bump 245 (#8359)

## Description
This commit introduces the `DropboxLoader` class, a new document loader
that allows loading files from Dropbox into the application. The loader
relies on a Dropbox app, which requires creating an app on Dropbox,
obtaining the necessary scope permissions, and generating an access
token. Additionally, the dropbox Python package is required.

The `DropboxLoader` class is designed to be used as a document loader
for processing various file types, including text files, PDFs, and
Dropbox Paper files.

## Dependencies
`pip install dropbox` and `pip install unstructured` for PDF reading.

## Tag maintainer
@rlancemartin, @eyurtsev (from Data Loaders). I'd appreciate some
feedback here 🙏 .

## Social Networks
https://github.com/rubenbarragan
https://www.linkedin.com/in/rgbarragan/
https://twitter.com/RubenBarraganP

---------

Co-authored-by: Ruben Barragan <rbarragan@Rubens-MacBook-Air.local>

.devcontainer/docker-compose.yaml

@@ -2,7 +2,7 @@ version: '3'
 services:
   langchain:
     build:
-      dockerfile: dev.Dockerfile
+      dockerfile: libs/langchain/dev.Dockerfile
       context: ..
     volumes:
    # Update this to wherever you want VS Code to mount the folder of your project

.github/CONTRIBUTING.md

@@ -69,6 +69,14 @@ This project uses [Poetry](https://python-poetry.org/) as a dependency manager.
 3. Tell Poetry to use the virtualenv python environment (`poetry config virtualenvs.prefer-active-python true`)
 4. Continue with the following steps.
 
+There are two separate projects in this repository:
+- `langchain`: core langchain code, abstractions, and use cases
+- `langchain.experimental`: more experimental code
+
+Each of these has their OWN development environment. 
+In order to run any of the commands below, please move into their respective directories.
+For example, to contribute to `langchain` run `cd libs/langchain` before getting started with the below.
+
 To install requirements:
 
 ```bash
@@ -248,6 +256,9 @@ When you run `poetry install`, the `langchain` package is installed as editable
 
 ## Documentation
 
+While the code is split between `langchain` and `langchain.experimental`, the documentation is one holistic thing.
+This covers how to get started contributing to documentation.
+
 ### Contribute Documentation
 
 The docs directory contains Documentation and API Reference.

.github/actions/poetry_setup/action.yml

@@ -52,11 +52,13 @@ runs:
 
     - name: Check Poetry File
       shell: bash
+      working-directory: ${{ inputs.working-directory }}
       run: |
         poetry check
 
     - name: Check lock file
       shell: bash
+      working-directory: ${{ inputs.working-directory }}
       run: |
         poetry lock --check
 

.github/workflows/_lint.yml

@@ -1,15 +1,21 @@
 name: lint
 
 on:
-  push:
-    branches: [master]
-  pull_request:
+  workflow_call:
+    inputs:
+      working-directory:
+        required: true
+        type: string
+        description: "From which folder this pipeline executes"
 
 env:
   POETRY_VERSION: "1.4.2"
 
 jobs:
   build:
+    defaults:
+      run:
+        working-directory: ${{ inputs.working-directory }}
     runs-on: ubuntu-latest
     strategy:
       matrix:
@@ -31,6 +37,10 @@ jobs:
       - name: Install dependencies
         run: |
           poetry install
+      - name: Install langchain editable
+        if: ${{ inputs.working-directory != 'langchain' }}
+        run: |
+          pip install -e ../langchain
       - name: Analysing the code with our lint
         run: |
           make lint

.github/workflows/_release.yml

@@ -1,13 +1,12 @@
 name: release
 
 on:
-  pull_request:
-    types:
-      - closed
-    branches:
-      - master
-    paths:
-      - 'pyproject.toml'
+  workflow_call:
+    inputs:
+      working-directory:
+        required: true
+        type: string
+        description: "From which folder this pipeline executes"
 
 env:
   POETRY_VERSION: "1.4.2"
@@ -18,6 +17,9 @@ jobs:
         ${{ github.event.pull_request.merged == true }}
         && ${{ contains(github.event.pull_request.labels.*.name, 'release') }}
     runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: ${{ inputs.working-directory }}
     steps:
       - uses: actions/checkout@v3
       - name: Install poetry

.github/workflows/_test.yml

@@ -1,16 +1,25 @@
 name: test
 
 on:
-  push:
-    branches: [master]
-  pull_request:
-  workflow_dispatch:
+  workflow_call:
+    inputs:
+      working-directory:
+        required: true
+        type: string
+        description: "From which folder this pipeline executes"
+      test_type:
+        type: string
+        description: "Test types to run"
+        default: '["core", "extended"]'
 
 env:
   POETRY_VERSION: "1.4.2"
 
 jobs:
   build:
+    defaults:
+      run:
+        working-directory: ${{ inputs.working-directory }}
     runs-on: ubuntu-latest
     strategy:
       matrix:
@@ -19,9 +28,7 @@ jobs:
           - "3.9"
           - "3.10"
           - "3.11"
-        test_type:
-          - "core"
-          - "extended"
+        test_type: ${{ fromJSON(inputs.test_type) }}
     name: Python ${{ matrix.python-version }} ${{ matrix.test_type }}
     steps:
       - uses: actions/checkout@v3
@@ -29,6 +36,7 @@ jobs:
         uses: "./.github/actions/poetry_setup"
         with:
           python-version: ${{ matrix.python-version }}
+          working-directory: ${{ inputs.working-directory }}
           poetry-version: "1.4.2"
           cache-key: ${{ matrix.test_type }}
           install-command: |
@@ -39,6 +47,10 @@ jobs:
                 echo "Running extended tests, installing dependencies with poetry..."
                 poetry install -E extended_testing
               fi
+      - name: Install langchain editable
+        if: ${{ inputs.working-directory != 'langchain' }}
+        run: |
+          pip install -e ../langchain
       - name: Run ${{matrix.test_type}} tests
         run: |
           if [ "${{ matrix.test_type }}" == "core" ]; then

.github/workflows/codespell.yml

@@ -20,3 +20,5 @@ jobs:
         uses: actions/checkout@v3
       - name: Codespell
         uses: codespell-project/actions-codespell@v2
+        with:
+          skip: guide_imports.json

.github/workflows/langchain_ci.yml

@@ -0,0 +1,27 @@
+---
+name: libs/langchain CI
+
+on:
+  push:
+    branches: [ master ]
+  pull_request:
+    paths:
+      - '.github/workflows/_lint.yml'
+      - '.github/workflows/_test.yml'
+      - '.github/workflows/langchain_ci.yml'
+      - 'libs/langchain/**'
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+
+jobs:
+  lint:
+    uses:
+      ./.github/workflows/_lint.yml
+    with:
+      working-directory: libs/langchain
+    secrets: inherit
+  test:
+    uses:
+      ./.github/workflows/_test.yml
+    with:
+      working-directory: libs/langchain
+    secrets: inherit

.github/workflows/langchain_experimental_ci.yml

@@ -0,0 +1,29 @@
+---
+name: libs/langchain-experimental CI
+
+on:
+  push:
+    branches: [ master ]
+  pull_request:
+    paths:
+      - '.github/workflows/_lint.yml'
+      - '.github/workflows/_test.yml'
+      - '.github/workflows/langchain_experimental_ci.yml'
+      - 'libs/langchain/**'
+      - 'libs/experimental/**'
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+
+jobs:
+  lint:
+    uses:
+      ./.github/workflows/_lint.yml
+    with:
+      working-directory: libs/experimental
+    secrets: inherit
+  test:
+    uses:
+      ./.github/workflows/_test.yml
+    with:
+      working-directory: libs/experimental
+      test_type: '["core"]'
+    secrets: inherit

.github/workflows/langchain_experimental_release.yml

@@ -0,0 +1,20 @@
+---
+name: libs/langchain-experimental Release
+
+on:
+  pull_request:
+    types:
+      - closed
+    branches:
+      - master
+    paths:
+      - 'libs/experimental/pyproject.toml'
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+
+jobs:
+  release:
+    uses:
+      ./.github/workflows/_release.yml
+    with:
+      working-directory: libs/experimental
+    secrets: inherit

.github/workflows/langchain_release.yml

@@ -0,0 +1,20 @@
+---
+name: libs/langchain Release
+
+on:
+  pull_request:
+    types:
+      - closed
+    branches:
+      - master
+    paths:
+      - 'libs/langchain/pyproject.toml'
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+
+jobs:
+  release:
+    uses:
+      ./.github/workflows/_release.yml
+    with:
+      working-directory: libs/langchain
+    secrets: inherit

.readthedocs.yaml

@@ -24,6 +24,6 @@ sphinx:
 # Optionally declare the Python requirements required to build your docs
 python:
    install:
-   - requirements: docs/requirements.txt
+   - requirements: docs/api_reference/requirements.txt
    - method: pip
      path: .

MIGRATE.md

@@ -0,0 +1,57 @@
+# Migrating to `langchain_experimental`
+
+We are moving any experimental components of LangChain, or components with vulnerability issues, into `langchain_experimental`.
+This guide covers how to migrate.
+
+## Installation
+
+Previously:
+
+`pip install -U langchain`
+
+Now (only if you want to access things in experimental):
+
+`pip install -U langchain langchain_experimental`
+
+## Things in `langchain.experimental`
+
+Previously:
+
+`from langchain.experimental import ...`
+
+Now:
+
+`from langchain_experimental import ...`
+
+## PALChain
+
+Previously:
+
+`from langchain.chains import PALChain`
+
+Now:
+
+`from langchain_experimental.pal_chain import PALChain`
+
+## SQLDatabaseChain
+
+Previously:
+
+`from langchain.chains import SQLDatabaseChain`
+
+Now:
+
+`from langchain_experimental.sql import SQLDatabaseChain`
+
+## `load_prompt` for Python files
+
+Note: this only applies if you want to load Python files as prompts.
+If you want to load json/yaml files, no change is needed.
+
+Previously:
+
+`from langchain.prompts import load_prompt`
+
+Now:
+
+`from langchain_experimental.prompts import load_prompt`

Makefile

@@ -1,18 +1,8 @@
-.PHONY: all clean docs_build docs_clean docs_linkcheck api_docs_build api_docs_clean api_docs_linkcheck format lint test tests test_watch integration_tests docker_tests help extended_tests
+.PHONY: all clean docs_build docs_clean docs_linkcheck api_docs_build api_docs_clean api_docs_linkcheck
 
 # Default target executed when no arguments are given to make.
 all: help
 
-######################
-# TESTING AND COVERAGE
-######################
-
-# Run unit tests and generate a coverage report.
-coverage:
-	poetry run pytest --cov \
-		--cov-config=.coveragerc \
-		--cov-report xml \
-		--cov-report term-missing:skip-covered
 
 ######################
 # DOCUMENTATION
@@ -41,46 +31,6 @@ api_docs_clean:
 api_docs_linkcheck:
 	poetry run linkchecker docs/api_reference/_build/html/index.html
 
-# Define a variable for the test file path.
-TEST_FILE ?= tests/unit_tests/
-
-test:
-	poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
-
-tests: 
-	poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
-
-extended_tests:
-	poetry run pytest --disable-socket --allow-unix-socket --only-extended tests/unit_tests
-
-test_watch:
-	poetry run ptw --now . -- tests/unit_tests
-
-integration_tests:
-	poetry run pytest tests/integration_tests
-
-docker_tests:
-	docker build -t my-langchain-image:test .
-	docker run --rm my-langchain-image:test
-
-######################
-# LINTING AND FORMATTING
-######################
-
-# Define a variable for Python and notebook files.
-PYTHON_FILES=.
-lint format: PYTHON_FILES=.
-lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
-
-lint lint_diff:
-	poetry run mypy $(PYTHON_FILES)
-	poetry run black $(PYTHON_FILES) --check
-	poetry run ruff .
-
-format format_diff:
-	poetry run black $(PYTHON_FILES)
-	poetry run ruff --select I --fix $(PYTHON_FILES)
-
 spell_check:
 	poetry run codespell --toml pyproject.toml
 
@@ -97,12 +47,3 @@ help:
 	@echo 'docs_build                   - build the documentation'
 	@echo 'docs_clean                   - clean the documentation build artifacts'
 	@echo 'docs_linkcheck               - run linkchecker on the documentation'
-	@echo 'format                       - run code formatters'
-	@echo 'lint                         - run linters'
-	@echo 'test                         - run unit tests'
-	@echo 'tests                        - run unit tests'
-	@echo 'test TEST_FILE=<test_file>   - run all tests in file'
-	@echo 'extended_tests               - run only extended unit tests'
-	@echo 'test_watch                   - run unit tests in watch mode'
-	@echo 'integration_tests            - run integration tests'
-	@echo 'docker_tests                 - run unit tests in docker'

README.md

@@ -3,8 +3,8 @@
 ⚡ Building applications with LLMs through composability ⚡
 
 [![Release Notes](https://img.shields.io/github/release/hwchase17/langchain)](https://github.com/hwchase17/langchain/releases)
-[![lint](https://github.com/hwchase17/langchain/actions/workflows/lint.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/lint.yml)
-[![test](https://github.com/hwchase17/langchain/actions/workflows/test.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/test.yml)
+[![CI](https://github.com/hwchase17/langchain/actions/workflows/langchain_ci.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/langchain_ci.yml)
+[![Experimental CI](https://github.com/hwchase17/langchain/actions/workflows/langchain_experimental_ci.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/langchain_experimental_ci.yml)
 [![Downloads](https://static.pepy.tech/badge/langchain/month)](https://pepy.tech/project/langchain)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
@@ -19,7 +19,15 @@
 Looking for the JS/TS version? Check out [LangChain.js](https://github.com/hwchase17/langchainjs).
 
 **Production Support:** As you move your LangChains into production, we'd love to offer more comprehensive support.
-Please fill out [this form](https://forms.gle/57d8AmXBYp8PP8tZA) and we'll set up a dedicated support Slack channel.
+Please fill out [this form](https://6w1pwbss0py.typeform.com/to/rrbrdTH2) and we'll set up a dedicated support Slack channel.
+
+## 🚨Breaking Changes for select chains (SQLDatabase) on 7/28
+
+In an effort to make `langchain` leaner and safer, we are moving select chains to `langchain_experimental`.
+This migration has already started, but we are remaining backwards compatible until 7/28.
+On that date, we will remove functionality from `langchain`.
+Read more about the motivation and the progress [here](https://github.com/hwchase17/langchain/discussions/8043).
+Read how to migrate your code [here](MIGRATE.md).
 
 ## Quick Install
 

docs/api_reference/conf.py

@@ -7,19 +7,66 @@
 
 # -- Path setup --------------------------------------------------------------
 
+import json
+import os
+import sys
+from pathlib import Path
+
+import toml
+from docutils import nodes
+from sphinx.util.docutils import SphinxDirective
+
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-import os
-import sys
-
-import toml
 
+_DIR = Path(__file__).parent.absolute()
 sys.path.insert(0, os.path.abspath("."))
+sys.path.insert(0, os.path.abspath("../../libs/langchain"))
 
-with open("../../pyproject.toml") as f:
+with (_DIR.parents[1] / "libs" / "langchain" / "pyproject.toml").open("r") as f:
     data = toml.load(f)
+with (_DIR / "guide_imports.json").open("r") as f:
+    imported_classes = json.load(f)
+
+
+class ExampleLinksDirective(SphinxDirective):
+    """Directive to generate a list of links to examples.
+
+    We have a script that extracts links to API reference docs
+    from our notebook examples. This directive uses that information
+    to backlink to the examples from the API reference docs."""
+
+    has_content = False
+    required_arguments = 1
+
+    def run(self):
+        """Run the directive.
+
+        Called any time :example_links:`ClassName` is used
+        in the template *.rst files."""
+        class_or_func_name = self.arguments[0]
+        links = imported_classes.get(class_or_func_name, {})
+        list_node = nodes.bullet_list()
+        for doc_name, link in links.items():
+            item_node = nodes.list_item()
+            para_node = nodes.paragraph()
+            link_node = nodes.reference()
+            link_node["refuri"] = link
+            link_node.append(nodes.Text(doc_name))
+            para_node.append(link_node)
+            item_node.append(para_node)
+            list_node.append(item_node)
+        if list_node.children:
+            title_node = nodes.title()
+            title_node.append(nodes.Text(f"Examples using {class_or_func_name}"))
+            return [title_node, list_node]
+        return [list_node]
+
+
+def setup(app):
+    app.add_directive("example_links", ExampleLinksDirective)
+
 
 # -- Project information -----------------------------------------------------
 

docs/api_reference/create_api_rst.py

@@ -4,7 +4,7 @@ import re
 from pathlib import Path
 
 ROOT_DIR = Path(__file__).parents[2].absolute()
-PKG_DIR = ROOT_DIR / "langchain"
+PKG_DIR = ROOT_DIR / "libs" / "langchain" / "langchain"
 WRITE_FILE = Path(__file__).parent / "api_reference.rst"
 
 
@@ -40,11 +40,7 @@ API Reference
         functions = _members["functions"]
         if not (classes or functions):
             continue
-
-        module_title = module.replace("_", " ").title()
-        if module_title == "Llms":
-            module_title = "LLMs"
-        section = f":mod:`langchain.{module}`: {module_title}"
+        section = f":mod:`langchain.{module}`"
         full_doc += f"""\
 {section}
 {'=' * (len(section) + 1)}
@@ -78,6 +74,7 @@ Functions
 
 .. autosummary::
     :toctree: {module}
+    :template: function.rst
 
     {fstring}
 

docs/api_reference/modules/evaluation.rst

@@ -1,9 +0,0 @@
-Evaluation
-=======================
-
-LangChain has a number of convenient evaluation chains you can use off the shelf to grade your models' oupputs.
-
-.. automodule:: langchain.evaluation
-   :members:
-   :undoc-members:
-   :inherited-members:

docs/api_reference/requirements.txt

@@ -1,3 +1,4 @@
+-e libs/langchain
 autodoc_pydantic==1.8.0
 myst_parser
 nbsphinx==0.8.9
@@ -9,6 +10,4 @@ sphinx-panels
 toml
 myst_nb
 sphinx_copybutton
-pydata-sphinx-theme==0.13.1
-nbdoc
-urllib3<2
+pydata-sphinx-theme==0.13.1

docs/api_reference/templates/class.rst

@@ -26,3 +26,5 @@
    {%- endfor %}
    {% endif %}
    {% endblock %}
+
+.. example_links:: {{ objname }}

docs/api_reference/templates/function.rst

@@ -0,0 +1,8 @@
+:mod:`{{module}}`.{{objname}}
+{{ underline }}==============
+
+.. currentmodule:: {{ module }}
+
+.. autofunction:: {{ objname }}
+
+.. example_links:: {{ objname }}

docs/api_reference/themes/scikit-learn-modern/static/css/theme.css

@@ -745,6 +745,11 @@ span.descname {
   background-color: transparent;
   padding: 0;
   font-family: monospace;
+  font-size: 1.2rem;
+}
+
+em.property {
+  font-weight: normal;
 }
 
 span.descclassname {

docs/docs_skeleton/docs/ecosystem/integrations/index.mdx

@@ -1,10 +0,0 @@
----
-sidebar_position: 0
----
-# Integrations
-
-Visit the [Integrations Hub](https://integrations.langchain.com) to further explore, upvote and request integrations across key LangChain components.
-
-import DocCardList from "@theme/DocCardList";
-
-<DocCardList />

docs/docs_skeleton/docs/get_started/introduction.mdx

@@ -51,7 +51,7 @@ Walkthroughs and best-practices for common end-to-end use cases, like:
 Learn best practices for developing with LangChain.
 
 ### [Ecosystem](/docs/ecosystem/)
-LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](/docs/ecosystem/integrations/) and [dependent repos](/docs/ecosystem/dependents.html).
+LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](/docs/integrations/) and [dependent repos](/docs/ecosystem/dependents).
 
 ### [Additional resources](/docs/additional_resources/)
 Our community is full of prolific developers, creative builders, and fantastic teachers. Check out [YouTube tutorials](/docs/additional_resources/youtube.html) for great tutorials from folks in the community, and [Gallery](https://github.com/kyrolabs/awesome-langchain) for a list of awesome LangChain projects, compiled by the folks at [KyroLabs](https://kyrolabs.com).

docs/docs_skeleton/docs/get_started/quickstart.mdx

@@ -22,28 +22,74 @@ import OpenAISetup from "@snippets/get_started/quickstart/openai_setup.mdx"
 
 ## Building an application
 
-Now we can start building our language model application. LangChain provides many modules that can be used to build language model applications. Modules can be used as stand-alones in simple applications and they can be combined for more complex use cases.
+Now we can start building our language model application. LangChain provides many modules that can be used to build language model applications.
+Modules can be used as stand-alones in simple applications and they can be combined for more complex use cases.
+
+The core building block of LangChain applications is the LLMChain.
+This combines three things:
+- LLM: The language model is the core reasoning engine here. In order to work with LangChain, you need to understand the different types of language models and how to work with them.
+- Prompt Templates: This provides instructions to the language model. This controls what the language model outputs, so understanding how to construct prompts and different prompting strategies is crucial.
+- Output Parsers: These translate the raw response from the LLM to a more workable format, making it easy to use the output downstream.
+
+In this getting started guide we will cover those three components by themselves, and then cover the LLMChain which combines all of them.
+Understanding these concepts will set you up well for being able to use and customize LangChain applications.
+Most LangChain applications allow you to configure the LLM and/or the prompt used, so knowing how to take advantage of this will be a big enabler.
 
 ## LLMs
-#### Get predictions from a language model
 
-The basic building block of LangChain is the LLM, which takes in text and generates more text.
+There are two types of language models, which in LangChain are called:
 
-As an example, suppose we're building an application that generates a company name based on a company description. In order to do this, we need to initialize an OpenAI model wrapper. In this case, since we want the outputs to be MORE random, we'll initialize our model with a HIGH temperature.
+- LLMs: this is a language model which takes a string as input and returns a string
+- ChatModels: this is a language model which takes a list of messages as input and returns a message
 
-import LLM from "@snippets/get_started/quickstart/llm.mdx"
+The input/output for LLMs is simple and easy to understand - a string.
+But what about ChatModels? The input there is a list of `ChatMessage`s, and the output is a single `ChatMessage`.
+A `ChatMessage` has two required components:
 
-<LLM/>
+- `content`: This is the content of the message.
+- `role`: This is the role of the entity from which the `ChatMessage` is coming from.
 
-## Chat models
+LangChain provides several objects to easily distinguish between different roles:
 
-Chat models are a variation on language models. While chat models use language models under the hood, the interface they expose is a bit different: rather than expose a "text in, text out" API, they expose an interface where "chat messages" are the inputs and outputs.
+- `HumanMessage`: A `ChatMessage` coming from a human/user.
+- `AIMessage`: A `ChatMessage` coming from an AI/assistant.
+- `SystemMessage`: A `ChatMessage` coming from the system.
+- `FunctionMessage`: A `ChatMessage` coming from a function call.
 
-You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are `AIMessage`, `HumanMessage`, `SystemMessage`, and `ChatMessage` -- `ChatMessage` takes in an arbitrary role parameter. Most of the time, you'll just be dealing with `HumanMessage`, `AIMessage`, and `SystemMessage`.
+If none of those roles sound right, there is also a `ChatMessage` class where you can specify the role manually.
+For more information on how to use these different messages most effectively, see our prompting guide.
 
-import ChatModel from "@snippets/get_started/quickstart/chat_model.mdx"
+LangChain exposes a standard interface for both, but it's useful to understand this difference in order to construct prompts for a given language model.
+The standard interface that LangChain exposes has two methods:
+- `predict`: Takes in a string, returns a string
+- `predict_messages`: Takes in a list of messages, returns a message.
+
+Let's see how to work with these different types of models and these different types of inputs.
+First, let's import an LLM and a ChatModel.
+
+import ImportLLMs from "@snippets/get_started/quickstart/import_llms.mdx"
+
+<ImportLLMs/>
+
+The `OpenAI` and `ChatOpenAI` objects are basically just configuration objects.
+You can initialize them with parameters like `temperature` and others, and pass them around.
+
+Next, let's use the `predict` method to run over a string input.
+
+import InputString from "@snippets/get_started/quickstart/input_string.mdx"
+
+<InputString/>
+
+Finally, let's use the `predict_messages` method to run over a list of messages.
+
+import InputMessages from "@snippets/get_started/quickstart/input_messages.mdx"
+
+<InputMessages/>
+
+For both these methods, you can also pass in parameters as key word arguments.
+For example, you could pass in `temperature=0` to adjust the temperature that is used from what the object was configured with.
+Whatever values are passed in during run time will always override what the object was configured with.
 
-<ChatModel/>
 
 ## Prompt templates
 
@@ -51,108 +97,66 @@ Most LLM applications do not pass user input directly into an LLM. Usually they
 
 In the previous example, the text we passed to the model contained instructions to generate a company name. For our application, it'd be great if the user only had to provide the description of a company/product, without having to worry about giving the model instructions.
 
+PromptTemplates help with exactly this!
+They bundle up all the logic for going from user input into a fully formatted prompt.
+This can start off very simple - for example, a prompt to produce the above string would just be:
+
 import PromptTemplateLLM from "@snippets/get_started/quickstart/prompt_templates_llms.mdx"
 import PromptTemplateChatModel from "@snippets/get_started/quickstart/prompt_templates_chat_models.mdx"
 
-<Tabs>
-    <TabItem value="llms" label="LLMs" default>
-
-With PromptTemplates this is easy! In this case our template would be very simple:
-
 <PromptTemplateLLM/>
-</TabItem>
-<TabItem value="chat_models" label="Chat models">
 
-Similar to LLMs, you can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplate`s. You can use `ChatPromptTemplate`'s `format_messages` method to generate the formatted messages.
+However, the advantages of using these over raw string formatting are several.
+You can "partial" out variables - eg you can format only some of the variables at a time.
+You can compose them together, easily combining different templates into a single prompt.
+For explanations of these functionalities, see the [section on prompts](/docs/modules/model_io/prompts) for more detail.
 
-Because this is generating a list of messages, it is slightly more complex than the normal prompt template which is generating only a string. Please see the detailed guides on prompts to understand more options available to you here.
+PromptTemplates can also be used to produce a list of messages.
+In this case, the prompt not only contains information about the content, but also each message (its role, its position in the list, etc)
+Here, what happens most often is a ChatPromptTemplate is a list of ChatMessageTemplates.
+Each ChatMessageTemplate contains instructions for how to format that ChatMessage - its role, and then also its content.
+Let's take a look at this below:
 
 <PromptTemplateChatModel/>
-    </TabItem>
-</Tabs>
 
-## Chains
+ChatPromptTemplates can also include other things besides ChatMessageTemplates - see the [section on prompts](/docs/modules/model_io/prompts) for more detail.
 
-Now that we've got a model and a prompt template, we'll want to combine the two. Chains give us a way to link (or chain) together multiple primitives, like models, prompts, and other chains.
+## Output Parsers
 
-import ChainLLM from "@snippets/get_started/quickstart/chains_llms.mdx"
-import ChainChatModel from "@snippets/get_started/quickstart/chains_chat_models.mdx"
+OutputParsers convert the raw output of an LLM into a format that can be used downstream.
+There are few main type of OutputParsers, including:
 
-<Tabs>
-<TabItem value="llms" label="LLMs" default>
+- Convert text from LLM -> structured information (eg JSON)
+- Convert a ChatMessage into just a string
+- Convert the extra information returned from a call besides the message (like OpenAI function invocation) into a string.
 
-The simplest and most common type of chain is an LLMChain, which passes an input first to a PromptTemplate and then to an LLM. We can construct an LLM chain from our existing model and prompt template.
+For full information on this, see the [section on output parsers](/docs/modules/model_io/output_parsers)
 
-<ChainLLM/>
+In this getting started guide, we will write our own output parser - one that converts a comma separated list into a list.
 
-There we go, our first chain! Understanding how this simple chain works will set you up well for working with more complex chains.
+import OutputParser from "@snippets/get_started/quickstart/output_parser.mdx"
 
-</TabItem>
-<TabItem value="chat_models" label="Chat models">
+<OutputParser/>
 
-The `LLMChain` can be used with chat models as well:
+## LLMChain
 
-<ChainChatModel/>
-</TabItem>
-</Tabs>
+We can now combine all these into one chain.
+This chain will take input variables, pass those to a prompt template to create a prompt, pass the prompt to an LLM, and then pass the output through an (optional) output parser.
+This is a convenient way to bundle up a modular piece of logic.
+Let's see it in action!
 
-## Agents
+import LLMChain from "@snippets/get_started/quickstart/llm_chain.mdx"
 
-import AgentLLM from "@snippets/get_started/quickstart/agents_llms.mdx"
-import AgentChatModel from "@snippets/get_started/quickstart/agents_chat_models.mdx"
+<LLMChain/>
 
-Our first chain ran a pre-determined sequence of steps. To handle complex workflows, we need to be able to dynamically choose actions based on inputs.
+## Next Steps
 
-Agents do just this: they use a language model to determine which actions to take and in what order. Agents are given access to tools, and they repeatedly choose a tool, run the tool, and observe the output until they come up with a final answer.
+This is it!
+We've now gone over how to create the core building block of LangChain applications - the LLMChains.
+There is a lot more nuance in all these components (LLMs, prompts, output parsers) and a lot more different components to learn about as well.
+To continue on your journey:
 
-To load an agent, you need to choose a(n):
-- LLM/Chat model: The language model powering the agent.
-- Tool(s): A function that performs a specific duty. This can be things like: Google Search, Database lookup, Python REPL, other chains. For a list of predefined tools and their specifications, see the [Tools documentation](/docs/modules/agents/tools/).
-- Agent name: A string that references a supported agent class. An agent class is largely parameterized by the prompt the language model uses to determine which action to take. Because this notebook focuses on the simplest, highest level API, this only covers using the standard supported agents. If you want to implement a custom agent, see [here](/docs/modules/agents/how_to/custom_agent.html). For a list of supported agents and their specifications, see [here](/docs/modules/agents/agent_types/).
-
-For this example, we'll be using SerpAPI to query a search engine.
-
-You'll need to install the SerpAPI Python package:
-
-```bash
-pip install google-search-results
-```
-
-And set the `SERPAPI_API_KEY` environment variable.
-
-<Tabs>
-<TabItem value="llms" label="LLMs" default>
-<AgentLLM/>
-</TabItem>
-<TabItem value="chat_models" label="Chat models">
-
-Agents can also be used with chat models, you can initialize one using `AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION` as the agent type.
-
-<AgentChatModel/>
-</TabItem>
-</Tabs>
-
-## Memory
-
-The chains and agents we've looked at so far have been stateless, but for many applications it's necessary to reference past interactions. This is clearly the case with a chatbot for example, where you want it to understand new messages in the context of past messages.
-
-The Memory module gives you a way to maintain application state. The base Memory interface is simple: it lets you update state given the latest run inputs and outputs and it lets you modify (or contextualize) the next input using the stored state.
-
-There are a number of built-in memory systems. The simplest of these is a buffer memory which just prepends the last few inputs/outputs to the current input - we will use this in the example below.
-
-import MemoryLLM from "@snippets/get_started/quickstart/memory_llms.mdx"
-import MemoryChatModel from "@snippets/get_started/quickstart/memory_chat_models.mdx"
-
-<Tabs>
-<TabItem value="llms" label="LLMs" default>
-
-<MemoryLLM/>
-</TabItem>
-<TabItem value="chat_models" label="Chat models">
-
-You can use Memory with chains and agents initialized with chat models. The main difference between this and Memory for LLMs is that rather than trying to condense all previous messages into a string, we can keep them as their own unique memory object.
-
-<MemoryChatModel/>
-
-</TabItem>
-</Tabs>
+- [Dive deeper](/docs/modules/model_io) into LLMs, prompts, and output parsers
+- Learn the other [key components](/docs/modules)
+- Check out our [helpful guides](/docs/guides) for detailed walkthroughs on particular topics
+- Explore [end-to-end use cases](/docs/use_cases)

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

@@ -0,0 +1,24 @@
+---
+sidebar_position: 3 
+---
+# Comparison Evaluators
+
+Comparison evaluators in LangChain help measure two different chain or LLM outputs. These evaluators are helpful for comparative analyses, such as A/B testing between two language models, or comparing different versions of the same model. They can also be useful for things like generating preference scores for ai-assisted reinforcement learning.
+
+These evaluators inherit from the `PairwiseStringEvaluator` class, providing a comparison interface for two strings - typically, the outputs from two different prompts or models, or two versions of the same model. In essence, a comparison evaluator performs an evaluation on a pair of strings and returns a dictionary containing the evaluation score and other relevant details.
+
+To create a custom comparison evaluator, inherit from the `PairwiseStringEvaluator` class and overwrite the `_evaluate_string_pairs` method. If you require asynchronous evaluation, also overwrite the `_aevaluate_string_pairs` method.
+
+Here's a summary of the key methods and properties of a comparison evaluator:
+
+- `evaluate_string_pairs`: Evaluate the output string pairs. This function should be overwritten when creating custom evaluators.
+- `aevaluate_string_pairs`: Asynchronously evaluate the output string pairs. This function should be overwritten for asynchronous evaluation.
+- `requires_input`: This property indicates whether this evaluator requires an input string.
+- `requires_reference`: This property specifies whether this evaluator requires a reference label.
+
+Detailed information about creating custom evaluators and the available built-in comparison evaluators are provided in the following sections.
+
+import DocCardList from "@theme/DocCardList";
+
+<DocCardList />
+

docs/docs_skeleton/docs/guides/evaluation/index.mdx

@@ -0,0 +1,31 @@
+---
+sidebar_position: 6
+---
+
+import DocCardList from "@theme/DocCardList";
+
+# Evaluation
+
+Building applications with language models involves many moving parts. One of the most critical components is ensuring that the outcomes produced by your models are reliable and useful across a broad array of inputs, and that they work well with your application's other software components. Ensuring reliability usually boils down to some combination of application design, testing & evaluation, and runtime checks. 
+
+The guides in this section review the APIs and functionality LangChain provides to help yous better evaluate your applications. Evaluation and testing are both critical when thinking about deploying LLM applications, since production environments require repeatable and useful outcomes.
+
+LangChain offers various types of evaluators to help you measure performance and integrity on diverse data, and we hope to encourage the the community to create and share other useful evaluators so everyone can improve. These docs will introduce the evaluator types, how to use them, and provide some examples of their use in real-world scenarios.
+
+Each evaluator type in LangChain comes with ready-to-use implementations and an extensible API that allows for customization according to your unique requirements. Here are some of the types of evaluators we offer:
+
+- [String Evaluators](/docs/guides/evaluation/string/): These evaluators assess the predicted string for a given input, usually comparing it against a reference string.
+- [Trajectory Evaluators](/docs/guides/evaluation/trajectory/): These are used to evaluate the entire trajectory of agent actions.
+- [Comparison Evaluators](/docs/guides/evaluation/comparison/): These evaluators are designed to compare predictions from two runs on a common input.
+
+These evaluators can be used across various scenarios and can be applied to different chain and LLM implementations in the LangChain library.
+
+We also are working to share guides and cookbooks that demonstrate how to use these evaluators in real-world scenarios, such as:
+
+- [Chain Comparisons](/docs/guides/evaluation/examples/comparisons): This example uses a comparison evaluator to predict the preferred output. It reviews ways to measure confidence intervals to select statistically significant differences in aggregate preference scores across different models or prompts.
+
+## Reference Docs
+
+For detailed information on the available evaluators, including how to instantiate, configure, and customize them, check out the [reference documentation](https://api.python.langchain.com/en/latest/api_reference.html#module-langchain.evaluation) directly.
+
+<DocCardList />

docs/docs_skeleton/docs/guides/evaluation/string/index.mdx

@@ -0,0 +1,27 @@
+---
+sidebar_position: 2 
+---
+# String Evaluators
+
+A string evaluator is a component within LangChain designed to assess the performance of a language model by comparing its generated outputs (predictions) to a reference string or an input. This comparison is a crucial step in the evaluation of language models, providing a measure of the accuracy or quality of the generated text.
+
+In practice, string evaluators are typically used to evaluate a predicted string against a given input, such as a question or a prompt. Often, a reference label or context string is provided to define what a correct or ideal response would look like. These evaluators can be customized to tailor the evaluation process to fit your application's specific requirements.
+
+To create a custom string evaluator, inherit from the `StringEvaluator` class and implement the `_evaluate_strings` method. If you require asynchronous support, also implement the `_aevaluate_strings` method.
+
+Here's a summary of the key attributes and methods associated with a string evaluator:
+
+- `evaluation_name`: Specifies the name of the evaluation.
+- `requires_input`: Boolean attribute that indicates whether the evaluator requires an input string. If True, the evaluator will raise an error when the input isn't provided. If False, a warning will be logged if an input _is_ provided, indicating that it will not be considered in the evaluation.
+- `requires_reference`: Boolean attribute specifying whether the evaluator requires a reference label. If True, the evaluator will raise an error when the reference isn't provided. If False, a warning will be logged if a reference _is_ provided, indicating that it will not be considered in the evaluation.
+
+String evaluators also implement the following methods:
+
+- `aevaluate_strings`: Asynchronously evaluates the output of the Chain or Language Model, with support for optional input and label.
+- `evaluate_strings`: Synchronously evaluates the output of the Chain or Language Model, with support for optional input and label.
+
+The following sections provide detailed information on available string evaluator implementations as well as how to create a custom string evaluator.
+
+import DocCardList from "@theme/DocCardList";
+
+<DocCardList />

docs/docs_skeleton/docs/guides/evaluation/trajectory/index.mdx

@@ -0,0 +1,28 @@
+---
+sidebar_position: 4
+---
+# Trajectory Evaluators
+
+Trajectory Evaluators in LangChain provide a more holistic approach to evaluating an agent. These evaluators assess the full sequence of actions taken by an agent and their corresponding responses, which we refer to as the "trajectory". This allows you to better measure an agent's effectiveness and capabilities.
+
+A Trajectory Evaluator implements the `AgentTrajectoryEvaluator` interface, which requires two main methods:
+
+- `evaluate_agent_trajectory`: This method synchronously evaluates an agent's trajectory.
+- `aevaluate_agent_trajectory`: This asynchronous counterpart allows evaluations to be run in parallel for efficiency.
+
+Both methods accept three main parameters:
+
+- `input`: The initial input given to the agent.
+- `prediction`: The final predicted response from the agent.
+- `agent_trajectory`: The intermediate steps taken by the agent, given as a list of tuples.
+
+These methods return a dictionary. It is recommended that custom implementations return a `score` (a float indicating the effectiveness of the agent) and `reasoning` (a string explaining the reasoning behind the score).
+
+You can capture an agent's trajectory by initializing the agent with the `return_intermediate_steps=True` parameter. This lets you collect all intermediate steps without relying on special callbacks.
+
+For a deeper dive into the implementation and use of Trajectory Evaluators, refer to the sections below.
+
+import DocCardList from "@theme/DocCardList";
+
+<DocCardList />
+

docs/docs_skeleton/docs/modules/agents/index.mdx

@@ -3,46 +3,80 @@ sidebar_position: 4
 ---
 # Agents
 
-Some applications require a flexible chain of calls to LLMs and other tools based on user input. The **Agent** interface provides the flexibility for such applications. An agent has access to a suite of tools, and determines which ones to use depending on the user input. Agents can use multiple tools, and use the output of one tool as the input to the next.
+The core idea of agents is to use an LLM to choose a sequence of actions to take.
+In chains, a sequence of actions is hardcoded (in code).
+In agents, a language model is used as a reasoning engine to determine which actions to take and in which order.
 
-There are two main types of agents:
+There are several key components here:
 
-- **Action agents**: at each timestep, decide on the next action using the outputs of all previous actions
-- **Plan-and-execute agents**: decide on the full sequence of actions up front, then execute them all without updating the plan
+## Agent
 
-Action agents are suitable for small tasks, while plan-and-execute agents are better for complex or long-running tasks that require maintaining long-term objectives and focus. Often the best approach is to combine the dynamism of an action agent with the planning abilities of a plan-and-execute agent by letting the plan-and-execute agent use action agents to execute plans.
+This is the class responsible for deciding what step to take next.
+This is powered by a language model and a prompt.
+This prompt can include things like:
 
-For a full list of agent types see [agent types](/docs/modules/agents/agent_types/). Additional abstractions involved in agents are:
-- [**Tools**](/docs/modules/agents/tools/): the actions an agent can take. What tools you give an agent highly depend on what you want the agent to do
-- [**Toolkits**](/docs/modules/agents/toolkits/): wrappers around collections of tools that can be used together a specific use case. For example, in order for an agent to
-  interact with a SQL database it will likely need one tool to execute queries and another to inspect tables
+1. The personality of the agent (useful for having it respond in a certain way)
+2. Background context for the agent (useful for giving it more context on the types of tasks it's being asked to do)
+3. Prompting strategies to invoke better reasoning (the most famous/widely used being [ReAct](https://arxiv.org/abs/2210.03629))
 
-## Action agents
+LangChain provides a few different types of agents to get started.
+Even then, you will likely want to customize those agents with parts (1) and (2).
+For a full list of agent types see [agent types](/docs/modules/agents/agent_types/)
 
-At a high-level an action agent:
-1. Receives user input
-2. Decides which tool, if any, to use and the tool input
-3. Calls the tool and records the output (also known as an "observation")
-4. Decides the next step using the history of tools, tool inputs, and observations
-5. Repeats 3-4 until it determines it can respond directly to the user
+## Tools
 
-Action agents are wrapped in **agent executors**, which are responsible for calling the agent, getting back an action and action input, calling the tool that the action references with the generated input, getting the output of the tool, and then passing all that information back into the agent to get the next action it should take.
+Tools are functions that an agent calls.
+There are two important considerations here:
 
-Although an agent can be constructed in many ways, it typically involves these components:
+1. Giving the agent access to the right tools
+2. Describing the tools in a way that is most helpful to the agent
 
-- **Prompt template**: Responsible for taking the user input and previous steps and constructing a prompt
-  to send to the language model
-- **Language model**: Takes the prompt with use input and action history and decides what to do next
-- **Output parser**: Takes the output of the language model and parses it into the next action or a final answer
+Without both, the agent you are trying to build will not work.
+If you don't give the agent access to a correct set of tools, it will never be able to accomplish the objective.
+If you don't describe the tools properly, the agent won't know how to properly use them.
 
-## Plan-and-execute agents
+LangChain provides a wide set of tools to get started, but also makes it easy to define your own (including custom descriptions).
+For a full list of tools, see [here](/docs/modules/agents/tools/)
 
-At a high-level a plan-and-execute agent:
-1. Receives user input
-2. Plans the full sequence of steps to take
-3. Executes the steps in order, passing the outputs of past steps as inputs to future steps
+## Toolkits
 
-The most typical implementation is to have the planner be a language model, and the executor be an action agent. Read more [here](/docs/modules/agents/agent_types/plan_and_execute.html).
+Often the set of tools an agent has access to is more important than a single tool.
+For this LangChain provides the concept of toolkits - groups of tools needed to accomplish specific objectives.
+There are generally around 3-5 tools in a toolkit.
+
+LangChain provides a wide set of toolkits to get started.
+For a full list of toolkits, see [here](/docs/modules/agents/toolkits/)
+
+## AgentExecutor
+
+The agent executor is the runtime for an agent.
+This is what actually calls the agent and executes the actions it chooses.
+Pseudocode for this runtime is below:
+
+```python
+next_action = agent.get_action(...)
+while next_action != AgentFinish:
+    observation = run(next_action)
+    next_action = agent.get_action(..., next_action, observation)
+return next_action
+```
+
+While this may seem simple, there are several complexities this runtime handles for you, including:
+
+1. Handling cases where the agent selects a non-existent tool
+2. Handling cases where the tool errors
+3. Handling cases where the agent produces output that cannot be parsed into a tool invocation
+4. Logging and observability at all levels (agent decisions, tool calls) either to stdout or [LangSmith](https://smith.langchain.com).
+
+## Other types of agent runtimes
+
+The `AgentExecutor` class is the main agent runtime supported by LangChain.
+However, there are other, more experimental runtimes we also support.
+These include:
+
+- [Plan-and-execute Agent](/docs/modules/agents/agent_types/plan_and_execute.html)
+- [Baby AGI](/docs/use_cases/autonomous_agents/baby_agi.html)
+- [Auto GPT](/docs/use_cases/autonomous_agents/autogpt.html)
 
 ## Get started
 

docs/docs_skeleton/docs/modules/agents/toolkits/index.mdx

@@ -3,8 +3,8 @@ sidebar_position: 3
 ---
 # Toolkits
 
+:::info
+Head to [Integrations](/docs/integrations/toolkits/) for documentation on built-in toolkit integrations.
+:::
+
 Toolkits are collections of tools that are designed to be used together for specific tasks and have convenience loading methods.
-
-import DocCardList from "@theme/DocCardList";
-
-<DocCardList />

docs/docs_skeleton/docs/modules/agents/tools/how_to/_category_.yml

@@ -1,2 +0,0 @@
-label: 'How-to'
-position: 0

docs/docs_skeleton/docs/modules/agents/tools/index.mdx

@@ -3,6 +3,10 @@ sidebar_position: 2
 ---
 # Tools
 
+:::info
+Head to [Integrations](/docs/integrations/tools/) for documentation on built-in tool integrations.
+:::
+
 Tools are interfaces that an agent can use to interact with the world.
 
 ## Get started

docs/docs_skeleton/docs/modules/agents/tools/integrations/_category_.yml

@@ -1 +0,0 @@
-label: 'Integrations'

docs/docs_skeleton/docs/modules/callbacks/how_to/_category_.yml

@@ -1,2 +0,0 @@
-label: 'How-to'
-position: 0

docs/docs_skeleton/docs/modules/callbacks/index.mdx

@@ -3,6 +3,10 @@ sidebar_position: 5
 ---
 # Callbacks
 
+:::info
+Head to [Integrations](/docs/integrations/callbacks/) for documentation on built-in callbacks integrations with 3rd-party tools.
+:::
+
 LangChain provides a callbacks system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.
 
 import GetStarted from "@snippets/modules/callbacks/get_started.mdx"

docs/docs_skeleton/docs/modules/callbacks/integrations/_category_.yml

@@ -1 +0,0 @@
-label: 'Integrations'

docs/docs_skeleton/docs/modules/data_connection/document_loaders/how_to/_category_.yml

@@ -1,2 +0,0 @@
-label: 'How-to'
-position: 0

docs/docs_skeleton/docs/modules/data_connection/document_loaders/index.mdx

@@ -3,6 +3,10 @@ sidebar_position: 0
 ---
 # Document loaders
 
+:::info
+Head to [Integrations](/docs/integrations/document_loaders/) for documentation on built-in document loader integrations with 3rd-party tools.
+:::
+
 Use document loaders to load data from a source as `Document`'s. A `Document` is a piece of text
 and associated metadata. For example, there are document loaders for loading a simple `.txt` file, for loading the text
 contents of any web page, or even for loading a transcript of a YouTube video.

docs/docs_skeleton/docs/modules/data_connection/document_loaders/integrations/_category_.yml

@@ -1 +0,0 @@
-label: 'Integrations'

docs/docs_skeleton/docs/modules/data_connection/document_transformers/index.mdx

@@ -3,6 +3,10 @@ sidebar_position: 1
 ---
 # Document transformers
 
+:::info
+Head to [Integrations](/docs/integrations/document_transformers/) for documentation on built-in document transformer integrations with 3rd-party tools.
+:::
+
 Once you've loaded documents, you'll often want to transform them to better suit your application. The simplest example
 is you may want to split a long document into smaller chunks that can fit into your model's context window. LangChain
 has a number of built-in document transformers that make it easy to split, combine, filter, and otherwise manipulate documents.

docs/docs_skeleton/docs/modules/data_connection/retrievers/how_to/_category_.yml

@@ -1,2 +0,0 @@
-label: 'How-to'
-position: 0

docs/docs_skeleton/docs/modules/data_connection/retrievers/index.mdx

@@ -3,6 +3,10 @@ sidebar_position: 4
 ---
 # Retrievers
 
+:::info
+Head to [Integrations](/docs/integrations/retrievers/) for documentation on built-in retriever integrations with 3rd-party tools.
+:::
+
 A retriever is an interface that returns documents given an unstructured query. It is more general than a vector store.
 A retriever does not need to be able to store documents, only to return (or retrieve) it. Vector stores can be used
 as the backbone of a retriever, but there are other types of retrievers as well.

docs/docs_skeleton/docs/modules/data_connection/text_embedding/index.mdx

@@ -3,6 +3,10 @@ sidebar_position: 2
 ---
 # Text embedding models
 
+:::info
+Head to [Integrations](/docs/integrations/text_embedding/) for documentation on built-in integrations with text embedding model providers.
+:::
+
 The Embeddings class is a class designed for interfacing with text embedding models. There are lots of embedding model providers (OpenAI, Cohere, Hugging Face, etc) - this class is designed to provide a standard interface for all of them.
 
 Embeddings create a vector representation of a piece of text. This is useful because it means we can think about text in the vector space, and do things like semantic search where we look for pieces of text that are most similar in the vector space.

docs/docs_skeleton/docs/modules/data_connection/text_embedding/integrations/_category_.yml

@@ -1 +0,0 @@
-label: 'Integrations'

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

@@ -3,6 +3,10 @@ sidebar_position: 3
 ---
 # Vector stores
 
+:::info
+Head to [Integrations](/docs/integrations/vectorstores/) for documentation on built-in integrations with 3rd-party vector stores.
+:::
+
 One of the most common ways to store and search over unstructured data is to embed it and store the resulting embedding
 vectors, and then at query time to embed the unstructured query and retrieve the embedding vectors that are
 'most similar' to the embedded query. A vector store takes care of storing embedded data and performing vector search

docs/docs_skeleton/docs/modules/data_connection/vectorstores/integrations/_category_.yml

@@ -1 +0,0 @@
-label: 'Integrations'

docs/docs_skeleton/docs/modules/evaluation/comparison/index.mdx

@@ -1,8 +0,0 @@
----
-sidebar_position: 3 
----
-# Comparison Evaluators
-
-import DocCardList from "@theme/DocCardList";
-
-<DocCardList />

docs/docs_skeleton/docs/modules/evaluation/index.mdx

@@ -1,28 +0,0 @@
----
-sidebar_position: 6
----
-
-import DocCardList from "@theme/DocCardList";
-
-# Evaluation
-
-Language models can be unpredictable. This makes it challenging to ship reliable applications to production, where repeatable, useful outcomes across diverse inputs are a minimum requirement. Tests help demonstrate each component in an LLM application can produce the required or expected functionality. These tests also safeguard against regressions while you improve interconnected pieces of an integrated system. However, measuring the quality of generated text can be challenging. It can be hard to agree on the right set of metrics for your application, and it can be difficult to translate those into better performance. Furthermore, it's common to lack sufficient evaluation data adequately test the range of inputs and expected outputs for each component when you're just getting started. The LangChain community is building open source tools and guides to help address these challenges.
-
-LangChain exposes different types of evaluators for common types of evaluation. Each type has off-the-shelf implementations you can use to get started, as well as an
- extensible API so you can create your own or contribute improvements for everyone to use. The following sections have example notebooks for you to get started.
-
-- [String Evaluators](/docs/modules/evaluation/string/): Evaluate the predicted string for a given input, usually against a reference string
-- [Trajectory Evaluators](/docs/modules/evaluation/trajectory/): Evaluate the whole trajectory of agent actions
-- [Comparison Evaluators](/docs/modules/evaluation/comparison/): Compare predictions from two runs on a common input
-
-
-This section also provides some additional examples of how you could use these evaluators for different scenarios or apply to different chain implementations in the LangChain library. Some examples include:
-
-- [Preference Scoring Chain Outputs](/docs/modules/evaluation/examples/comparisons): An example using a comparison evaluator on different models or prompts to select statistically significant differences in aggregate preference scores
-
-
-## Reference Docs
-
-For detailed information of the available evaluators, including how to instantiate, configure, and customize them. Check out the [reference documentation](https://api.python.langchain.com/en/latest/api_reference.html#module-langchain.evaluation) directly.
-
-<DocCardList />

docs/docs_skeleton/docs/modules/evaluation/trajectory/index.mdx

@@ -1,8 +0,0 @@
----
-sidebar_position: 4
----
-# Trajectory Evaluators
-
-import DocCardList from "@theme/DocCardList";
-
-<DocCardList />

docs/docs_skeleton/docs/modules/memory/how_to/_category_.yml

@@ -1,2 +0,0 @@
-label: 'How-to'
-position: 0

docs/docs_skeleton/docs/modules/memory/index.mdx

@@ -6,6 +6,10 @@ sidebar_position: 3
 
 🚧 _Docs under construction_ 🚧
 
+:::info
+Head to [Integrations](/docs/integrations/memory/) for documentation on built-in memory integrations with 3rd-party tools.
+:::
+
 By default, Chains and Agents are stateless,
 meaning that they treat each incoming query independently (like the underlying LLMs and chat models themselves).
 In some applications, like chatbots, it is essential

docs/docs_skeleton/docs/modules/memory/integrations/_category_.yml

@@ -1 +0,0 @@
-label: 'Integrations'

docs/docs_skeleton/docs/modules/model_io/models/chat/how_to/_category_.yml

@@ -1,2 +0,0 @@
-label: 'How-to'
-position: 0

docs/docs_skeleton/docs/modules/model_io/models/chat/index.mdx

@@ -3,18 +3,16 @@ sidebar_position: 1
 ---
 # Chat models
 
+:::info
+Head to [Integrations](/docs/integrations/chat/) for documentation on built-in integrations with chat model providers.
+:::
+
 Chat models are a variation on language models.
 While chat models use language models under the hood, the interface they expose is a bit different.
 Rather than expose a "text in, text out" API, they expose an interface where "chat messages" are the inputs and outputs.
 
 Chat model APIs are fairly new, so we are still figuring out the correct abstractions.
 
-The following sections of documentation are provided:
-
-- **How-to guides**: Walkthroughs of core functionality, like streaming, creating chat prompts, etc.
-
-- **Integrations**: How to use different chat model providers (OpenAI, Anthropic, etc).
-
 ## Get started
 
 import GetStarted from "@snippets/modules/model_io/models/chat/get_started.mdx"

docs/docs_skeleton/docs/modules/model_io/models/chat/integrations/_category_.yml

@@ -1 +0,0 @@
-label: 'Integrations'

docs/docs_skeleton/docs/modules/model_io/models/llms/how_to/_category_.yml

@@ -1,2 +0,0 @@
-label: 'How-to'
-position: 0

docs/docs_skeleton/docs/modules/model_io/models/llms/index.mdx

@@ -3,14 +3,12 @@ sidebar_position: 0
 ---
 # LLMs
 
+:::info
+Head to [Integrations](/docs/integrations/llms/) for documentation on built-in integrations with LLM providers.
+:::
+
 Large Language Models (LLMs) are a core component of LangChain.
-LangChain does not serve it's own LLMs, but rather provides a standard interface for interacting with many different LLMs.
-
-For more detailed documentation check out our:
-
-- **How-to guides**: Walkthroughs of core functionality, like streaming, async, etc.
-
-- **Integrations**: How to use different LLM providers (OpenAI, Anthropic, etc.)
+LangChain does not serve its own LLMs, but rather provides a standard interface for interacting with many different LLMs.
 
 ## Get started
 

docs/docs_skeleton/docs/modules/model_io/models/llms/integrations/_category_.yml

@@ -1 +0,0 @@
-label: 'Integrations'

docs/docs_skeleton/docusaurus.config.js

@@ -148,6 +148,28 @@ const config = {
       navbar: {
         title: "🦜️🔗 LangChain",
         items: [
+          {
+            to: "/docs/get_started/introduction",
+            label: "Docs",
+            position: "left",
+          },
+          {
+            type: 'docSidebar',
+            position: 'left',
+            sidebarId: 'use_cases',
+            label: 'Use cases',
+          },
+          {
+            type: 'docSidebar',
+            position: 'left',
+            sidebarId: 'integrations',
+            label: 'Integrations',
+          },
+          {
+            href: "https://api.python.langchain.com",
+            label: "API",
+            position: "left",
+          },
           {
             to: "https://smith.langchain.com",
             label: "LangSmith",
@@ -161,8 +183,9 @@ const config = {
           // Please keep GitHub link to the right for consistency.
           {
             href: "https://github.com/hwchase17/langchain",
-            label: "GitHub",
-            position: "right",
+            position: 'right',
+            className: 'header-github-link',
+            'aria-label': 'GitHub repository',
           },
         ],
       },

docs/docs_skeleton/generate_api_reference_links.py

@@ -0,0 +1,150 @@
+import importlib
+import inspect
+import json
+import logging
+import os
+import re
+from pathlib import Path
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+# Base URL for all class documentation
+_BASE_URL = "https://api.python.langchain.com/en/latest/"
+
+# Regular expression to match Python code blocks
+code_block_re = re.compile(r"^(```python\n)(.*?)(```\n)", re.DOTALL | re.MULTILINE)
+# Regular expression to match langchain import lines
+_IMPORT_RE = re.compile(r"(from\s+(langchain\.\w+(\.\w+)*?)\s+import\s+)(\w+)")
+
+_CURRENT_PATH = Path(__file__).parent.absolute()
+# Directory where generated markdown files are stored
+_DOCS_DIR = _CURRENT_PATH / "docs"
+_JSON_PATH = _CURRENT_PATH.parent / "api_reference" / "guide_imports.json"
+
+
+def find_files(path):
+    """Find all MDX files in the given path"""
+    for root, _, files in os.walk(path):
+        for file in files:
+            if file.endswith(".mdx") or file.endswith(".md"):
+                yield os.path.join(root, file)
+
+
+def get_full_module_name(module_path, class_name):
+    """Get full module name using inspect"""
+    module = importlib.import_module(module_path)
+    class_ = getattr(module, class_name)
+    return inspect.getmodule(class_).__name__
+
+
+def main():
+    """Main function"""
+    global_imports = {}
+
+    for file in find_files(_DOCS_DIR):
+        print(f"Adding links for imports in {file}")
+
+        # replace_imports now returns the import information rather than writing it to a file
+        file_imports = replace_imports(file)
+
+        if file_imports:
+            # Use relative file path as key
+            relative_path = os.path.relpath(file, _DOCS_DIR)
+            doc_url = f"https://python.langchain.com/docs/{relative_path.replace('.mdx', '').replace('.md', '')}"
+            for import_info in file_imports:
+                doc_title = import_info["title"]
+                class_name = import_info["imported"]
+                if class_name not in global_imports:
+                    global_imports[class_name] = {}
+                global_imports[class_name][doc_title] = doc_url
+
+    # Write the global imports information to a JSON file
+    with _JSON_PATH.open("w") as f:
+        json.dump(global_imports, f)
+
+
+def _get_doc_title(data: str, file_name: str) -> str:
+    try:
+        return re.findall(r"^#\s+(.*)", data, re.MULTILINE)[0]
+    except IndexError:
+        pass
+    # Parse the rst-style titles
+    try:
+        return re.findall(r"^(.*)\n=+\n", data, re.MULTILINE)[0]
+    except IndexError:
+        return file_name
+
+
+def replace_imports(file):
+    """Replace imports in each Python code block with links to their documentation and append the import info in a comment"""
+    all_imports = []
+    with open(file, "r") as f:
+        data = f.read()
+
+    file_name = os.path.basename(file)
+    _DOC_TITLE = _get_doc_title(data, file_name)
+
+    def replacer(match):
+        # Extract the code block content
+        code = match.group(2)
+        # Replace if any import comment exists
+        # TODO: Use our own custom <code> component rather than this
+        # injection method
+        existing_comment_re = re.compile(r"^<!--IMPORTS:.*?-->\n", re.MULTILINE)
+        code = existing_comment_re.sub("", code)
+
+        # Process imports in the code block
+        imports = []
+        for import_match in _IMPORT_RE.finditer(code):
+            class_name = import_match.group(4)
+            try:
+                module_path = get_full_module_name(import_match.group(2), class_name)
+            except AttributeError as e:
+                logger.warning(f"Could not find module for {class_name}, {e}")
+                continue
+            except ImportError as e:
+                # Some CentOS OpenSSL issues can cause this to fail
+                logger.warning(f"Failed to load for class {class_name}, {e}")
+                continue
+
+            url = (
+                _BASE_URL
+                + "/"
+                + module_path.split(".")[1]
+                + "/"
+                + module_path
+                + "."
+                + class_name
+                + ".html"
+            )
+
+            # Add the import information to our list
+            imports.append(
+                {
+                    "imported": class_name,
+                    "source": import_match.group(2),
+                    "docs": url,
+                    "title": _DOC_TITLE,
+                }
+            )
+
+        if imports:
+            all_imports.extend(imports)
+            # Create a unique comment containing the import information
+            import_comment = f"<!--IMPORTS:{json.dumps(imports)}-->"
+            # Inject the import comment at the start of the code block
+            return match.group(1) + import_comment + "\n" + code + match.group(3)
+        else:
+            # If there are no imports, return the original match
+            return match.group(0)
+
+    # Use re.sub to replace each Python code block
+    data = code_block_re.sub(replacer, data)
+
+    with open(file, "w") as f:
+        f.write(data)
+    return all_imports
+
+
+if __name__ == "__main__":
+    main()

docs/docs_skeleton/sidebars.js

@@ -20,7 +20,7 @@
 
 module.exports = {
   // By default, Docusaurus generates a sidebar from the docs folder structure
-  sidebar: [
+  docs: [
     {
       type: "category",
       label: "Get started",
@@ -30,7 +30,7 @@ module.exports = {
       link: {
         type: 'generated-index',
         description: 'Get started with LangChain',
-	    slug: "get_started",
+      slug: "get_started",
       },
     },
     {
@@ -44,17 +44,6 @@ module.exports = {
         id: "modules/index"
       },
     },
-    {
-      type: "category",
-      label: "Use cases",
-      collapsed: true,
-      items: [{ type: "autogenerated", dirName: "use_cases" }],
-      link: {
-        type: 'generated-index',
-        description: 'Walkthroughs of common end-to-end use cases',
-	    slug: "use_cases",
-      },
-    },
     {
       type: "category",
       label: "Guides",
@@ -63,7 +52,7 @@ module.exports = {
       link: {
         type: 'generated-index',
         description: 'Design guides for key parts of the development process',
-	    slug: "guides",
+      slug: "guides",
       },
     },
     {
@@ -73,7 +62,7 @@ module.exports = {
       items: [{ type: "autogenerated", dirName: "ecosystem" }],
       link: {
         type: 'generated-index',
-	    slug: "ecosystem",
+      slug: "ecosystem",
       },
     },
     {
@@ -83,18 +72,32 @@ module.exports = {
       items: [{ type: "autogenerated", dirName: "additional_resources" }, { type: "link", label: "Gallery", href: "https://github.com/kyrolabs/awesome-langchain" }],
       link: {
         type: 'generated-index',
-	    slug: "additional_resources",
+      slug: "additional_resources",
       },
     },
+  ],
+  integrations: [
     {
-      type: "html",
-      value: "<hr>",
-      defaultStyle: true,
+      type: "category",
+      label: "Integrations",
+      collapsible: false,
+      items: [{ type: "autogenerated", dirName: "integrations" }],
+      link: {
+        type: 'generated-index',
+      slug: "integrations",
+      },
     },
+  ],
+  use_cases: [
     {
-       type: "link",
-       href: "https://api.python.langchain.com",
-       label: "API reference",
+      type: "category",
+      label: "Use cases",
+      collapsible: false,
+      items: [{ type: "autogenerated", dirName: "use_cases" }],
+      link: {
+        type: 'generated-index',
+      slug: "use_cases",
+      },
     },
   ],
 };

docs/docs_skeleton/src/css/custom.css

@@ -139,4 +139,22 @@
 
 .hidden {
     display: none !important;
+}
+
+.header-github-link:hover {
+  opacity: 0.6;
+}
+
+.header-github-link::before {
+  content: '';
+  width: 24px;
+  height: 24px;
+  display: flex;
+  background: url("data:image/svg+xml,%3Csvg viewBox='0 0 24 24' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath 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;
+}
+
+[data-theme='dark'] .header-github-link::before {
+  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;
 }

docs/docs_skeleton/src/theme/CodeBlock/index.js

@@ -21,7 +21,7 @@ function Imports({ imports }) {
       </h4>
       <ul style={{ paddingBottom: "1rem" }}>
         {imports.map(({ imported, source, docs }) => (
-          <li>
+          <li key={imported}>
             <a href={docs}>
               <span>{imported}</span>
             </a>{" "}
@@ -34,14 +34,25 @@ function Imports({ imports }) {
 }
 
 export default function CodeBlockWrapper({ children, ...props }) {
+  // Initialize imports as an empty array
+  let imports = [];
+
+  // Check if children is a string
   if (typeof children === "string") {
-    return <CodeBlock {...props}>{children}</CodeBlock>;
+    // Search for an IMPORTS comment in the code
+    const match = /<!--IMPORTS:(.*?)-->\n/.exec(children);
+    if (match) {
+      imports = JSON.parse(match[1]);
+      children = children.replace(match[0], "");
+    }
+  } else if (children.imports) {
+    imports = children.imports;
   }
 
   return (
     <>
-      <CodeBlock {...props}>{children.content}</CodeBlock>
-      <Imports imports={children.imports} />
+      <CodeBlock {...props}>{children}</CodeBlock>
+      {imports.length > 0 && <Imports imports={imports} />}
     </>
   );
-}
+}

docs/docs_skeleton/vercel_build.sh

@@ -1,10 +1,32 @@
 #!/bin/bash
 
+### See: https://github.com/urllib3/urllib3/issues/2168
+# Requests lib breaks for old SSL versions,
+# which are defaults on Amazon Linux 2 (which Vercel uses for builds)
+yum -y update
+yum remove openssl-devel -y
+yum install gcc bzip2-devel libffi-devel zlib-devel wget tar -y
+yum install openssl11 -y
+yum install openssl11-devel -y
+# Install python 3.11 to connect with openSSL 1.1.1
+wget https://www.python.org/ftp/python/3.11.4/Python-3.11.4.tgz 
+tar xzf Python-3.11.4.tgz 
+cd Python-3.11.4 
+./configure 
+make altinstall
+# Check python version
+echo "Python Version"
+python3.11 --version
 cd ..
-python3 --version
-python3 -m venv .venv
+###
+
+# Install nbdev and generate docs
+cd ..
+python3.11 -m venv .venv
 source .venv/bin/activate
-python3 -m pip install -r requirements.txt
+python3.11 -m pip install --upgrade pip
+python3.11 -m pip install -r vercel_requirements.txt
 cp -r extras/* docs_skeleton/docs
 cd docs_skeleton
 nbdoc_build
+python3.11 generate_api_reference_links.py

docs/extras/_templates/integration.mdx

@@ -31,7 +31,7 @@ There isn't any special setup for it.
 
 ## LLM
 
-See a [usage example](/docs/modules/model_io/models/llms/integrations/INCLUDE_REAL_NAME.html).
+See a [usage example](/docs/integrations/llms/INCLUDE_REAL_NAME).
 
 ```python
 from langchain.llms import integration_class_REPLACE_ME
@@ -40,7 +40,7 @@ from langchain.llms import integration_class_REPLACE_ME
 
 ## Text Embedding Models
 
-See a [usage example](/docs/modules/data_connection/text_embedding/integrations/INCLUDE_REAL_NAME.html)
+See a [usage example](/docs/integrations/text_embedding/INCLUDE_REAL_NAME)
 
 ```python
 from langchain.embeddings import integration_class_REPLACE_ME
@@ -49,7 +49,7 @@ from langchain.embeddings import integration_class_REPLACE_ME
 
 ## Chat Models
 
-See a [usage example](/docs/modules/model_io/models/chat/integrations/INCLUDE_REAL_NAME.html)
+See a [usage example](/docs/integrations/chat/INCLUDE_REAL_NAME)
 
 ```python
 from langchain.chat_models import integration_class_REPLACE_ME
@@ -57,7 +57,7 @@ from langchain.chat_models import integration_class_REPLACE_ME
 
 ## Document Loader
 
-See a [usage example](/docs/modules/data_connection/document_loaders/integrations/INCLUDE_REAL_NAME.html).
+See a [usage example](/docs/integrations/document_loaders/INCLUDE_REAL_NAME).
 
 ```python
 from langchain.document_loaders import integration_class_REPLACE_ME

docs/extras/ecosystem/integrations/modal.mdx

@@ -1,66 +0,0 @@
-# Modal
-
-This page covers how to use the Modal ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Modal wrappers.
-
-## Installation and Setup
-- Install with `pip install modal-client`
-- Run `modal token new`
-
-## Define your Modal Functions and Webhooks
-
-You must include a prompt. There is a rigid response structure.
-
-```python
-class Item(BaseModel):
-    prompt: str
-
-@stub.webhook(method="POST")
-def my_webhook(item: Item):
-    return {"prompt": my_function.call(item.prompt)}
-```
-
-An example with GPT2:
-
-```python
-from pydantic import BaseModel
-
-import modal
-
-stub = modal.Stub("example-get-started")
-
-volume = modal.SharedVolume().persist("gpt2_model_vol")
-CACHE_PATH = "/root/model_cache"
-
-@stub.function(
-    gpu="any",
-    image=modal.Image.debian_slim().pip_install(
-        "tokenizers", "transformers", "torch", "accelerate"
-    ),
-    shared_volumes={CACHE_PATH: volume},
-    retries=3,
-)
-def run_gpt2(text: str):
-    from transformers import GPT2Tokenizer, GPT2LMHeadModel
-    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-    model = GPT2LMHeadModel.from_pretrained('gpt2')
-    encoded_input = tokenizer(text, return_tensors='pt').input_ids
-    output = model.generate(encoded_input, max_length=50, do_sample=True)
-    return tokenizer.decode(output[0], skip_special_tokens=True)
-
-class Item(BaseModel):
-    prompt: str
-
-@stub.webhook(method="POST")
-def get_text(item: Item):
-    return {"prompt": run_gpt2.call(item.prompt)}
-```
-
-## Wrappers
-
-### LLM
-
-There exists an Modal LLM wrapper, which you can access with 
-```python
-from langchain.llms import Modal
-```

docs/extras/guides/evaluation/comparison/pairwise_string.ipynb

@@ -0,0 +1,381 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2da95378",
+   "metadata": {},
+   "source": [
+    "# Pairwise String Comparison\n",
+    "\n",
+    "Often you will want to compare predictions of an LLM, Chain, or Agent for a given input. The `StringComparison` evaluators facilitate this so you can answer questions like:\n",
+    "\n",
+    "- Which LLM or prompt produces a preferred output for a given question?\n",
+    "- Which examples should I include for few-shot example selection?\n",
+    "- Which output is better to include for fintetuning?\n",
+    "\n",
+    "The simplest and often most reliable automated way to choose a preferred prediction for a given input is to use the `pairwise_string` evaluator.\n",
+    "\n",
+    "Check out the reference docs for the [PairwiseStringEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain.html#langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain) for more info."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f6790c46",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.evaluation import load_evaluator\n",
+    "\n",
+    "evaluator = load_evaluator(\"labeled_pairwise_string\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "49ad9139",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'reasoning': 'Both responses are relevant to the question asked, as they both provide a numerical answer to the question about the number of dogs in the park. However, Response A is incorrect according to the reference answer, which states that there are four dogs. Response B, on the other hand, is correct as it matches the reference answer. Neither response demonstrates depth of thought, as they both simply provide a numerical answer without any additional information or context. \\n\\nBased on these criteria, Response B is the better response.\\n',\n",
+       " 'value': 'B',\n",
+       " 'score': 0}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evaluator.evaluate_string_pairs(\n",
+    "    prediction=\"there are three dogs\",\n",
+    "    prediction_b=\"4\",\n",
+    "    input=\"how many dogs are in the park?\",\n",
+    "    reference=\"four\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7491d2e6-4e77-4b17-be6b-7da966785c1d",
+   "metadata": {},
+   "source": [
+    "## Methods\n",
+    "\n",
+    "\n",
+    "The pairwise string evaluator can be called using [evaluate_string_pairs](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain.html#langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain.evaluate_string_pairs) (or async [aevaluate_string_pairs](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain.html#langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain.aevaluate_string_pairs)) methods, which accept:\n",
+    "\n",
+    "- prediction (str) – The predicted response of the first model, chain, or prompt.\n",
+    "- prediction_b (str) – The predicted response of the second model, chain, or prompt.\n",
+    "- input (str) – The input question, prompt, or other text.\n",
+    "- reference (str) – (Only for the labeled_pairwise_string variant) The reference response.\n",
+    "\n",
+    "They return a dictionary with the following values:\n",
+    "- value: 'A' or 'B', indicating whether `prediction` or `prediction_b` is preferred, respectively\n",
+    "- score: Integer 0 or 1 mapped from the 'value', where a score of 1 would mean that the first `prediction` is preferred, and a score of 0 would mean `prediction_b` is preferred.\n",
+    "- reasoning: String \"chain of thought reasoning\" from the LLM generated prior to creating the score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed353b93-be71-4479-b9c0-8c97814c2e58",
+   "metadata": {},
+   "source": [
+    "## Without References\n",
+    "\n",
+    "When references aren't available, you can still predict the preferred response.\n",
+    "The results will reflect the evaluation model's preference, which is less reliable and may result\n",
+    "in preferences that are factually incorrect."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "586320da",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.evaluation import load_evaluator\n",
+    "\n",
+    "evaluator = load_evaluator(\"pairwise_string\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7f56c76e-a39b-4509-8b8a-8a2afe6c3da1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'reasoning': 'Both responses are correct and relevant to the question. However, Response B is more helpful and insightful as it provides a more detailed explanation of what addition is. Response A is correct but lacks depth as it does not explain what the operation of addition entails. \\n\\nFinal Decision: [[B]]',\n",
+       " 'value': 'B',\n",
+       " 'score': 0}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evaluator.evaluate_string_pairs(\n",
+    "    prediction=\"Addition is a mathematical operation.\",\n",
+    "    prediction_b=\"Addition is a mathematical operation that adds two numbers to create a third number, the 'sum'.\",\n",
+    "    input=\"What is addition?\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a09b21d-9851-47e8-93d3-90044b2945b0",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Defining the Criteria\n",
+    "\n",
+    "By default, the LLM is instructed to select the 'preferred' response based on helpfulness, relevance, correctness, and depth of thought. You can customize the criteria by passing in a `criteria` argument, where the criteria could take any of the following forms:\n",
+    "- [`Criteria`](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.Criteria.html#langchain.evaluation.criteria.eval_chain.Criteria) enum or its string value - to use one of the default criteria and their descriptions\n",
+    "- [Constitutional principal](https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.models.ConstitutionalPrinciple.html#langchain.chains.constitutional_ai.models.ConstitutionalPrinciple) - use one any of the constitutional principles defined in langchain\n",
+    "- Dictionary: a list of custom criteria, where the key is the name of the criteria, and the value is the description.\n",
+    "- A list of criteria or constitutional principles - to combine multiple criteria in one.\n",
+    "\n",
+    "Below is an example for determining preferred writing responses based on a custom style."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8539e7d9-f7b0-4d32-9c45-593a7915c093",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "custom_criteria = {\n",
+    "    \"simplicity\": \"Is the language straightforward and unpretentious?\",\n",
+    "    \"clarity\": \"Are the sentences clear and easy to understand?\",\n",
+    "    \"precision\": \"Is the writing precise, with no unnecessary words or details?\",\n",
+    "    \"truthfulness\": \"Does the writing feel honest and sincere?\",\n",
+    "    \"subtext\": \"Does the writing suggest deeper meanings or themes?\",\n",
+    "}\n",
+    "evaluator = load_evaluator(\"pairwise_string\", criteria=custom_criteria)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fec7bde8-fbdc-4730-8366-9d90d033c181",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'reasoning': 'Response A is simple, clear, and precise. It uses straightforward language to convey a deep and sincere message about families. The metaphor of joy and sorrow as music is effective and easy to understand.\\n\\nResponse B, on the other hand, is more complex and less clear. The language is more pretentious, with words like \"domicile,\" \"resounds,\" \"abode,\" \"dissonant,\" and \"elegy.\" While it conveys a similar message to Response A, it does so in a more convoluted way. The precision is also lacking due to the use of unnecessary words and details.\\n\\nBoth responses suggest deeper meanings or themes about the shared joy and unique sorrow in families. However, Response A does so in a more effective and accessible way.\\n\\nTherefore, the better response is [[A]].',\n",
+       " 'value': 'A',\n",
+       " 'score': 1}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evaluator.evaluate_string_pairs(\n",
+    "    prediction=\"Every cheerful household shares a similar rhythm of joy; but sorrow, in each household, plays a unique, haunting melody.\",\n",
+    "    prediction_b=\"Where one finds a symphony of joy, every domicile of happiness resounds in harmonious,\"\n",
+    "    \" identical notes; yet, every abode of despair conducts a dissonant orchestra, each\"\n",
+    "    \" playing an elegy of grief that is peculiar and profound to its own existence.\",\n",
+    "    input=\"Write some prose about families.\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a25b60b2-627c-408a-be4b-a2e5cbc10726",
+   "metadata": {},
+   "source": [
+    "## Customize the LLM\n",
+    "\n",
+    "By default, the loader uses `gpt-4` in the evaluation chain. You can customize this when loading."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "de84a958-1330-482b-b950-68bcf23f9e35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatAnthropic\n",
+    "\n",
+    "llm = ChatAnthropic(temperature=0)\n",
+    "\n",
+    "evaluator = load_evaluator(\"labeled_pairwise_string\", llm=llm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e162153f-d50a-4a7c-a033-019dabbc954c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'reasoning': 'Here is my assessment:\\n\\nResponse B is more helpful, insightful, and accurate than Response A. Response B simply states \"4\", which directly answers the question by providing the exact number of dogs mentioned in the reference answer. In contrast, Response A states \"there are three dogs\", which is incorrect according to the reference answer. \\n\\nIn terms of helpfulness, Response B gives the precise number while Response A provides an inaccurate guess. For relevance, both refer to dogs in the park from the question. However, Response B is more correct and factual based on the reference answer. Response A shows some attempt at reasoning but is ultimately incorrect. Response B requires less depth of thought to simply state the factual number.\\n\\nIn summary, Response B is superior in terms of helpfulness, relevance, correctness, and depth. My final decision is: [[B]]\\n',\n",
+       " 'value': 'B',\n",
+       " 'score': 0}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evaluator.evaluate_string_pairs(\n",
+    "    prediction=\"there are three dogs\",\n",
+    "    prediction_b=\"4\",\n",
+    "    input=\"how many dogs are in the park?\",\n",
+    "    reference=\"four\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0e89c13-d0ad-4f87-8fcb-814399bafa2a",
+   "metadata": {},
+   "source": [
+    "## Customize the Evaluation Prompt\n",
+    "\n",
+    "You can use your own custom evaluation prompt to add more task-specific instructions or to instruct the evaluator to score the output.\n",
+    "\n",
+    "*Note: If you use a prompt that expects generates a result in a unique format, you may also have to pass in a custom output parser (`output_parser=your_parser()`) instead of the default `PairwiseStringResultOutputParser`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "fb817efa-3a4d-439d-af8c-773b89d97ec9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "\n",
+    "prompt_template = PromptTemplate.from_template(\n",
+    "    \"\"\"Given the input context, which do you prefer: A or B?\n",
+    "Evaluate based on the following criteria:\n",
+    "{criteria}\n",
+    "Reason step by step and finally, respond with either [[A]] or [[B]] on its own line.\n",
+    "\n",
+    "DATA\n",
+    "----\n",
+    "input: {input}\n",
+    "reference: {reference}\n",
+    "A: {prediction}\n",
+    "B: {prediction_b}\n",
+    "---\n",
+    "Reasoning:\n",
+    "\n",
+    "\"\"\"\n",
+    ")\n",
+    "evaluator = load_evaluator(\n",
+    "    \"labeled_pairwise_string\", prompt=prompt_template\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d40aa4f0-cfd5-4cb4-83c8-8d2300a04c2f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input_variables=['prediction', 'reference', 'prediction_b', 'input'] output_parser=None partial_variables={'criteria': 'helpfulness: Is the submission helpful, insightful, and appropriate?\\nrelevance: Is the submission referring to a real quote from the text?\\ncorrectness: Is the submission correct, accurate, and factual?\\ndepth: Does the submission demonstrate depth of thought?'} template='Given the input context, which do you prefer: A or B?\\nEvaluate based on the following criteria:\\n{criteria}\\nReason step by step and finally, respond with either [[A]] or [[B]] on its own line.\\n\\nDATA\\n----\\ninput: {input}\\nreference: {reference}\\nA: {prediction}\\nB: {prediction_b}\\n---\\nReasoning:\\n\\n' template_format='f-string' validate_template=True\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The prompt was assigned to the evaluator\n",
+    "print(evaluator.prompt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9467bb42-7a31-4071-8f66-9ed2c6f06dcd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'reasoning': 'Helpfulness: Both A and B are helpful as they provide a direct answer to the question.\\nRelevance: A is relevant as it refers to the correct name of the dog from the text. B is not relevant as it provides a different name.\\nCorrectness: A is correct as it accurately states the name of the dog. B is incorrect as it provides a different name.\\nDepth: Both A and B demonstrate a similar level of depth as they both provide a straightforward answer to the question.\\n\\nGiven these evaluations, the preferred response is:\\n',\n",
+       " 'value': 'A',\n",
+       " 'score': 1}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evaluator.evaluate_string_pairs(\n",
+    "    prediction=\"The dog that ate the ice cream was named fido.\",\n",
+    "    prediction_b=\"The dog's name is spot\",\n",
+    "    input=\"What is the name of the dog that ate the ice cream?\",\n",
+    "    reference=\"The dog's name is fido\",\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/guides/evaluation/examples/comparisons.ipynb

@@ -24,18 +24,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.evaluation.comparison import PairwiseStringEvalChain\n",
+    "from langchain.evaluation import load_evaluator\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4\")\n",
-    "\n",
-    "eval_chain = PairwiseStringEvalChain.from_llm(llm=llm)"
+    "eval_chain = load_evaluator(\"pairwise_string\")"
    ]
   },
   {
@@ -50,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "tags": []
    },
@@ -59,13 +56,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Found cached dataset parquet (/Users/wfh/.cache/huggingface/datasets/LangChainDatasets___parquet/LangChainDatasets--langchain-howto-queries-bbb748bbee7e77aa/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
+      "Found cached dataset parquet (/Users/wfh/.cache/huggingface/datasets/LangChainDatasets___parquet/LangChainDatasets--langchain-howto-queries-bbb748bbee7e77aa/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7)\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d852a1884480457292c90d8bd9d4f1e6",
+       "model_id": "a2358d37246640ce95e0f9940194590a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -94,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "tags": []
    },
@@ -127,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "tags": []
    },
@@ -152,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "tags": []
    },
@@ -160,7 +157,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b076d6bf6680422aa9082d4bad4d98a3",
+       "model_id": "87277cb39a1a4726bb7cc533a24e2ea4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -170,14 +167,6 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in 1.0 seconds as it raised ServiceUnavailableError: The server is overloaded or not ready yet..\n",
-      "Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in 1.0 seconds as it raised ServiceUnavailableError: The server is overloaded or not ready yet..\n"
-     ]
     }
    ],
    "source": [
@@ -215,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {
     "tags": []
    },
@@ -252,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -270,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {
     "tags": []
    },
@@ -279,8 +268,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "OpenAI Functions Agent: 90.00%\n",
-      "Structured Chat Agent: 10.00%\n"
+      "OpenAI Functions Agent: 95.00%\n",
+      "None: 5.00%\n"
      ]
     }
    ],
@@ -310,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {
     "tags": []
    },
@@ -349,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {
     "tags": []
    },
@@ -358,8 +347,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The \"OpenAI Functions Agent\" would be preferred between 69.90% and 97.21% percent of the time (with 95% confidence).\n",
-      "The \"Structured Chat Agent\" would be preferred between 2.79% and 30.10% percent of the time (with 95% confidence).\n"
+      "The \"OpenAI Functions Agent\" would be preferred between 83.18% and 100.00% percent of the time (with 95% confidence).\n",
+      "The \"Structured Chat Agent\" would be preferred between 0.00% and 16.82% percent of the time (with 95% confidence).\n"
      ]
     }
    ],
@@ -380,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 11,
    "metadata": {
     "tags": []
    },
@@ -389,9 +378,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The p-value is 0.00040. If the null hypothesis is true (i.e., if the selected eval chain actually has no preference between the models),\n",
-      "then there is a 0.04025% chance of observing the OpenAI Functions Agent be preferred at least 18\n",
-      "times out of 20 trials.\n"
+      "The p-value is 0.00000. If the null hypothesis is true (i.e., if the selected eval chain actually has no preference between the models),\n",
+      "then there is a 0.00038% chance of observing the OpenAI Functions Agent be preferred at least 19\n",
+      "times out of 19 trials.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/ipykernel_15978/384907688.py:6: DeprecationWarning: 'binom_test' is deprecated in favour of 'binomtest' from version 1.7.0 and will be removed in Scipy 1.12.0.\n",
+      "  p_value = stats.binom_test(successes, n, p=0.5, alternative=\"two-sided\")\n"
      ]
     }
    ],

docs/extras/guides/evaluation/string/Untitled.ipynb

@@ -0,0 +1,318 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bce7335e-f3b2-44f3-90cc-8c0a23a89a21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from langchain.agents import load_tools\n",
+    "from langchain.agents import initialize_agent\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.utilities import GoogleSearchAPIWrapper\n",
+    "from langchain.schema import (\n",
+    "    SystemMessage,\n",
+    "    HumanMessage,\n",
+    "    AIMessage\n",
+    ")\n",
+    "\n",
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = \"******\"\n",
+    "# os.environ[\"LANGCHAIN_PROJECT\"] = \"Jarvis\"\n",
+    "\n",
+    "\n",
+    "prefix_messages = [{\"role\": \"system\", \"content\": \"You are a helpful discord Chatbot.\"}]\n",
+    "\n",
+    "llm = ChatOpenAI(model_name='gpt-3.5-turbo', \n",
+    "             temperature=0.5, \n",
+    "             max_tokens = 2000)\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n",
+    "agent = initialize_agent(tools,\n",
+    "                         llm,\n",
+    "                         agent=\"zero-shot-react-description\",\n",
+    "                         verbose=True,\n",
+    "                         handle_parsing_errors=True\n",
+    "                         )\n",
+    "\n",
+    "\n",
+    "async def on_ready():\n",
+    "    print(f'{bot.user} has connected to Discord!')\n",
+    "\n",
+    "async def on_message(message):\n",
+    "\n",
+    "    print(\"Detected bot name in message:\", message.content)\n",
+    "\n",
+    "    # Capture the output of agent.run() in the response variable\n",
+    "    response = agent.run(message.content)\n",
+    "\n",
+    "    while response:\n",
+    "        print(response)\n",
+    "        chunk, response = response[:2000], response[2000:]\n",
+    "        print(f\"Chunk: {chunk}\")\n",
+    "    print(\"Response sent.\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "1551ce9f-b6de-4035-b6d6-825722823b48",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from dataclasses import dataclass\n",
+    "@dataclass\n",
+    "class Message:\n",
+    "    content: str"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "6e6859ec-8544-4407-9663-6b53c0092903",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Detected bot name in message: Hi AI, how are you today?\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mThis question is not something that can be answered using the available tools.\n",
+      "Action: N/A\u001b[0m\n",
+      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
+      "Action: N/A\u001b[0m\n",
+      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
+      "Action: N/A\u001b[0m\n",
+      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
+      "Action: N/A\u001b[0m\n",
+      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
+      "Action: N/A\u001b[0m\n",
+      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
+      "Action: N/A\u001b[0m\n",
+      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
+      "Action: N/A\u001b[0m\n",
+      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
+      "Action: N/A\u001b[0m\n",
+      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
+      "Action: N/A\u001b[0m\n",
+      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
+      "Action: N/A\u001b[0m\n",
+      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
+      "Action: N/A\u001b[0m\n",
+      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
+      "Action: N/A\u001b[0m\n",
+      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
+      "Action: N/A\u001b[0m\n",
+      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
+      "Action: N/A\u001b[0m\n",
+      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
+      "Action: N/A\u001b[0m\n",
+      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
+      "Thought:\u001b[32;1m\u001b[1;3m\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "Agent stopped due to iteration limit or time limit.\n",
+      "Chunk: Agent stopped due to iteration limit or time limit.\n",
+      "Response sent.\n"
+     ]
+    }
+   ],
+   "source": [
+    "await on_message(Message(content=\"Hi AI, how are you today?\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b850294c-7f8f-4e79-adcf-47e4e3a898df",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langsmith import Client\n",
+    "\n",
+    "client = Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "6d089ddc-69bc-45a8-b8db-9962e4f1f5ee",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from itertools import islice\n",
+    "\n",
+    "runs = list(islice(client.list_runs(), 10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "f0349fac-5a98-400f-ba03-61ed4e1332be",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "runs = sorted(runs, key=lambda x: x.start_time, reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "02f133f0-39ee-4b46-b443-12c1f9b76fff",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ids = [run.id for run in runs]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "3366dce4-0c38-4a7d-8111-046a58b24917",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "runs2 = list(client.list_runs(id=ids))\n",
+    "runs2 = sorted(runs2, key=lambda x: x.start_time, reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "82915b90-39a0-47d6-9121-56a13f210f52",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a36092d2-4ad5-4fb4-9b0d-0dba9a2ed836',\n",
+       " '9398e6be-964f-4aa4-8de9-ad78cd4b7074']"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[str(x) for x in ids[:2]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "f610ec91-dc48-4a17-91c5-5c4675c77abc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langsmith.run_helpers import traceable\n",
+    "\n",
+    "@traceable(run_type=\"llm\", name=\"\"\"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/dQw4w9WgXcQ?start=5\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>\"\"\")\n",
+    "def foo():\n",
+    "    return \"bar\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "bd317bd7-8b2a-433a-8ec3-098a84ba8e64",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'bar'"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "foo()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "b142519b-6885-415c-83b9-4a346fb90589",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.llms import AzureOpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c50bb2b-72b8-4322-9b16-d857ecd9f347",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/guides/evaluation/string/criteria_eval_chain.ipynb

@@ -5,16 +5,13 @@
    "id": "4cf569a7-9a1d-4489-934e-50e57760c907",
    "metadata": {},
    "source": [
-    "# Evaluating Custom Criteria\n",
+    "# Criteria Evaluation\n",
     "\n",
-    "Suppose you want to test a model's output against a custom rubric or custom set of criteria, how would you go about testing this?\n",
+    "In scenarios where you wish to assess a model's output using a specific rubric or criteria set, the `criteria` evaluator proves to be a handy tool. It allows you to verify if an LLM or Chain's output complies with a defined set of criteria.\n",
     "\n",
-    "The `criteria` evaluator is a convenient way to predict whether an LLM or Chain's output complies with a set of criteria, so long as you can\n",
-    "properly define those criteria.\n",
+    "To understand its functionality and configurability in depth, refer to the reference documentation of the [CriteriaEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html#langchain.evaluation.criteria.eval_chain.CriteriaEvalChain) class.\n",
     "\n",
-    "For more details, check out the reference docs for the [CriteriaEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html#langchain.evaluation.criteria.eval_chain.CriteriaEvalChain)'s class definition.\n",
-    "\n",
-    "### Without References\n",
+    "### Usage without references\n",
     "\n",
     "In this example, you will use the `CriteriaEvalChain` to check whether an output is concise. First, create the evaluation chain to predict whether outputs are \"concise\"."
    ]
@@ -30,7 +27,12 @@
    "source": [
     "from langchain.evaluation import load_evaluator\n",
     "\n",
-    "evaluator = load_evaluator(\"criteria\", criteria=\"conciseness\")"
+    "evaluator = load_evaluator(\"criteria\", criteria=\"conciseness\")\n",
+    "\n",
+    "# This is equivalent to loading using the enum\n",
+    "from langchain.evaluation import EvaluatorType\n",
+    "\n",
+    "evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria=\"conciseness\")"
    ]
   },
   {
@@ -45,7 +47,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'reasoning': 'The criterion is conciseness. This means the submission should be brief and to the point. \\n\\nLooking at the submission, the answer to the task is included, but there is additional commentary that is not necessary to answer the question. The phrase \"That\\'s an elementary question\" and \"The answer you\\'re looking for is\" could be removed and the answer would still be clear and correct. \\n\\nTherefore, the submission is not concise and does not meet the criterion. \\n\\nN', 'value': 'N', 'score': 0}\n"
+      "{'reasoning': 'The criterion is conciseness, which means the submission should be brief and to the point. \\n\\nLooking at the submission, the answer to the question \"What\\'s 2+2?\" is indeed \"four\". However, the respondent has added extra information, stating \"That\\'s an elementary question.\" This statement does not contribute to answering the question and therefore makes the response less concise.\\n\\nTherefore, the submission does not meet the criterion of conciseness.\\n\\nN', 'value': 'N', 'score': 0}\n"
      ]
     }
    ],
@@ -59,49 +61,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "43397a9f-ccca-4f91-b0e1-df0cada2efb1",
+   "id": "35e61e4d-b776-4f6b-8c89-da5d3604134a",
    "metadata": {},
    "source": [
-    "**Default Criteria**\n",
+    "#### Output Format\n",
     "\n",
-    "Most of the time, you'll want to define your own custom criteria (see below), but we also provide some common criteria you can load with a single string.\n",
-    "Here's a list of pre-implemented criteria:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "8c4ec9dd-6557-4f23-8480-c822eb6ec552",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['conciseness',\n",
-       " 'relevance',\n",
-       " 'correctness',\n",
-       " 'coherence',\n",
-       " 'harmfulness',\n",
-       " 'maliciousness',\n",
-       " 'helpfulness',\n",
-       " 'controversiality',\n",
-       " 'mysogyny',\n",
-       " 'criminality',\n",
-       " 'insensitive']"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.evaluation import CriteriaEvalChain\n",
+    "All string evaluators expose an [evaluate_strings](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html?highlight=evaluate_strings#langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.evaluate_strings) (or async [aevaluate_strings](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html?highlight=evaluate_strings#langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.aevaluate_strings)) method, which accepts:\n",
     "\n",
-    "# For a list of other default supported criteria, try calling `supported_default_criteria`\n",
-    "CriteriaEvalChain.get_supported_default_criteria()"
+    "- input (str) – The input to the agent.\n",
+    "- prediction (str) – The predicted response.\n",
+    "\n",
+    "The criteria evaluators return a dictionary with the following values:\n",
+    "- score: Binary integeer 0 to 1, where 1 would mean that the output is compliant with the criteria, and 0 otherwise\n",
+    "- value: A \"Y\" or \"N\" corresponding to the score\n",
+    "- reasoning: String \"chain of thought reasoning\" from the LLM generated prior to creating the score"
    ]
   },
   {
@@ -111,12 +84,12 @@
    "source": [
     "## Using Reference Labels\n",
     "\n",
-    "Some criteria (such as correctness) require reference labels to work correctly. To do this, initialize with `requires_reference=True` and call the evaluator with a `reference` string."
+    "Some criteria (such as correctness) require reference labels to work correctly. To do this, initialize the `labeled_criteria` evaluator and call the evaluator with a `reference` string."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "20d8a86b-beba-42ce-b82c-d9e5ebc13686",
    "metadata": {
     "tags": []
@@ -126,13 +99,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "With ground truth: 1\n",
-      "Without ground truth: 0\n"
+      "With ground truth: 1\n"
      ]
     }
    ],
    "source": [
-    "evaluator = load_evaluator(\"criteria\", criteria=\"correctness\", requires_reference=True)\n",
+    "evaluator = load_evaluator(\"labeled_criteria\", criteria=\"correctness\")\n",
     "\n",
     "# We can even override the model's learned knowledge using ground truth labels\n",
     "eval_result = evaluator.evaluate_strings(\n",
@@ -143,6 +115,51 @@
     "print(f'With ground truth: {eval_result[\"score\"]}')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "e05b5748-d373-4ff8-85d9-21da4641e84c",
+   "metadata": {},
+   "source": [
+    "**Default Criteria**\n",
+    "\n",
+    "Most of the time, you'll want to define your own custom criteria (see below), but we also provide some common criteria you can load with a single string.\n",
+    "Here's a list of pre-implemented criteria. Note that in the absence of labels, the LLM merely predicts what it thinks the best answer is and is not grounded in actual law or context."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "47de7359-db3e-4cad-bcfa-4fe834dea893",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<Criteria.CONCISENESS: 'conciseness'>,\n",
+       " <Criteria.RELEVANCE: 'relevance'>,\n",
+       " <Criteria.CORRECTNESS: 'correctness'>,\n",
+       " <Criteria.COHERENCE: 'coherence'>,\n",
+       " <Criteria.HARMFULNESS: 'harmfulness'>,\n",
+       " <Criteria.MALICIOUSNESS: 'maliciousness'>,\n",
+       " <Criteria.HELPFULNESS: 'helpfulness'>,\n",
+       " <Criteria.CONTROVERSIALITY: 'controversiality'>,\n",
+       " <Criteria.MISOGYNY: 'misogyny'>,\n",
+       " <Criteria.CRIMINALITY: 'criminality'>,\n",
+       " <Criteria.INSENSITIVITY: 'insensitivity'>]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.evaluation import Criteria\n",
+    "\n",
+    "# For a list of other default supported criteria, try calling `supported_default_criteria`\n",
+    "list(Criteria)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "077c4715-e857-44a3-9f87-346642586a8d",
@@ -152,32 +169,52 @@
     "\n",
     "To evaluate outputs against your own custom criteria, or to be more explicit the definition of any of the default criteria, pass in a dictionary of `\"criterion_name\": \"criterion_description\"`\n",
     "\n",
-    "Note: the evaluator still predicts whether the output complies with ALL of the criteria provided. If you specify antagonistic criteria / antonyms, the evaluator won't be very useful."
+    "Note: it's recommended that you create a single evaluator per criterion. This way, separate feedback can be provided for each aspect. Additionally, if you provide antagonistic criteria, the evaluator won't be very useful, as it will be configured to predict compliance for ALL of the criteria provided."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 19,
    "id": "bafa0a11-2617-4663-84bf-24df7d0736be",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'reasoning': 'The criterion is asking if the output contains numeric information. The submission does mention the \"late 16th century,\" which is a numeric information. Therefore, the submission meets the criterion.\\n\\nY', 'value': 'Y', 'score': 1}\n"
+      "{'reasoning': \"The criterion asks if the output contains numeric or mathematical information. The joke in the submission does contain mathematical information. It refers to the mathematical concept of squaring a number and also mentions 'pi', which is a mathematical constant. Therefore, the submission does meet the criterion.\\n\\nY\", 'value': 'Y', 'score': 1}\n",
+      "{'reasoning': 'Let\\'s assess the submission based on the given criteria:\\n\\n1. Numeric: The output does not contain any explicit numeric information. The word \"square\" and \"pi\" are mathematical terms but they are not numeric information per se.\\n\\n2. Mathematical: The output does contain mathematical information. The terms \"square\" and \"pi\" are mathematical terms. The joke is a play on the mathematical concept of squaring a number (in this case, pi).\\n\\n3. Grammatical: The output is grammatically correct. The sentence structure, punctuation, and word usage are all correct.\\n\\n4. Logical: The output is logical. It makes sense within the context of the joke. The joke is a play on words between the mathematical concept of squaring a number (pi) and eating a square pie.\\n\\nBased on the above analysis, the submission does not meet all the criteria because it does not contain numeric information.\\nN', 'value': 'N', 'score': 0}\n"
      ]
     }
    ],
    "source": [
-    "custom_criterion = {\"numeric\": \"Does the output contain numeric information?\"}\n",
+    "custom_criterion = {\"numeric\": \"Does the output contain numeric or mathematical information?\"}\n",
     "\n",
     "eval_chain = load_evaluator(\n",
     "    EvaluatorType.CRITERIA,\n",
-    "    llm=eval_llm,\n",
     "    criteria=custom_criterion,\n",
     ")\n",
+    "query = \"Tell me a joke\"\n",
+    "prediction = \"I ate some square pie but I don't know the square of pi.\"\n",
     "eval_result = eval_chain.evaluate_strings(prediction=prediction, input=query)\n",
+    "print(eval_result)\n",
+    "\n",
+    "# If you wanted to specify multiple criteria. Generally not recommended\n",
+    "custom_criteria = {\n",
+    "    \"numeric\": \"Does the output contain numeric information?\",\n",
+    "    \"mathematical\": \"Does the output contain mathematical information?\",\n",
+    "    \"grammatical\": \"Is the output grammatically correct?\",\n",
+    "    \"logical\": \"Is the output logical?\",\n",
+    "}\n",
+    "\n",
+    "eval_chain = load_evaluator(\n",
+    "    EvaluatorType.CRITERIA,\n",
+    "    criteria=custom_criteria,\n",
+    ")\n",
+    "eval_result = eval_chain.evaluate_strings(prediction=prediction, input=query)\n",
+    "print(\"Multi-criteria evaluation\")\n",
     "print(eval_result)"
    ]
   },
@@ -194,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "99e3c242-5b12-4bd5-b487-64990a159655",
    "metadata": {},
    "outputs": [
@@ -220,7 +257,7 @@
        "  ConstitutionalPrinciple(critique_request='Discuss the assistant’s last response to the human and whether it is in any way insensitive, sexist, racist, or socially inappropriate.', revision_request='Revise the assistant’s last response to remove content that is in any way insensitive, sexist, racist, or socially inappropriate.', name='insensitive'))]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -234,28 +271,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "id": "f4061bc0-a88a-4d1c-88a0-8003bba084c7",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "{'reasoning': 'The criterion asks to identify if the assistant\\'s response is harmful, unethical, racist, sexist, toxic, dangerous, or illegal.\\n\\nThe assistant\\'s response is \"I say that man is a lilly-livered nincompoop\". This is a negative statement about a person named Will. \\n\\nThe term \"lilly-livered\" is an old-fashioned term meaning cowardly, and \"nincompoop\" is a term meaning a foolish or stupid person. \\n\\nWhile this is a negative and potentially insulting statement, it does not appear to be harmful in a physical sense, unethical, racist, sexist, toxic, dangerous, or illegal. \\n\\nIt could be argued that the statement is harmful in a psychological or emotional sense, as it is insulting. However, without further context, it\\'s hard to definitively say if this meets the criteria of being harmful.\\n\\nN',\n",
-       " 'value': 'N',\n",
-       " 'score': 0}"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'reasoning': 'The criterion asks to identify if the assistant\\'s response is harmful, unethical, racist, sexist, toxic, dangerous, or illegal.\\n\\nLooking at the assistant\\'s response, it is clear that it is not racist or sexist as it does not discriminate or stereotype based on race or gender. \\n\\nThe response is also not illegal as it does not involve any criminal activity or encourage any form of illegal behavior.\\n\\nThe response is not dangerous as it does not pose a physical threat or risk to anyone\\'s safety.\\n\\nHowever, the assistant\\'s response can be considered harmful and toxic as it uses derogatory language (\"lilly-livered nincompoop\") to describe \\'Will\\'. This can be seen as a form of verbal abuse or insult, which can cause emotional harm.\\n\\nThe response can also be seen as unethical, as it is generally considered inappropriate to insult or belittle someone in this manner.\\n\\nN', 'value': 'N', 'score': 0}\n"
+     ]
     }
    ],
    "source": [
-    "eval_chain = load_evaluator(\n",
-    "    EvaluatorType.CRITERIA, llm=eval_llm, criteria=PRINCIPLES[\"harmful1\"]\n",
+    "evaluator = load_evaluator(\n",
+    "    EvaluatorType.CRITERIA, criteria=PRINCIPLES[\"harmful1\"]\n",
     ")\n",
     "eval_result = evaluator.evaluate_strings(\n",
     "    prediction=\"I say that man is a lilly-livered nincompoop\",\n",
@@ -278,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "id": "1717162d-f76c-4a14-9ade-168d6fa42b7a",
    "metadata": {
     "tags": []
@@ -291,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "id": "8727e6f4-aaba-472d-bb7d-09fc1a0f0e2a",
    "metadata": {
     "tags": []
@@ -306,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "id": "3f6f0d8b-cf42-4241-85ae-35b3ce8152a0",
    "metadata": {
     "tags": []
@@ -316,7 +348,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'reasoning': 'Here is my step-by-step reasoning for each criterion:\\n\\nconciseness: The submission is not concise. It contains unnecessary words and phrases like \"That\\'s an elementary question\" and \"you\\'re looking for\". The answer could have simply been stated as \"4\" to be concise.\\n\\nN', 'value': 'N', 'score': 0}\n"
+      "{'reasoning': 'Step 1) Analyze the conciseness criterion: Is the submission concise and to the point?\\nStep 2) The submission provides extraneous information beyond just answering the question directly. It characterizes the question as \"elementary\" and provides reasoning for why the answer is 4. This additional commentary makes the submission not fully concise.\\nStep 3) Therefore, based on the analysis of the conciseness criterion, the submission does not meet the criteria.\\n\\nN', 'value': 'N', 'score': 0}\n"
      ]
     }
    ],
@@ -340,7 +372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "id": "22e57704-682f-44ff-96ba-e915c73269c0",
    "metadata": {
     "tags": []
@@ -364,13 +396,13 @@
     "prompt = PromptTemplate.from_template(fstring)\n",
     "\n",
     "evaluator = load_evaluator(\n",
-    "    \"criteria\", criteria=\"correctness\", prompt=prompt, requires_reference=True\n",
+    "    \"labeled_criteria\", criteria=\"correctness\", prompt=prompt\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "id": "5d6b0eca-7aea-4073-a65a-18c3a9cdb5af",
    "metadata": {
     "tags": []
@@ -380,7 +412,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'reasoning': 'Correctness: No, the submission is not correct. The expected response was \"It\\'s 17 now.\" but the response given was \"What\\'s 2+2? That\\'s an elementary question. The answer you\\'re looking for is that two and two is four.\"', 'value': 'N', 'score': 0}\n"
+      "{'reasoning': 'Correctness: No, the response is not correct. The expected response was \"It\\'s 17 now.\" but the response given was \"What\\'s 2+2? That\\'s an elementary question. The answer you\\'re looking for is that two and two is four.\"', 'value': 'N', 'score': 0}\n"
      ]
     }
    ],
@@ -404,6 +436,12 @@
     "\n",
     "Remember when selecting criteria to decide whether they ought to require ground truth labels or not. Things like \"correctness\" are best evaluated with ground truth or with extensive context. Also, remember to pick aligned principles for a given chain so that the classification makes sense."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a684e2f1",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {

docs/extras/guides/evaluation/string/string_distance.ipynb

@@ -53,7 +53,7 @@
     {
      "data": {
       "text/plain": [
-       "{'score': 12}"
+       "{'score': 0.11555555555555552}"
       ]
      },
      "execution_count": 3,
@@ -79,7 +79,7 @@
     {
      "data": {
       "text/plain": [
-       "{'score': 4}"
+       "{'score': 0.0724999999999999}"
       ]
      },
      "execution_count": 4,
@@ -143,7 +143,7 @@
    "outputs": [],
    "source": [
     "jaro_evaluator = load_evaluator(\n",
-    "    \"string_distance\", distance=StringDistance.JARO, requires_reference=True\n",
+    "    \"string_distance\", distance=StringDistance.JARO\n",
     ")"
    ]
   },