Merge branch 'openai_v0.3' into jacob/o1_structured_output

.github/scripts/check_diff.py

@@ -30,6 +30,9 @@
     # specifically in huggingface jobs
     # https://github.com/langchain-ai/langchain/issues/25558
     "huggingface",
+    # prompty exhibiting issues with numpy for Python 3.13
+    # https://github.com/langchain-ai/langchain/actions/runs/12651104685/job/35251034969?pr=29065
+    "prompty",
 ]
 
 PY_312_MAX_PACKAGES = [

.github/workflows/_test.yml

@@ -59,7 +59,8 @@ jobs:
         env:
           MIN_VERSIONS: ${{ steps.min-version.outputs.min-versions }}
         run: |
-          poetry run pip install $MIN_VERSIONS
+          poetry run pip install uv
+          poetry run uv pip install $MIN_VERSIONS
           make tests
         working-directory: ${{ inputs.working-directory }}
 

cookbook/README.md

@@ -50,7 +50,7 @@ Notebook | Description
 [press_releases.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/press_releases.ipynb) | Retrieve and query company press release data powered by [Kay.ai](https://kay.ai).
 [program_aided_language_model.i...](https://github.com/langchain-ai/langchain/tree/master/cookbook/program_aided_language_model.ipynb) | Implement program-aided language models as described in the provided research paper.
 [qa_citations.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/qa_citations.ipynb) | Different ways to get a model to cite its sources.
-[rag_upstage_layout_analysis_groundedness_check.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/rag_upstage_layout_analysis_groundedness_check.ipynb) | End-to-end RAG example using Upstage Layout Analysis and Groundedness Check.
+[rag_upstage_document_parse_groundedness_check.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/rag_upstage_document_parse_groundedness_check.ipynb) | End-to-end RAG example using Upstage Document Parse and Groundedness Check.
 [retrieval_in_sql.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/retrieval_in_sql.ipynb) | Perform retrieval-augmented-generation (rag) on a PostgreSQL database using pgvector.
 [sales_agent_with_context.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/sales_agent_with_context.ipynb) | Implement a context-aware ai sales agent, salesgpt, that can have natural sales conversations, interact with other systems, and use a product knowledge base to discuss a company's offerings.
 [self_query_hotel_search.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/self_query_hotel_search.ipynb) | Build a hotel room search feature with self-querying retrieval, using a specific hotel recommendation dataset.

cookbook/rag_upstage_layout_analysis_groundedness_check.ipynb → cookbook/rag_upstage_document_parse_groundedness_check.ipynb

@@ -4,8 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# RAG using Upstage Layout Analysis and Groundedness Check\n",
-    "This example illustrates RAG using [Upstage](https://python.langchain.com/docs/integrations/providers/upstage/) Layout Analysis and Groundedness Check."
+    "# RAG using Upstage Document Parse and Groundedness Check\n",
+    "This example illustrates RAG using [Upstage](https://python.langchain.com/docs/integrations/providers/upstage/) Document Parse and Groundedness Check."
    ]
   },
   {
@@ -23,16 +23,16 @@
     "from langchain_core.runnables.base import RunnableSerializable\n",
     "from langchain_upstage import (\n",
     "    ChatUpstage,\n",
+    "    UpstageDocumentParseLoader,\n",
     "    UpstageEmbeddings,\n",
     "    UpstageGroundednessCheck,\n",
-    "    UpstageLayoutAnalysisLoader,\n",
     ")\n",
     "\n",
     "model = ChatUpstage()\n",
     "\n",
     "files = [\"/PATH/TO/YOUR/FILE.pdf\", \"/PATH/TO/YOUR/FILE2.pdf\"]\n",
     "\n",
-    "loader = UpstageLayoutAnalysisLoader(file_path=files, split=\"element\")\n",
+    "loader = UpstageDocumentParseLoader(file_path=files, split=\"element\")\n",
     "\n",
     "docs = loader.load()\n",
     "\n",

docs/docs/concepts/lcel.mdx

@@ -140,7 +140,7 @@ is Equivalent to:
 chain = RunnableSequence([runnable1, runnable2])
 ```
 
-### The `.pipe` method`
+### The `.pipe` method
 
 If you have moral qualms with operator overloading, you can use the `.pipe` method instead. This is equivalent to the `|` operator.
 

docs/docs/concepts/streaming.mdx

@@ -39,7 +39,7 @@ In some cases, you may need to stream **custom data** that goes beyond the infor
 
 ## Streaming APIs
 
-LangChain two main APIs for streaming output in real-time. These APIs are supported by any component that implements the [Runnable Interface](/docs/concepts/runnables), including [LLMs](/docs/concepts/chat_models), [compiled LangGraph graphs](https://langchain-ai.github.io/langgraph/concepts/low_level/), and any Runnable generated with [LCEL](/docs/concepts/lcel).
+LangChain has two main APIs for streaming output in real-time. These APIs are supported by any component that implements the [Runnable Interface](/docs/concepts/runnables), including [LLMs](/docs/concepts/chat_models), [compiled LangGraph graphs](https://langchain-ai.github.io/langgraph/concepts/low_level/), and any Runnable generated with [LCEL](/docs/concepts/lcel).
 
 1. sync [stream](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.stream) and async [astream](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream): Use to stream outputs from individual Runnables (e.g., a chat model) as they are generated or stream any workflow created with LangGraph.
 2. The async only [astream_events](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream_events): Use this API to get access to custom events and intermediate outputs from LLM  applications built entirely with [LCEL](/docs/concepts/lcel). Note that this API is available, but not needed when working with LangGraph.

docs/docs/how_to/callbacks_custom_events.ipynb

@@ -15,7 +15,7 @@
     "- [Astream Events API](/docs/concepts/streaming/#astream_events) the `astream_events` method will surface custom callback events.\n",
     ":::\n",
     "\n",
-    "In some situations, you may want to dipsatch a custom callback event from within a [Runnable](/docs/concepts/runnables) so it can be surfaced\n",
+    "In some situations, you may want to dispatch a custom callback event from within a [Runnable](/docs/concepts/runnables) so it can be surfaced\n",
     "in a custom callback handler or via the [Astream Events API](/docs/concepts/streaming/#astream_events).\n",
     "\n",
     "For example, if you have a long running tool with multiple steps, you can dispatch custom events between the steps and use these custom events to monitor progress.\n",

docs/docs/how_to/custom_tools.ipynb

@@ -169,7 +169,7 @@
     "    return a * max(b)\n",
     "\n",
     "\n",
-    "multiply_by_max.args_schema.schema()"
+    "print(multiply_by_max.args_schema.model_json_schema())"
    ]
   },
   {
@@ -285,7 +285,7 @@
     "    return bar\n",
     "\n",
     "\n",
-    "foo.args_schema.schema()"
+    "print(foo.args_schema.model_json_schema())"
    ]
   },
   {

docs/docs/how_to/indexing.ipynb

@@ -61,7 +61,7 @@
     "   * document addition by id (`add_documents` method with `ids` argument)\n",
     "   * delete by id (`delete` method with `ids` argument)\n",
     "\n",
-    "Compatible Vectorstores: `Aerospike`, `AnalyticDB`, `AstraDB`, `AwaDB`, `AzureCosmosDBNoSqlVectorSearch`, `AzureCosmosDBVectorSearch`, `Bagel`, `Cassandra`, `Chroma`, `CouchbaseVectorStore`, `DashVector`, `DatabricksVectorSearch`, `DeepLake`, `Dingo`, `ElasticVectorSearch`, `ElasticsearchStore`, `FAISS`, `HanaDB`, `Milvus`, `MongoDBAtlasVectorSearch`, `MyScale`, `OpenSearchVectorSearch`, `PGVector`, `Pinecone`, `Qdrant`, `Redis`, `Rockset`, `ScaNN`, `SingleStoreDB`, `SupabaseVectorStore`, `SurrealDBStore`, `TimescaleVector`, `Vald`, `VDMS`, `Vearch`, `VespaStore`, `Weaviate`, `Yellowbrick`, `ZepVectorStore`, `TencentVectorDB`, `OpenSearchVectorSearch`.\n",
+    "Compatible Vectorstores: `Aerospike`, `AnalyticDB`, `AstraDB`, `AwaDB`, `AzureCosmosDBNoSqlVectorSearch`, `AzureCosmosDBVectorSearch`, `AzureSearch`, `Bagel`, `Cassandra`, `Chroma`, `CouchbaseVectorStore`, `DashVector`, `DatabricksVectorSearch`, `DeepLake`, `Dingo`, `ElasticVectorSearch`, `ElasticsearchStore`, `FAISS`, `HanaDB`, `Milvus`, `MongoDBAtlasVectorSearch`, `MyScale`, `OpenSearchVectorSearch`, `PGVector`, `Pinecone`, `Qdrant`, `Redis`, `Rockset`, `ScaNN`, `SingleStoreDB`, `SupabaseVectorStore`, `SurrealDBStore`, `TimescaleVector`, `Vald`, `VDMS`, `Vearch`, `VespaStore`, `Weaviate`, `Yellowbrick`, `ZepVectorStore`, `TencentVectorDB`, `OpenSearchVectorSearch`.\n",
     "  \n",
     "## Caution\n",
     "\n",

docs/docs/how_to/installation.mdx

@@ -31,7 +31,7 @@ By default, the dependencies needed to do that are NOT installed. You will need
 ## Ecosystem packages
 
 With the exception of the `langsmith` SDK, all packages in the LangChain ecosystem depend on `langchain-core`, which contains base
-classes and abstractions that other packages use. The dependency graph below shows how the difference packages are related.
+classes and abstractions that other packages use. The dependency graph below shows how the different packages are related.
 A directed arrow indicates that the source package depends on the target package:
 
 ![](/img/ecosystem_packages.png)
@@ -115,4 +115,4 @@ If you want to install a package from source, you can do so by cloning the [main
 pip install -e .
 ```
 
-LangGraph, LangSmith SDK, and certain integration packages live outside the main LangChain repo. You can see [all repos here](https://github.com/langchain-ai).
+LangGraph, LangSmith SDK, and certain integration packages live outside the main LangChain repo. You can see [all repos here](https://github.com/langchain-ai).

docs/docs/how_to/multi_vector.ipynb

@@ -292,7 +292,7 @@
    "id": "3faa9fde-1b09-4849-a815-8b2e89c30a02",
    "metadata": {},
    "source": [
-    "Note that we can [batch](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable) the chain accross documents:"
+    "Note that we can [batch](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable) the chain across documents:"
    ]
   },
   {

docs/docs/how_to/structured_output.ipynb

@@ -165,6 +165,8 @@
     }
    ],
    "source": [
+    "from typing import Optional\n",
+    "\n",
     "from typing_extensions import Annotated, TypedDict\n",
     "\n",
     "\n",
@@ -206,10 +208,10 @@
     {
      "data": {
       "text/plain": [
-        "{'setup': 'Why was the cat sitting on the computer?',\n",
-        " 'punchline': 'Because it wanted to keep an eye on the mouse!',\n",
-        " 'rating': 7}"
-       ]
+       "{'setup': 'Why was the cat sitting on the computer?',\n",
+       " 'punchline': 'Because it wanted to keep an eye on the mouse!',\n",
+       " 'rating': 7}"
+      ]
      },
      "execution_count": 4,
      "metadata": {},

docs/docs/integrations/chat/friendli.ipynb

@@ -2,10 +2,14 @@
  "cells": [
   {
    "cell_type": "raw",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
    "source": [
     "---\n",
-    "sidebar_label: Friendli\n",
+    "sidebar_label: ChatFriendli\n",
     "---"
    ]
   },
@@ -37,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,13 +61,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_community.chat_models.friendli import ChatFriendli\n",
     "\n",
-    "chat = ChatFriendli(model=\"llama-2-13b-chat\", max_tokens=100, temperature=0)"
+    "chat = ChatFriendli(model=\"meta-llama-3.1-8b-instruct\", max_tokens=100, temperature=0)"
    ]
   },
   {
@@ -84,16 +88,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\")"
+       "AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-d47c1056-54e8-4ea9-ad63-07cf74b834b7-0')"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -111,17 +115,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"),\n",
-       " AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\")]"
+       "[AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-36775b84-2a7a-48f0-8c68-df23ffffe4b2-0'),\n",
+       " AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-b204be41-bc06-4d3a-9f74-e66ab1e60e4f-0')]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -132,16 +136,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LLMResult(generations=[[ChatGeneration(text=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\", message=AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"))], [ChatGeneration(text=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\", message=AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"))]], llm_output={}, run=[RunInfo(run_id=UUID('a0c2d733-6971-4ae7-beea-653856f4e57c')), RunInfo(run_id=UUID('f3d35e44-ac9a-459a-9e4b-b8e3a73a91e1'))])"
+       "LLMResult(generations=[[ChatGeneration(text=\"Why don't eggs tell jokes? They'd crack each other up.\", message=AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-2e4cb949-8c51-40d5-92a0-cd0ac577db83-0'))], [ChatGeneration(text=\"Why don't eggs tell jokes? They'd crack each other up.\", message=AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-afcdd1be-463c-4e50-9731-7a9f5958e396-0'))]], llm_output={}, run=[RunInfo(run_id=UUID('2e4cb949-8c51-40d5-92a0-cd0ac577db83')), RunInfo(run_id=UUID('afcdd1be-463c-4e50-9731-7a9f5958e396'))], type='LLMResult')"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -152,18 +156,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " Knock, knock!\n",
-      "Who's there?\n",
-      "Cows go.\n",
-      "Cows go who?\n",
-      "MOO!"
+      "Why don't eggs tell jokes? They'd crack each other up."
      ]
     }
    ],
@@ -181,16 +181,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\")"
+       "AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-ba8062fb-68af-47b8-bd7b-d1e01b914744-0')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -201,17 +201,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"),\n",
-       " AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\")]"
+       "[AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-5d2c77ab-2637-45da-8bbe-1b1f18a22369-0'),\n",
+       " AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-f1338470-8b52-4d6e-9428-a694a08ae484-0')]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -222,16 +222,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LLMResult(generations=[[ChatGeneration(text=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\", message=AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"))], [ChatGeneration(text=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\", message=AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"))]], llm_output={}, run=[RunInfo(run_id=UUID('f2255321-2d8e-41cc-adbd-3f4facec7573')), RunInfo(run_id=UUID('fcc297d0-6ca9-48cb-9d86-e6f78cade8ee'))])"
+       "LLMResult(generations=[[ChatGeneration(text=\"Why don't eggs tell jokes? They'd crack each other up.\", message=AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-d4e44569-39cc-40cc-93fc-de53e599fd51-0'))], [ChatGeneration(text=\"Why don't eggs tell jokes? They'd crack each other up.\", message=AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-54647cc2-bee3-4154-ad00-2e547993e6d7-0'))]], llm_output={}, run=[RunInfo(run_id=UUID('d4e44569-39cc-40cc-93fc-de53e599fd51')), RunInfo(run_id=UUID('54647cc2-bee3-4154-ad00-2e547993e6d7'))], type='LLMResult')"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -242,18 +242,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " Knock, knock!\n",
-      "Who's there?\n",
-      "Cows go.\n",
-      "Cows go who?\n",
-      "MOO!"
+      "Why don't eggs tell jokes? They'd crack each other up."
      ]
     }
    ],
@@ -265,7 +261,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "langchain",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -279,7 +275,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,