(\n",
+    "docs = retriever_from_llm.invoke(\n",
     "    \"I live in San Francisco. What are the Types of Memory?\"\n",
     ")"
    ]
@@ -182,7 +182,7 @@
     }
    ],
    "source": [
-    "docs = retriever_from_llm_chain.get_relevant_documents(\n",
+    "docs = retriever_from_llm_chain.invoke(\n",
     "    \"Hi I'm Lance. What is Maximum Inner Product Search?\"\n",
     ")"
    ]

docs/docs/integrations/retrievers/self_query/activeloop_deeplake_self_query.ipynb

@@ -270,7 +270,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+    "retriever.invoke(\"What are some movies about dinosaurs\")"
    ]
   },
   {
@@ -300,7 +300,7 @@
    ],
    "source": [
     "# This example only specifies a filter\n",
-    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")\n",
+    "retriever.invoke(\"I want to watch a movie rated higher than 8.5\")\n",
     "\n",
     "# in case if this example errored out, consider installing libdeeplake manually: `pip install libdeeplake`, and then restart notebook."
    ]
@@ -331,7 +331,7 @@
    ],
    "source": [
     "# This example specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
+    "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
    ]
   },
   {
@@ -360,9 +360,7 @@
    ],
    "source": [
     "# This example specifies a composite filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What's a highly rated (above 8.5) science fiction film?\"\n",
-    ")"
+    "retriever.invoke(\"What's a highly rated (above 8.5) science fiction film?\")"
    ]
   },
   {
@@ -391,7 +389,7 @@
    ],
    "source": [
     "# This example specifies a query and composite filter\n",
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\n",
     ")"
    ]
@@ -457,7 +455,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
+    "retriever.invoke(\"what are two movies about dinosaurs\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/self_query/astradb.ipynb

@@ -192,7 +192,7 @@
    "outputs": [],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs?\")"
+    "retriever.invoke(\"What are some movies about dinosaurs?\")"
    ]
   },
   {
@@ -202,7 +202,7 @@
    "outputs": [],
    "source": [
     "# This example specifies a filter\n",
-    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
+    "retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
    ]
   },
   {
@@ -212,7 +212,7 @@
    "outputs": [],
    "source": [
     "# This example only specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
+    "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
    ]
   },
   {
@@ -222,9 +222,7 @@
    "outputs": [],
    "source": [
     "# This example specifies a composite filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What's a highly rated (above 8.5), science fiction movie ?\"\n",
-    ")"
+    "retriever.invoke(\"What's a highly rated (above 8.5), science fiction movie ?\")"
    ]
   },
   {
@@ -234,7 +232,7 @@
    "outputs": [],
    "source": [
     "# This example specifies a query and composite filter\n",
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"What's a movie about toys after 1990 but before 2005, and is animated\"\n",
     ")"
    ]
@@ -273,7 +271,7 @@
    "outputs": [],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are two movies about dinosaurs?\")"
+    "retriever.invoke(\"What are two movies about dinosaurs?\")"
    ]
   },
   {

docs/docs/integrations/retrievers/self_query/chroma_self_query.ipynb

@@ -232,7 +232,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+    "retriever.invoke(\"What are some movies about dinosaurs\")"
    ]
   },
   {
@@ -262,7 +262,7 @@
    ],
    "source": [
     "# This example only specifies a filter\n",
-    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
+    "retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
    ]
   },
   {
@@ -291,7 +291,7 @@
    ],
    "source": [
     "# This example specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
+    "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
    ]
   },
   {
@@ -320,9 +320,7 @@
    ],
    "source": [
     "# This example specifies a composite filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What's a highly rated (above 8.5) science fiction film?\"\n",
-    ")"
+    "retriever.invoke(\"What's a highly rated (above 8.5) science fiction film?\")"
    ]
   },
   {
@@ -351,7 +349,7 @@
    ],
    "source": [
     "# This example specifies a query and composite filter\n",
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\n",
     ")"
    ]
@@ -418,7 +416,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
+    "retriever.invoke(\"what are two movies about dinosaurs\")"
    ]
   },
   {

docs/docs/integrations/retrievers/self_query/dashvector.ipynb

@@ -269,7 +269,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+    "retriever.invoke(\"What are some movies about dinosaurs\")"
    ]
   },
   {
@@ -308,7 +308,7 @@
    ],
    "source": [
     "# This example only specifies a filter\n",
-    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
+    "retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
    ]
   },
   {
@@ -346,7 +346,7 @@
    ],
    "source": [
     "# This example specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
+    "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
    ]
   },
   {
@@ -384,9 +384,7 @@
    ],
    "source": [
     "# This example specifies a composite filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What's a highly rated (above 8.5) science fiction film?\"\n",
-    ")"
+    "retriever.invoke(\"What's a highly rated (above 8.5) science fiction film?\")"
    ]
   },
   {
@@ -468,7 +466,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
+    "retriever.invoke(\"what are two movies about dinosaurs\")"
    ]
   },
   {

docs/docs/integrations/retrievers/self_query/databricks_vector_search.ipynb

@@ -352,7 +352,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+    "retriever.invoke(\"What are some movies about dinosaurs\")"
    ]
   },
   {
@@ -384,7 +384,7 @@
    ],
    "source": [
     "# This example specifies a filter\n",
-    "retriever.get_relevant_documents(\"What are some highly rated movies (above 9)?\")"
+    "retriever.invoke(\"What are some highly rated movies (above 9)?\")"
    ]
   },
   {
@@ -416,7 +416,7 @@
    ],
    "source": [
     "# This example specifies both a relevant query and a filter\n",
-    "retriever.get_relevant_documents(\"What are the thriller movies that are highly rated?\")"
+    "retriever.invoke(\"What are the thriller movies that are highly rated?\")"
    ]
   },
   {
@@ -438,7 +438,7 @@
    ],
    "source": [
     "# This example specifies a query and composite filter\n",
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"What's a movie after 1990 but before 2005 that's all about dinosaurs, \\\n",
     "    and preferably has a lot of action\"\n",
     ")"
@@ -520,7 +520,7 @@
    },
    "outputs": [],
    "source": [
-    "retriever.get_relevant_documents(\"What are two movies about dinosaurs?\")"
+    "retriever.invoke(\"What are two movies about dinosaurs?\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/self_query/dingo.ipynb

@@ -265,7 +265,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+    "retriever.invoke(\"What are some movies about dinosaurs\")"
    ]
   },
   {
@@ -296,7 +296,7 @@
    ],
    "source": [
     "# This example only specifies a filter\n",
-    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
+    "retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
    ]
   },
   {
@@ -326,7 +326,7 @@
    ],
    "source": [
     "# This example specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
+    "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
    ]
   },
   {
@@ -357,9 +357,7 @@
    ],
    "source": [
     "# This example specifies a composite filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What's a highly rated (above 8.5) science fiction film?\"\n",
-    ")"
+    "retriever.invoke(\"What's a highly rated (above 8.5) science fiction film?\")"
    ]
   },
   {
@@ -389,7 +387,7 @@
    ],
    "source": [
     "# This example specifies a query and composite filter\n",
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\n",
     ")"
    ]
@@ -450,7 +448,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are two movies about dinosaurs\")"
+    "retriever.invoke(\"What are two movies about dinosaurs\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/self_query/elasticsearch_self_query.ipynb

@@ -197,7 +197,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+    "retriever.invoke(\"What are some movies about dinosaurs\")"
    ]
   },
   {
@@ -219,7 +219,7 @@
    ],
    "source": [
     "# This example specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
+    "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
    ]
   },
   {
@@ -275,7 +275,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
+    "retriever.invoke(\"what are two movies about dinosaurs\")"
    ]
   },
   {
@@ -305,7 +305,7 @@
     }
    ],
    "source": [
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"what animated or comedy movies have been released in the last 30 years about animated toys?\"\n",
     ")"
    ]

docs/docs/integrations/retrievers/self_query/milvus_self_query.ipynb

@@ -190,7 +190,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+    "retriever.invoke(\"What are some movies about dinosaurs\")"
    ]
   },
   {
@@ -219,7 +219,7 @@
    ],
    "source": [
     "# This example specifies a filter\n",
-    "retriever.get_relevant_documents(\"What are some highly rated movies (above 9)?\")"
+    "retriever.invoke(\"What are some highly rated movies (above 9)?\")"
    ]
   },
   {
@@ -248,9 +248,7 @@
    ],
    "source": [
     "# This example only specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"I want to watch a movie about toys rated higher than 9\"\n",
-    ")"
+    "retriever.invoke(\"I want to watch a movie about toys rated higher than 9\")"
    ]
   },
   {
@@ -278,9 +276,7 @@
    ],
    "source": [
     "# This example specifies a composite filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What's a highly rated (above or equal 9) thriller film?\"\n",
-    ")"
+    "retriever.invoke(\"What's a highly rated (above or equal 9) thriller film?\")"
    ]
   },
   {
@@ -308,7 +304,7 @@
    ],
    "source": [
     "# This example specifies a query and composite filter\n",
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"What's a movie after 1990 but before 2005 that's all about dinosaurs, \\\n",
     "    and preferably has a lot of action\"\n",
     ")"
@@ -367,7 +363,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are two movies about dinosaurs?\")"
+    "retriever.invoke(\"What are two movies about dinosaurs?\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/self_query/mongodb_atlas.ipynb

@@ -209,7 +209,7 @@
    "outputs": [],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+    "retriever.invoke(\"What are some movies about dinosaurs\")"
    ]
   },
   {
@@ -219,7 +219,7 @@
    "outputs": [],
    "source": [
     "# This example specifies a filter\n",
-    "retriever.get_relevant_documents(\"What are some highly rated movies (above 9)?\")"
+    "retriever.invoke(\"What are some highly rated movies (above 9)?\")"
    ]
   },
   {
@@ -229,9 +229,7 @@
    "outputs": [],
    "source": [
     "# This example only specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"I want to watch a movie about toys rated higher than 9\"\n",
-    ")"
+    "retriever.invoke(\"I want to watch a movie about toys rated higher than 9\")"
    ]
   },
   {
@@ -241,9 +239,7 @@
    "outputs": [],
    "source": [
     "# This example specifies a composite filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What's a highly rated (above or equal 9) thriller film?\"\n",
-    ")"
+    "retriever.invoke(\"What's a highly rated (above or equal 9) thriller film?\")"
    ]
   },
   {
@@ -253,7 +249,7 @@
    "outputs": [],
    "source": [
     "# This example specifies a query and composite filter\n",
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"What's a movie after 1990 but before 2005 that's all about dinosaurs, \\\n",
     "    and preferably has a lot of action\"\n",
     ")"
@@ -293,7 +289,7 @@
    "outputs": [],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are two movies about dinosaurs?\")"
+    "retriever.invoke(\"What are two movies about dinosaurs?\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/self_query/myscale_self_query.ipynb

@@ -216,7 +216,7 @@
    "outputs": [],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+    "retriever.invoke(\"What are some movies about dinosaurs\")"
    ]
   },
   {
@@ -227,7 +227,7 @@
    "outputs": [],
    "source": [
     "# This example only specifies a filter\n",
-    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
+    "retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
    ]
   },
   {
@@ -238,7 +238,7 @@
    "outputs": [],
    "source": [
     "# This example specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
+    "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
    ]
   },
   {
@@ -249,9 +249,7 @@
    "outputs": [],
    "source": [
     "# This example specifies a composite filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What's a highly rated (above 8.5) science fiction film?\"\n",
-    ")"
+    "retriever.invoke(\"What's a highly rated (above 8.5) science fiction film?\")"
    ]
   },
   {
@@ -262,7 +260,7 @@
    "outputs": [],
    "source": [
     "# This example specifies a query and composite filter\n",
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\n",
     ")"
    ]
@@ -285,7 +283,7 @@
    "outputs": [],
    "source": [
     "# You can use length(genres) to do anything you want\n",
-    "retriever.get_relevant_documents(\"What's a movie that have more than 1 genres?\")"
+    "retriever.invoke(\"What's a movie that have more than 1 genres?\")"
    ]
   },
   {
@@ -296,7 +294,7 @@
    "outputs": [],
    "source": [
     "# Fine-grained datetime? You got it already.\n",
-    "retriever.get_relevant_documents(\"What's a movie that release after feb 1995?\")"
+    "retriever.invoke(\"What's a movie that release after feb 1995?\")"
    ]
   },
   {
@@ -307,7 +305,7 @@
    "outputs": [],
    "source": [
     "# Don't know what your exact filter should be? Use string pattern match!\n",
-    "retriever.get_relevant_documents(\"What's a movie whose name is like Andrei?\")"
+    "retriever.invoke(\"What's a movie whose name is like Andrei?\")"
    ]
   },
   {
@@ -318,9 +316,7 @@
    "outputs": [],
    "source": [
     "# Contain works for lists: so you can match a list with contain comparator!\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What's a movie who has genres science fiction and adventure?\"\n",
-    ")"
+    "retriever.invoke(\"What's a movie who has genres science fiction and adventure?\")"
    ]
   },
   {
@@ -364,7 +360,7 @@
    "outputs": [],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
+    "retriever.invoke(\"what are two movies about dinosaurs\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/self_query/opensearch_self_query.ipynb

@@ -203,7 +203,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+    "retriever.invoke(\"What are some movies about dinosaurs\")"
    ]
   },
   {
@@ -233,7 +233,7 @@
    ],
    "source": [
     "# This example only specifies a filter\n",
-    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
+    "retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
    ]
   },
   {
@@ -262,7 +262,7 @@
    ],
    "source": [
     "# This example specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
+    "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
    ]
   },
   {
@@ -291,9 +291,7 @@
    ],
    "source": [
     "# This example specifies a composite filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What's a highly rated (above 8.5) science fiction film?\"\n",
-    ")"
+    "retriever.invoke(\"What's a highly rated (above 8.5) science fiction film?\")"
    ]
   },
   {
@@ -356,7 +354,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
+    "retriever.invoke(\"what are two movies about dinosaurs\")"
    ]
   },
   {
@@ -393,7 +391,7 @@
     }
    ],
    "source": [
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"what animated or comedy movies have been released in the last 30 years about animated toys?\"\n",
     ")"
    ]

docs/docs/integrations/retrievers/self_query/pgvector_self_query.ipynb

@@ -188,7 +188,7 @@
    "outputs": [],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+    "retriever.invoke(\"What are some movies about dinosaurs\")"
    ]
   },
   {
@@ -199,7 +199,7 @@
    "outputs": [],
    "source": [
     "# This example only specifies a filter\n",
-    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
+    "retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
    ]
   },
   {
@@ -210,7 +210,7 @@
    "outputs": [],
    "source": [
     "# This example specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
+    "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
    ]
   },
   {
@@ -221,9 +221,7 @@
    "outputs": [],
    "source": [
     "# This example specifies a composite filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What's a highly rated (above 8.5) science fiction film?\"\n",
-    ")"
+    "retriever.invoke(\"What's a highly rated (above 8.5) science fiction film?\")"
    ]
   },
   {
@@ -234,7 +232,7 @@
    "outputs": [],
    "source": [
     "# This example specifies a query and composite filter\n",
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\n",
     ")"
    ]
@@ -280,7 +278,7 @@
    "outputs": [],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
+    "retriever.invoke(\"what are two movies about dinosaurs\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/self_query/pinecone.ipynb

@@ -214,7 +214,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+    "retriever.invoke(\"What are some movies about dinosaurs\")"
    ]
   },
   {
@@ -244,7 +244,7 @@
    ],
    "source": [
     "# This example only specifies a filter\n",
-    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
+    "retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
    ]
   },
   {
@@ -273,7 +273,7 @@
    ],
    "source": [
     "# This example specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
+    "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
    ]
   },
   {
@@ -302,9 +302,7 @@
    ],
    "source": [
     "# This example specifies a composite filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What's a highly rated (above 8.5) science fiction film?\"\n",
-    ")"
+    "retriever.invoke(\"What's a highly rated (above 8.5) science fiction film?\")"
    ]
   },
   {
@@ -333,7 +331,7 @@
    ],
    "source": [
     "# This example specifies a query and composite filter\n",
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\n",
     ")"
    ]
@@ -375,7 +373,7 @@
    "outputs": [],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are two movies about dinosaurs\")"
+    "retriever.invoke(\"What are two movies about dinosaurs\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/self_query/qdrant_self_query.ipynb

@@ -214,7 +214,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+    "retriever.invoke(\"What are some movies about dinosaurs\")"
    ]
   },
   {
@@ -244,7 +244,7 @@
    ],
    "source": [
     "# This example only specifies a filter\n",
-    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
+    "retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
    ]
   },
   {
@@ -273,7 +273,7 @@
    ],
    "source": [
     "# This example specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
+    "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
    ]
   },
   {
@@ -302,9 +302,7 @@
    ],
    "source": [
     "# This example specifies a composite filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What's a highly rated (above 8.5) science fiction film?\"\n",
-    ")"
+    "retriever.invoke(\"What's a highly rated (above 8.5) science fiction film?\")"
    ]
   },
   {
@@ -333,7 +331,7 @@
    ],
    "source": [
     "# This example specifies a query and composite filter\n",
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\n",
     ")"
    ]
@@ -399,7 +397,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
+    "retriever.invoke(\"what are two movies about dinosaurs\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/self_query/redis_self_query.ipynb

@@ -279,7 +279,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+    "retriever.invoke(\"What are some movies about dinosaurs\")"
    ]
   },
   {
@@ -310,7 +310,7 @@
    ],
    "source": [
     "# This example only specifies a filter\n",
-    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.4\")"
+    "retriever.invoke(\"I want to watch a movie rated higher than 8.4\")"
    ]
   },
   {
@@ -339,7 +339,7 @@
    ],
    "source": [
     "# This example specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
+    "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
    ]
   },
   {
@@ -369,9 +369,7 @@
    ],
    "source": [
     "# This example specifies a composite filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What's a highly rated (above 8.5) science fiction film?\"\n",
-    ")"
+    "retriever.invoke(\"What's a highly rated (above 8.5) science fiction film?\")"
    ]
   },
   {
@@ -400,7 +398,7 @@
    ],
    "source": [
     "# This example specifies a query and composite filter\n",
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\n",
     ")"
    ]
@@ -465,7 +463,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
+    "retriever.invoke(\"what are two movies about dinosaurs\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/self_query/supabase_self_query.ipynb

@@ -374,7 +374,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+    "retriever.invoke(\"What are some movies about dinosaurs\")"
    ]
   },
   {
@@ -404,7 +404,7 @@
    ],
    "source": [
     "# This example only specifies a filter\n",
-    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
+    "retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
    ]
   },
   {
@@ -433,7 +433,7 @@
    ],
    "source": [
     "# This example specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women?\")"
+    "retriever.invoke(\"Has Greta Gerwig directed any movies about women?\")"
    ]
   },
   {
@@ -462,9 +462,7 @@
    ],
    "source": [
     "# This example specifies a composite filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What's a highly rated (above 8.5) science fiction film?\"\n",
-    ")"
+    "retriever.invoke(\"What's a highly rated (above 8.5) science fiction film?\")"
    ]
   },
   {
@@ -493,7 +491,7 @@
    ],
    "source": [
     "# This example specifies a query and composite filter\n",
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"What's a movie after 1990 but before (or on) 2005 that's all about toys, and preferably is animated\"\n",
     ")"
    ]
@@ -558,7 +556,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
+    "retriever.invoke(\"what are two movies about dinosaurs\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/self_query/tencentvectordb.ipynb

@@ -297,7 +297,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"movies about a superhero\")"
+    "retriever.invoke(\"movies about a superhero\")"
    ]
   },
   {
@@ -323,7 +323,7 @@
    ],
    "source": [
     "# This example only specifies a filter\n",
-    "retriever.get_relevant_documents(\"movies that were released after 2010\")"
+    "retriever.invoke(\"movies that were released after 2010\")"
    ]
   },
   {
@@ -349,9 +349,7 @@
    ],
    "source": [
     "# This example specifies both a relevant query and a filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"movies about a superhero which were released after 2010\"\n",
-    ")"
+    "retriever.invoke(\"movies about a superhero which were released after 2010\")"
    ]
   },
   {
@@ -413,7 +411,7 @@
     }
    ],
    "source": [
-    "retriever.get_relevant_documents(\"what are two movies about a superhero\")"
+    "retriever.invoke(\"what are two movies about a superhero\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/self_query/timescalevector_self_query.ipynb

@@ -334,7 +334,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+    "retriever.invoke(\"What are some movies about dinosaurs\")"
    ]
   },
   {
@@ -366,7 +366,7 @@
    ],
    "source": [
     "# This example only specifies a filter\n",
-    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
+    "retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
    ]
   },
   {
@@ -396,7 +396,7 @@
    ],
    "source": [
     "# This example specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
+    "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
    ]
   },
   {
@@ -426,9 +426,7 @@
    ],
    "source": [
     "# This example specifies a composite filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What's a highly rated (above 8.5) science fiction film?\"\n",
-    ")"
+    "retriever.invoke(\"What's a highly rated (above 8.5) science fiction film?\")"
    ]
   },
   {
@@ -457,7 +455,7 @@
    ],
    "source": [
     "# This example specifies a query and composite filter\n",
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\n",
     ")"
    ]
@@ -523,7 +521,7 @@
    ],
    "source": [
     "# This example specifies a query with a LIMIT value\n",
-    "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
+    "retriever.invoke(\"what are two movies about dinosaurs\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/self_query/vectara_self_query.ipynb

@@ -225,7 +225,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+    "retriever.invoke(\"What are some movies about dinosaurs\")"
    ]
   },
   {
@@ -248,7 +248,7 @@
    ],
    "source": [
     "# This example only specifies a filter\n",
-    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
+    "retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
    ]
   },
   {
@@ -270,7 +270,7 @@
    ],
    "source": [
     "# This example specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
+    "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
    ]
   },
   {
@@ -292,9 +292,7 @@
    ],
    "source": [
     "# This example specifies a composite filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What's a highly rated (above 8.5) science fiction film?\"\n",
-    ")"
+    "retriever.invoke(\"What's a highly rated (above 8.5) science fiction film?\")"
    ]
   },
   {
@@ -316,7 +314,7 @@
    ],
    "source": [
     "# This example specifies a query and composite filter\n",
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\n",
     ")"
    ]
@@ -374,7 +372,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
+    "retriever.invoke(\"what are two movies about dinosaurs\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/self_query/weaviate_self_query.ipynb

@@ -184,7 +184,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+    "retriever.invoke(\"What are some movies about dinosaurs\")"
    ]
   },
   {
@@ -213,7 +213,7 @@
    ],
    "source": [
     "# This example specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
+    "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
    ]
   },
   {
@@ -276,7 +276,7 @@
    ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
+    "retriever.invoke(\"what are two movies about dinosaurs\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/singlestoredb.ipynb

@@ -91,9 +91,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = retriever.get_relevant_documents(\n",
-    "    \"What did the president say about Ketanji Brown Jackson\"\n",
-    ")\n",
+    "result = retriever.invoke(\"What did the president say about Ketanji Brown Jackson\")\n",
     "print(docs[0].page_content)"
    ]
   }

docs/docs/integrations/retrievers/svm.ipynb

@@ -125,7 +125,7 @@
    },
    "outputs": [],
    "source": [
-    "result = retriever.get_relevant_documents(\"foo\")"
+    "result = retriever.invoke(\"foo\")"
    ]
   },
   {

docs/docs/integrations/retrievers/tf_idf.ipynb

@@ -105,7 +105,7 @@
    },
    "outputs": [],
    "source": [
-    "result = retriever.get_relevant_documents(\"foo\")"
+    "result = retriever.invoke(\"foo\")"
    ]
   },
   {
@@ -185,7 +185,7 @@
     }
    ],
    "source": [
-    "retriever_copy.get_relevant_documents(\"foo\")"
+    "retriever_copy.invoke(\"foo\")"
    ]
   },
   {

docs/docs/integrations/retrievers/thirdai_neuraldb.ipynb

@@ -95,7 +95,7 @@
    "outputs": [],
    "source": [
     "# This returns a list of LangChain Document objects\n",
-    "documents = retriever.get_relevant_documents(\"query\", top_k=10)"
+    "documents = retriever.invoke(\"query\", top_k=10)"
    ]
   },
   {

docs/docs/integrations/retrievers/vespa.ipynb

@@ -110,7 +110,7 @@
    },
    "outputs": [],
    "source": [
-    "retriever.get_relevant_documents(\"what is vespa?\")"
+    "retriever.invoke(\"what is vespa?\")"
    ]
   }
  ],

docs/docs/integrations/retrievers/weaviate-hybrid.ipynb

@@ -202,7 +202,7 @@
     }
    ],
    "source": [
-    "retriever.get_relevant_documents(\"the ethical implications of AI\")"
+    "retriever.invoke(\"the ethical implications of AI\")"
    ]
   },
   {
@@ -233,7 +233,7 @@
     }
    ],
    "source": [
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"AI integration in society\",\n",
     "    where_filter={\n",
     "        \"path\": [\"author\"],\n",
@@ -272,7 +272,7 @@
     }
    ],
    "source": [
-    "retriever.get_relevant_documents(\n",
+    "retriever.invoke(\n",
     "    \"AI integration in society\",\n",
     "    score=True,\n",
     ")"

docs/docs/integrations/retrievers/wikipedia.ipynb

@@ -98,7 +98,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "docs = retriever.get_relevant_documents(query=\"HUNTER X HUNTER\")"
+    "docs = retriever.invoke(\"HUNTER X HUNTER\")"
    ]
   },
   {

docs/docs/integrations/retrievers/zep_memorystore.ipynb

@@ -314,7 +314,7 @@
     "    api_key=zep_api_key,\n",
     ")\n",
     "\n",
-    "await zep_retriever.aget_relevant_documents(\"Who wrote Parable of the Sower?\")"
+    "await zep_retriever.ainvoke(\"Who wrote Parable of the Sower?\")"
    ]
   },
   {
@@ -355,7 +355,7 @@
     }
    ],
    "source": [
-    "zep_retriever.get_relevant_documents(\"Who wrote Parable of the Sower?\")"
+    "zep_retriever.invoke(\"Who wrote Parable of the Sower?\")"
    ]
   },
   {
@@ -407,7 +407,7 @@
     "    mmr_lambda=0.5,\n",
     ")\n",
     "\n",
-    "await zep_retriever.aget_relevant_documents(\"Who wrote Parable of the Sower?\")"
+    "await zep_retriever.ainvoke(\"Who wrote Parable of the Sower?\")"
    ]
   },
   {
@@ -445,9 +445,7 @@
    "source": [
     "filter = {\"where\": {\"jsonpath\": '$[*] ? (@.Label == \"WORK_OF_ART\")'}}\n",
     "\n",
-    "await zep_retriever.aget_relevant_documents(\n",
-    "    \"Who wrote Parable of the Sower?\", metadata=filter\n",
-    ")"
+    "await zep_retriever.ainvoke(\"Who wrote Parable of the Sower?\", metadata=filter)"
    ]
   },
   {
@@ -491,7 +489,7 @@
     "    mmr_lambda=0.5,\n",
     ")\n",
     "\n",
-    "await zep_retriever.aget_relevant_documents(\"Who wrote Parable of the Sower?\")"
+    "await zep_retriever.ainvoke(\"Who wrote Parable of the Sower?\")"
    ]
   },
   {

docs/docs/integrations/text_embedding/upstage.ipynb

@@ -187,7 +187,7 @@
     "    embedding=UpstageEmbeddings(),\n",
     ")\n",
     "retriever = vectorstore.as_retriever()\n",
-    "docs = retriever.get_relevant_documents(\"Where did Harrison work?\")\n",
+    "docs = retriever.invoke(\"Where did Harrison work?\")\n",
     "print(docs)"
    ]
   }

docs/docs/integrations/text_embedding/voyageai.ipynb

@@ -196,7 +196,7 @@
     "retriever = KNNRetriever.from_texts(documents, embeddings)\n",
     "\n",
     "# retrieve the most relevant documents\n",
-    "result = retriever.get_relevant_documents(query)\n",
+    "result = retriever.invoke(query)\n",
     "top1_retrieved_doc = result[0].page_content  # return the top1 retrieved result\n",
     "\n",
     "print(top1_retrieved_doc)"

docs/docs/integrations/vectorstores/chroma.ipynb

@@ -510,7 +510,7 @@
     }
    ],
    "source": [
-    "retriever.get_relevant_documents(query)[0]"
+    "retriever.invoke(query)[0]"
    ]
   },
   {

docs/docs/integrations/vectorstores/dingo.ipynb

@@ -197,7 +197,7 @@
    "outputs": [],
    "source": [
     "retriever = docsearch.as_retriever(search_type=\"mmr\")\n",
-    "matched_docs = retriever.get_relevant_documents(query)\n",
+    "matched_docs = retriever.invoke(query)\n",
     "for i, d in enumerate(matched_docs):\n",
     "    print(f\"\\n## Document {i}\\n\")\n",
     "    print(d.page_content)"

docs/docs/integrations/vectorstores/google_vertex_ai_vector_search.ipynb

@@ -487,7 +487,7 @@
    "outputs": [],
    "source": [
     "# perform simple similarity search on retriever\n",
-    "retriever.get_relevant_documents(\"What are my options in breathable fabric?\")"
+    "retriever.invoke(\"What are my options in breathable fabric?\")"
    ]
   },
   {
@@ -503,7 +503,7 @@
     "retriever.search_kwargs = {\"filter\": filters}\n",
     "\n",
     "# perform similarity search with filters on retriever\n",
-    "retriever.get_relevant_documents(\"What are my options in breathable fabric?\")"
+    "retriever.invoke(\"What are my options in breathable fabric?\")"
    ]
   },
   {
@@ -520,7 +520,7 @@
     "\n",
     "retriever.search_kwargs = {\"filter\": filters, \"numeric_filter\": numeric_filters}\n",
     "\n",
-    "retriever.get_relevant_documents(\"What are my options in breathable fabric?\")"
+    "retriever.invoke(\"What are my options in breathable fabric?\")"
    ]
   },
   {

docs/docs/integrations/vectorstores/milvus.ipynb

@@ -291,9 +291,9 @@
    ],
    "source": [
     "# This will only get documents for Ankush\n",
-    "vectorstore.as_retriever(\n",
-    "    search_kwargs={\"expr\": 'namespace == \"ankush\"'}\n",
-    ").get_relevant_documents(\"where did i work?\")"
+    "vectorstore.as_retriever(search_kwargs={\"expr\": 'namespace == \"ankush\"'}).invoke(\n",
+    "    \"where did i work?\"\n",
+    ")"
    ]
   },
   {
@@ -320,9 +320,9 @@
    ],
    "source": [
     "# This will only get documents for Harrison\n",
-    "vectorstore.as_retriever(\n",
-    "    search_kwargs={\"expr\": 'namespace == \"harrison\"'}\n",
-    ").get_relevant_documents(\"where did i work?\")"
+    "vectorstore.as_retriever(search_kwargs={\"expr\": 'namespace == \"harrison\"'}).invoke(\n",
+    "    \"where did i work?\"\n",
+    ")"
    ]
   },
   {

docs/docs/integrations/vectorstores/neo4jvector.ipynb

@@ -8,7 +8,7 @@
     "\n",
     ">[Neo4j](https://neo4j.com/) is an open-source graph database with integrated support for vector similarity search\n",
     "\n",
-    "It supports:\n",
+    "It supports:\n\n",
     "- approximate nearest neighbor search\n",
     "- Euclidean similarity and cosine similarity\n",
     "- Hybrid search combining vector and keyword searches\n",
@@ -694,7 +694,7 @@
    ],
    "source": [
     "retriever = store.as_retriever()\n",
-    "retriever.get_relevant_documents(query)[0]"
+    "retriever.invoke(query)[0]"
    ]
   },
   {

docs/docs/integrations/vectorstores/pinecone.ipynb

@@ -256,7 +256,7 @@
    ],
    "source": [
     "retriever = docsearch.as_retriever(search_type=\"mmr\")\n",
-    "matched_docs = retriever.get_relevant_documents(query)\n",
+    "matched_docs = retriever.invoke(query)\n",
     "for i, d in enumerate(matched_docs):\n",
     "    print(f\"\\n## Document {i}\\n\")\n",
     "    print(d.page_content)"