docs: v02_url [rfc]

Thank you for contributing to LangChain!

- [x] **PR title**: docs: add Pinecone tab to [vector stores
page](https://python.langchain.com/v0.1/docs/modules/data_connection/vectorstores/).


- [x] **PR message**: Recreation of
https://github.com/langchain-ai/langchain/pull/21721.
Adds information about PineconeVectorStore to the LangChain vector
stores page. Although this page is deprecated, it still shows up
prominently in Google search results, so it will still be very helpful
to users to have correct information.
![search
results](https://github.com/langchain-ai/langchain/assets/19216250/e05d8d74-03da-44a1-b87f-0f8087d3c13a)


- [x] **Add tests and docs**: N/A


- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

.github/workflows/codespell.yml

@@ -3,9 +3,9 @@ name: CI / cd . / make spell_check
 
 on:
   push:
-    branches: [master]
+    branches: [master, v0.1]
   pull_request:
-    branches: [master]
+    branches: [master, v0.1]
 
 permissions:
   contents: read

docs/Makefile

@@ -13,7 +13,7 @@ OUTPUT_NEW_DOCS_DIR = $(OUTPUT_NEW_DIR)/docs
 
 PYTHON = .venv/bin/python
 
-PARTNER_DEPS_LIST := $(shell ls -1 ../libs/partners | grep -vE "airbyte|ibm" | xargs -I {} echo "../libs/partners/{}" | tr '\n' ' ')
+PARTNER_DEPS_LIST := $(shell find ../libs/partners -mindepth 1 -maxdepth 1 -type d -exec test -e "{}/pyproject.toml" \; -print | grep -vE "airbyte|ibm|ai21" | tr '\n' ' ')
 
 PORT ?= 3001
 
@@ -48,8 +48,6 @@ generate-files:
 	wget -q https://raw.githubusercontent.com/langchain-ai/langgraph/main/README.md -O $(INTERMEDIATE_DIR)/langgraph.md
 	$(PYTHON) scripts/resolve_local_links.py $(INTERMEDIATE_DIR)/langgraph.md https://github.com/langchain-ai/langgraph/tree/main/
 
-	$(PYTHON) scripts/generate_api_reference_links.py --docs_dir $(INTERMEDIATE_DIR)
-
 copy-infra:
 	mkdir -p $(OUTPUT_NEW_DIR)
 	cp -r src $(OUTPUT_NEW_DIR)
@@ -68,7 +66,20 @@ render:
 md-sync:
 	rsync -avm --include="*/" --include="*.mdx" --include="*.md" --include="*.png" --exclude="*" $(INTERMEDIATE_DIR)/ $(OUTPUT_NEW_DOCS_DIR)
 
-build: install-py-deps generate-files copy-infra render md-sync
+generate-references:
+	$(PYTHON) scripts/generate_api_reference_links.py --docs_dir $(OUTPUT_NEW_DOCS_DIR)
+
+build: install-py-deps generate-files copy-infra render md-sync generate-references
+
+vercel-build: install-vercel-deps build
+	rm -rf docs
+	mv $(OUTPUT_NEW_DOCS_DIR) docs
+	rm -rf build
+	yarn run docusaurus build
+	mv build v0.1
+	mkdir build
+	mv v0.1 build
+	mv build/v0.1/404.html build
 
 start:
 	cd $(OUTPUT_NEW_DIR) && yarn && yarn start --port=$(PORT)

docs/docs/expression_language/how_to/decorator.ipynb

@@ -7,9 +7,9 @@
    "source": [
     "# Create a runnable with the @chain decorator\n",
     "\n",
-    "You can also turn an arbitrary function into a chain by adding a `@chain` decorator. This is functionaly equivalent to wrapping in a [`RunnableLambda`](/docs/expression_language/primitives/functions).\n",
+    "You can also turn an arbitrary function into a chain by adding a `@chain` decorator. This is functionally equivalent to wrapping in a [`RunnableLambda`](/docs/expression_language/primitives/functions).\n",
     "\n",
-    "This will have the benefit of improved observability by tracing your chain correctly. Any calls to runnables inside this function will be traced as nested childen.\n",
+    "This will have the benefit of improved observability by tracing your chain correctly. Any calls to runnables inside this function will be traced as nested children.\n",
     "\n",
     "It will also allow you to use this as any other runnable, compose it in chain, etc.\n",
     "\n",

docs/docs/get_started/introduction.mdx

@@ -13,12 +13,13 @@ LangChain simplifies every stage of the LLM application lifecycle:
 - **Deployment**: Turn any chain into an API with [LangServe](/docs/langserve).
 
 import ThemedImage from '@theme/ThemedImage';
+import useBaseUrl from '@docusaurus/useBaseUrl';
 
 <ThemedImage
   alt="Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers."
   sources={{
-    light: '/svg/langchain_stack.svg',
-    dark: '/svg/langchain_stack_dark.svg',
+    light: useBaseUrl('/svg/langchain_stack.svg'),
+    dark: useBaseUrl('/svg/langchain_stack_dark.svg'),
   }}
   title="LangChain Framework Overview"
 />

docs/docs/guides/productionization/evaluation/comparison/index.mdx

@@ -17,7 +17,7 @@ Here's a summary of the key methods and properties of a comparison evaluator:
 - `requires_reference`: This property specifies whether this evaluator requires a reference label.
 
 :::note LangSmith Support
-The [run_on_dataset](https://api.python.langchain.com/en/latest/langchain_api_reference.html#module-langchain.smith) evaluation method is designed to evaluate only a single model at a time, and thus, doesn't support these evaluators.
+Pairwise evaluations are supported in LangSmith via the [`evaluate_comparative`](https://docs.smith.langchain.com/how_to_guides/evaluation/evaluate_pairwise) function.
 :::
 
 Detailed information about creating custom evaluators and the available built-in comparison evaluators is provided in the following sections.

docs/docs/integrations/chat/anthropic.ipynb

@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "238bdbaa-526a-4130-89e9-523aa44bb196",
    "metadata": {},
    "outputs": [],
@@ -250,16 +250,7 @@
    "execution_count": 3,
    "id": "42f87466-cb8e-490d-a9f8-aa0f8e9b4217",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/bagatur/langchain/libs/core/langchain_core/_api/beta_decorator.py:87: LangChainBetaWarning: The function `bind_tools` is in beta. It is actively being worked on, so the API may change.\n",
-      "  warn_beta(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
@@ -369,13 +360,49 @@
    "id": "90e015e0-c6e5-4ff5-8fb9-be0cd3c86395",
    "metadata": {},
    "source": [
-    "::: {.callout-tip}\n",
+    ":::tip\n",
     "\n",
     "ChatAnthropic model outputs are always a single AI message that can have either a single string or a list of content blocks. The content blocks can be text blocks or tool-duse blocks. There can be multiple of each and they can be interspersed.\n",
     "\n",
     ":::"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b5145dea-0183-4cab-b9e2-0e35fb8370cf",
+   "metadata": {},
+   "source": [
+    "### Forcing tool calls\n",
+    "\n",
+    "By default the model can choose whether to call any tools. To force the model to call at least one tool we can specify `bind_tools(..., tool_choice=\"any\")` and to force the model to call a specific tool we can pass in that tool name `bind_tools(..., tool_choice=\"GetWeather\")`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "05993626-060c-449f-8069-e52d31442977",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'GetWeather',\n",
+       "  'args': {'location': '<UNKNOWN>'},\n",
+       "  'id': 'toolu_01DwWjKzHPs6EHCUPxsGm9bN'}]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm_with_force_tools = llm.bind_tools([GetWeather], tool_choice=\"GetWeather\")\n",
+    "# Notice the model will still return tool calls despite a message that\n",
+    "# doesn't have anything to do with the tools.\n",
+    "llm_with_force_tools.invoke(\"this doesn't really require tool use\").tool_calls"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "8652ee98-814c-4ed6-9def-275eeaa9651e",

docs/docs/integrations/chat/openai.ipynb

@@ -147,7 +147,7 @@
     "\n",
     "### ChatOpenAI.bind_tools()\n",
     "\n",
-    "With `ChatAnthropic.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to an Anthropic tool schemas, which looks like:\n",
+    "With `ChatOpenAI.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to an OpenAI tool schemas, which looks like:\n",
     "```\n",
     "{\n",
     "    \"name\": \"...\",\n",

docs/docs/modules/data_connection/vectorstores/index.mdx

@@ -1,6 +1,7 @@
 ---
 sidebar_position: 3
 sidebar_class_name: hidden
+v02_url: /v0.2/docs/how_to/vectorstores/
 ---
 # Vector stores
 
@@ -56,6 +57,50 @@ documents = text_splitter.split_documents(raw_documents)
 db = Chroma.from_documents(documents, OpenAIEmbeddings())
 ```
 
+  </TabItem>
+  <TabItem value="pinecone" label="Pinecone">
+
+This walkthrough uses the `Pinecone` vector database, which provides broad functionality to store and search over vectors.
+
+```bash
+pip install langchain-pinecone
+```
+
+We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
+
+```python
+import os
+import getpass
+
+os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
+```
+
+```python
+from langchain_community.document_loaders import TextLoader
+from langchain_openai import OpenAIEmbeddings
+from langchain_text_splitters import CharacterTextSplitter
+
+# Load the document, split it into chunks, and embed each chunk.
+loader = TextLoader("../../modules/state_of_the_union.txt")
+documents = loader.load()
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+docs = text_splitter.split_documents(documents)
+
+embeddings = OpenAIEmbeddings()
+```
+
+Next, go to the [Pinecone console](https://app.pinecone.io) and create a new index with `dimension=1536` called "langchain-test-index". Then, copy the API key and index name.
+
+```python
+from langchain_pinecone import PineconeVectorStore
+
+os.environ['PINECONE_API_KEY'] = '<YOUR_PINECONE_API_KEY>'
+
+index_name = "langchain-test-index"
+
+# Connect to Pinecone index and insert the chunked docs as contents
+docsearch = PineconeVectorStore.from_documents(docs, embeddings, index_name=index_name)
+```
   </TabItem>
   <TabItem value="faiss" label="FAISS">
 
@@ -280,4 +325,4 @@ I’ve worked on these issues a long time.
 I know what works: Investing in crime prevention and community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.
 ```
 
-</CodeOutputBlock>
+</CodeOutputBlock>

docs/docs/use_cases/apis.ipynb

@@ -16,7 +16,7 @@
    "id": "a15e6a18",
    "metadata": {},
    "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/apis.ipynb)\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/v0.1/docs/docs/use_cases/apis.ipynb)\n",
     "\n",
     "## Use case \n",
     "\n",

docs/docs/use_cases/code_understanding.ipynb

@@ -14,7 +14,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/code_understanding.ipynb)\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/v0.1/docs/docs/use_cases/code_understanding.ipynb)\n",
     "\n",
     "## Use case\n",
     "\n",

docs/docs/use_cases/data_generation.ipynb

@@ -17,7 +17,7 @@
    "id": "aa3571cc",
    "metadata": {},
    "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/data_generation.ipynb)\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/v0.1/docs/docs/use_cases/data_generation.ipynb)\n",
     "\n",
     "## Use case\n",
     "\n",

docs/docs/use_cases/extraction/index.ipynb

@@ -56,9 +56,9 @@
     "\n",
     "Head to the [Guidelines](/docs/use_cases/extraction/guidelines) page to see a list of opinionated guidelines on how to get the best performance for extraction use cases.\n",
     "\n",
-    "## Use Case Accelerant\n",
+    "## Reference Application\n",
     "\n",
-    "[langchain-extract](https://github.com/langchain-ai/langchain-extract) is a starter repo that implements a simple web server for information extraction from text and files using LLMs. It is build using **FastAPI**, **LangChain** and **Postgresql**. Feel free to adapt it to your own use cases.\n",
+    "[langchain-extract](https://github.com/langchain-ai/langchain-extract) is a starter repo that implements a simple web server for information extraction from text and files using LLMs. It is built using **FastAPI**, **LangChain** and **Postgresql**. Feel free to adapt it to your own use cases.\n",
     "\n",
     "## Other Resources\n",
     "\n",

docs/docs/use_cases/summarization.ipynb

@@ -16,7 +16,7 @@
    "id": "cf13f702",
    "metadata": {},
    "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/summarization.ipynb)\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/v0.1/docs/docs/use_cases/summarization.ipynb)\n",
     "\n",
     "## Use case\n",
     "\n",

docs/docs/use_cases/tagging.ipynb

@@ -16,7 +16,7 @@
    "id": "a0507a4b",
    "metadata": {},
    "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/tagging.ipynb)\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/v0.1/docs/docs/use_cases/tagging.ipynb)\n",
     "\n",
     "## Use case\n",
     "\n",

docs/docs/use_cases/web_scraping.ipynb

@@ -16,7 +16,7 @@
    "id": "6605e7f7",
    "metadata": {},
    "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/web_scraping.ipynb)\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/v0.1/docs/docs/use_cases/web_scraping.ipynb)\n",
     "\n",
     "## Use case\n",
     "\n",

docs/docusaurus.config.js

@@ -9,6 +9,8 @@ require("dotenv").config();
 const baseLightCodeBlockTheme = require("prism-react-renderer/themes/vsLight");
 const baseDarkCodeBlockTheme = require("prism-react-renderer/themes/vsDark");
 
+const baseUrl = "/v0.1/";
+
 /** @type {import('@docusaurus/types').Config} */
 const config = {
   title: "🦜️🔗 LangChain",
@@ -18,7 +20,7 @@ const config = {
   url: "https://python.langchain.com",
   // Set the /<baseUrl>/ pathname under which your site is served
   // For GitHub pages deployment, it is often '/<projectName>/'
-  baseUrl: "/",
+  baseUrl: baseUrl,
   trailingSlash: true,
   onBrokenLinks: "throw",
   onBrokenMarkdownLinks: "throw",
@@ -118,6 +120,10 @@ const config = {
   themeConfig:
     /** @type {import('@docusaurus/preset-classic').ThemeConfig} */
     ({
+      announcementBar: {
+        content: 'LangChain v0.2 is out! You are currently viewing the old v0.1 docs. <strong>View the latest docs <a href="/v0.2/docs/introduction/">here</a>.</strong>',
+        isCloseable: true,
+      },
       docs: {
         sidebar: {
           hideable: true,
@@ -165,7 +171,7 @@ const config = {
             position: "left",
           },
           {
-            href: "https://api.python.langchain.com",
+            href: "https://api.python.langchain.com/en/v0.1/",
             label: "API Reference",
             position: "left",
           },
@@ -205,6 +211,21 @@ const config = {
               },
             ]
           },
+          {
+            type: "dropdown",
+            label: "v0.1",
+            position: "right",
+            items: [
+              {
+                label: "v0.2",
+                href: "https://python.langchain.com/v0.2/docs/introduction"
+              },
+              {
+                label: "v0.1",
+                href: "/docs/get_started/introduction"
+              }
+            ]
+          },
           {
             type: "dropdown",
             label: "🦜️🔗",
@@ -318,7 +339,7 @@ const config = {
     }),
 
   scripts: [
-    "/js/google_analytics.js",
+    baseUrl + "js/google_analytics.js",
     {
       src: "https://www.googletagmanager.com/gtag/js?id=G-9B66JQQH2F",
       async: true,

docs/package.json

@@ -5,7 +5,7 @@
   "scripts": {
     "docusaurus": "docusaurus",
     "start": "rm -rf ./docs/api && docusaurus start",
-    "build": "bash vercel_build.sh && rm -rf ./build && docusaurus build",
+    "build": "make vercel-build",
     "swizzle": "docusaurus swizzle",
     "deploy": "docusaurus deploy",
     "clear": "docusaurus clear",

docs/scripts/notebook_convert.py

@@ -7,7 +7,7 @@ from typing import Iterable, Tuple
 
 import nbformat
 from nbconvert.exporters import MarkdownExporter
-from nbconvert.preprocessors import Preprocessor, RegexRemovePreprocessor
+from nbconvert.preprocessors import Preprocessor
 
 
 class EscapePreprocessor(Preprocessor):
@@ -27,7 +27,9 @@ class EscapePreprocessor(Preprocessor):
                 )
             # rewrite .ipynb links to .md
             cell.source = re.sub(
-                r"\[([^\]]*)\]\(([^)]*).ipynb\)", r"[\1](\2.md)", cell.source
+                r"\[([^\]]*)\]\((?![^\)]*//)([^)]*)\.ipynb\)",
+                r"[\1](\2.md)",
+                cell.source,
             )
         return cell, resources
 
@@ -79,11 +81,26 @@ class ExtractAttachmentsPreprocessor(Preprocessor):
         return cell, resources
 
 
+class CustomRegexRemovePreprocessor(Preprocessor):
+    def check_conditions(self, cell):
+        pattern = re.compile(r"(?s)(?:\s*\Z)|(?:.*#\s*\|\s*output:\s*false.*)")
+        rtn = not pattern.match(cell.source)
+        if not rtn:
+            return False
+        else:
+            return True
+
+    def preprocess(self, nb, resources):
+        nb.cells = [cell for cell in nb.cells if self.check_conditions(cell)]
+
+        return nb, resources
+
+
 exporter = MarkdownExporter(
     preprocessors=[
         EscapePreprocessor,
         ExtractAttachmentsPreprocessor,
-        RegexRemovePreprocessor(patterns=[r"^\s*$"]),
+        CustomRegexRemovePreprocessor,
     ],
     template_name="mdoutput",
     extra_template_basedirs=["./scripts/notebook_convert_templates"],

docs/src/css/custom.css

@@ -243,3 +243,17 @@ nav, h1, h2, h3, h4 {
   background: url("data:image/svg+xml,%3Csvg viewBox='0 0 24 24' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill='white' d='M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12'/%3E%3C/svg%3E")
     no-repeat;
 }
+
+div[class^=announcementBar_] {
+  height:40px !important;
+  font-size: 20px !important;
+}
+
+[data-theme='dark'] div[class^=announcementBar_] {
+  background-color: #1b1b1b;
+  color: #fff;
+}
+
+[data-theme='dark'] div[class^=announcementBar_] button {
+  color: #fff;
+}

docs/src/pages/index.js

@@ -9,7 +9,8 @@
 
 import React from "react";
 import { Redirect } from "@docusaurus/router";
+import useBaseUrl from "@docusaurus/useBaseUrl";
 
 export default function Home() {
-  return <Redirect to="docs/get_started/introduction" />;
+  return <Redirect to={useBaseUrl("/docs/get_started/introduction")} />;
 }

docs/vercel_requirements.txt

@@ -1,6 +1,6 @@
+-e ../libs/core
 -e ../libs/langchain
 -e ../libs/community
--e ../libs/core
 -e ../libs/experimental
 -e ../libs/text-splitters
 langchain-cohere

libs/community/poetry.lock

@@ -3964,7 +3964,7 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.1.51"
+version = "0.1.52"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -10044,4 +10044,4 @@ extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "as
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "ca64e52a60e8ee6f2f4ea303e1779a4508f401e283f63861161cb6a9560e2178"
+content-hash = "6427b52f752b6b8e46c1bc56f03db5a25e959fb471d0b2de3607696a97fdcd77"

libs/community/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain-community"
-version = "0.0.37"
+version = "0.0.38"
 description = "Community contributed LangChain integrations."
 authors = []
 license = "MIT"
@@ -9,7 +9,7 @@ repository = "https://github.com/langchain-ai/langchain"
 
 [tool.poetry.dependencies]
 python = ">=3.8.1,<4.0"
-langchain-core = "^0.1.51"
+langchain-core = "^0.1.52"
 SQLAlchemy = ">=1.4,<3"
 requests = "^2"
 PyYAML = ">=5.3"

libs/langchain/langchain/chains/moderation.py

@@ -1,9 +1,13 @@
 """Pass input through a moderation endpoint."""
+
 from typing import Any, Dict, List, Optional
 
-from langchain_core.callbacks import CallbackManagerForChainRun
-from langchain_core.pydantic_v1 import root_validator
-from langchain_core.utils import get_from_dict_or_env
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForChainRun,
+    CallbackManagerForChainRun,
+)
+from langchain_core.pydantic_v1 import Field, root_validator
+from langchain_core.utils import check_package_version, get_from_dict_or_env
 
 from langchain.chains.base import Chain
 
@@ -25,6 +29,7 @@ class OpenAIModerationChain(Chain):
     """
 
     client: Any  #: :meta private:
+    async_client: Any  #: :meta private:
     model_name: Optional[str] = None
     """Moderation model name to use."""
     error: bool = False
@@ -33,6 +38,7 @@ class OpenAIModerationChain(Chain):
     output_key: str = "output"  #: :meta private:
     openai_api_key: Optional[str] = None
     openai_organization: Optional[str] = None
+    _openai_pre_1_0: bool = Field(default=None)
 
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
@@ -52,7 +58,16 @@ class OpenAIModerationChain(Chain):
             openai.api_key = openai_api_key
             if openai_organization:
                 openai.organization = openai_organization
-            values["client"] = openai.Moderation  # type: ignore
+            values["_openai_pre_1_0"] = False
+            try:
+                check_package_version("openai", gte_version="1.0")
+            except ValueError:
+                values["_openai_pre_1_0"] = True
+            if values["_openai_pre_1_0"]:
+                values["client"] = openai.Moderation
+            else:
+                values["client"] = openai.OpenAI()
+                values["async_client"] = openai.AsyncOpenAI()
         except ImportError:
             raise ImportError(
                 "Could not import openai python package. "
@@ -76,8 +91,12 @@ class OpenAIModerationChain(Chain):
         """
         return [self.output_key]
 
-    def _moderate(self, text: str, results: dict) -> str:
-        if results["flagged"]:
+    def _moderate(self, text: str, results: Any) -> str:
+        if self._openai_pre_1_0:
+            condition = results["flagged"]
+        else:
+            condition = results.flagged
+        if condition:
             error_str = "Text was found that violates OpenAI's content policy."
             if self.error:
                 raise ValueError(error_str)
@@ -87,10 +106,26 @@ class OpenAIModerationChain(Chain):
 
     def _call(
         self,
-        inputs: Dict[str, str],
+        inputs: Dict[str, Any],
         run_manager: Optional[CallbackManagerForChainRun] = None,
-    ) -> Dict[str, str]:
+    ) -> Dict[str, Any]:
         text = inputs[self.input_key]
-        results = self.client.create(text)
-        output = self._moderate(text, results["results"][0])
+        if self._openai_pre_1_0:
+            results = self.client.create(text)
+            output = self._moderate(text, results["results"][0])
+        else:
+            results = self.client.moderations.create(input=text)
+            output = self._moderate(text, results.results[0])
+        return {self.output_key: output}
+
+    async def _acall(
+        self,
+        inputs: Dict[str, Any],
+        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
+        if self._openai_pre_1_0:
+            return await super()._acall(inputs, run_manager=run_manager)
+        text = inputs[self.input_key]
+        results = await self.async_client.moderations.create(input=text)
+        output = self._moderate(text, results.results[0])
         return {self.output_key: output}

libs/langchain/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
 name = "aiodns"
@@ -3469,7 +3469,7 @@ files = [
 
 [[package]]
 name = "langchain-community"
-version = "0.0.37"
+version = "0.0.38"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -3479,7 +3479,7 @@ develop = true
 [package.dependencies]
 aiohttp = "^3.8.3"
 dataclasses-json = ">= 0.5.7, < 0.7"
-langchain-core = "^0.1.51"
+langchain-core = "^0.1.52"
 langsmith = "^0.1.0"
 numpy = "^1"
 PyYAML = ">=5.3"
@@ -3497,7 +3497,7 @@ url = "../community"
 
 [[package]]
 name = "langchain-core"
-version = "0.1.51"
+version = "0.1.52"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -4727,6 +4727,7 @@ description = "Nvidia JIT LTO Library"
 optional = true
 python-versions = ">=3"
 files = [
+    {file = "nvidia_nvjitlink_cu12-12.4.99-py3-none-manylinux2014_aarch64.whl", hash = "sha256:75d6498c96d9adb9435f2bbdbddb479805ddfb97b5c1b32395c694185c20ca57"},
     {file = "nvidia_nvjitlink_cu12-12.4.99-py3-none-manylinux2014_x86_64.whl", hash = "sha256:c6428836d20fe7e327191c175791d38570e10762edc588fb46749217cd444c74"},
     {file = "nvidia_nvjitlink_cu12-12.4.99-py3-none-win_amd64.whl", hash = "sha256:991905ffa2144cb603d8ca7962d75c35334ae82bf92820b6ba78157277da1ad2"},
 ]
@@ -6074,26 +6075,31 @@ python-versions = ">=3.8"
 files = [
     {file = "PyMuPDF-1.23.26-cp310-none-macosx_10_9_x86_64.whl", hash = "sha256:645a05321aecc8c45739f71f0eb574ce33138d19189582ffa5241fea3a8e2549"},
     {file = "PyMuPDF-1.23.26-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:2dfc9e010669ae92fade6fb72aaea49ebe3b8dcd7ee4dcbbe50115abcaa4d3fe"},
+    {file = "PyMuPDF-1.23.26-cp310-none-manylinux2014_aarch64.whl", hash = "sha256:734ee380b3abd038602be79114194a3cb74ac102b7c943bcb333104575922c50"},
     {file = "PyMuPDF-1.23.26-cp310-none-manylinux2014_x86_64.whl", hash = "sha256:b22f8d854f8196ad5b20308c1cebad3d5189ed9f0988acbafa043947ea7e6c55"},
     {file = "PyMuPDF-1.23.26-cp310-none-win32.whl", hash = "sha256:cc0f794e3466bc96b5bf79d42fbc1551428751e3fef38ebc10ac70396b676144"},
     {file = "PyMuPDF-1.23.26-cp310-none-win_amd64.whl", hash = "sha256:2eb701247d8e685a24e45899d1175f01a3ce5fc792a4431c91fbb68633b29298"},
     {file = "PyMuPDF-1.23.26-cp311-none-macosx_10_9_x86_64.whl", hash = "sha256:e2804a64bb57da414781e312fb0561f6be67658ad57ed4a73dce008b23fc70a6"},
     {file = "PyMuPDF-1.23.26-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:97b40bb22e3056874634617a90e0ed24a5172cf71791b9e25d1d91c6743bc567"},
+    {file = "PyMuPDF-1.23.26-cp311-none-manylinux2014_aarch64.whl", hash = "sha256:fab8833559bc47ab26ce736f915b8fc1dd37c108049b90396f7cd5e1004d7593"},
     {file = "PyMuPDF-1.23.26-cp311-none-manylinux2014_x86_64.whl", hash = "sha256:f25aafd3e7fb9d7761a22acf2b67d704f04cc36d4dc33a3773f0eb3f4ec3606f"},
     {file = "PyMuPDF-1.23.26-cp311-none-win32.whl", hash = "sha256:05e672ed3e82caca7ef02a88ace30130b1dd392a1190f03b2b58ffe7aa331400"},
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+    {file = "PyMuPDF-1.23.26-cp312-none-manylinux2014_aarch64.whl", hash = "sha256:99607649f89a02bba7d8ebe96e2410664316adc95e9337f7dfeff6a154f93049"},
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+    {file = "PyMuPDF-1.23.26-cp38-none-manylinux2014_aarch64.whl", hash = "sha256:5ef4360f20015673c20cf59b7e19afc97168795188c584254ed3778cde43ce77"},
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+    {file = "PyMuPDF-1.23.26-cp39-none-manylinux2014_aarch64.whl", hash = "sha256:879e7f5ad35709d8760ab6103c3d5dac8ab8043a856ab3653fd324af7358ee87"},
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@@ -9404,4 +9410,4 @@ text-helpers = ["chardet"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "9ed4d0b11749d1f98e8fbe2895a94e4bc90975817873e52a70f2bbcee934ce19"
+content-hash = "56e0417edc75e591b9265c6986b87115c27ddd98b87466f698c68df6f3fb60d3"

libs/langchain/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain"
-version = "0.1.18"
+version = "0.1.20"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"
@@ -12,9 +12,9 @@ langchain-server = "langchain.server:main"
 
 [tool.poetry.dependencies]
 python = ">=3.8.1,<4.0"
-langchain-core = "^0.1.48"
+langchain-core = "^0.1.52"
 langchain-text-splitters = ">=0.0.1,<0.1"
-langchain-community = ">=0.0.37,<0.1"
+langchain-community = ">=0.0.38,<0.1"
 langsmith = "^0.1.17"
 pydantic = ">=1,<3"
 SQLAlchemy = ">=1.4,<3"