bump ver to 195 (#5949)

# Expose full params in Qdrant

There were many questions regarding supporting some additional
parameters in Qdrant integration. Qdrant supports many vector search
optimizations that were impossible to use directly in Qdrant before.
That includes:

1. Possibility to manipulate collection params while using
`Qdrant.from_texts`. The PR allows setting things such as quantization,
HNWS config, optimizers config, etc. That makes it consistent with raw
`QdrantClient`.
2. Extended options while searching. It includes HNSW options, exact
search, score threshold filtering, and read consistency in distributed
mode.

After merging that PR, #4858 might also be closed.

## Who can review?

VectorStores / Retrievers / Memory

@dev2049 @hwchase17

.github/CONTRIBUTING.md

@@ -2,60 +2,62 @@
 
 Hi there! Thank you for even being interested in contributing to LangChain.
 As an open source project in a rapidly developing field, we are extremely open
-to contributions, whether it be in the form of a new feature, improved infra, or better documentation.
+to contributions, whether they be in the form of new features, improved infra, better documentation, or bug fixes.
+
+## 🗺️ Guidelines
+
+### 👩‍💻 Contributing Code
 
 To contribute to this project, please follow a ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
 Please do not try to push directly to this repo unless you are maintainer.
 
-## 🗺️Contributing Guidelines
+Please follow the checked-in pull request template when opening pull requests. Note related issues and tag relevant
+maintainers.
+
+Pull requests cannot land without passing the formatting, linting and testing checks first. See
+[Common Tasks](#-common-tasks) for how to run these checks locally.
+
+It's essential that we maintain great documentation and testing. If you:
+- Fix a bug
+  - Add a relevant unit or integration test when possible. These live in `tests/unit_tests` and `tests/integration_tests`.
+- Make an improvement
+  - Update any affected example notebooks and documentation. These lives in `docs`.
+  - Update unit and integration tests when relevant.
+- Add a feature
+  - Add a demo notebook in `docs/modules`.
+  - Add unit and integration tests.
+
+We're a small, building-oriented team. If there's something you'd like to add or change, opening a pull request is the
+best way to get our attention.
 
 ### 🚩GitHub Issues
 
 Our [issues](https://github.com/hwchase17/langchain/issues) page is kept up to date
-with bugs, improvements, and feature requests. There is a taxonomy of labels to help
-with sorting and discovery of issues of interest. These include:
+with bugs, improvements, and feature requests. 
 
-- prompts: related to prompt tooling/infra.
-- llms: related to LLM wrappers/tooling/infra.
-- chains
-- utilities: related to different types of utilities to integrate with (Python, SQL, etc.).
-- agents
-- memory
-- applications: related to example applications to build
+There is a taxonomy of labels to help with sorting and discovery of issues of interest. Please use these to help
+organize issues.
 
 If you start working on an issue, please assign it to yourself.
 
-If you are adding an issue, please try to keep it focused on a single modular bug/improvement/feature.
-If the two issues are related, or blocking, please link them rather than keep them as one single one.
+If you are adding an issue, please try to keep it focused on a single, modular bug/improvement/feature.
+If two issues are related, or blocking, please link them rather than combining them.
 
 We will try to keep these issues as up to date as possible, though
 with the rapid rate of develop in this field some may get out of date.
-If you notice this happening, please just let us know.
+If you notice this happening, please let us know.
 
 ### 🙋Getting Help
 
-Although we try to have a developer setup to make it as easy as possible for others to contribute (see below)
-it is possible that some pain point may arise around environment setup, linting, documentation, or other.
-Should that occur, please contact a maintainer! Not only do we want to help get you unblocked,
-but we also want to make sure that the process is smooth for future contributors.
+Our goal is to have the simplest developer setup possible. Should you experience any difficulty getting setup, please
+contact a maintainer! Not only do we want to help get you unblocked, but we also want to make sure that the process is
+smooth for future contributors.
 
 In a similar vein, we do enforce certain linting, formatting, and documentation standards in the codebase.
-If you are finding these difficult (or even just annoying) to work with,
-feel free to contact a maintainer for help - we do not want these to get in the way of getting
-good code into the codebase.
+If you are finding these difficult (or even just annoying) to work with, feel free to contact a maintainer for help -
+we do not want these to get in the way of getting good code into the codebase.
 
-### 🏭Release process
-
-As of now, LangChain has an ad hoc release process: releases are cut with high frequency by
-a developer and published to [PyPI](https://pypi.org/project/langchain/).
-
-LangChain follows the [semver](https://semver.org/) versioning standard. However, as pre-1.0 software,
-even patch releases may contain [non-backwards-compatible changes](https://semver.org/#spec-item-4).
-
-If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
-If you have a Twitter account you would like us to mention, please let us know in the PR or in another manner.
-
-## 🚀Quick Start
+## 🚀 Quick Start
 
 This project uses [Poetry](https://python-poetry.org/) as a dependency manager. Check out Poetry's [documentation on how to install it](https://python-poetry.org/docs/#installation) on your system before proceeding.
 
@@ -77,7 +79,7 @@ This will install all requirements for running the package, examples, linting, f
 
 Now, you should be able to run the common tasks in the following section. To double check, run `make test`, all tests should pass. If they don't you may need to pip install additional dependencies, such as `numexpr` and `openapi_schema_pydantic`.
 
-## ✅Common Tasks
+## ✅ Common Tasks
 
 Type `make` for a list of common tasks.
 
@@ -113,8 +115,37 @@ To get a report of current coverage, run the following:
 make coverage
 ```
 
+### Working with Optional Dependencies
+
+Langchain relies heavily on optional dependencies to keep the Langchain package lightweight.
+
+If you're adding a new dependency to Langchain, assume that it will be an optional dependency, and
+that most users won't have it installed.
+
+Users that do not have the dependency installed should be able to **import** your code without
+any side effects (no warnings, no errors, no exceptions). 
+
+To introduce the dependency to the pyproject.toml file correctly, please do the following: 
+
+1. Add the dependency to the main group as an optional dependency
+  ```bash
+  poetry add --optional [package_name]
+  ```
+2. Open pyproject.toml and add the dependency to the `extended_testing` extra
+3. Relock the poetry file to update the extra.
+  ```bash
+  poetry lock --no-update
+  ```
+4. Add a unit test that the very least attempts to import the new code. Ideally the unit
+test makes use of lightweight fixtures to test the logic of the code.
+5. Please use the `@pytest.mark.requires(package_name)` decorator for any tests that require the dependency.
+
 ### Testing
 
+See section about optional dependencies.
+
+#### Unit Tests
+
 Unit tests cover modular logic that does not require calls to outside APIs.
 
 To run unit tests:
@@ -131,8 +162,20 @@ make docker_tests
 
 If you add new logic, please add a unit test.
 
+
+
+#### Integration Tests
+
 Integration tests cover logic that requires making calls to outside APIs (often integration with other services).
 
+**warning** Almost no tests should be integration tests. 
+
+  Tests that require making network connections make it difficult for other
+  developers to test the code.
+
+  Instead favor relying on `responses` library and/or mock.patch to mock
+  requests using small fixtures.
+
 To run integration tests:
 
 ```bash
@@ -188,3 +231,17 @@ Finally, you can build the documentation as outlined below:
 ```bash
 make docs_build
 ```
+
+## 🏭 Release Process
+
+As of now, LangChain has an ad hoc release process: releases are cut with high frequency by
+a developer and published to [PyPI](https://pypi.org/project/langchain/).
+
+LangChain follows the [semver](https://semver.org/) versioning standard. However, as pre-1.0 software,
+even patch releases may contain [non-backwards-compatible changes](https://semver.org/#spec-item-4).
+
+### 🌟 Recognition
+
+If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
+If you have a Twitter account you would like us to mention, please let us know in the PR or in another manner.
+

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,42 +1,56 @@
-# Your PR Title (What it does)
-
 <!--
-Thank you for contributing to LangChain! Your PR will appear in our next release under the title you set. Please make sure it highlights your valuable contribution.
+Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
 
 Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
 
 After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
+
+Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle!
 -->
 
 <!-- Remove if not applicable -->
 
 Fixes # (issue)
 
-## Before submitting
+#### Before submitting
 
-<!-- If you're adding a new integration, include an integration test and an example notebook showing its use! -->
+<!-- If you're adding a new integration, please include:
 
-## Who can review?
+1. a test for the integration - favor unit tests that does not rely on network access.
+2. an example notebook showing its use
 
-Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested:
+
+See contribution guidelines for more information on how to write tests, lint
+etc:
+
+https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
+-->
+
+#### Who can review?
+
+Tag maintainers/contributors who might be interested:
 
 <!-- For a quicker response, figure out the right person to tag with @
 
-        @hwchase17 - project lead
+  @hwchase17 - project lead
 
-        Tracing / Callbacks
-        - @agola11
+  Tracing / Callbacks
+  - @agola11
 
-        Async
-        - @agola11
+  Async
+  - @agola11
 
-        DataLoader Abstractions
-        - @eyurtsev
+  DataLoaders
+  - @eyurtsev
 
-        LLM/Chat Wrappers
-        - @hwchase17
-        - @agola11
+  Models
+  - @hwchase17
+  - @agola11
+
+  Agents / Tools / Toolkits
+  - @vowelparrot
+
+  VectorStores / Retrievers / Memory
+  - @dev2049
 
-        Tools / Toolkits
-        - @vowelparrot
  -->

.github/actions/poetry_setup/action.yml

@@ -33,11 +33,13 @@ runs:
   using: composite
   steps:
     - uses: actions/setup-python@v4
+      name: Setup python $${ inputs.python-version }}
       with:
         python-version: ${{ inputs.python-version }}
 
     - uses: actions/cache@v3
       id: cache-pip
+      name: Cache Pip ${{ inputs.python-version }}
       env:
         SEGMENT_DOWNLOAD_TIMEOUT_MIN: "15"
       with:
@@ -48,6 +50,16 @@ runs:
     - run: pipx install poetry==${{ inputs.poetry-version }} --python python${{ inputs.python-version }}
       shell: bash
 
+    - name: Check Poetry File
+      shell: bash
+      run: |
+        poetry check
+
+    - name: Check lock file
+      shell: bash
+      run: |
+        poetry lock --check
+
     - uses: actions/cache@v3
       id: cache-poetry
       env:

.github/workflows/linkcheck.yml

@@ -4,6 +4,8 @@ on:
   push:
     branches: [master]
   pull_request:
+    paths:
+      - 'docs/**'
 
 env:
   POETRY_VERSION: "1.4.2"

.github/workflows/test.yml

@@ -4,6 +4,7 @@ on:
   push:
     branches: [master]
   pull_request:
+  workflow_dispatch:
 
 env:
   POETRY_VERSION: "1.4.2"
@@ -18,6 +19,10 @@ jobs:
           - "3.9"
           - "3.10"
           - "3.11"
+        test_type:
+          - "core"
+          - "extended"
+    name: Python ${{ matrix.python-version }} ${{ matrix.test_type }}
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python ${{ matrix.python-version }}
@@ -25,8 +30,20 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           poetry-version: "1.4.2"
-          cache-key: "main"
-          install-command: "poetry install"
-      - name: Run unit tests
+          cache-key: ${{ matrix.test_type }}
+          install-command: |
+              if [ "${{ matrix.test_type }}" == "core" ]; then
+                echo "Running core tests, installing dependencies with poetry..."
+                poetry install
+              else
+                echo "Running extended tests, installing dependencies with poetry..."
+                poetry install -E extended_testing
+              fi
+      - name: Run ${{matrix.test_type}} tests
         run: |
-          make test
+          if [ "${{ matrix.test_type }}" == "core" ]; then
+            make test
+          else
+            make extended_tests
+          fi
+        shell: bash

.github/workflows/test_all.yml

@@ -1,33 +0,0 @@
-# Run unit tests with all optional packages installed.
-name: test_all
-
-on:
-  push:
-    branches: [master]
-  pull_request:
-
-env:
-  POETRY_VERSION: "1.4.2"
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version:
-          - "3.8"
-          - "3.9"
-          - "3.10"
-          - "3.11"
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: "./.github/actions/poetry_setup"
-        with:
-          python-version: ${{ matrix.python-version }}
-          poetry-version: "1.4.2"
-          cache-key: "extended"
-          install-command: "poetry install -E extended_testing"
-      - name: Run unit tests
-        run: |
-          make test

.gitignore

@@ -149,4 +149,7 @@ wandb/
 
 # integration test artifacts
 data_map*
-\[('_type', 'fake'), ('stop', None)]
+\[('_type', 'fake'), ('stop', None)]
+
+# Replit files
+*replit*

Makefile

@@ -1,4 +1,4 @@
-.PHONY: all clean format lint test tests test_watch integration_tests docker_tests help
+.PHONY: all clean format lint test tests test_watch integration_tests docker_tests help extended_tests
 
 all: help
 
@@ -35,10 +35,13 @@ lint lint_diff:
 TEST_FILE ?= tests/unit_tests/
 
 test:
-	poetry run pytest $(TEST_FILE)
+	poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
 
-tests:
-	poetry run pytest $(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
@@ -59,7 +62,9 @@ help:
 	@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

@@ -2,6 +2,7 @@
 
 ⚡ 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)
 [![linkcheck](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml)
@@ -12,6 +13,8 @@
 [![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/hwchase17/langchain)
 [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/hwchase17/langchain)
 [![GitHub star chart](https://img.shields.io/github/stars/hwchase17/langchain?style=social)](https://star-history.com/#hwchase17/langchain)
+[![Dependency Status](https://img.shields.io/librariesio/github/hwchase17/langchain)](https://libraries.io/github/hwchase17/langchain)
+[![Open Issues](https://img.shields.io/github/issues-raw/hwchase17/langchain)](https://github.com/hwchase17/langchain/issues)
 
 
 Looking for the JS/TS version? Check out [LangChain.js](https://github.com/hwchase17/langchainjs).

docs/_static/css/custom.css

@@ -13,5 +13,5 @@ pre {
 }
 
 #my-component-root *, #headlessui-portal-root * {
-  z-index: 1000000000000;
+  z-index: 10000;
 }

docs/_static/js/mendablesearch.js

@@ -30,18 +30,17 @@ document.addEventListener('DOMContentLoaded', () => {
     const icon = React.createElement('p', {
       style: { color: '#ffffff', fontSize: '22px',width: '48px', height: '48px', margin: '0px', padding: '0px', display: 'flex', alignItems: 'center', justifyContent: 'center', textAlign: 'center' },
     }, [iconSpan1, iconSpan2]);
-
     
-    
-
     const mendableFloatingButton = React.createElement(
       MendableFloatingButton,
       {
         style: { darkMode: false, accentColor: '#010810' },
         floatingButtonStyle: { color: '#ffffff', backgroundColor: '#010810' },
         anon_key: '82842b36-3ea6-49b2-9fb8-52cfc4bde6bf', // Mendable Search Public ANON key, ok to be public
+        cmdShortcutKey:'j',
         messageSettings: {
           openSourcesInNewTab: false,
+          prettySources: true // Prettify the sources displayed now
         },
         icon: icon,
       }
@@ -52,7 +51,7 @@ document.addEventListener('DOMContentLoaded', () => {
 
   loadScript('https://unpkg.com/react@17/umd/react.production.min.js', () => {
     loadScript('https://unpkg.com/react-dom@17/umd/react-dom.production.min.js', () => {
-      loadScript('https://unpkg.com/@mendable/search@0.0.93/dist/umd/mendable.min.js', initializeMendable);
+      loadScript('https://unpkg.com/@mendable/search@0.0.102/dist/umd/mendable.min.js', initializeMendable);
     });
   });
 });

docs/additional_resources/deploy_llms.rst

@@ -0,0 +1,137 @@
+
+===========================
+Deploying LLMs in Production
+===========================
+
+In today's fast-paced technological landscape, the use of Large Language Models (LLMs) is rapidly expanding. As a result, it's crucial for developers to understand how to effectively deploy these models in production environments. LLM interfaces typically fall into two categories:
+
+- **Case 1: Utilizing External LLM Providers (OpenAI, Anthropic, etc.)**
+    In this scenario, most of the computational burden is handled by the LLM providers, while LangChain simplifies the implementation of business logic around these services. This approach includes features such as prompt templating, chat message generation, caching, vector embedding database creation, preprocessing, etc.
+
+- **Case 2: Self-hosted Open-Source Models**
+    Alternatively, developers can opt to use smaller, yet comparably capable, self-hosted open-source LLM models. This approach can significantly decrease costs, latency, and privacy concerns associated with transferring data to external LLM providers.
+
+Regardless of the framework that forms the backbone of your product, deploying LLM applications comes with its own set of challenges. It's vital to understand the trade-offs and key considerations when evaluating serving frameworks.
+
+Outline
+=======
+
+This guide aims to provide a comprehensive overview of the requirements for deploying LLMs in a production setting, focusing on:
+
+- `Designing a Robust LLM Application Service <#robust>`_
+- `Maintaining Cost-Efficiency <#cost>`_
+- `Ensuring Rapid Iteration <#iteration>`_
+
+Understanding these components is crucial when assessing serving systems. LangChain integrates with several open-source projects designed to tackle these issues, providing a robust framework for productionizing your LLM applications. Some notable frameworks include:
+
+- `Ray Serve <../integrations/ray_serve.html>`_
+- `BentoML <https://github.com/ssheng/BentoChain>`_
+- `Modal <../integrations/modal.html>`_
+
+These links will provide further information on each ecosystem, assisting you in finding the best fit for your LLM deployment needs.
+
+Designing a Robust LLM Application Service
+===========================================
+.. _robust:
+
+When deploying an LLM service in production, it's imperative to provide a seamless user experience free from outages. Achieving 24/7 service availability involves creating and maintaining several sub-systems surrounding your application.
+
+Monitoring
+----------
+
+Monitoring forms an integral part of any system running in a production environment. In the context of LLMs, it is essential to monitor both performance and quality metrics.
+
+**Performance Metrics:** These metrics provide insights into the efficiency and capacity of your model. Here are some key examples:
+
+- Query per second (QPS): This measures the number of queries your model processes in a second, offering insights into its utilization.
+- Latency: This metric quantifies the delay from when your client sends a request to when they receive a response.
+- Tokens Per Second (TPS): This represents the number of tokens your model can generate in a second.
+
+**Quality Metrics:** These metrics are typically customized according to the business use-case. For instance, how does the output of your system compare to a baseline, such as a previous version? Although these metrics can be calculated offline, you need to log the necessary data to use them later.
+
+Fault tolerance
+---------------
+
+Your application may encounter errors such as exceptions in your model inference or business logic code, causing failures and disrupting traffic. Other potential issues could arise from the machine running your application, such as unexpected hardware breakdowns or loss of spot-instances during high-demand periods. One way to mitigate these risks is by increasing redundancy through replica scaling and implementing recovery mechanisms for failed replicas. However, model replicas aren't the only potential points of failure. It's essential to build resilience against various failures that could occur at any point in your stack.
+
+
+Zero down time upgrade
+----------------------
+
+System upgrades are often necessary but can result in service disruptions if not handled correctly. One way to prevent downtime during upgrades is by implementing a smooth transition process from the old version to the new one. Ideally, the new version of your LLM service is deployed, and traffic gradually shifts from the old to the new version, maintaining a constant QPS throughout the process.
+
+
+Load balancing
+--------------
+
+Load balancing, in simple terms, is a technique to distribute work evenly across multiple computers, servers, or other resources to optimize the utilization of the system, maximize throughput, minimize response time, and avoid overload of any single resource. Think of it as a traffic officer directing cars (requests) to different roads (servers) so that no single road becomes too congested.
+
+There are several strategies for load balancing. For example, one common method is the *Round Robin* strategy, where each request is sent to the next server in line, cycling back to the first when all servers have received a request. This works well when all servers are equally capable. However, if some servers are more powerful than others, you might use a *Weighted Round Robin* or *Least Connections* strategy, where more requests are sent to the more powerful servers, or to those currently handling the fewest active requests. Let's imagine you're running a LLM chain. If your application becomes popular, you could have hundreds or even thousands of users asking questions at the same time. If one server gets too busy (high load), the load balancer would direct new requests to another server that is less busy. This way, all your users get a timely response and the system remains stable.
+
+
+
+Maintaining Cost-Efficiency and Scalability
+============================================
+.. _cost:
+
+Deploying LLM services can be costly, especially when you're handling a large volume of user interactions. Charges by LLM providers are usually based on tokens used, making a chat system inference on these models potentially expensive. However, several strategies can help manage these costs without compromising the quality of the service.
+
+
+Self-hosting models
+-------------------
+
+Several smaller and open-source LLMs are emerging to tackle the issue of reliance on LLM providers. Self-hosting allows you to maintain similar quality to LLM provider models while managing costs. The challenge lies in building a reliable, high-performing LLM serving system on your own machines. 
+
+Resource Management and Auto-Scaling
+------------------------------------
+
+Computational logic within your application requires precise resource allocation. For instance, if part of your traffic is served by an OpenAI endpoint and another part by a self-hosted model, it's crucial to allocate suitable resources for each. Auto-scaling—adjusting resource allocation based on traffic—can significantly impact the cost of running your application. This strategy requires a balance between cost and responsiveness, ensuring neither resource over-provisioning nor compromised application responsiveness.
+
+Utilizing Spot Instances
+------------------------
+
+On platforms like AWS, spot instances offer substantial cost savings, typically priced at about a third of on-demand instances. The trade-off is a higher crash rate, necessitating a robust fault-tolerance mechanism for effective use.
+
+Independent Scaling
+-------------------
+
+When self-hosting your models, you should consider independent scaling. For example, if you have two translation models, one fine-tuned for French and another for Spanish, incoming requests might necessitate different scaling requirements for each.
+
+Batching requests
+-----------------
+
+In the context of Large Language Models, batching requests can enhance efficiency by better utilizing your GPU resources. GPUs are inherently parallel processors, designed to handle multiple tasks simultaneously. If you send individual requests to the model, the GPU might not be fully utilized as it's only working on a single task at a time. On the other hand, by batching requests together, you're allowing the GPU to work on multiple tasks at once, maximizing its utilization and improving inference speed. This not only leads to cost savings but can also improve the overall latency of your LLM service.
+
+
+In summary, managing costs while scaling your LLM services requires a strategic approach. Utilizing self-hosting models, managing resources effectively, employing auto-scaling, using spot instances, independently scaling models, and batching requests are key strategies to consider. Open-source libraries such as Ray Serve and BentoML are designed to deal with these complexities. 
+
+
+
+Ensuring Rapid Iteration
+========================
+
+.. _iteration:
+
+The LLM landscape is evolving at an unprecedented pace, with new libraries and model architectures being introduced constantly. Consequently, it's crucial to avoid tying yourself to a solution specific to one particular framework. This is especially relevant in serving, where changes to your infrastructure can be time-consuming, expensive, and risky. Strive for infrastructure that is not locked into any specific machine learning library or framework, but instead offers a general-purpose, scalable serving layer. Here are some aspects where flexibility plays a key role:
+
+Model composition
+-----------------
+
+Deploying systems like LangChain demands the ability to piece together different models and connect them via logic. Take the example of building a natural language input SQL query engine. Querying an LLM and obtaining the SQL command is only part of the system. You need to extract metadata from the connected database, construct a prompt for the LLM, run the SQL query on an engine, collect and feed back the response to the LLM as the query runs, and present the results to the user. This demonstrates the need to seamlessly integrate various complex components built in Python into a dynamic chain of logical blocks that can be served together.
+
+Cloud providers
+---------------
+
+Many hosted solutions are restricted to a single cloud provider, which can limit your options in today's multi-cloud world. Depending on where your other infrastructure components are built, you might prefer to stick with your chosen cloud provider.
+
+
+Infrastructure as Code (IaC)
+---------------------------
+
+Rapid iteration also involves the ability to recreate your infrastructure quickly and reliably. This is where Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Kubernetes YAML files come into play. They allow you to define your infrastructure in code files, which can be version controlled and quickly deployed, enabling faster and more reliable iterations.
+
+
+CI/CD
+-----
+
+In a fast-paced environment, implementing CI/CD pipelines can significantly speed up the iteration process. They help automate the testing and deployment of your LLM applications, reducing the risk of errors and enabling faster feedback and iteration.

docs/additional_resources/tracing.md

@@ -6,8 +6,8 @@ First, you should install tracing and set up your environment properly.
 You can use either a locally hosted version of this (uses Docker) or a cloud hosted version (in closed alpha).
 If you're interested in using the hosted platform, please fill out the form [here](https://forms.gle/tRCEMSeopZf6TE3b6).
 
-- [Locally Hosted Setup](./tracing/local_installation.md)
-- [Cloud Hosted Setup](./tracing/hosted_installation.md)
+- [Locally Hosted Setup](../tracing/local_installation.md)
+- [Cloud Hosted Setup](../tracing/hosted_installation.md)
 
 ## Tracing Walkthrough
 
@@ -17,32 +17,32 @@ A session is just a way to group traces together.
 If you click on a session, it will take you to a page with no recorded traces that says "No Runs."
 You can create a new session with the new session form.
 
-![](tracing/homepage.png)
+![](../tracing/homepage.png)
 
 If we click on the `default` session, we can see that to start we have no traces stored.
 
-![](tracing/default_empty.png)
+![](../tracing/default_empty.png)
 
 If we now start running chains and agents with tracing enabled, we will see data show up here.
-To do so, we can run [this notebook](tracing/agent_with_tracing.ipynb) as an example.
+To do so, we can run [this notebook](../tracing/agent_with_tracing.ipynb) as an example.
 After running it, we will see an initial trace show up.
 
-![](tracing/first_trace.png)
+![](../tracing/first_trace.png)
 
 From here we can explore the trace at a high level by clicking on the arrow to show nested runs.
 We can keep on clicking further and further down to explore deeper and deeper.
 
-![](tracing/explore.png)
+![](../tracing/explore.png)
 
 We can also click on the "Explore" button of the top level run to dive even deeper.
 Here, we can see the inputs and outputs in full, as well as all the nested traces.
 
-![](tracing/explore_trace.png)
+![](../tracing/explore_trace.png)
 
 We can keep on exploring each of these nested traces in more detail.
 For example, here is the lowest level trace with the exact inputs/outputs to the LLM.
 
-![](tracing/explore_llm.png)
+![](../tracing/explore_llm.png)
 
 ## Changing Sessions
 

docs/additional_resources/youtube.md

@@ -0,0 +1,90 @@
+# YouTube
+
+This is a collection of `LangChain` videos on `YouTube`.
+
+### ⛓️[Official LangChain YouTube channel](https://www.youtube.com/@LangChain)⛓️
+
+### Introduction to LangChain with Harrison Chase, creator of LangChain
+- [Building the Future with LLMs, `LangChain`, & `Pinecone`](https://youtu.be/nMniwlGyX-c) by [Pinecone](https://www.youtube.com/@pinecone-io)
+- [LangChain and Weaviate with Harrison Chase and Bob van Luijt - Weaviate Podcast #36](https://youtu.be/lhby7Ql7hbk) by [Weaviate • Vector Database](https://www.youtube.com/@Weaviate)
+- [LangChain Demo + Q&A with Harrison Chase](https://youtu.be/zaYTXQFR0_s?t=788) by [Full Stack Deep Learning](https://www.youtube.com/@FullStackDeepLearning)
+- [LangChain Agents: Build Personal Assistants For Your Data (Q&A with Harrison Chase and Mayo Oshin)](https://youtu.be/gVkF8cwfBLI) by [Chat with data](https://www.youtube.com/@chatwithdata)
+- ⛓️ [LangChain "Agents in Production" Webinar](https://youtu.be/k8GNCCs16F4) by [LangChain](https://www.youtube.com/@LangChain)
+
+## Videos (sorted by views)
+
+- [Building AI LLM Apps with LangChain (and more?) - LIVE STREAM](https://www.youtube.com/live/M-2Cj_2fzWI?feature=share) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
+- [First look - `ChatGPT` + `WolframAlpha` (`GPT-3.5` and Wolfram|Alpha via LangChain by James Weaver)](https://youtu.be/wYGbY811oMo) by [Dr Alan D. Thompson](https://www.youtube.com/@DrAlanDThompson) 
+- [LangChain explained - The hottest new Python framework](https://youtu.be/RoR4XJw8wIc) by [AssemblyAI](https://www.youtube.com/@AssemblyAI)
+- [Chatbot with INFINITE MEMORY using `OpenAI` & `Pinecone` - `GPT-3`, `Embeddings`, `ADA`, `Vector DB`, `Semantic`](https://youtu.be/2xNzB7xq8nk) by [David Shapiro ~ AI](https://www.youtube.com/@DavidShapiroAutomator)
+- [LangChain for LLMs is... basically just an Ansible playbook](https://youtu.be/X51N9C-OhlE) by [David Shapiro ~ AI](https://www.youtube.com/@DavidShapiroAutomator)
+- [Build your own LLM Apps with LangChain & `GPT-Index`](https://youtu.be/-75p09zFUJY) by [1littlecoder](https://www.youtube.com/@1littlecoder)
+- [`BabyAGI` - New System of Autonomous AI Agents with LangChain](https://youtu.be/lg3kJvf1kXo) by [1littlecoder](https://www.youtube.com/@1littlecoder)
+- [Run `BabyAGI` with Langchain Agents (with Python Code)](https://youtu.be/WosPGHPObx8) by [1littlecoder](https://www.youtube.com/@1littlecoder)
+- [How to Use Langchain With `Zapier` | Write and Send Email with GPT-3 | OpenAI API Tutorial](https://youtu.be/p9v2-xEa9A0) by [StarMorph AI](https://www.youtube.com/@starmorph)
+- [Use Your Locally Stored Files To Get Response From GPT - `OpenAI` | Langchain | Python](https://youtu.be/NC1Ni9KS-rk) by [Shweta Lodha](https://www.youtube.com/@shweta-lodha)
+- [`Langchain JS` | How to Use GPT-3, GPT-4 to Reference your own Data | `OpenAI Embeddings` Intro](https://youtu.be/veV2I-NEjaM) by [StarMorph AI](https://www.youtube.com/@starmorph)
+- [The easiest way to work with large language models | Learn LangChain in 10min](https://youtu.be/kmbS6FDQh7c) by [Sophia Yang](https://www.youtube.com/@SophiaYangDS)
+- [4 Autonomous AI Agents: “Westworld” simulation `BabyAGI`, `AutoGPT`, `Camel`, `LangChain`](https://youtu.be/yWbnH6inT_U) by [Sophia Yang](https://www.youtube.com/@SophiaYangDS)
+- [AI CAN SEARCH THE INTERNET? Langchain Agents + OpenAI ChatGPT](https://youtu.be/J-GL0htqda8) by [tylerwhatsgood](https://www.youtube.com/@tylerwhatsgood)
+- [Query Your Data with GPT-4 | Embeddings, Vector Databases | Langchain JS Knowledgebase](https://youtu.be/jRnUPUTkZmU) by [StarMorph AI](https://www.youtube.com/@starmorph)
+- [`Weaviate` + LangChain for LLM apps presented by Erika Cardenas](https://youtu.be/7AGj4Td5Lgw) by [`Weaviate` • Vector Database](https://www.youtube.com/@Weaviate)
+- [Langchain Overview — How to Use Langchain & `ChatGPT`](https://youtu.be/oYVYIq0lOtI) by [Python In Office](https://www.youtube.com/@pythoninoffice6568)
+- [Langchain Overview - How to Use Langchain & `ChatGPT`](https://youtu.be/oYVYIq0lOtI) by [Python In Office](https://www.youtube.com/@pythoninoffice6568)
+- [Custom langchain Agent & Tools with memory. Turn any `Python function` into langchain tool with Gpt 3](https://youtu.be/NIG8lXk0ULg) by [echohive](https://www.youtube.com/@echohive)
+- [LangChain: Run Language Models Locally - `Hugging Face Models`](https://youtu.be/Xxxuw4_iCzw) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
+- [`ChatGPT` with any `YouTube` video using langchain and `chromadb`](https://youtu.be/TQZfB2bzVwU) by [echohive](https://www.youtube.com/@echohive)
+- [How to Talk to a `PDF` using LangChain and `ChatGPT`](https://youtu.be/v2i1YDtrIwk) by [Automata Learning Lab](https://www.youtube.com/@automatalearninglab)
+- [Langchain Document Loaders Part 1: Unstructured Files](https://youtu.be/O5C0wfsen98) by [Merk](https://www.youtube.com/@merksworld) 
+- [LangChain - Prompt Templates (what all the best prompt engineers use)](https://youtu.be/1aRu8b0XNOQ) by [Nick Daigler](https://www.youtube.com/@nick_daigs)
+- [LangChain. Crear aplicaciones Python impulsadas por GPT](https://youtu.be/DkW_rDndts8) by [Jesús Conde](https://www.youtube.com/@0utKast)
+- [Easiest Way to Use GPT In Your Products | LangChain Basics Tutorial](https://youtu.be/fLy0VenZyGc) by [Rachel Woods](https://www.youtube.com/@therachelwoods)
+- [`BabyAGI` + `GPT-4` Langchain Agent with Internet Access](https://youtu.be/wx1z_hs5P6E) by [tylerwhatsgood](https://www.youtube.com/@tylerwhatsgood)
+- [Learning LLM Agents. How does it actually work? LangChain, AutoGPT & OpenAI](https://youtu.be/mb_YAABSplk) by [Arnoldas Kemeklis](https://www.youtube.com/@processusAI)
+- [Get Started with LangChain in `Node.js`](https://youtu.be/Wxx1KUWJFv4) by [Developers Digest](https://www.youtube.com/@DevelopersDigest)
+- [LangChain + `OpenAI` tutorial: Building a Q&A system w/ own text data](https://youtu.be/DYOU_Z0hAwo) by [Samuel Chan](https://www.youtube.com/@SamuelChan)
+- [Langchain + `Zapier` Agent](https://youtu.be/yribLAb-pxA) by [Merk](https://www.youtube.com/@merksworld)
+- [Connecting the Internet with `ChatGPT` (LLMs) using Langchain And Answers Your Questions](https://youtu.be/9Y0TBC63yZg) by [Kamalraj M M](https://www.youtube.com/@insightbuilder)
+- [Build More Powerful LLM Applications for Business’s with LangChain (Beginners Guide)](https://youtu.be/sp3-WLKEcBg) by[ No Code Blackbox](https://www.youtube.com/@nocodeblackbox)
+- ⛓️ [LangFlow LLM Agent Demo for 🦜🔗LangChain](https://youtu.be/zJxDHaWt-6o) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
+- ⛓️ [Chatbot Factory: Streamline Python Chatbot Creation with LLMs and Langchain](https://youtu.be/eYer3uzrcuM) by [Finxter](https://www.youtube.com/@CobusGreylingZA)
+- ⛓️ [LangChain Tutorial - ChatGPT mit eigenen Daten](https://youtu.be/0XDLyY90E2c) by [Coding Crashkurse](https://www.youtube.com/@codingcrashkurse6429)
+- ⛓️ [Chat with a `CSV` | LangChain Agents Tutorial (Beginners)](https://youtu.be/tjeti5vXWOU) by [GoDataProf](https://www.youtube.com/@godataprof)
+- ⛓️ [Introdução ao Langchain - #Cortes - Live DataHackers](https://youtu.be/fw8y5VRei5Y) by [Prof. João Gabriel Lima](https://www.youtube.com/@profjoaogabriellima)
+- ⛓️ [LangChain: Level up `ChatGPT` !? | LangChain Tutorial Part 1](https://youtu.be/vxUGx8aZpDE) by [Code Affinity](https://www.youtube.com/@codeaffinitydev)
+- ⛓️ [KI schreibt krasses Youtube Skript 😲😳 | LangChain Tutorial Deutsch](https://youtu.be/QpTiXyK1jus) by [SimpleKI](https://www.youtube.com/@simpleki)
+- ⛓️ [Chat with Audio: Langchain, `Chroma DB`, OpenAI, and `Assembly AI`](https://youtu.be/Kjy7cx1r75g) by [AI Anytime](https://www.youtube.com/@AIAnytime)
+- ⛓️ [QA over documents with Auto vector index selection with Langchain router chains](https://youtu.be/9G05qybShv8) by [echohive](https://www.youtube.com/@echohive)
+- ⛓️ [Build your own custom LLM application with `Bubble.io` & Langchain (No Code & Beginner friendly)](https://youtu.be/O7NhQGu1m6c) by [No Code Blackbox](https://www.youtube.com/@nocodeblackbox)
+- ⛓️ [Simple App to Question Your Docs: Leveraging `Streamlit`, `Hugging Face Spaces`, LangChain, and `Claude`!](https://youtu.be/X4YbNECRr7o) by [Chris Alexiuk](https://www.youtube.com/@chrisalexiuk)
+- ⛓️ [LANGCHAIN AI- `ConstitutionalChainAI` + Databutton AI ASSISTANT Web App](https://youtu.be/5zIU6_rdJCU) by [Avra](https://www.youtube.com/@Avra_b)
+- ⛓️ [LANGCHAIN AI AUTONOMOUS AGENT WEB APP - 👶 `BABY AGI` 🤖 with EMAIL AUTOMATION using `DATABUTTON`](https://youtu.be/cvAwOGfeHgw) by [Avra](https://www.youtube.com/@Avra_b)
+- ⛓️ [The Future of Data Analysis: Using A.I. Models in Data Analysis (LangChain)](https://youtu.be/v_LIcVyg5dk) by [Absent Data](https://www.youtube.com/@absentdata)
+- ⛓️ [Memory in LangChain | Deep dive (python)](https://youtu.be/70lqvTFh_Yg) by [Eden Marco](https://www.youtube.com/@EdenMarco)
+- ⛓️ [9 LangChain UseCases | Beginner's Guide | 2023](https://youtu.be/zS8_qosHNMw) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
+- ⛓️ [Use Large Language Models in Jupyter Notebook | LangChain | Agents & Indexes](https://youtu.be/JSe11L1a_QQ) by [Abhinaw Tiwari](https://www.youtube.com/@AbhinawTiwariAT)
+- ⛓️ [How to Talk to Your Langchain Agent | `11 Labs` + `Whisper`](https://youtu.be/N4k459Zw2PU) by [VRSEN](https://www.youtube.com/@vrsen)
+- ⛓️ [LangChain Deep Dive: 5 FUN AI App Ideas To Build Quickly and Easily](https://youtu.be/mPYEPzLkeks) by [James NoCode](https://www.youtube.com/@jamesnocode)
+- ⛓️ [BEST OPEN Alternative to OPENAI's EMBEDDINGs for Retrieval QA: LangChain](https://youtu.be/ogEalPMUCSY) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
+- ⛓️ [LangChain 101: Models](https://youtu.be/T6c_XsyaNSQ) by [Mckay Wrigley](https://www.youtube.com/@realmckaywrigley)
+- ⛓️ [LangChain with JavaScript Tutorial #1 | Setup & Using LLMs](https://youtu.be/W3AoeMrg27o) by [Leon van Zyl](https://www.youtube.com/@leonvanzyl)
+- ⛓️ [LangChain Overview & Tutorial for Beginners: Build Powerful AI Apps Quickly & Easily (ZERO CODE)](https://youtu.be/iI84yym473Q) by [James NoCode](https://www.youtube.com/@jamesnocode)
+- ⛓️ [LangChain In Action: Real-World Use Case With Step-by-Step Tutorial](https://youtu.be/UO699Szp82M) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
+- ⛓️ [Summarizing and Querying Multiple Papers with LangChain](https://youtu.be/p_MQRWH5Y6k) by [Automata Learning Lab](https://www.youtube.com/@automatalearninglab)
+- ⛓️ [Using Langchain (and `Replit`) through `Tana`, ask `Google`/`Wikipedia`/`Wolfram Alpha` to fill out a table](https://youtu.be/Webau9lEzoI) by [Stian Håklev](https://www.youtube.com/@StianHaklev)
+- ⛓️ [Langchain PDF App (GUI) | Create a ChatGPT For Your `PDF` in Python](https://youtu.be/wUAUdEw5oxM) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
+- ⛓️ [Auto-GPT with LangChain 🔥 | Create Your Own Personal AI Assistant](https://youtu.be/imDfPmMKEjM) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
+- ⛓️ [Create Your OWN Slack AI Assistant with Python & LangChain](https://youtu.be/3jFXRNn2Bu8) by [Dave Ebbelaar](https://www.youtube.com/@daveebbelaar)
+- ⛓️ [How to Create LOCAL Chatbots with GPT4All and LangChain [Full Guide]](https://youtu.be/4p1Fojur8Zw) by [Liam Ottley](https://www.youtube.com/@LiamOttley)
+- ⛓️ [Build a `Multilingual PDF` Search App with LangChain, `Cohere` and `Bubble`](https://youtu.be/hOrtuumOrv8) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
+- ⛓️ [Building a LangChain Agent (code-free!) Using `Bubble` and `Flowise`](https://youtu.be/jDJIIVWTZDE) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
+- ⛓️ [Build a LangChain-based Semantic PDF Search App with No-Code Tools Bubble and Flowise](https://youtu.be/s33v5cIeqA4) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
+- ⛓️ [LangChain Memory Tutorial | Building a ChatGPT Clone in Python](https://youtu.be/Cwq91cj2Pnc) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
+- ⛓️ [ChatGPT For Your DATA | Chat with Multiple Documents Using LangChain](https://youtu.be/TeDgIDqQmzs) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
+- ⛓️ [`Llama Index`: Chat with Documentation using URL Loader](https://youtu.be/XJRoDEctAwA) by [Merk](https://www.youtube.com/@merksworld)
+- ⛓️ [Using OpenAI, LangChain, and `Gradio` to Build Custom GenAI Applications](https://youtu.be/1MsmqMg3yUc) by [David Hundley](https://www.youtube.com/@dkhundley)
+
+
+
+---------------------
+⛓ icon marks a new video [last update 2023-05-15]

docs/dependents.md

@@ -0,0 +1,231 @@
+# Dependents
+
+Dependents stats for `hwchase17/langchain`
+
+[![](https://img.shields.io/static/v1?label=Used%20by&message=7484&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
+[![](https://img.shields.io/static/v1?label=Used%20by%20(public)&message=212&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
+[![](https://img.shields.io/static/v1?label=Used%20by%20(private)&message=7272&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
+[![](https://img.shields.io/static/v1?label=Used%20by%20(stars)&message=19095&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
+
+[update: 2023-06-05; only dependent repositories with Stars > 100]
+
+
+| Repository | Stars  |
+| :--------  | -----: |
+|[openai/openai-cookbook](https://github.com/openai/openai-cookbook) | 38024 |
+|[LAION-AI/Open-Assistant](https://github.com/LAION-AI/Open-Assistant) | 33609 |
+|[microsoft/TaskMatrix](https://github.com/microsoft/TaskMatrix) | 33136 |
+|[hpcaitech/ColossalAI](https://github.com/hpcaitech/ColossalAI) | 30032 |
+|[imartinez/privateGPT](https://github.com/imartinez/privateGPT) | 28094 |
+|[reworkd/AgentGPT](https://github.com/reworkd/AgentGPT) | 23430 |
+|[openai/chatgpt-retrieval-plugin](https://github.com/openai/chatgpt-retrieval-plugin) | 17942 |
+|[jerryjliu/llama_index](https://github.com/jerryjliu/llama_index) | 16697 |
+|[mindsdb/mindsdb](https://github.com/mindsdb/mindsdb) | 16410 |
+|[mlflow/mlflow](https://github.com/mlflow/mlflow) | 14517 |
+|[GaiZhenbiao/ChuanhuChatGPT](https://github.com/GaiZhenbiao/ChuanhuChatGPT) | 10793 |
+|[databrickslabs/dolly](https://github.com/databrickslabs/dolly) | 10155 |
+|[openai/evals](https://github.com/openai/evals) | 10076 |
+|[AIGC-Audio/AudioGPT](https://github.com/AIGC-Audio/AudioGPT) | 8619 |
+|[logspace-ai/langflow](https://github.com/logspace-ai/langflow) | 8211 |
+|[imClumsyPanda/langchain-ChatGLM](https://github.com/imClumsyPanda/langchain-ChatGLM) | 8154 |
+|[PromtEngineer/localGPT](https://github.com/PromtEngineer/localGPT) | 6853 |
+|[StanGirard/quivr](https://github.com/StanGirard/quivr) | 6830 |
+|[PipedreamHQ/pipedream](https://github.com/PipedreamHQ/pipedream) | 6520 |
+|[go-skynet/LocalAI](https://github.com/go-skynet/LocalAI) | 6018 |
+|[arc53/DocsGPT](https://github.com/arc53/DocsGPT) | 5643 |
+|[e2b-dev/e2b](https://github.com/e2b-dev/e2b) | 5075 |
+|[langgenius/dify](https://github.com/langgenius/dify) | 4281 |
+|[nsarrazin/serge](https://github.com/nsarrazin/serge) | 4228 |
+|[zauberzeug/nicegui](https://github.com/zauberzeug/nicegui) | 4084 |
+|[madawei2699/myGPTReader](https://github.com/madawei2699/myGPTReader) | 4039 |
+|[wenda-LLM/wenda](https://github.com/wenda-LLM/wenda) | 3871 |
+|[GreyDGL/PentestGPT](https://github.com/GreyDGL/PentestGPT) | 3837 |
+|[zilliztech/GPTCache](https://github.com/zilliztech/GPTCache) | 3625 |
+|[csunny/DB-GPT](https://github.com/csunny/DB-GPT) | 3545 |
+|[gkamradt/langchain-tutorials](https://github.com/gkamradt/langchain-tutorials) | 3404 |
+|[mmabrouk/chatgpt-wrapper](https://github.com/mmabrouk/chatgpt-wrapper) | 3303 |
+|[postgresml/postgresml](https://github.com/postgresml/postgresml) | 3052 |
+|[marqo-ai/marqo](https://github.com/marqo-ai/marqo) | 3014 |
+|[MineDojo/Voyager](https://github.com/MineDojo/Voyager) | 2945 |
+|[PrefectHQ/marvin](https://github.com/PrefectHQ/marvin) | 2761 |
+|[project-baize/baize-chatbot](https://github.com/project-baize/baize-chatbot) | 2673 |
+|[hwchase17/chat-langchain](https://github.com/hwchase17/chat-langchain) | 2589 |
+|[whitead/paper-qa](https://github.com/whitead/paper-qa) | 2572 |
+|[Azure-Samples/azure-search-openai-demo](https://github.com/Azure-Samples/azure-search-openai-demo) | 2366 |
+|[GerevAI/gerev](https://github.com/GerevAI/gerev) | 2330 |
+|[OpenGVLab/InternGPT](https://github.com/OpenGVLab/InternGPT) | 2289 |
+|[ParisNeo/gpt4all-ui](https://github.com/ParisNeo/gpt4all-ui) | 2159 |
+|[OpenBMB/BMTools](https://github.com/OpenBMB/BMTools) | 2158 |
+|[guangzhengli/ChatFiles](https://github.com/guangzhengli/ChatFiles) | 2005 |
+|[h2oai/h2ogpt](https://github.com/h2oai/h2ogpt) | 1939 |
+|[Farama-Foundation/PettingZoo](https://github.com/Farama-Foundation/PettingZoo) | 1845 |
+|[OpenGVLab/Ask-Anything](https://github.com/OpenGVLab/Ask-Anything) | 1749 |
+|[IntelligenzaArtificiale/Free-Auto-GPT](https://github.com/IntelligenzaArtificiale/Free-Auto-GPT) | 1740 |
+|[Unstructured-IO/unstructured](https://github.com/Unstructured-IO/unstructured) | 1628 |
+|[hwchase17/notion-qa](https://github.com/hwchase17/notion-qa) | 1607 |
+|[NVIDIA/NeMo-Guardrails](https://github.com/NVIDIA/NeMo-Guardrails) | 1544 |
+|[SamurAIGPT/privateGPT](https://github.com/SamurAIGPT/privateGPT) | 1543 |
+|[paulpierre/RasaGPT](https://github.com/paulpierre/RasaGPT) | 1526 |
+|[yanqiangmiffy/Chinese-LangChain](https://github.com/yanqiangmiffy/Chinese-LangChain) | 1485 |
+|[Kav-K/GPTDiscord](https://github.com/Kav-K/GPTDiscord) | 1402 |
+|[vocodedev/vocode-python](https://github.com/vocodedev/vocode-python) | 1387 |
+|[Chainlit/chainlit](https://github.com/Chainlit/chainlit) | 1336 |
+|[lunasec-io/lunasec](https://github.com/lunasec-io/lunasec) | 1323 |
+|[psychic-api/psychic](https://github.com/psychic-api/psychic) | 1248 |
+|[agiresearch/OpenAGI](https://github.com/agiresearch/OpenAGI) | 1208 |
+|[jina-ai/thinkgpt](https://github.com/jina-ai/thinkgpt) | 1193 |
+|[thomas-yanxin/LangChain-ChatGLM-Webui](https://github.com/thomas-yanxin/LangChain-ChatGLM-Webui) | 1182 |
+|[ttengwang/Caption-Anything](https://github.com/ttengwang/Caption-Anything) | 1137 |
+|[jina-ai/dev-gpt](https://github.com/jina-ai/dev-gpt) | 1135 |
+|[greshake/llm-security](https://github.com/greshake/llm-security) | 1086 |
+|[keephq/keep](https://github.com/keephq/keep) | 1063 |
+|[juncongmoo/chatllama](https://github.com/juncongmoo/chatllama) | 1037 |
+|[richardyc/Chrome-GPT](https://github.com/richardyc/Chrome-GPT) | 1035 |
+|[visual-openllm/visual-openllm](https://github.com/visual-openllm/visual-openllm) | 997 |
+|[mmz-001/knowledge_gpt](https://github.com/mmz-001/knowledge_gpt) | 995 |
+|[jina-ai/langchain-serve](https://github.com/jina-ai/langchain-serve) | 949 |
+|[irgolic/AutoPR](https://github.com/irgolic/AutoPR) | 936 |
+|[microsoft/X-Decoder](https://github.com/microsoft/X-Decoder) | 908 |
+|[poe-platform/api-bot-tutorial](https://github.com/poe-platform/api-bot-tutorial) | 902 |
+|[peterw/Chat-with-Github-Repo](https://github.com/peterw/Chat-with-Github-Repo) | 875 |
+|[cirediatpl/FigmaChain](https://github.com/cirediatpl/FigmaChain) | 822 |
+|[homanp/superagent](https://github.com/homanp/superagent) | 806 |
+|[seanpixel/Teenage-AGI](https://github.com/seanpixel/Teenage-AGI) | 800 |
+|[chatarena/chatarena](https://github.com/chatarena/chatarena) | 796 |
+|[hashintel/hash](https://github.com/hashintel/hash) | 795 |
+|[SamurAIGPT/Camel-AutoGPT](https://github.com/SamurAIGPT/Camel-AutoGPT) | 786 |
+|[rlancemartin/auto-evaluator](https://github.com/rlancemartin/auto-evaluator) | 770 |
+|[corca-ai/EVAL](https://github.com/corca-ai/EVAL) | 769 |
+|[101dotxyz/GPTeam](https://github.com/101dotxyz/GPTeam) | 755 |
+|[noahshinn024/reflexion](https://github.com/noahshinn024/reflexion) | 706 |
+|[eyurtsev/kor](https://github.com/eyurtsev/kor) | 695 |
+|[cheshire-cat-ai/core](https://github.com/cheshire-cat-ai/core) | 681 |
+|[e-johnstonn/BriefGPT](https://github.com/e-johnstonn/BriefGPT) | 656 |
+|[run-llama/llama-lab](https://github.com/run-llama/llama-lab) | 635 |
+|[griptape-ai/griptape](https://github.com/griptape-ai/griptape) | 583 |
+|[namuan/dr-doc-search](https://github.com/namuan/dr-doc-search) | 555 |
+|[getmetal/motorhead](https://github.com/getmetal/motorhead) | 550 |
+|[kreneskyp/ix](https://github.com/kreneskyp/ix) | 543 |
+|[hwchase17/chat-your-data](https://github.com/hwchase17/chat-your-data) | 510 |
+|[Anil-matcha/ChatPDF](https://github.com/Anil-matcha/ChatPDF) | 501 |
+|[whyiyhw/chatgpt-wechat](https://github.com/whyiyhw/chatgpt-wechat) | 497 |
+|[SamurAIGPT/ChatGPT-Developer-Plugins](https://github.com/SamurAIGPT/ChatGPT-Developer-Plugins) | 496 |
+|[microsoft/PodcastCopilot](https://github.com/microsoft/PodcastCopilot) | 492 |
+|[debanjum/khoj](https://github.com/debanjum/khoj) | 485 |
+|[akshata29/chatpdf](https://github.com/akshata29/chatpdf) | 485 |
+|[langchain-ai/langchain-aiplugin](https://github.com/langchain-ai/langchain-aiplugin) | 462 |
+|[jina-ai/agentchain](https://github.com/jina-ai/agentchain) | 460 |
+|[alexanderatallah/window.ai](https://github.com/alexanderatallah/window.ai) | 457 |
+|[yeagerai/yeagerai-agent](https://github.com/yeagerai/yeagerai-agent) | 451 |
+|[mckaywrigley/repo-chat](https://github.com/mckaywrigley/repo-chat) | 446 |
+|[michaelthwan/searchGPT](https://github.com/michaelthwan/searchGPT) | 446 |
+|[mpaepper/content-chatbot](https://github.com/mpaepper/content-chatbot) | 441 |
+|[freddyaboulton/gradio-tools](https://github.com/freddyaboulton/gradio-tools) | 439 |
+|[ruoccofabrizio/azure-open-ai-embeddings-qna](https://github.com/ruoccofabrizio/azure-open-ai-embeddings-qna) | 429 |
+|[StevenGrove/GPT4Tools](https://github.com/StevenGrove/GPT4Tools) | 422 |
+|[jonra1993/fastapi-alembic-sqlmodel-async](https://github.com/jonra1993/fastapi-alembic-sqlmodel-async) | 407 |
+|[msoedov/langcorn](https://github.com/msoedov/langcorn) | 405 |
+|[amosjyng/langchain-visualizer](https://github.com/amosjyng/langchain-visualizer) | 395 |
+|[ajndkr/lanarky](https://github.com/ajndkr/lanarky) | 384 |
+|[mtenenholtz/chat-twitter](https://github.com/mtenenholtz/chat-twitter) | 376 |
+|[steamship-core/steamship-langchain](https://github.com/steamship-core/steamship-langchain) | 371 |
+|[langchain-ai/auto-evaluator](https://github.com/langchain-ai/auto-evaluator) | 365 |
+|[xuwenhao/geektime-ai-course](https://github.com/xuwenhao/geektime-ai-course) | 358 |
+|[continuum-llms/chatgpt-memory](https://github.com/continuum-llms/chatgpt-memory) | 357 |
+|[opentensor/bittensor](https://github.com/opentensor/bittensor) | 347 |
+|[showlab/VLog](https://github.com/showlab/VLog) | 345 |
+|[daodao97/chatdoc](https://github.com/daodao97/chatdoc) | 345 |
+|[logan-markewich/llama_index_starter_pack](https://github.com/logan-markewich/llama_index_starter_pack) | 332 |
+|[poe-platform/poe-protocol](https://github.com/poe-platform/poe-protocol) | 320 |
+|[explosion/spacy-llm](https://github.com/explosion/spacy-llm) | 312 |
+|[andylokandy/gpt-4-search](https://github.com/andylokandy/gpt-4-search) | 311 |
+|[alejandro-ao/langchain-ask-pdf](https://github.com/alejandro-ao/langchain-ask-pdf) | 310 |
+|[jupyterlab/jupyter-ai](https://github.com/jupyterlab/jupyter-ai) | 294 |
+|[BlackHC/llm-strategy](https://github.com/BlackHC/llm-strategy) | 283 |
+|[itamargol/openai](https://github.com/itamargol/openai) | 281 |
+|[momegas/megabots](https://github.com/momegas/megabots) | 279 |
+|[personoids/personoids-lite](https://github.com/personoids/personoids-lite) | 277 |
+|[yvann-hub/Robby-chatbot](https://github.com/yvann-hub/Robby-chatbot) | 267 |
+|[Anil-matcha/Website-to-Chatbot](https://github.com/Anil-matcha/Website-to-Chatbot) | 266 |
+|[Cheems-Seminar/grounded-segment-any-parts](https://github.com/Cheems-Seminar/grounded-segment-any-parts) | 260 |
+|[sullivan-sean/chat-langchainjs](https://github.com/sullivan-sean/chat-langchainjs) | 248 |
+|[bborn/howdoi.ai](https://github.com/bborn/howdoi.ai) | 245 |
+|[daveebbelaar/langchain-experiments](https://github.com/daveebbelaar/langchain-experiments) | 240 |
+|[MagnivOrg/prompt-layer-library](https://github.com/MagnivOrg/prompt-layer-library) | 237 |
+|[ur-whitelab/exmol](https://github.com/ur-whitelab/exmol) | 234 |
+|[conceptofmind/toolformer](https://github.com/conceptofmind/toolformer) | 234 |
+|[recalign/RecAlign](https://github.com/recalign/RecAlign) | 226 |
+|[OpenBMB/AgentVerse](https://github.com/OpenBMB/AgentVerse) | 220 |
+|[alvarosevilla95/autolang](https://github.com/alvarosevilla95/autolang) | 219 |
+|[JohnSnowLabs/nlptest](https://github.com/JohnSnowLabs/nlptest) | 216 |
+|[kaleido-lab/dolphin](https://github.com/kaleido-lab/dolphin) | 215 |
+|[truera/trulens](https://github.com/truera/trulens) | 208 |
+|[NimbleBoxAI/ChainFury](https://github.com/NimbleBoxAI/ChainFury) | 208 |
+|[airobotlab/KoChatGPT](https://github.com/airobotlab/KoChatGPT) | 207 |
+|[monarch-initiative/ontogpt](https://github.com/monarch-initiative/ontogpt) | 200 |
+|[paolorechia/learn-langchain](https://github.com/paolorechia/learn-langchain) | 195 |
+|[shaman-ai/agent-actors](https://github.com/shaman-ai/agent-actors) | 185 |
+|[Haste171/langchain-chatbot](https://github.com/Haste171/langchain-chatbot) | 184 |
+|[plchld/InsightFlow](https://github.com/plchld/InsightFlow) | 182 |
+|[su77ungr/CASALIOY](https://github.com/su77ungr/CASALIOY) | 180 |
+|[jbrukh/gpt-jargon](https://github.com/jbrukh/gpt-jargon) | 177 |
+|[benthecoder/ClassGPT](https://github.com/benthecoder/ClassGPT) | 174 |
+|[billxbf/ReWOO](https://github.com/billxbf/ReWOO) | 170 |
+|[filip-michalsky/SalesGPT](https://github.com/filip-michalsky/SalesGPT) | 168 |
+|[hwchase17/langchain-streamlit-template](https://github.com/hwchase17/langchain-streamlit-template) | 168 |
+|[radi-cho/datasetGPT](https://github.com/radi-cho/datasetGPT) | 164 |
+|[hardbyte/qabot](https://github.com/hardbyte/qabot) | 164 |
+|[gia-guar/JARVIS-ChatGPT](https://github.com/gia-guar/JARVIS-ChatGPT) | 158 |
+|[plastic-labs/tutor-gpt](https://github.com/plastic-labs/tutor-gpt) | 154 |
+|[yasyf/compress-gpt](https://github.com/yasyf/compress-gpt) | 154 |
+|[fengyuli-dev/multimedia-gpt](https://github.com/fengyuli-dev/multimedia-gpt) | 154 |
+|[ethanyanjiali/minChatGPT](https://github.com/ethanyanjiali/minChatGPT) | 153 |
+|[hwchase17/chroma-langchain](https://github.com/hwchase17/chroma-langchain) | 153 |
+|[edreisMD/plugnplai](https://github.com/edreisMD/plugnplai) | 148 |
+|[chakkaradeep/pyCodeAGI](https://github.com/chakkaradeep/pyCodeAGI) | 145 |
+|[ccurme/yolopandas](https://github.com/ccurme/yolopandas) | 145 |
+|[shamspias/customizable-gpt-chatbot](https://github.com/shamspias/customizable-gpt-chatbot) | 144 |
+|[realminchoi/babyagi-ui](https://github.com/realminchoi/babyagi-ui) | 143 |
+|[PradipNichite/Youtube-Tutorials](https://github.com/PradipNichite/Youtube-Tutorials) | 140 |
+|[gustavz/DataChad](https://github.com/gustavz/DataChad) | 140 |
+|[Klingefjord/chatgpt-telegram](https://github.com/Klingefjord/chatgpt-telegram) | 140 |
+|[Jaseci-Labs/jaseci](https://github.com/Jaseci-Labs/jaseci) | 139 |
+|[handrew/browserpilot](https://github.com/handrew/browserpilot) | 137 |
+|[jmpaz/promptlib](https://github.com/jmpaz/promptlib) | 137 |
+|[SamPink/dev-gpt](https://github.com/SamPink/dev-gpt) | 135 |
+|[menloparklab/langchain-cohere-qdrant-doc-retrieval](https://github.com/menloparklab/langchain-cohere-qdrant-doc-retrieval) | 135 |
+|[hirokidaichi/wanna](https://github.com/hirokidaichi/wanna) | 135 |
+|[steamship-core/vercel-examples](https://github.com/steamship-core/vercel-examples) | 134 |
+|[pablomarin/GPT-Azure-Search-Engine](https://github.com/pablomarin/GPT-Azure-Search-Engine) | 133 |
+|[ibiscp/LLM-IMDB](https://github.com/ibiscp/LLM-IMDB) | 133 |
+|[shauryr/S2QA](https://github.com/shauryr/S2QA) | 133 |
+|[jerlendds/osintbuddy](https://github.com/jerlendds/osintbuddy) | 132 |
+|[yuanjie-ai/ChatLLM](https://github.com/yuanjie-ai/ChatLLM) | 132 |
+|[yasyf/summ](https://github.com/yasyf/summ) | 132 |
+|[WongSaang/chatgpt-ui-server](https://github.com/WongSaang/chatgpt-ui-server) | 130 |
+|[peterw/StoryStorm](https://github.com/peterw/StoryStorm) | 127 |
+|[Teahouse-Studios/akari-bot](https://github.com/Teahouse-Studios/akari-bot) | 126 |
+|[vaibkumr/prompt-optimizer](https://github.com/vaibkumr/prompt-optimizer) | 125 |
+|[preset-io/promptimize](https://github.com/preset-io/promptimize) | 124 |
+|[homanp/vercel-langchain](https://github.com/homanp/vercel-langchain) | 124 |
+|[petehunt/langchain-github-bot](https://github.com/petehunt/langchain-github-bot) | 123 |
+|[eunomia-bpf/GPTtrace](https://github.com/eunomia-bpf/GPTtrace) | 118 |
+|[nicknochnack/LangchainDocuments](https://github.com/nicknochnack/LangchainDocuments) | 116 |
+|[jiran214/GPT-vup](https://github.com/jiran214/GPT-vup) | 112 |
+|[rsaryev/talk-codebase](https://github.com/rsaryev/talk-codebase) | 112 |
+|[zenml-io/zenml-projects](https://github.com/zenml-io/zenml-projects) | 112 |
+|[microsoft/azure-openai-in-a-day-workshop](https://github.com/microsoft/azure-openai-in-a-day-workshop) | 112 |
+|[davila7/file-gpt](https://github.com/davila7/file-gpt) | 112 |
+|[prof-frink-lab/slangchain](https://github.com/prof-frink-lab/slangchain) | 111 |
+|[aurelio-labs/arxiv-bot](https://github.com/aurelio-labs/arxiv-bot) | 110 |
+|[fixie-ai/fixie-examples](https://github.com/fixie-ai/fixie-examples) | 108 |
+|[miaoshouai/miaoshouai-assistant](https://github.com/miaoshouai/miaoshouai-assistant) | 105 |
+|[flurb18/AgentOoba](https://github.com/flurb18/AgentOoba) | 103 |
+|[solana-labs/chatgpt-plugin](https://github.com/solana-labs/chatgpt-plugin) | 102 |
+|[Significant-Gravitas/Auto-GPT-Benchmarks](https://github.com/Significant-Gravitas/Auto-GPT-Benchmarks) | 102 |
+|[kaarthik108/snowChat](https://github.com/kaarthik108/snowChat) | 100 |
+
+
+_Generated by [github-dependents-info](https://github.com/nvuillam/github-dependents-info)_
+
+`github-dependents-info --repo hwchase17/langchain --markdownfile dependents.md --minstars 100 --sort stars`

docs/ecosystem/baseten.md

@@ -0,0 +1,25 @@
+# Baseten
+
+Learn how to use LangChain with models deployed on Baseten.
+
+## Installation and setup
+
+- Create a [Baseten](https://baseten.co) account and [API key](https://docs.baseten.co/settings/api-keys).
+- Install the Baseten Python client with `pip install baseten`
+- Use your API key to authenticate with `baseten login`
+
+## Invoking a model
+
+Baseten integrates with LangChain through the LLM module, which provides a standardized and interoperable interface for models that are deployed on your Baseten workspace.
+
+You can deploy foundation models like WizardLM and Alpaca with one click from the [Baseten model library](https://app.baseten.co/explore/) or if you have your own model, [deploy it with this tutorial](https://docs.baseten.co/deploying-models/deploy).
+
+In this example, we'll work with WizardLM. [Deploy WizardLM here](https://app.baseten.co/explore/wizardlm) and follow along with the deployed [model's version ID](https://docs.baseten.co/managing-models/manage).
+
+```python
+from langchain.llms import Baseten
+
+wizardlm = Baseten(model="MODEL_VERSION_ID", verbose=True)
+
+wizardlm("What is the difference between a Wizard and a Sorcerer?")
+```

docs/ecosystem/cohere.md

@@ -1,25 +0,0 @@
-# Cohere
-
-This page covers how to use the Cohere ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Cohere wrappers.
-
-## Installation and Setup
-- Install the Python SDK with `pip install cohere`
-- Get an Cohere api key and set it as an environment variable (`COHERE_API_KEY`)
-
-## Wrappers
-
-### LLM
-
-There exists an Cohere LLM wrapper, which you can access with 
-```python
-from langchain.llms import Cohere
-```
-
-### Embeddings
-
-There exists an Cohere Embeddings wrapper, which you can access with 
-```python
-from langchain.embeddings import CohereEmbeddings
-```
-For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/cohere.ipynb)

docs/ecosystem/databerry.md

@@ -1,25 +0,0 @@
-# Databerry
-
-This page covers how to use the [Databerry](https://databerry.ai) within LangChain.
-
-## What is Databerry?
-
-Databerry is an [open source](https://github.com/gmpetrov/databerry) document retrievial platform that helps to connect your personal data with Large Language Models.
-
-![Databerry](../_static/DataberryDashboard.png)
-
-## Quick start
-
-Retrieving documents stored in Databerry from LangChain is very easy!
-
-```python
-from langchain.retrievers import DataberryRetriever
-
-retriever = DataberryRetriever(
-    datastore_url="https://api.databerry.ai/query/clg1xg2h80000l708dymr0fxc",
-    # api_key="DATABERRY_API_KEY", # optional if datastore is public
-    # top_k=10 # optional
-)
-
-docs = retriever.get_relevant_documents("What's Databerry?")
-```

docs/ecosystem/deployments.md

@@ -6,6 +6,11 @@ This section covers several options for that. Note that these options are meant
 
 What follows is a list of template GitHub repositories designed to be easily forked and modified to use your chain. This list is far from exhaustive, and we are EXTREMELY open to contributions here.
 
+## [Anyscale](https://www.anyscale.com/model-serving)
+
+Anyscale is a unified compute platform that makes it easy to develop, deploy, and manage scalable LLM applications in production using Ray.
+With Anyscale you can scale the most challenging LLM-based workloads and both develop and deploy LLM-based apps on a single compute platform.
+
 ## [Streamlit](https://github.com/hwchase17/langchain-streamlit-template)
 
 This repo serves as a template for how to deploy a LangChain with Streamlit.
@@ -19,6 +24,12 @@ It implements a chatbot interface, with a "Bring-Your-Own-Token" approach (nice
 It also contains instructions for how to deploy this app on the Hugging Face platform.
 This is heavily influenced by James Weaver's [excellent examples](https://huggingface.co/JavaFXpert).
 
+## [Chainlit](https://github.com/Chainlit/cookbook)
+
+This repo is a cookbook explaining how to visualize and deploy LangChain agents with Chainlit.
+You create ChatGPT-like UIs with Chainlit. Some of the key features include intermediary steps visualisation, element management & display (images, text, carousel, etc.) as well as cloud deployment.
+Chainlit [doc](https://docs.chainlit.io/langchain) on the integration with LangChain
+
 ## [Beam](https://github.com/slai-labs/get-beam/tree/main/examples/langchain-question-answering)
 
 This repo serves as a template for how deploy a LangChain with [Beam](https://beam.cloud).
@@ -29,6 +40,14 @@ It implements a Question Answering app and contains instructions for deploying t
 
 A minimal example on how to run LangChain on Vercel using Flask.
 
+## [FastAPI + Vercel](https://github.com/msoedov/langcorn)
+
+A minimal example on how to run LangChain on Vercel using FastAPI and LangCorn/Uvicorn.
+
+## [Kinsta](https://github.com/kinsta/hello-world-langchain)
+
+A minimal example on how to deploy LangChain to [Kinsta](https://kinsta.com) using Flask.
+
 ## [Fly.io](https://github.com/fly-apps/hello-fly-langchain)
 
 A minimal example of how to deploy LangChain to [Fly.io](https://fly.io/) using Flask.

docs/ecosystem/modelscope.md

@@ -0,0 +1,20 @@
+# ModelScope
+
+This page covers how to use the modelscope ecosystem within LangChain.
+It is broken into two parts: installation and setup, and then references to specific modelscope wrappers.
+
+## Installation and Setup
+
+* Install the Python SDK with `pip install modelscope`
+
+## Wrappers
+
+### Embeddings
+
+There exists a modelscope Embeddings wrapper, which you can access with 
+
+```python
+from langchain.embeddings import ModelScopeEmbeddings
+```
+
+For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/modelscope_hub.ipynb)

docs/ecosystem/openai.md

@@ -1,55 +0,0 @@
-# OpenAI
-
-This page covers how to use the OpenAI ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific OpenAI wrappers.
-
-## Installation and Setup
-- Install the Python SDK with `pip install openai`
-- Get an OpenAI api key and set it as an environment variable (`OPENAI_API_KEY`)
-- If you want to use OpenAI's tokenizer (only available for Python 3.9+), install it with `pip install tiktoken`
-
-## Wrappers
-
-### LLM
-
-There exists an OpenAI LLM wrapper, which you can access with 
-```python
-from langchain.llms import OpenAI
-```
-
-If you are using a model hosted on Azure, you should use different wrapper for that:
-```python
-from langchain.llms import AzureOpenAI
-```
-For a more detailed walkthrough of the Azure wrapper, see [this notebook](../modules/models/llms/integrations/azure_openai_example.ipynb)
-
-
-
-### Embeddings
-
-There exists an OpenAI Embeddings wrapper, which you can access with 
-```python
-from langchain.embeddings import OpenAIEmbeddings
-```
-For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/openai.ipynb)
-
-
-### Tokenizer
-
-There are several places you can use the `tiktoken` tokenizer. By default, it is used to count tokens
-for OpenAI LLMs.
-
-You can also use it to count tokens when splitting documents with 
-```python
-from langchain.text_splitter import CharacterTextSplitter
-CharacterTextSplitter.from_tiktoken_encoder(...)
-```
-For a more detailed walkthrough of this, see [this notebook](../modules/indexes/text_splitters/examples/tiktoken.ipynb)
-
-### Moderation
-You can also access the OpenAI content moderation endpoint with
-
-```python
-from langchain.chains import OpenAIModerationChain
-```
-For a more detailed walkthrough of this, see [this notebook](../modules/chains/examples/moderation.ipynb)

docs/ecosystem/predictionguard.md

@@ -1,56 +0,0 @@
-# Prediction Guard
-
-This page covers how to use the Prediction Guard ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Prediction Guard wrappers.
-
-## Installation and Setup
-- Install the Python SDK with `pip install predictionguard`
-- Get an Prediction Guard access token (as described [here](https://docs.predictionguard.com/)) and set it as an environment variable (`PREDICTIONGUARD_TOKEN`)
-
-## LLM Wrapper
-
-There exists a Prediction Guard LLM wrapper, which you can access with 
-```python
-from langchain.llms import PredictionGuard
-```
-
-You can provide the name of your Prediction Guard "proxy" as an argument when initializing the LLM:
-```python
-pgllm = PredictionGuard(name="your-text-gen-proxy")
-```
-
-Alternatively, you can use Prediction Guard's default proxy for SOTA LLMs:
-```python
-pgllm = PredictionGuard(name="default-text-gen")
-```
-
-You can also provide your access token directly as an argument:
-```python
-pgllm = PredictionGuard(name="default-text-gen", token="<your access token>")
-```
-
-## Example usage
-
-Basic usage of the LLM wrapper:
-```python
-from langchain.llms import PredictionGuard
-
-pgllm = PredictionGuard(name="default-text-gen")
-pgllm("Tell me a joke")
-```
-
-Basic LLM Chaining with the Prediction Guard wrapper:
-```python
-from langchain import PromptTemplate, LLMChain
-from langchain.llms import PredictionGuard
-
-template = """Question: {question}
-
-Answer: Let's think step by step."""
-prompt = PromptTemplate(template=template, input_variables=["question"])
-llm_chain = LLMChain(prompt=prompt, llm=PredictionGuard(name="default-text-gen"), verbose=True)
-
-question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
-
-llm_chain.predict(question=question)
-```

docs/ecosystem/zilliz.md

@@ -1,21 +0,0 @@
-# Zilliz
-
-This page covers how to use the Zilliz Cloud ecosystem within LangChain.
-Zilliz uses the Milvus integration. 
-It is broken into two parts: installation and setup, and then references to specific Milvus wrappers.
-
-## Installation and Setup
-- Install the Python SDK with `pip install pymilvus`
-## Wrappers
-
-### VectorStore
-
-There exists a wrapper around Zilliz indexes, allowing you to use it as a vectorstore,
-whether for semantic search or example selection.
-
-To import this vectorstore:
-```python
-from langchain.vectorstores import Milvus
-```
-
-For a more detailed walkthrough of the Miluvs wrapper, see [this notebook](../modules/indexes/vectorstores/examples/zilliz.ipynb)

docs/gallery.rst

@@ -1,354 +0,0 @@
-LangChain Gallery
-=================
-
-Lots of people have built some pretty awesome stuff with LangChain.
-This is a collection of our favorites.
-If you see any other demos that you think we should highlight, be sure to let us know!
-
-
-Open Source
------------
-
-.. panels::
-    :body: text-center
-
-    ---
-
-    .. link-button:: https://github.com/bborn/howdoi.ai
-        :type: url
-        :text: HowDoI.ai
-        :classes: stretched-link btn-lg
-
-    +++
-
-    This is an experiment in building a large-language-model-backed chatbot. It can hold a conversation, remember previous comments/questions, 
-    and answer all types of queries (history, web search, movie data, weather, news, and more).
-
-    ---
-
-    .. link-button:: https://colab.research.google.com/drive/1sKSTjt9cPstl_WMZ86JsgEqFG-aSAwkn?usp=sharing
-        :type: url
-        :text: YouTube Transcription QA with Sources
-        :classes: stretched-link btn-lg
-
-    +++
-
-    An end-to-end example of doing question answering on YouTube transcripts, returning the timestamps as sources to legitimize the answer.
-
-    ---
-
-    .. link-button:: https://github.com/normandmickey/MrsStax
-        :type: url
-        :text: QA Slack Bot
-        :classes: stretched-link btn-lg
-
-    +++
-
-    This application is a Slack Bot that uses Langchain and OpenAI's GPT3 language model to provide domain specific answers. You provide the documents.
-
-    ---
-
-    .. link-button:: https://github.com/OpenBioLink/ThoughtSource
-        :type: url
-        :text: ThoughtSource
-        :classes: stretched-link btn-lg
-
-    +++
-
-    A central, open resource and community around data and tools related to chain-of-thought reasoning in large language models.
-
-    ---
-
-    .. link-button:: https://github.com/blackhc/llm-strategy
-        :type: url
-        :text: LLM Strategy
-        :classes: stretched-link btn-lg
-    
-    +++
-
-    This Python package adds a decorator llm_strategy that connects to an LLM (such as OpenAI’s GPT-3) and uses the LLM to "implement" abstract methods in interface classes. It does this by forwarding requests to the LLM and converting the responses back to Python data using Python's @dataclasses.
-
-    ---
-
-    .. link-button:: https://github.com/JohnNay/llm-lobbyist
-        :type: url
-        :text: Zero-Shot Corporate Lobbyist
-        :classes: stretched-link btn-lg
-
-    +++
-
-    A notebook showing how to use GPT to help with the work of a corporate lobbyist.
-
-    ---
-
-    .. link-button:: https://dagster.io/blog/chatgpt-langchain
-        :type: url
-        :text: Dagster Documentation ChatBot
-        :classes: stretched-link btn-lg
-
-    +++
-
-    A jupyter notebook demonstrating how you could create a semantic search engine on documents in one of your Google Folders
-
-    ---
-
-    .. link-button:: https://github.com/venuv/langchain_semantic_search
-        :type: url
-        :text: Google Folder Semantic Search
-        :classes: stretched-link btn-lg
-
-    +++
-
-    Build a GitHub support bot with GPT3, LangChain, and Python.
-
-    ---
-
-    .. link-button:: https://huggingface.co/spaces/team7/talk_with_wind
-        :type: url
-        :text: Talk With Wind
-        :classes: stretched-link btn-lg
-
-    +++
-
-    Record sounds of anything (birds, wind, fire, train station) and chat with it.
-
-    ---
-
-    .. link-button:: https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain
-        :type: url
-        :text: ChatGPT LangChain
-        :classes: stretched-link btn-lg
-
-    +++
-
-    This simple application demonstrates a conversational agent implemented with OpenAI GPT-3.5 and LangChain. When necessary, it leverages tools for complex math, searching the internet, and accessing news and weather.
-
-    ---
-
-    .. link-button:: https://huggingface.co/spaces/JavaFXpert/gpt-math-techniques
-        :type: url
-        :text: GPT Math Techniques
-        :classes: stretched-link btn-lg
-
-    +++
-
-    A Hugging Face spaces project showing off the benefits of using PAL for math problems.
-
-    ---
-
-    .. link-button:: https://colab.research.google.com/drive/1xt2IsFPGYMEQdoJFNgWNAjWGxa60VXdV
-        :type: url
-        :text: GPT Political Compass
-        :classes: stretched-link btn-lg
-
-    +++
-
-    Measure the political compass of GPT.
-
-    ---
-
-    .. link-button:: https://github.com/hwchase17/notion-qa
-        :type: url
-        :text: Notion Database Question-Answering Bot
-        :classes: stretched-link btn-lg
-    
-    +++
-
-    Open source GitHub project shows how to use LangChain to create a chatbot that can answer questions about an arbitrary Notion database.
-
-    ---
-
-    .. link-button:: https://github.com/jerryjliu/llama_index
-        :type: url
-        :text: LlamaIndex
-        :classes: stretched-link btn-lg
-    
-    +++
-
-    LlamaIndex (formerly GPT Index) is a project consisting of a set of data structures that are created using GPT-3 and can be traversed using GPT-3 in order to answer queries.
-
-    ---
-
-    .. link-button:: https://github.com/JavaFXpert/llm-grovers-search-party
-        :type: url
-        :text: Grover's Algorithm
-        :classes: stretched-link btn-lg
-
-    +++
-
-    Leveraging Qiskit, OpenAI and LangChain to demonstrate Grover's algorithm
-
-    ---
-
-    .. link-button:: https://huggingface.co/spaces/rituthombre/QNim
-        :type: url
-        :text: QNimGPT
-        :classes: stretched-link btn-lg
-
-    +++
-
-    A chat UI to play Nim, where a player can select an opponent, either a quantum computer or an AI
-
-    ---
-
-    .. link-button:: https://colab.research.google.com/drive/19WTIWC3prw5LDMHmRMvqNV2loD9FHls6?usp=sharing
-        :type: url
-        :text: ReAct TextWorld
-        :classes: stretched-link btn-lg
-
-    +++
-
-    Leveraging the ReActTextWorldAgent to play TextWorld with an LLM!
-
-    ---
-
-    .. link-button:: https://github.com/jagilley/fact-checker
-        :type: url
-        :text: Fact Checker
-        :classes: stretched-link btn-lg
-    
-    +++
-
-    This repo is a simple demonstration of using LangChain to do fact-checking with prompt chaining.
-    
-    ---
-
-    .. link-button:: https://github.com/arc53/docsgpt
-        :type: url
-        :text: DocsGPT
-        :classes: stretched-link btn-lg
-    
-    +++
-
-    Answer questions about the documentation of any project    
-
-Misc. Colab Notebooks
-~~~~~~~~~~~~~~~~~~~~~
-
-.. panels::
-    :body: text-center
-
-    ---
-
-    .. link-button:: https://colab.research.google.com/drive/1AAyEdTz-Z6ShKvewbt1ZHUICqak0MiwR?usp=sharing
-        :type: url
-        :text: Wolfram Alpha in Conversational Agent
-        :classes: stretched-link btn-lg
-    
-    +++
-
-    Give ChatGPT a WolframAlpha neural implant
-    
-    ---
-
-    .. link-button:: https://colab.research.google.com/drive/1UsCLcPy8q5PMNQ5ytgrAAAHa124dzLJg?usp=sharing
-        :type: url
-        :text: Tool Updates in Agents
-        :classes: stretched-link btn-lg
-    
-    +++
-
-    Agent improvements (6th Jan 2023)
-    
-    ---
-
-    .. link-button:: https://colab.research.google.com/drive/1UsCLcPy8q5PMNQ5ytgrAAAHa124dzLJg?usp=sharing
-        :type: url
-        :text: Conversational Agent with Tools (Langchain AGI)
-        :classes: stretched-link btn-lg
-    
-    +++
-
-    Langchain AGI (23rd Dec 2022)    
-
-Proprietary
------------
-
-.. panels::
-    :body: text-center
-
-    ---
-
-    .. link-button:: https://twitter.com/sjwhitmore/status/1580593217153531908?s=20&t=neQvtZZTlp623U3LZwz3bQ
-        :type: url
-        :text: Daimon
-        :classes: stretched-link btn-lg
-
-    +++
-
-    A chat-based AI personal assistant with long-term memory about you.
-
-    ---
-
-    .. link-button:: https://anysummary.app
-        :type: url
-        :text: Summarize any file with AI
-        :classes: stretched-link btn-lg
-
-    +++
-
-    Summarize not only long docs, interview audio or video files quickly, but also entire websites and YouTube videos. Share or download your generated summaries to collaborate with others, or revisit them at any time! Bonus: `@anysummary <https://twitter.com/anysummary>`_ on Twitter will also summarize any thread it is tagged in.
-    
-    ---
-
-    .. link-button:: https://twitter.com/dory111111/status/1608406234646052870?s=20&t=XYlrbKM0ornJsrtGa0br-g
-        :type: url
-        :text: AI Assisted SQL Query Generator
-        :classes: stretched-link btn-lg
-
-    +++
-
-    An app to write SQL using natural language, and execute against real DB.
-
-    ---
-
-    .. link-button:: https://twitter.com/krrish_dh/status/1581028925618106368?s=20&t=neQvtZZTlp623U3LZwz3bQ
-        :type: url
-        :text: Clerkie
-        :classes: stretched-link btn-lg
-
-    +++
-
-    Stack Tracing QA Bot to help debug complex stack tracing (especially the ones that go multi-function/file deep).
-
-    ---
-
-    .. link-button:: https://twitter.com/Raza_Habib496/status/1596880140490838017?s=20&t=6MqEQYWfSqmJwsKahjCVOA
-        :type: url
-        :text: Sales Email Writer
-        :classes: stretched-link btn-lg
-
-    +++
-
-    By Raza Habib, this demo utilizes LangChain + SerpAPI + HumanLoop to write sales emails. Give it a company name and a person, this application will use Google Search (via SerpAPI) to get more information on the company and the person, and then write them a sales message.
-
-    ---
-
-    .. link-button:: https://twitter.com/chillzaza_/status/1592961099384905730?s=20&t=EhU8jl0KyCPJ7vE9Rnz-cQ
-        :type: url
-        :text: Question-Answering on a Web Browser
-        :classes: stretched-link btn-lg
-
-    +++
-
-    By Zahid Khawaja, this demo utilizes question answering to answer questions about a given website. A followup added this for `YouTube videos <https://twitter.com/chillzaza_/status/1593739682013220865?s=20&t=EhU8jl0KyCPJ7vE9Rnz-cQ>`_, and then another followup added it for `Wikipedia <https://twitter.com/chillzaza_/status/1594847151238037505?s=20&t=EhU8jl0KyCPJ7vE9Rnz-cQ>`_.
-
-    ---
-
-    .. link-button:: https://mynd.so
-        :type: url
-        :text: Mynd
-        :classes: stretched-link btn-lg
-
-    +++
-
-    A journaling app for self-care that uses AI to uncover insights and patterns over time.
-
-
-Articles on **Google Scholar**
------------------------------
-
-LangChain is used in many scientific and research projects.
-
-**Google Scholar** presents a `list of the papers <https://scholar.google.com/scholar?q=%22langchain%22&hl=en&as_sdt=0,5&as_vis=1>`_
-with references to LangChain.

docs/getting_started/concepts.md

@@ -0,0 +1,75 @@
+# Concepts
+
+These are concepts and terminology commonly used when developing LLM applications.
+It contains reference to external papers or sources where the concept was first introduced,
+as well as to places in LangChain where the concept is used.
+
+## Chain of Thought
+
+`Chain of Thought (CoT)` is a prompting technique used to encourage the model to generate a series of intermediate reasoning steps.
+A less formal way to induce this behavior is to include “Let’s think step-by-step” in the prompt.
+
+- [Chain-of-Thought Paper](https://arxiv.org/pdf/2201.11903.pdf)
+- [Step-by-Step Paper](https://arxiv.org/abs/2112.00114)
+
+## Action Plan Generation
+
+`Action Plan Generation` is a prompting technique that uses a language model to generate actions to take.
+The results of these actions can then be fed back into the language model to generate a subsequent action.
+
+- [WebGPT Paper](https://arxiv.org/pdf/2112.09332.pdf)
+- [SayCan Paper](https://say-can.github.io/assets/palm_saycan.pdf)
+
+## ReAct
+
+`ReAct` is a prompting technique that combines Chain-of-Thought prompting with action plan generation.
+This induces the model to think about what action to take, then take it.
+
+- [Paper](https://arxiv.org/pdf/2210.03629.pdf)
+- [LangChain Example](../modules/agents/agents/examples/react.ipynb)
+
+## Self-ask
+
+`Self-ask` is a prompting method that builds on top of chain-of-thought prompting.
+In this method, the model explicitly asks itself follow-up questions, which are then answered by an external search engine.
+
+- [Paper](https://ofir.io/self-ask.pdf)
+- [LangChain Example](../modules/agents/agents/examples/self_ask_with_search.ipynb)
+
+## Prompt Chaining
+
+`Prompt Chaining` is combining multiple LLM calls, with the output of one-step being the input to the next.
+
+- [PromptChainer Paper](https://arxiv.org/pdf/2203.06566.pdf)
+- [Language Model Cascades](https://arxiv.org/abs/2207.10342)
+- [ICE Primer Book](https://primer.ought.org/)
+- [Socratic Models](https://socraticmodels.github.io/)
+
+## Memetic Proxy
+
+`Memetic Proxy` is encouraging the LLM
+to respond in a certain way framing the discussion in a context that the model knows of and that 
+will result in that type of response.
+For example, as a conversation between a student and a teacher.
+
+- [Paper](https://arxiv.org/pdf/2102.07350.pdf)
+
+## Self Consistency
+
+`Self Consistency` is a decoding strategy that samples a diverse set of reasoning paths and then selects the most consistent answer.
+Is most effective when combined with Chain-of-thought prompting.
+
+- [Paper](https://arxiv.org/pdf/2203.11171.pdf)
+
+## Inception
+
+`Inception` is also called `First Person Instruction`.
+It is encouraging the model to think a certain way by including the start of the model’s response in the prompt.
+
+- [Example](https://twitter.com/goodside/status/1583262455207460865?s=20&t=8Hz7XBnK1OF8siQrxxCIGQ)
+
+## MemPrompt
+
+`MemPrompt` maintains a memory of errors and user feedback, and uses them to prevent repetition of mistakes.
+
+- [Paper](https://memprompt.com/)

docs/getting_started/getting_started.md

@@ -37,6 +37,12 @@ import os
 os.environ["OPENAI_API_KEY"] = "..."
 ```
 
+If you want to set the API key dynamically, you can use the openai_api_key parameter when initiating OpenAI class—for instance, each user's API key.
+
+```python
+from langchain.llms import OpenAI
+llm = OpenAI(openai_api_key="OPENAI_API_KEY")
+```
 
 ## Building a Language Model Application: LLMs
 

docs/getting_started/tutorials.md

@@ -0,0 +1,113 @@
+# Tutorials
+
+⛓ icon marks a new addition [last update 2023-05-15]
+
+### DeepLearning.AI course
+⛓[LangChain for LLM Application Development](https://learn.deeplearning.ai/langchain) by Harrison Chase presented by [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng)
+
+### Handbook
+[LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham**
+
+### Tutorials
+[LangChain Tutorials](https://www.youtube.com/watch?v=FuqdVNB_8c0&list=PL9V0lbeJ69brU-ojMpU1Y7Ic58Tap0Cw6) by [Edrick](https://www.youtube.com/@edrickdch):
+- ⛓ [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0)
+- ⛓ [LangChain 101: The Complete Beginner's Guide](https://youtu.be/P3MAbZ2eMUI) 
+
+[LangChain Crash Course: Build an AutoGPT app in 25 minutes](https://youtu.be/MlK6SIjcjE8) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
+
+
+[LangChain Crash Course - Build apps with language models](https://youtu.be/LbT1yp6quS8) by [Patrick Loeber](https://www.youtube.com/@patloeber)
+
+
+[LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners](https://youtu.be/aywZrzNaKjs) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
+
+
+###
+[LangChain for Gen AI and LLMs](https://www.youtube.com/playlist?list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F) by [James Briggs](https://www.youtube.com/@jamesbriggs):
+- #1 [Getting Started with `GPT-3` vs. Open Source LLMs](https://youtu.be/nE2skSRWTTs)
+- #2 [Prompt Templates for `GPT 3.5` and other LLMs](https://youtu.be/RflBcK0oDH0)
+- #3 [LLM Chains using `GPT 3.5` and other LLMs](https://youtu.be/S8j9Tk0lZHU)
+- #4 [Chatbot Memory for `Chat-GPT`, `Davinci` + other LLMs](https://youtu.be/X05uK0TZozM)
+- #5 [Chat with OpenAI in LangChain](https://youtu.be/CnAgB3A5OlU)
+- ⛓ #6 [Fixing LLM Hallucinations with Retrieval Augmentation in LangChain](https://youtu.be/kvdVduIJsc8)
+- ⛓ #7 [LangChain Agents Deep Dive with GPT 3.5](https://youtu.be/jSP-gSEyVeI)
+- ⛓ #8 [Create Custom Tools for Chatbots in LangChain](https://youtu.be/q-HNphrWsDE)
+- ⛓ #9 [Build Conversational Agents with Vector DBs](https://youtu.be/H6bCqqw9xyI)
+
+
+###
+[LangChain 101](https://www.youtube.com/playlist?list=PLqZXAkvF1bPNQER9mLmDbntNfSpzdDIU5) by [Data Independent](https://www.youtube.com/@DataIndependent):
+- [What Is LangChain? - LangChain + `ChatGPT` Overview](https://youtu.be/_v_fgW2SkkQ)
+- [Quickstart Guide](https://youtu.be/kYRB-vJFy38)
+- [Beginner Guide To 7 Essential Concepts](https://youtu.be/2xxziIWmaSA)
+- [`OpenAI` + `Wolfram Alpha`](https://youtu.be/UijbzCIJ99g)
+- [Ask Questions On Your Custom (or Private) Files](https://youtu.be/EnT-ZTrcPrg)
+- [Connect `Google Drive Files` To `OpenAI`](https://youtu.be/IqqHqDcXLww)
+- [`YouTube Transcripts` + `OpenAI`](https://youtu.be/pNcQ5XXMgH4)
+- [Question A 300 Page Book (w/ `OpenAI` + `Pinecone`)](https://youtu.be/h0DHDp1FbmQ)
+- [Workaround `OpenAI's` Token Limit With Chain Types](https://youtu.be/f9_BWhCI4Zo)
+- [Build Your Own OpenAI + LangChain Web App in 23 Minutes](https://youtu.be/U_eV8wfMkXU)
+- [Working With The New `ChatGPT API`](https://youtu.be/e9P7FLi5Zy8)
+- [OpenAI + LangChain Wrote Me 100 Custom Sales Emails](https://youtu.be/y1pyAQM-3Bo)
+- [Structured Output From `OpenAI` (Clean Dirty Data)](https://youtu.be/KwAXfey-xQk)
+- [Connect `OpenAI` To +5,000 Tools (LangChain + `Zapier`)](https://youtu.be/7tNm0yiDigU)
+- [Use LLMs To Extract Data From Text (Expert Mode)](https://youtu.be/xZzvwR9jdPA)
+- ⛓ [Extract Insights From Interview Transcripts Using LLMs](https://youtu.be/shkMOHwJ4SM)
+- ⛓ [5 Levels Of LLM Summarizing: Novice to Expert](https://youtu.be/qaPMdcCqtWk)
+
+
+###
+[LangChain How to and guides](https://www.youtube.com/playlist?list=PL8motc6AQftk1Bs42EW45kwYbyJ4jOdiZ) by [Sam Witteveen](https://www.youtube.com/@samwitteveenai):
+- [LangChain Basics - LLMs & PromptTemplates with Colab](https://youtu.be/J_0qvRt4LNk)
+- [LangChain Basics - Tools and Chains](https://youtu.be/hI2BY7yl_Ac)
+- [`ChatGPT API` Announcement & Code Walkthrough with LangChain](https://youtu.be/phHqvLHCwH4)
+- [Conversations with Memory (explanation & code walkthrough)](https://youtu.be/X550Zbz_ROE)
+- [Chat with `Flan20B`](https://youtu.be/VW5LBavIfY4)
+- [Using `Hugging Face Models` locally (code walkthrough)](https://youtu.be/Kn7SX2Mx_Jk)
+- [`PAL` : Program-aided Language Models with LangChain code](https://youtu.be/dy7-LvDu-3s)
+- [Building a Summarization System with LangChain and `GPT-3` - Part 1](https://youtu.be/LNq_2s_H01Y)
+- [Building a Summarization System with LangChain and `GPT-3` - Part 2](https://youtu.be/d-yeHDLgKHw)
+- [Microsoft's `Visual ChatGPT` using LangChain](https://youtu.be/7YEiEyfPF5U)
+- [LangChain Agents - Joining Tools and Chains with Decisions](https://youtu.be/ziu87EXZVUE)
+- [Comparing LLMs with LangChain](https://youtu.be/rFNG0MIEuW0)
+- [Using `Constitutional AI` in LangChain](https://youtu.be/uoVqNFDwpX4)
+- [Talking to `Alpaca` with LangChain - Creating an Alpaca Chatbot](https://youtu.be/v6sF8Ed3nTE)
+- [Talk to your `CSV` & `Excel` with LangChain](https://youtu.be/xQ3mZhw69bc)
+- [`BabyAGI`: Discover the Power of Task-Driven Autonomous Agents!](https://youtu.be/QBcDLSE2ERA)
+- [Improve your `BabyAGI` with LangChain](https://youtu.be/DRgPyOXZ-oE)
+- ⛓ [Master `PDF` Chat with LangChain - Your essential guide to queries on documents](https://youtu.be/ZzgUqFtxgXI)
+- ⛓ [Using LangChain with `DuckDuckGO` `Wikipedia` & `PythonREPL` Tools](https://youtu.be/KerHlb8nuVc)
+- ⛓ [Building Custom Tools and Agents with LangChain (gpt-3.5-turbo)](https://youtu.be/biS8G8x8DdA)
+- ⛓ [LangChain Retrieval QA Over Multiple Files with `ChromaDB`](https://youtu.be/3yPBVii7Ct0)
+- ⛓ [LangChain Retrieval QA with Instructor Embeddings & `ChromaDB` for PDFs](https://youtu.be/cFCGUjc33aU)
+- ⛓ [LangChain + Retrieval Local LLMs for Retrieval QA - No OpenAI!!!](https://youtu.be/9ISVjh8mdlA)
+
+
+###
+[LangChain](https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr) by [Prompt Engineering](https://www.youtube.com/@engineerprompt):
+- [LangChain Crash Course — All You Need to Know to Build Powerful Apps with LLMs](https://youtu.be/5-fc4Tlgmro)
+- [Working with MULTIPLE `PDF` Files in LangChain: `ChatGPT` for your Data](https://youtu.be/s5LhRdh5fu4)
+- [`ChatGPT` for YOUR OWN `PDF` files with LangChain](https://youtu.be/TLf90ipMzfE)
+- [Talk to YOUR DATA without OpenAI APIs: LangChain](https://youtu.be/wrD-fZvT6UI)
+- ⛓️ [CHATGPT For WEBSITES: Custom ChatBOT](https://youtu.be/RBnuhhmD21U)
+
+
+###
+LangChain by [Chat with data](https://www.youtube.com/@chatwithdata)
+- [LangChain Beginner's Tutorial for `Typescript`/`Javascript`](https://youtu.be/bH722QgRlhQ)
+- [`GPT-4` Tutorial: How to Chat With Multiple `PDF` Files (~1000 pages of Tesla's 10-K Annual Reports)](https://youtu.be/Ix9WIZpArm0)
+- [`GPT-4` & LangChain Tutorial: How to Chat With A 56-Page `PDF` Document (w/`Pinecone`)](https://youtu.be/ih9PBGVVOO4)
+- ⛓ [LangChain & Supabase Tutorial: How to Build a ChatGPT Chatbot For Your Website](https://youtu.be/R2FMzcsmQY8)
+
+
+###
+[Get SH\*T Done with Prompt Engineering and LangChain](https://www.youtube.com/watch?v=muXbPpG_ys4&list=PLEJK-H61Xlwzm5FYLDdKt_6yibO33zoMW) by [Venelin Valkov](https://www.youtube.com/@venelin_valkov)
+- [Getting Started with LangChain: Load Custom Data, Run OpenAI Models, Embeddings and `ChatGPT`](https://www.youtube.com/watch?v=muXbPpG_ys4)
+- [Loaders, Indexes & Vectorstores in LangChain: Question Answering on `PDF` files with `ChatGPT`](https://www.youtube.com/watch?v=FQnvfR8Dmr0)
+- [LangChain Models: `ChatGPT`, `Flan Alpaca`, `OpenAI Embeddings`, Prompt Templates & Streaming](https://www.youtube.com/watch?v=zy6LiK5F5-s)
+- [LangChain Chains: Use `ChatGPT` to Build Conversational Agents, Summaries and Q&A on Text With LLMs](https://www.youtube.com/watch?v=h1tJZQPcimM)
+- [Analyze Custom CSV Data with `GPT-4` using Langchain](https://www.youtube.com/watch?v=Ew3sGdX8at4)
+- ⛓ [Build ChatGPT Chatbots with LangChain Memory: Understanding and Implementing Memory in Conversations](https://youtu.be/CyuUlf54wTs)
+
+---------------------
+⛓ icon marks a new addition [last update 2023-05-15]

docs/glossary.md

@@ -1,90 +0,0 @@
-# Glossary
-
-This is a collection of terminology commonly used when developing LLM applications.
-It contains reference to external papers or sources where the concept was first introduced,
-as well as to places in LangChain where the concept is used.
-
-## Chain of Thought Prompting
-
-A prompting technique used to encourage the model to generate a series of intermediate reasoning steps.
-A less formal way to induce this behavior is to include “Let’s think step-by-step” in the prompt.
-
-Resources:
-
-- [Chain-of-Thought Paper](https://arxiv.org/pdf/2201.11903.pdf)
-- [Step-by-Step Paper](https://arxiv.org/abs/2112.00114)
-
-## Action Plan Generation
-
-A prompt usage that uses a language model to generate actions to take.
-The results of these actions can then be fed back into the language model to generate a subsequent action.
-
-Resources:
-
-- [WebGPT Paper](https://arxiv.org/pdf/2112.09332.pdf)
-- [SayCan Paper](https://say-can.github.io/assets/palm_saycan.pdf)
-
-## ReAct Prompting
-
-A prompting technique that combines Chain-of-Thought prompting with action plan generation.
-This induces the to model to think about what action to take, then take it.
-
-Resources:
-
-- [Paper](https://arxiv.org/pdf/2210.03629.pdf)
-- [LangChain Example](modules/agents/agents/examples/react.ipynb)
-
-## Self-ask
-
-A prompting method that builds on top of chain-of-thought prompting.
-In this method, the model explicitly asks itself follow-up questions, which are then answered by an external search engine.
-
-Resources:
-
-- [Paper](https://ofir.io/self-ask.pdf)
-- [LangChain Example](modules/agents/agents/examples/self_ask_with_search.ipynb)
-
-## Prompt Chaining
-
-Combining multiple LLM calls together, with the output of one-step being the input to the next.
-
-Resources:
-
-- [PromptChainer Paper](https://arxiv.org/pdf/2203.06566.pdf)
-- [Language Model Cascades](https://arxiv.org/abs/2207.10342)
-- [ICE Primer Book](https://primer.ought.org/)
-- [Socratic Models](https://socraticmodels.github.io/)
-
-## Memetic Proxy
-
-Encouraging the LLM to respond in a certain way framing the discussion in a context that the model knows of and that will result in that type of response. For example, as a conversation between a student and a teacher.
-
-Resources:
-
-- [Paper](https://arxiv.org/pdf/2102.07350.pdf)
-
-## Self Consistency
-
-A decoding strategy that samples a diverse set of reasoning paths and then selects the most consistent answer.
-Is most effective when combined with Chain-of-thought prompting.
-
-Resources:
-
-- [Paper](https://arxiv.org/pdf/2203.11171.pdf)
-
-## Inception
-
-Also called “First Person Instruction”.
-Encouraging the model to think a certain way by including the start of the model’s response in the prompt.
-
-Resources:
-
-- [Example](https://twitter.com/goodside/status/1583262455207460865?s=20&t=8Hz7XBnK1OF8siQrxxCIGQ)
-
-## MemPrompt
-
-MemPrompt maintains a memory of errors and user feedback, and uses them to prevent repetition of mistakes.
-
-Resources:
-
-- [Paper](https://memprompt.com/)

docs/index.rst

@@ -1,51 +1,63 @@
 Welcome to LangChain
 ==========================
 
-LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model via an API, but will also:
+| **LangChain** is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model, but will also be:
+1. *Data-aware*: connect a language model to other sources of data
+2. *Agentic*: allow a language model to interact with its environment
 
-- *Be data-aware*: connect a language model to other sources of data
-- *Be agentic*: allow a language model to interact with its environment
+| The LangChain framework is designed around these principles.
 
-The LangChain framework is designed with the above principles in mind.
-
-This is the Python specific portion of the documentation. For a purely conceptual guide to LangChain, see `here <https://docs.langchain.com/docs/>`_. For the JavaScript documentation, see `here <https://js.langchain.com/docs/>`_.
+| This is the Python specific portion of the documentation. For a purely conceptual guide to LangChain, see `here <https://docs.langchain.com/docs/>`_. For the JavaScript documentation, see `here <https://js.langchain.com/docs/>`_.
 
 Getting Started
 ----------------
 
-Checkout the below guide for a walkthrough of how to get started using LangChain to create an Language Model application.
+| How to get started using LangChain to create an Language Model application.
 
-- `Getting Started Documentation <./getting_started/getting_started.html>`_
+- `Quickstart Guide <./getting_started/getting_started.html>`_
+
+| Concepts and terminology.
+
+- `Concepts and terminology <./getting_started/concepts.html>`_
+
+| Tutorials created by community experts and presented on YouTube.
+
+- `Tutorials <./getting_started/tutorials.html>`_
 
 .. toctree::
-   :maxdepth: 1
+   :maxdepth: 2
    :caption: Getting Started
    :name: getting_started
    :hidden:
 
    getting_started/getting_started.md
+   getting_started/concepts.md
+   getting_started/tutorials.md
+
 
 Modules
 -----------
 
-There are several main modules that LangChain provides support for.
-For each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides.
-These modules are, in increasing order of complexity:
+| These modules are the core abstractions which we view as the building blocks of any LLM-powered application.
+For each module LangChain provides standard, extendable interfaces. LangChain also provides external integrations and even end-to-end implementations for off-the-shelf use.
 
-- `Models <./modules/models.html>`_: The various model types and model integrations LangChain supports.
+| The docs for each module contain quickstart examples, how-to guides, reference docs, and conceptual guides.
 
-- `Prompts <./modules/prompts.html>`_: This includes prompt management, prompt optimization, and prompt serialization.
+| The modules are (from least to most complex):
 
-- `Memory <./modules/memory.html>`_: Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
+- `Models <./modules/models.html>`_: Supported model types and integrations.
 
-- `Indexes <./modules/indexes.html>`_: Language models are often more powerful when combined with your own text data - this module covers best practices for doing exactly that.
+- `Prompts <./modules/prompts.html>`_: Prompt management, optimization, and serialization.
 
-- `Chains <./modules/chains.html>`_: Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
+- `Memory <./modules/memory.html>`_: Memory refers to state that is persisted between calls of a chain/agent.
 
-- `Agents <./modules/agents.html>`_: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.
+- `Indexes <./modules/indexes.html>`_: Language models become much more powerful when combined with application-specific data - this module contains interfaces and integrations for loading, querying and updating external data.
 
-- `Callbacks <./modules/callbacks/getting_started.html>`_: It can be difficult to track all that occurs inside a chain or agent - callbacks help add a level of observability and introspection.
+- `Chains <./modules/chains.html>`_: Chains are structured sequences of calls (to an LLM or to a different utility).
 
+- `Agents <./modules/agents.html>`_: An agent is a Chain in which an LLM, given a high-level directive and a set of tools, repeatedly decides an action, executes the action and observes the outcome until the high-level directive is complete.
+
+- `Callbacks <./modules/callbacks/getting_started.html>`_: Callbacks let you log and stream the intermediate steps of any chain, making it easy to observe, debug, and evaluate the internals of an application.
 
 .. toctree::
    :maxdepth: 1
@@ -55,8 +67,8 @@ These modules are, in increasing order of complexity:
 
    ./modules/models.rst
    ./modules/prompts.rst
-   ./modules/indexes.md
    ./modules/memory.md
+   ./modules/indexes.md
    ./modules/chains.md
    ./modules/agents.md
    ./modules/callbacks/getting_started.ipynb
@@ -64,29 +76,29 @@ These modules are, in increasing order of complexity:
 Use Cases
 ----------
 
-The above modules can be used in a variety of ways. LangChain also provides guidance and assistance in this. Below are some of the common use cases LangChain supports.
+| Best practices and built-in implementations for common LangChain use cases:
 
-- `Autonomous Agents <./use_cases/autonomous_agents.html>`_: Autonomous agents are long running agents that take many steps in an attempt to accomplish an objective. Examples include AutoGPT and BabyAGI.
+- `Autonomous Agents <./use_cases/autonomous_agents.html>`_: Autonomous agents are long-running agents that take many steps in an attempt to accomplish an objective. Examples include AutoGPT and BabyAGI.
 
-- `Agent Simulations <./use_cases/agent_simulations.html>`_: Putting agents in a sandbox and observing how they interact with each other or to events can be an interesting way to observe their long-term memory abilities.
+- `Agent Simulations <./use_cases/agent_simulations.html>`_: Putting agents in a sandbox and observing how they interact with each other and react to events can be an effective way to evaluate their long-range reasoning and planning abilities.
 
-- `Personal Assistants <./use_cases/personal_assistants.html>`_: The main LangChain use case. Personal assistants need to take actions, remember interactions, and have knowledge about your data.
+- `Personal Assistants <./use_cases/personal_assistants.html>`_: One of the primary LangChain use cases. Personal assistants need to take actions, remember interactions, and have knowledge about your data.
 
-- `Question Answering <./use_cases/question_answering.html>`_: The second big LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer.
+- `Question Answering <./use_cases/question_answering.html>`_: Another common LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer.
 
-- `Chatbots <./use_cases/chatbots.html>`_: Since language models are good at producing text, that makes them ideal for creating chatbots.
+- `Chatbots <./use_cases/chatbots.html>`_: Language models love to chat, making this a very natural use of them.
 
-- `Querying Tabular Data <./use_cases/tabular.html>`_: If you want to understand how to use LLMs to query data that is stored in a tabular format (csvs, SQL, dataframes, etc) you should read this page.
+- `Querying Tabular Data <./use_cases/tabular.html>`_: Recommended reading if you want to use language models to query structured data (CSVs, SQL, dataframes, etc).
 
-- `Code Understanding <./use_cases/code.html>`_: If you want to understand how to use LLMs to query source code from github, you should read this page.
+- `Code Understanding <./use_cases/code.html>`_: Recommended reading if you want to use language models to analyze code.
 
-- `Interacting with APIs <./use_cases/apis.html>`_: Enabling LLMs to interact with APIs is extremely powerful in order to give them more up-to-date information and allow them to take actions.
+- `Interacting with APIs <./use_cases/apis.html>`_: Enabling language models to interact with APIs is extremely powerful. It gives them access to up-to-date information and allows them to take actions.
 
 - `Extraction <./use_cases/extraction.html>`_: Extract structured information from text.
 
-- `Summarization <./use_cases/summarization.html>`_: Summarizing longer documents into shorter, more condensed chunks of information. A type of Data Augmented Generation.
+- `Summarization <./use_cases/summarization.html>`_: Compressing longer documents. A type of Data-Augmented Generation.
 
-- `Evaluation <./use_cases/evaluation.html>`_: Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
+- `Evaluation <./use_cases/evaluation.html>`_: Generative models are hard to evaluate with traditional metrics. One promising approach is to use language models themselves to do the evaluation.
 
 
 .. toctree::
@@ -95,26 +107,29 @@ The above modules can be used in a variety of ways. LangChain also provides guid
    :name: use_cases
    :hidden:
 
-   ./use_cases/personal_assistants.md
    ./use_cases/autonomous_agents.md
    ./use_cases/agent_simulations.md
+   ./use_cases/personal_assistants.md
    ./use_cases/question_answering.md
    ./use_cases/chatbots.md
    ./use_cases/tabular.rst
    ./use_cases/code.md
    ./use_cases/apis.md
-   ./use_cases/summarization.md
    ./use_cases/extraction.md
+   ./use_cases/summarization.md
    ./use_cases/evaluation.rst
 
 
 Reference Docs
 ---------------
 
-All of LangChain's reference documentation, in one place. Full documentation on all methods, classes, installation methods, and integration setups for LangChain.
+| Full documentation on all methods, classes, installation methods, and integration setups for LangChain.
 
 
+- `LangChain Installation <./reference/installation.html>`_
+
 - `Reference Documentation <./reference.html>`_
+
 .. toctree::
    :maxdepth: 1
    :caption: Reference
@@ -122,47 +137,54 @@ All of LangChain's reference documentation, in one place. Full documentation on
    :hidden:
 
    ./reference/installation.md
-   ./reference/integrations.md
    ./reference.rst
 
 
-LangChain Ecosystem
--------------------
+Ecosystem
+------------
 
-Guides for how other companies/products can be used with LangChain
+| LangChain integrates a lot of different LLMs, systems, and products.
+| From the other side, many systems and products depend on LangChain.
+| It creates a vibrant and thriving ecosystem.
+
+
+- `Integrations <./integrations.html>`_: Guides for how other products can be used with LangChain.
+
+- `Dependents <./dependents.html>`_: List of repositories that use LangChain.
+
+- `Deployments <./ecosystem/deployments.html>`_: A collection of instructions, code snippets, and template repositories for deploying LangChain apps.
 
-- `LangChain Ecosystem <./ecosystem.html>`_
 
 .. toctree::
-   :maxdepth: 1
+   :maxdepth: 2
    :glob:
    :caption: Ecosystem
    :name: ecosystem
    :hidden:
 
-   ./ecosystem.rst
+   ./integrations.rst
+   ./dependents.md
+   ./ecosystem/deployments.md
 
 
 Additional Resources
 ---------------------
 
-Additional collection of resources we think may be useful as you develop your application!
+| Additional resources we think may be useful as you develop your application!
 
 - `LangChainHub <https://github.com/hwchase17/langchain-hub>`_: The LangChainHub is a place to share and explore other prompts, chains, and agents.
 
-- `Glossary <./glossary.html>`_: A glossary of all related terms, papers, methods, etc. Whether implemented in LangChain or not!
+- `Gallery <https://github.com/kyrolabs/awesome-langchain>`_: A collection of great projects that use Langchain, compiled by the folks at `Kyrolabs <https://kyrolabs.com>`_. Useful for finding inspiration and example implementations.
 
-- `Gallery <./gallery.html>`_: A collection of our favorite projects that use LangChain. Useful for finding inspiration or seeing how things were done in other applications.
+- `Deploying LLMs in Production <./additional_resources/deploy_llms.html>`_: A collection of best practices and tutorials for deploying LLMs in production.
 
-- `Deployments <./deployments.html>`_: A collection of instructions, code snippets, and template repositories for deploying LangChain apps.
+- `Tracing <./additional_resources/tracing.html>`_: A guide on using tracing in LangChain to visualize the execution of chains and agents.
 
-- `Tracing <./tracing.html>`_: A guide on using tracing in LangChain to visualize the execution of chains and agents.
-
-- `Model Laboratory <./model_laboratory.html>`_: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.
+- `Model Laboratory <./additional_resources/model_laboratory.html>`_: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.
 
 - `Discord <https://discord.gg/6adMQxSpJS>`_: Join us on our Discord to discuss all things LangChain!
 
-- `YouTube <./youtube.html>`_: A collection of the LangChain tutorials and videos.
+- `YouTube <./additional_resources/youtube.html>`_: A collection of the LangChain tutorials and videos.
 
 - `Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>`_: As you move your LangChains into production, we'd love to offer more comprehensive support. Please fill out this form and we'll set up a dedicated support Slack channel.
 
@@ -174,11 +196,11 @@ Additional collection of resources we think may be useful as you develop your ap
    :hidden:
 
    LangChainHub <https://github.com/hwchase17/langchain-hub>
-   ./glossary.md
-   ./gallery.rst
-   ./deployments.md
-   ./tracing.md
-   ./use_cases/model_laboratory.ipynb
+   ./additional_resources/deployments.md
+   ./additional_resources/deploy_llms.rst
+   Gallery <https://github.com/kyrolabs/awesome-langchain>
+   ./additional_resources/tracing.md
+   ./additional_resources/model_laboratory.ipynb
    Discord <https://discord.gg/6adMQxSpJS>
-   ./youtube.md
+   ./additional_resources/youtube.md
    Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>

docs/integrations.rst

@@ -1,12 +1,13 @@
-LangChain Ecosystem
+Integrations
 ===================
 
-Guides for how other companies/products can be used with LangChain
+LangChain integrates with many LLMs, systems, and products.
 
-Groups
-----------
+Integrations by Module
+--------------------------------
+
+| Integrations grouped by the core LangChain module they map to:
 
-LangChain provides integration with many LLMs and systems:
 
 - `LLM Providers <./modules/models/llms/integrations.html>`_
 - `Chat Model Providers <./modules/models/chat/integrations.html>`_
@@ -18,12 +19,21 @@ LangChain provides integration with many LLMs and systems:
 - `Tool Providers <./modules/agents/tools.html>`_
 - `Toolkit Integrations <./modules/agents/toolkits.html>`_
 
-Companies / Products
-----------
+
+Dependencies
+----------------
+
+| LangChain depends on `several hungered Python packages <https://github.com/hwchase17/langchain/network/dependencies>`_.
+
+
+All Integrations
+-------------------------------------------
+
+| A comprehensive list of LLMs, systems, and products integrated with LangChain:
 
 
 .. toctree::
    :maxdepth: 1
    :glob:
 
-   ecosystem/*
+   integrations/*

docs/integrations/airbyte.md

@@ -0,0 +1,29 @@
+# Airbyte
+
+>[Airbyte](https://github.com/airbytehq/airbyte) is a data integration platform for ELT pipelines from APIs, 
+> databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.
+
+## Installation and Setup
+
+This instruction shows how to load any source from `Airbyte` into a local `JSON` file that can be read in as a document.
+
+**Prerequisites:**
+Have `docker desktop` installed.
+
+**Steps:**
+1. Clone Airbyte from GitHub - `git clone https://github.com/airbytehq/airbyte.git`.
+2. Switch into Airbyte directory - `cd airbyte`.
+3. Start Airbyte - `docker compose up`.
+4. In your browser, just visit http://localhost:8000. You will be asked for a username and password. By default, that's username `airbyte` and password `password`.
+5. Setup any source you wish.
+6. Set destination as Local JSON, with specified destination path - lets say `/json_data`. Set up a manual sync.
+7. Run the connection.
+8. To see what files are created, navigate to: `file:///tmp/airbyte_local/`.
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/airbyte_json.ipynb).
+
+```python
+from langchain.document_loaders import AirbyteJSONLoader
+```

docs/integrations/aleph_alpha.md

@@ -0,0 +1,36 @@
+# Aleph Alpha
+
+>[Aleph Alpha](https://docs.aleph-alpha.com/) was founded in 2019 with the mission to research and build the foundational technology for an era of strong AI. The team of international scientists, engineers, and innovators researches, develops, and deploys transformative AI like large language and multimodal models and runs the fastest European commercial AI cluster.
+
+>[The Luminous series](https://docs.aleph-alpha.com/docs/introduction/luminous/) is a family of large language models.
+
+## Installation and Setup
+
+```bash
+pip install aleph-alpha-client
+```
+
+You have to create a new token. Please, see [instructions](https://docs.aleph-alpha.com/docs/account/#create-a-new-token).
+
+```python
+from getpass import getpass
+
+ALEPH_ALPHA_API_KEY = getpass()
+```
+
+
+## LLM
+
+See a [usage example](../modules/models/llms/integrations/aleph_alpha.ipynb).
+
+```python
+from langchain.llms import AlephAlpha
+```
+
+## Text Embedding Models
+
+See a [usage example](../modules/models/text_embedding/examples/aleph_alpha.ipynb).
+
+```python
+from langchain.embeddings import AlephAlphaSymmetricSemanticEmbedding, AlephAlphaAsymmetricSemanticEmbedding
+```

docs/integrations/amazon_bedrock.md

@@ -0,0 +1,24 @@
+# Amazon Bedrock
+
+>[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case.
+
+## Installation and Setup
+
+```bash
+pip install boto3
+```
+
+## LLM
+
+See a [usage example](../modules/models/llms/integrations/bedrock.ipynb).
+
+```python
+from langchain import Bedrock
+```
+
+## Text Embedding Models
+
+See a [usage example](../modules/models/text_embedding/examples/amazon_bedrock.ipynb).
+```python
+from langchain.embeddings import BedrockEmbeddings
+```

docs/integrations/annoy.md

@@ -0,0 +1,18 @@
+# Annoy
+
+> [Annoy](https://github.com/spotify/annoy) (`Approximate Nearest Neighbors Oh Yeah`) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data. 
+## Installation and Setup
+
+
+```bash
+pip install annoy
+```
+
+
+## Vectorstore
+
+See a [usage example](../modules/indexes/vectorstores/examples/annoy.ipynb).
+
+```python
+from langchain.vectorstores import Annoy
+```

docs/integrations/anthropic.md

@@ -0,0 +1,26 @@
+# Anthropic
+
+>[Anthropic](https://en.wikipedia.org/wiki/Anthropic) is an American artificial intelligence (AI) startup and 
+> public-benefit corporation, founded by former members of OpenAI. `Anthropic` specializes in developing general AI 
+> systems and language models, with a company ethos of responsible AI usage.
+> `Anthropic` develops a chatbot, named `Claude`. Similar to `ChatGPT`, `Claude` uses a messaging 
+> interface where users can submit questions or requests and receive highly detailed and relevant responses.
+ 
+## Installation and Setup
+
+
+```bash
+pip install anthropic
+```
+
+See the [setup documentation](https://console.anthropic.com/docs/access).
+
+
+
+## Chat Models
+
+See a [usage example](../modules/models/chat/integrations/anthropic.ipynb)
+
+```python
+from langchain.chat_models import ChatAnthropic
+```

docs/integrations/anyscale.md

@@ -0,0 +1,17 @@
+# Anyscale
+
+This page covers how to use the Anyscale ecosystem within LangChain.
+It is broken into two parts: installation and setup, and then references to specific Anyscale wrappers.
+
+## Installation and Setup
+- Get an Anyscale Service URL, route and API key and set them as environment variables (`ANYSCALE_SERVICE_URL`,`ANYSCALE_SERVICE_ROUTE`, `ANYSCALE_SERVICE_TOKEN`). 
+- Please see [the Anyscale docs](https://docs.anyscale.com/productionize/services-v2/get-started) for more details.
+
+## Wrappers
+
+### LLM
+
+There exists an Anyscale LLM wrapper, which you can access with 
+```python
+from langchain.llms import Anyscale
+```

docs/integrations/argilla.md

@@ -0,0 +1,29 @@
+# Argilla
+
+![Argilla - Open-source data platform for LLMs](https://argilla.io/og.png)
+
+>[Argilla](https://argilla.io/) is an open-source data curation platform for LLMs.
+> Using Argilla, everyone can build robust language models through faster data curation 
+> using both human and machine feedback. We provide support for each step in the MLOps cycle, 
+> from data labeling to model monitoring.
+
+## Installation and Setup
+
+First, you'll need to install the  `argilla` Python package as follows:
+
+```bash
+pip install argilla --upgrade
+```
+
+If you already have an Argilla Server running, then you're good to go; but if
+you don't, follow the next steps to install it.
+
+If you don't you can refer to [Argilla - 🚀 Quickstart](https://docs.argilla.io/en/latest/getting_started/quickstart.html#Running-Argilla-Quickstart) to deploy Argilla either on HuggingFace Spaces, locally, or on a server.
+
+## Tracking
+
+See a [usage example of `ArgillaCallbackHandler`](../modules/callbacks/examples/examples/argilla.ipynb).
+
+```python
+from langchain.callbacks import ArgillaCallbackHandler
+```

docs/integrations/arxiv.md

@@ -0,0 +1,36 @@
+# Arxiv
+
+>[arXiv](https://arxiv.org/) is an open-access archive for 2 million scholarly articles in the fields of physics, 
+> mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and 
+> systems science, and economics.
+
+
+## Installation and Setup
+
+First, you need to install `arxiv` python package.
+
+```bash
+pip install arxiv
+```
+
+Second, you need to install `PyMuPDF` python package which transforms PDF files downloaded from the `arxiv.org` site into the text format.
+
+```bash
+pip install pymupdf
+```
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/arxiv.ipynb).
+
+```python
+from langchain.document_loaders import ArxivLoader
+```
+
+## Retriever
+
+See a [usage example](../modules/indexes/retrievers/examples/arxiv.ipynb).
+
+```python
+from langchain.retrievers import ArxivRetriever
+```

docs/integrations/aws_s3.md

@@ -0,0 +1,25 @@
+# AWS S3 Directory
+
+>[Amazon Simple Storage Service (Amazon S3)](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html) is an object storage service.
+
+>[AWS S3 Directory](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html)
+
+>[AWS S3 Buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingBucket.html)
+
+
+## Installation and Setup
+
+```bash
+pip install boto3
+```
+
+
+## Document Loader
+
+See a [usage example for S3DirectoryLoader](../modules/indexes/document_loaders/examples/aws_s3_directory.ipynb).
+
+See a [usage example for S3FileLoader](../modules/indexes/document_loaders/examples/aws_s3_file.ipynb).
+
+```python
+from langchain.document_loaders import S3DirectoryLoader, S3FileLoader
+```

docs/integrations/azlyrics.md

@@ -0,0 +1,16 @@
+# AZLyrics
+
+>[AZLyrics](https://www.azlyrics.com/) is a large, legal, every day growing collection of lyrics.
+
+## Installation and Setup
+
+There isn't any special setup for it.
+
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/azlyrics.ipynb).
+
+```python
+from langchain.document_loaders import AZLyricsLoader
+```

docs/integrations/azure_blob_storage.md

@@ -0,0 +1,36 @@
+# Azure Blob Storage
+
+>[Azure Blob Storage](https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction) is Microsoft's object storage solution for the cloud. Blob Storage is optimized for storing massive amounts of unstructured data. Unstructured data is data that doesn't adhere to a particular data model or definition, such as text or binary data.
+
+>[Azure Files](https://learn.microsoft.com/en-us/azure/storage/files/storage-files-introduction) offers fully managed
+> file shares in the cloud that are accessible via the industry standard Server Message Block (`SMB`) protocol, 
+> Network File System (`NFS`) protocol, and `Azure Files REST API`. `Azure Files` are based on the `Azure Blob Storage`.
+
+`Azure Blob Storage` is designed for:
+- Serving images or documents directly to a browser.
+- Storing files for distributed access.
+- Streaming video and audio.
+- Writing to log files.
+- Storing data for backup and restore, disaster recovery, and archiving.
+- Storing data for analysis by an on-premises or Azure-hosted service.
+
+## Installation and Setup
+
+```bash
+pip install azure-storage-blob
+```
+
+
+## Document Loader
+
+See a [usage example for the Azure Blob Storage](../modules/indexes/document_loaders/examples/azure_blob_storage_container.ipynb).
+
+```python
+from langchain.document_loaders import AzureBlobStorageContainerLoader
+```
+
+See a [usage example for the Azure Files](../modules/indexes/document_loaders/examples/azure_blob_storage_file.ipynb).
+
+```python
+from langchain.document_loaders import AzureBlobStorageFileLoader
+```

docs/integrations/azure_cognitive_search_.md

@@ -0,0 +1,24 @@
+# Azure Cognitive Search
+
+>[Azure Cognitive Search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search) (formerly known as `Azure Search`) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.
+
+>Search is foundational to any app that surfaces text to users, where common scenarios include catalog or document search, online retail apps, or data exploration over proprietary content. When you create a search service, you'll work with the following capabilities:
+>- A search engine for full text search over a search index containing user-owned content
+>- Rich indexing, with lexical analysis and optional AI enrichment for content extraction and transformation
+>- Rich query syntax for text search, fuzzy search, autocomplete, geo-search and more
+>- Programmability through REST APIs and client libraries in Azure SDKs
+>- Azure integration at the data layer, machine learning layer, and AI (Cognitive Services)
+
+
+## Installation and Setup
+
+See [set up instructions](https://learn.microsoft.com/en-us/azure/search/search-create-service-portal).
+
+
+## Retriever
+
+See a [usage example](../modules/indexes/retrievers/examples/azure_cognitive_search.ipynb).
+
+```python
+from langchain.retrievers import AzureCognitiveSearchRetriever
+```

docs/integrations/azure_openai.md

@@ -0,0 +1,50 @@
+# Azure OpenAI
+
+>[Microsoft Azure](https://en.wikipedia.org/wiki/Microsoft_Azure), often referred to as `Azure` is a cloud computing platform run by `Microsoft`, which offers access, management, and development of applications and services through global data centers. It provides a range of capabilities, including software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). `Microsoft Azure` supports many programming languages, tools, and frameworks, including Microsoft-specific and third-party software and systems.
+
+
+>[Azure OpenAI](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/) is an `Azure` service with powerful language models from `OpenAI` including the `GPT-3`, `Codex` and `Embeddings model` series for content generation, summarization, semantic search, and natural language to code translation.
+
+
+## Installation and Setup
+
+```bash
+pip install openai
+pip install tiktoken
+```
+
+
+Set the environment variables to get access to the `Azure OpenAI` service.
+
+```python
+import os
+
+os.environ["OPENAI_API_TYPE"] = "azure"
+os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
+os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
+os.environ["OPENAI_API_VERSION"] = "2023-03-15-preview"
+```
+
+## LLM
+
+See a [usage example](../modules/models/llms/integrations/azure_openai_example.ipynb).
+
+```python
+from langchain.llms import AzureOpenAI
+```
+
+## Text Embedding Models
+
+See a [usage example](../modules/models/text_embedding/examples/azureopenai.ipynb)
+
+```python
+from langchain.embeddings import OpenAIEmbeddings
+```
+
+## Chat Models
+
+See a [usage example](../modules/models/chat/integrations/azure_chat_openai.ipynb)
+
+```python
+from langchain.chat_models import AzureChatOpenAI
+```

docs/integrations/beam.md

@@ -0,0 +1,93 @@
+# Beam
+
+>[Beam](https://docs.beam.cloud/introduction) makes it easy to run code on GPUs, deploy scalable web APIs, 
+> schedule cron jobs, and run massively parallel workloads — without managing any infrastructure.
+ 
+
+## Installation and Setup
+
+- [Create an account](https://www.beam.cloud/)
+- Install the Beam CLI with `curl https://raw.githubusercontent.com/slai-labs/get-beam/main/get-beam.sh -sSfL | sh`
+- Register API keys with `beam configure`
+- Set environment variables (`BEAM_CLIENT_ID`) and (`BEAM_CLIENT_SECRET`)
+- Install the Beam SDK:
+```bash
+pip install beam-sdk
+```
+
+## LLM
+
+
+```python
+from langchain.llms.beam import Beam
+```
+
+### Example of the Beam app
+
+This is the environment you’ll be developing against once you start the app.
+It's also used to define the maximum response length from the model.
+```python
+llm = Beam(model_name="gpt2",
+           name="langchain-gpt2-test",
+           cpu=8,
+           memory="32Gi",
+           gpu="A10G",
+           python_version="python3.8",
+           python_packages=[
+               "diffusers[torch]>=0.10",
+               "transformers",
+               "torch",
+               "pillow",
+               "accelerate",
+               "safetensors",
+               "xformers",],
+           max_length="50",
+           verbose=False)
+```
+
+### Deploy the Beam app
+
+Once defined, you can deploy your Beam app by calling your model's `_deploy()` method.
+
+```python
+llm._deploy()
+```
+
+### Call the Beam app
+
+Once a beam model is deployed, it can be called by calling your model's `_call()` method.
+This returns the GPT2 text response to your prompt.
+
+```python
+response = llm._call("Running machine learning on a remote GPU")
+```
+
+An example script which deploys the model and calls it would be:
+
+```python
+from langchain.llms.beam import Beam
+import time
+
+llm = Beam(model_name="gpt2",
+           name="langchain-gpt2-test",
+           cpu=8,
+           memory="32Gi",
+           gpu="A10G",
+           python_version="python3.8",
+           python_packages=[
+               "diffusers[torch]>=0.10",
+               "transformers",
+               "torch",
+               "pillow",
+               "accelerate",
+               "safetensors",
+               "xformers",],
+           max_length="50",
+           verbose=False)
+
+llm._deploy()
+
+response = llm._call("Running machine learning on a remote GPU")
+
+print(response)
+```

docs/integrations/bilibili.md

@@ -0,0 +1,17 @@
+# BiliBili
+
+>[Bilibili](https://www.bilibili.tv/) is one of the most beloved long-form video sites in China.
+
+## Installation and Setup
+
+```bash
+pip install bilibili-api-python
+```
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/bilibili.ipynb).
+
+```python
+from langchain.document_loaders import BiliBiliLoader
+```

docs/integrations/blackboard.md

@@ -0,0 +1,22 @@
+# Blackboard
+
+>[Blackboard Learn](https://en.wikipedia.org/wiki/Blackboard_Learn) (previously the `Blackboard Learning Management System`)
+> is a web-based virtual learning environment and learning management system developed by Blackboard Inc. 
+> The software features course management, customizable open architecture, and scalable design that allows 
+> integration with student information systems and authentication protocols. It may be installed on local servers, 
+> hosted by `Blackboard ASP Solutions`, or provided as Software as a Service hosted on Amazon Web Services. 
+> Its main purposes are stated to include the addition of online elements to courses traditionally delivered 
+> face-to-face and development of completely online courses with few or no face-to-face meetings.
+
+## Installation and Setup
+
+There isn't any special setup for it.
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/blackboard.ipynb).
+
+```python
+from langchain.document_loaders import BlackboardLoader
+
+```

docs/integrations/cassandra.md

@@ -0,0 +1,23 @@
+# Cassandra
+
+>[Cassandra](https://en.wikipedia.org/wiki/Apache_Cassandra) is a free and open-source, distributed, wide-column 
+> store, NoSQL database management system designed to handle large amounts of data across many commodity servers, 
+> providing high availability with no single point of failure. `Cassandra` offers support for clusters spanning 
+> multiple datacenters, with asynchronous masterless replication allowing low latency operations for all clients. 
+> `Cassandra` was designed to implement a combination of `Amazon's Dynamo` distributed storage and replication 
+> techniques combined with `Google's Bigtable` data and storage engine model.
+ 
+## Installation and Setup
+
+```bash
+pip install cassandra-drive
+```
+
+
+## Memory
+
+See a [usage example](../modules/memory/examples/cassandra_chat_message_history.ipynb).
+
+```python
+from langchain.memory import CassandraChatMessageHistory
+```

docs/integrations/chroma.md

@@ -1,20 +1,29 @@
 # Chroma
 
-This page covers how to use the Chroma ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Chroma wrappers.
+>[Chroma](https://docs.trychroma.com/getting-started) is a database for building AI applications with embeddings.
 
 ## Installation and Setup
-- Install the Python package with `pip install chromadb`
-## Wrappers
 
-### VectorStore
+```bash
+pip install chromadb
+```
+
+
+## VectorStore
 
 There exists a wrapper around Chroma vector databases, allowing you to use it as a vectorstore,
 whether for semantic search or example selection.
 
-To import this vectorstore:
 ```python
 from langchain.vectorstores import Chroma
 ```
 
 For a more detailed walkthrough of the Chroma wrapper, see [this notebook](../modules/indexes/vectorstores/getting_started.ipynb)
+
+## Retriever
+
+See a [usage example](../modules/indexes/retrievers/examples/chroma_self_query.ipynb).
+
+```python
+from langchain.retrievers import SelfQueryRetriever
+```

docs/integrations/clearml_tracking.ipynb

@@ -1,13 +1,22 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# ClearML Integration\n",
+    "# ClearML\n",
     "\n",
-    "In order to properly keep track of your langchain experiments and their results, you can enable the ClearML integration. ClearML is an experiment manager that neatly tracks and organizes all your experiment runs.\n",
+    "> [ClearML](https://github.com/allegroai/clearml) is a ML/DL development and production suite, it contains 5 main modules:\n",
+    "> - `Experiment Manager` - Automagical experiment tracking, environments and results\n",
+    "> - `MLOps` - Orchestration, Automation & Pipelines solution for ML/DL jobs (K8s / Cloud / bare-metal)\n",
+    "> - `Data-Management` - Fully differentiable data management & version control solution on top of object-storage (S3 / GS / Azure / NAS)\n",
+    "> - `Model-Serving` - cloud-ready Scalable model serving solution!\n",
+    "    Deploy new model endpoints in under 5 minutes\n",
+    "    Includes optimized GPU serving support backed by Nvidia-Triton\n",
+    "    with out-of-the-box Model Monitoring\n",
+    "> - `Fire Reports` - Create and share rich MarkDown documents supporting embeddable online content\n",
+    "\n",
+    "In order to properly keep track of your langchain experiments and their results, you can enable the `ClearML` integration. We use the `ClearML Experiment Manager` that neatly tracks and organizes all your experiment runs.\n",
     "\n",
     "<a target=\"_blank\" href=\"https://colab.research.google.com/github/hwchase17/langchain/blob/master/docs/ecosystem/clearml_tracking.ipynb\">\n",
     "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
@@ -15,11 +24,32 @@
    ]
   },
   {
-   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Installation and Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install clearml\n",
+    "!pip install pandas\n",
+    "!pip install textstat\n",
+    "!pip install spacy\n",
+    "!python -m spacy download en_core_web_sm"
+   ]
+  },
+  {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Getting API Credentials\n",
+    "### Getting API Credentials\n",
     "\n",
     "We'll be using quite some APIs in this notebook, here is a list and where to get them:\n",
     "\n",
@@ -43,24 +73,21 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setting Up"
+    "## Callbacks"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "!pip install clearml\n",
-    "!pip install pandas\n",
-    "!pip install textstat\n",
-    "!pip install spacy\n",
-    "!python -m spacy download en_core_web_sm"
+    "from langchain.callbacks import ClearMLCallbackHandler"
    ]
   },
   {
@@ -78,7 +105,7 @@
    ],
    "source": [
     "from datetime import datetime\n",
-    "from langchain.callbacks import ClearMLCallbackHandler, StdOutCallbackHandler\n",
+    "from langchain.callbacks import StdOutCallbackHandler\n",
     "from langchain.llms import OpenAI\n",
     "\n",
     "# Setup and use the ClearML Callback\n",
@@ -98,11 +125,10 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Scenario 1: Just an LLM\n",
+    "### Scenario 1: Just an LLM\n",
     "\n",
     "First, let's just run a single LLM a few times and capture the resulting prompt-answer conversation in ClearML"
    ]
@@ -344,7 +370,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -356,11 +381,10 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Scenario 2: Creating an agent with tools\n",
+    "### Scenario 2: Creating an agent with tools\n",
     "\n",
     "To show a more advanced workflow, let's create an agent with access to tools. The way ClearML tracks the results is not different though, only the table will look slightly different as there are other types of actions taken when compared to the earlier, simpler example.\n",
     "\n",
@@ -536,11 +560,10 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Tips and Next Steps\n",
+    "### Tips and Next Steps\n",
     "\n",
     "- Make sure you always use a unique `name` argument for the `clearml_callback.flush_tracker` function. If not, the model parameters used for a run will override the previous run!\n",
     "\n",
@@ -559,7 +582,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -573,9 +596,8 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.10.6"
   },
-  "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
     "hash": "a53ebf4a859167383b364e7e7521d0add3c2dbbdecce4edf676e8c4634ff3fbb"
@@ -583,5 +605,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/integrations/clickhouse.md

@@ -0,0 +1,52 @@
+# ClickHouse
+
+This page covers how to use ClickHouse Vector Search within LangChain.
+
+[ClickHouse](https://clickhouse.com) is a open source real-time OLAP database with full SQL support and a wide range of functions to assist users in writing analytical queries. Some of these functions and data structures perform distance operations between vectors, enabling ClickHouse to be used as a vector database.
+
+Due to the fully parallelized query pipeline, ClickHouse can process vector search operations very quickly, especially when performing exact matching through a linear scan over all rows, delivering processing speed comparable to dedicated vector databases.
+
+High compression levels, tunable through custom compression codecs, enable very large datasets to be stored and queried. ClickHouse is not memory-bound, allowing multi-TB datasets containing embeddings to be queried.
+
+The capabilities for computing the distance between two vectors are just another SQL function and can be effectively combined with more traditional SQL filtering and aggregation capabilities. This allows vectors to be stored and queried alongside metadata, and even rich text, enabling a broad array of use cases and applications.
+
+Finally, experimental ClickHouse capabilities like [Approximate Nearest Neighbour (ANN) indices](https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/annindexes) support faster approximate matching of vectors and provide a promising development aimed to further enhance the vector matching capabilities of ClickHouse.
+
+## Installation
+- Install clickhouse server by [binary](https://clickhouse.com/docs/en/install) or [docker image](https://hub.docker.com/r/clickhouse/clickhouse-server/)
+- Install the Python SDK with `pip install clickhouse-connect`
+
+### Configure clickhouse vector index
+
+Customize `ClickhouseSettings` object with parameters
+
+    ```python
+    from langchain.vectorstores import ClickHouse, ClickhouseSettings
+    config = ClickhouseSettings(host="<clickhouse-server-host>", port=8123, ...)
+    index = Clickhouse(embedding_function, config)
+    index.add_documents(...)
+    ```
+  
+## Wrappers
+supported functions:
+- `add_texts`
+- `add_documents`
+- `from_texts`
+- `from_documents`
+- `similarity_search`
+- `asimilarity_search`
+- `similarity_search_by_vector`
+- `asimilarity_search_by_vector`
+- `similarity_search_with_relevance_scores`
+
+### VectorStore
+
+There exists a wrapper around open source Clickhouse database, allowing you to use it as a vectorstore,
+whether for semantic search or similar example retrieval.
+
+To import this vectorstore:
+```python
+from langchain.vectorstores import Clickhouse
+```
+
+For a more detailed walkthrough of the MyScale wrapper, see [this notebook](../modules/indexes/vectorstores/examples/clickhouse.ipynb)

docs/integrations/cohere.md

@@ -0,0 +1,38 @@
+# Cohere
+
+>[Cohere](https://cohere.ai/about) is a Canadian startup that provides natural language processing models
+> that help companies improve human-machine interactions.
+
+## Installation and Setup
+- Install the Python SDK :
+```bash
+pip install cohere
+```
+
+Get a [Cohere api key](https://dashboard.cohere.ai/) and set it as an environment variable (`COHERE_API_KEY`)
+
+
+## LLM
+
+There exists an Cohere LLM wrapper, which you can access with 
+See a [usage example](../modules/models/llms/integrations/cohere.ipynb).
+
+```python
+from langchain.llms import Cohere
+```
+
+## Text Embedding Model
+
+There exists an Cohere Embedding model, which you can access with 
+```python
+from langchain.embeddings import CohereEmbeddings
+```
+For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/cohere.ipynb)
+
+## Retriever
+
+See a [usage example](../modules/indexes/retrievers/examples/cohere-reranker.ipynb).
+
+```python
+from langchain.retrievers.document_compressors import CohereRerank
+```

docs/integrations/college_confidential.md

@@ -0,0 +1,16 @@
+# College Confidential
+
+>[College Confidential](https://www.collegeconfidential.com/) gives information on 3,800+ colleges and universities.
+
+## Installation and Setup
+
+There isn't any special setup for it.
+
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/college_confidential.ipynb).
+
+```python
+from langchain.document_loaders import CollegeConfidentialLoader
+```

docs/integrations/confluence.md

@@ -0,0 +1,22 @@
+# Confluence
+
+>[Confluence](https://www.atlassian.com/software/confluence) is a wiki collaboration platform that saves and organizes all of the project-related material. `Confluence` is a knowledge base that primarily handles content management activities. 
+
+
+## Installation and Setup
+
+```bash
+pip install atlassian-python-api
+```
+
+We need to set up `username/api_key` or `Oauth2 login`. 
+See [instructions](https://support.atlassian.com/atlassian-account/docs/manage-api-tokens-for-your-atlassian-account/).
+
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/confluence.ipynb).
+
+```python
+from langchain.document_loaders import ConfluenceLoader
+```

docs/integrations/ctransformers.md

@@ -0,0 +1,57 @@
+# C Transformers
+
+This page covers how to use the [C Transformers](https://github.com/marella/ctransformers) library within LangChain.
+It is broken into two parts: installation and setup, and then references to specific C Transformers wrappers.
+
+## Installation and Setup
+
+- Install the Python package with `pip install ctransformers`
+- Download a supported [GGML model](https://huggingface.co/TheBloke) (see [Supported Models](https://github.com/marella/ctransformers#supported-models))
+
+## Wrappers
+
+### LLM
+
+There exists a CTransformers LLM wrapper, which you can access with:
+
+```python
+from langchain.llms import CTransformers
+```
+
+It provides a unified interface for all models:
+
+```python
+llm = CTransformers(model='/path/to/ggml-gpt-2.bin', model_type='gpt2')
+
+print(llm('AI is going to'))
+```
+
+If you are getting `illegal instruction` error, try using `lib='avx'` or `lib='basic'`:
+
+```py
+llm = CTransformers(model='/path/to/ggml-gpt-2.bin', model_type='gpt2', lib='avx')
+```
+
+It can be used with models hosted on the Hugging Face Hub:
+
+```py
+llm = CTransformers(model='marella/gpt-2-ggml')
+```
+
+If a model repo has multiple model files (`.bin` files), specify a model file using:
+
+```py
+llm = CTransformers(model='marella/gpt-2-ggml', model_file='ggml-model.bin')
+```
+
+Additional parameters can be passed using the `config` parameter:
+
+```py
+config = {'max_new_tokens': 256, 'repetition_penalty': 1.1}
+
+llm = CTransformers(model='marella/gpt-2-ggml', config=config)
+```
+
+See [Documentation](https://github.com/marella/ctransformers#config) for a list of available parameters.
+
+For a more detailed walkthrough of this, see [this notebook](../modules/models/llms/integrations/ctransformers.ipynb).

docs/integrations/databerry.md

@@ -0,0 +1,17 @@
+# Databerry
+
+>[Databerry](https://databerry.ai) is an [open source](https://github.com/gmpetrov/databerry) document retrieval platform that helps to connect your personal data with Large Language Models.
+
+
+## Installation and Setup
+
+We need to sign up for Databerry, create a datastore, add some data and get your datastore api endpoint url. 
+We need the [API Key](https://docs.databerry.ai/api-reference/authentication).
+
+## Retriever
+
+See a [usage example](../modules/indexes/retrievers/examples/databerry.ipynb).
+
+```python
+from langchain.retrievers import DataberryRetriever
+```

docs/integrations/databricks.md

@@ -0,0 +1,36 @@
+Databricks
+==========
+
+The [Databricks](https://www.databricks.com/) Lakehouse Platform unifies data, analytics, and AI on one platform.
+
+Databricks embraces the LangChain ecosystem in various ways:
+
+1. Databricks connector for the SQLDatabase Chain: SQLDatabase.from_databricks() provides an easy way to query your data on Databricks through LangChain
+2. Databricks-managed MLflow integrates with LangChain: Tracking and serving LangChain applications with fewer steps
+3. Databricks as an LLM provider: Deploy your fine-tuned LLMs on Databricks via serving endpoints or cluster driver proxy apps, and query it as langchain.llms.Databricks
+4. Databricks Dolly: Databricks open-sourced Dolly which allows for commercial use, and can be accessed through the HuggingFace Hub
+
+Databricks connector for the SQLDatabase Chain
+----------------------------------------------
+You can connect to [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the SQLDatabase wrapper of LangChain. See the notebook [Connect to Databricks](./databricks/databricks.html) for details.
+
+Databricks-managed MLflow integrates with LangChain
+---------------------------------------------------
+
+MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. See the notebook [MLflow Callback Handler](./mlflow_tracking.ipynb) for details about MLflow's integration with LangChain.
+
+Databricks provides a fully managed and hosted version of MLflow integrated with enterprise security features, high availability, and other Databricks workspace features such as experiment and run management and notebook revision capture. MLflow on Databricks offers an integrated experience for tracking and securing machine learning model training runs and running machine learning projects. See [MLflow guide](https://docs.databricks.com/mlflow/index.html) for more details.
+
+Databricks-managed MLflow makes it more convenient to develop LangChain applications on Databricks. For MLflow tracking, you don't need to set the tracking uri. For MLflow Model Serving, you can save LangChain Chains in the MLflow langchain flavor, and then register and serve the Chain with a few clicks on Databricks, with credentials securely managed by MLflow Model Serving.
+
+Databricks as an LLM provider
+-----------------------------
+
+The notebook [Wrap Databricks endpoints as LLMs](../modules/models/llms/integrations/databricks.html) illustrates the method to wrap Databricks endpoints as LLMs in LangChain. It supports two types of endpoints: the serving endpoint, which is recommended for both production and development, and the cluster driver proxy app, which is recommended for interactive development. 
+
+Databricks endpoints support Dolly, but are also great for hosting models like MPT-7B or any other models from the HuggingFace ecosystem. Databricks endpoints can also be used with proprietary models like OpenAI to provide a governance layer for enterprises.
+
+Databricks Dolly
+----------------
+
+Databricks’ Dolly is an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. The model is available on Hugging Face Hub as databricks/dolly-v2-12b. See the notebook [HuggingFace Hub](../modules/models/llms/integrations/huggingface_hub.html) for instructions to access it through the HuggingFace Hub integration with LangChain. 

docs/integrations/databricks/databricks.ipynb

@@ -0,0 +1,280 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Databricks\n",
+    "\n",
+    "This notebook covers how to connect to the [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the SQLDatabase wrapper of LangChain.\n",
+    "It is broken into 3 parts: installation and setup, connecting to Databricks, and examples."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Installation and Setup"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [],
+   "source": [
+    "!pip install databricks-sql-connector"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Connecting to Databricks\n",
+    "\n",
+    "You can connect to [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the `SQLDatabase.from_databricks()` method.\n",
+    "\n",
+    "### Syntax\n",
+    "```python\n",
+    "SQLDatabase.from_databricks(\n",
+    "    catalog: str,\n",
+    "    schema: str,\n",
+    "    host: Optional[str] = None,\n",
+    "    api_token: Optional[str] = None,\n",
+    "    warehouse_id: Optional[str] = None,\n",
+    "    cluster_id: Optional[str] = None,\n",
+    "    engine_args: Optional[dict] = None,\n",
+    "    **kwargs: Any)\n",
+    "```\n",
+    "### Required Parameters\n",
+    "* `catalog`: The catalog name in the Databricks database.\n",
+    "* `schema`: The schema name in the catalog.\n",
+    "\n",
+    "### Optional Parameters\n",
+    "There following parameters are optional. When executing the method in a Databricks notebook, you don't need to provide them in most of the cases.\n",
+    "* `host`: The Databricks workspace hostname, excluding 'https://' part. Defaults to 'DATABRICKS_HOST' environment variable or current workspace if in a Databricks notebook.\n",
+    "* `api_token`: The Databricks personal access token for accessing the Databricks SQL warehouse or the cluster. Defaults to 'DATABRICKS_TOKEN' environment variable or a temporary one is generated if in a Databricks notebook.\n",
+    "* `warehouse_id`: The warehouse ID in the Databricks SQL.\n",
+    "* `cluster_id`: The cluster ID in the Databricks Runtime. If running in a Databricks notebook and both 'warehouse_id' and 'cluster_id' are None, it uses the ID of the cluster the notebook is attached to.\n",
+    "* `engine_args`: The arguments to be used when connecting Databricks.\n",
+    "* `**kwargs`: Additional keyword arguments for the `SQLDatabase.from_uri` method."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Examples"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "# Connecting to Databricks with SQLDatabase wrapper\n",
+    "from langchain import SQLDatabase\n",
+    "\n",
+    "db = SQLDatabase.from_databricks(catalog='samples', schema='nyctaxi')"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [],
+   "source": [
+    "# Creating a OpenAI Chat LLM wrapper\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(temperature=0, model_name=\"gpt-4\")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### SQL Chain example\n",
+    "\n",
+    "This example demonstrates the use of the [SQL Chain](https://python.langchain.com/en/latest/modules/chains/examples/sqlite.html) for answering a question over a Databricks database."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "36f2270b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import SQLDatabaseChain\n",
+    "\n",
+    "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4e2b5f25",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001B[1m> Entering new SQLDatabaseChain chain...\u001B[0m\n",
+      "What is the average duration of taxi rides that start between midnight and 6am?\n",
+      "SQLQuery:\u001B[32;1m\u001B[1;3mSELECT AVG(UNIX_TIMESTAMP(tpep_dropoff_datetime) - UNIX_TIMESTAMP(tpep_pickup_datetime)) as avg_duration\n",
+      "FROM trips\n",
+      "WHERE HOUR(tpep_pickup_datetime) >= 0 AND HOUR(tpep_pickup_datetime) < 6\u001B[0m\n",
+      "SQLResult: \u001B[33;1m\u001B[1;3m[(987.8122786304605,)]\u001B[0m\n",
+      "Answer:\u001B[32;1m\u001B[1;3mThe average duration of taxi rides that start between midnight and 6am is 987.81 seconds.\u001B[0m\n",
+      "\u001B[1m> Finished chain.\u001B[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "'The average duration of taxi rides that start between midnight and 6am is 987.81 seconds.'"
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "db_chain.run(\"What is the average duration of taxi rides that start between midnight and 6am?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### SQL Database Agent example\n",
+    "\n",
+    "This example demonstrates the use of the [SQL Database Agent](https://python.langchain.com/en/latest/modules/agents/toolkits/examples/sql_database.html) for answering questions over a Databricks database."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9918e86a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import create_sql_agent\n",
+    "from langchain.agents.agent_toolkits import SQLDatabaseToolkit\n",
+    "\n",
+    "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n",
+    "agent = create_sql_agent(\n",
+    "    llm=llm,\n",
+    "    toolkit=toolkit,\n",
+    "    verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c484a76e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3mAction: list_tables_sql_db\n",
+      "Action Input: \u001B[0m\n",
+      "Observation: \u001B[38;5;200m\u001B[1;3mtrips\u001B[0m\n",
+      "Thought:\u001B[32;1m\u001B[1;3mI should check the schema of the trips table to see if it has the necessary columns for trip distance and duration.\n",
+      "Action: schema_sql_db\n",
+      "Action Input: trips\u001B[0m\n",
+      "Observation: \u001B[33;1m\u001B[1;3m\n",
+      "CREATE TABLE trips (\n",
+      "\ttpep_pickup_datetime TIMESTAMP, \n",
+      "\ttpep_dropoff_datetime TIMESTAMP, \n",
+      "\ttrip_distance FLOAT, \n",
+      "\tfare_amount FLOAT, \n",
+      "\tpickup_zip INT, \n",
+      "\tdropoff_zip INT\n",
+      ") USING DELTA\n",
+      "\n",
+      "/*\n",
+      "3 rows from trips table:\n",
+      "tpep_pickup_datetime\ttpep_dropoff_datetime\ttrip_distance\tfare_amount\tpickup_zip\tdropoff_zip\n",
+      "2016-02-14 16:52:13+00:00\t2016-02-14 17:16:04+00:00\t4.94\t19.0\t10282\t10171\n",
+      "2016-02-04 18:44:19+00:00\t2016-02-04 18:46:00+00:00\t0.28\t3.5\t10110\t10110\n",
+      "2016-02-17 17:13:57+00:00\t2016-02-17 17:17:55+00:00\t0.7\t5.0\t10103\t10023\n",
+      "*/\u001B[0m\n",
+      "Thought:\u001B[32;1m\u001B[1;3mThe trips table has the necessary columns for trip distance and duration. I will write a query to find the longest trip distance and its duration.\n",
+      "Action: query_checker_sql_db\n",
+      "Action Input: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001B[0m\n",
+      "Observation: \u001B[31;1m\u001B[1;3mSELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001B[0m\n",
+      "Thought:\u001B[32;1m\u001B[1;3mThe query is correct. I will now execute it to find the longest trip distance and its duration.\n",
+      "Action: query_sql_db\n",
+      "Action Input: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001B[0m\n",
+      "Observation: \u001B[36;1m\u001B[1;3m[(30.6, '0 00:43:31.000000000')]\u001B[0m\n",
+      "Thought:\u001B[32;1m\u001B[1;3mI now know the final answer.\n",
+      "Final Answer: The longest trip distance is 30.6 miles and it took 43 minutes and 31 seconds.\u001B[0m\n",
+      "\n",
+      "\u001B[1m> Finished chain.\u001B[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "'The longest trip distance is 30.6 miles and it took 43 minutes and 31 seconds.'"
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"What is the longest trip distance and how long did it take?\")"
+   ]
+  }
+ ],
+ "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/integrations/deepinfra.md

@@ -7,6 +7,14 @@ It is broken into two parts: installation and setup, and then references to spec
 - Get your DeepInfra api key from this link [here](https://deepinfra.com/).
 - Get an DeepInfra api key and set it as an environment variable (`DEEPINFRA_API_TOKEN`)
 
+## Available Models
+
+DeepInfra provides a range of Open Source LLMs ready for deployment.
+You can list supported models [here](https://deepinfra.com/models?type=text-generation).
+google/flan\* models can be viewed [here](https://deepinfra.com/models?type=text2text-generation).
+
+You can view a list of request and response parameters [here](https://deepinfra.com/databricks/dolly-v2-12b#API)
+
 ## Wrappers
 
 ### LLM

docs/integrations/diffbot.md

@@ -0,0 +1,18 @@
+# Diffbot
+
+>[Diffbot](https://docs.diffbot.com/docs) is a service to read web pages. Unlike traditional web scraping tools, 
+> `Diffbot` doesn't require any rules to read the content on a page.
+>It starts with computer vision, which classifies a page into one of 20 possible types. Content is then interpreted by a machine learning model trained to identify the key attributes on a page based on its type.
+>The result is a website transformed into clean-structured data (like JSON or CSV), ready for your application.
+
+## Installation and Setup
+
+Read [instructions](https://docs.diffbot.com/reference/authentication) how to get the Diffbot API Token.
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/diffbot.ipynb).
+
+```python
+from langchain.document_loaders import DiffbotLoader
+```

docs/integrations/discord.md

@@ -0,0 +1,30 @@
+# Discord
+
+>[Discord](https://discord.com/) is a VoIP and instant messaging social platform. Users have the ability to communicate 
+> with voice calls, video calls, text messaging, media and files in private chats or as part of communities called 
+> "servers". A server is a collection of persistent chat rooms and voice channels which can be accessed via invite links.
+
+## Installation and Setup
+
+
+```bash
+pip install pandas
+```
+
+Follow these steps to download your `Discord` data:
+
+1. Go to your **User Settings**
+2. Then go to **Privacy and Safety**
+3. Head over to the **Request all of my Data** and click on **Request Data** button
+
+It might take 30 days for you to receive your data. You'll receive an email at the address which is registered 
+with Discord. That email will have a download button using which you would be able to download your personal Discord data.
+
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/discord.ipynb).
+
+```python
+from langchain.document_loaders import DiscordChatLoader
+```

docs/integrations/docugami.md

@@ -0,0 +1,20 @@
+# Docugami
+
+>[Docugami](https://docugami.com) converts business documents into a Document XML Knowledge Graph, generating forests 
+> of XML semantic trees representing entire documents. This is a rich representation that includes the semantic and 
+> structural characteristics of various chunks in the document as an XML tree.
+
+## Installation and Setup
+
+
+```bash
+pip install lxml
+```
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/docugami.ipynb).
+
+```python
+from langchain.document_loaders import DocugamiLoader
+```

docs/integrations/duckdb.md

@@ -0,0 +1,19 @@
+# DuckDB
+
+>[DuckDB](https://duckdb.org/) is an in-process SQL OLAP database management system.
+
+## Installation and Setup
+
+First, you need to install `duckdb` python package.
+
+```bash
+pip install duckdb
+```
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/duckdb.ipynb).
+
+```python
+from langchain.document_loaders import DuckDBLoader
+```

docs/integrations/elasticsearch.md

@@ -0,0 +1,24 @@
+# Elasticsearch
+
+>[Elasticsearch](https://www.elastic.co/elasticsearch/) is a distributed, RESTful search and analytics engine. 
+> It provides a distributed, multi-tenant-capable full-text search engine with an HTTP web interface and schema-free 
+> JSON documents.
+
+
+## Installation and Setup
+
+```bash
+pip install elasticsearch
+```
+
+## Retriever
+
+>In information retrieval, [Okapi BM25](https://en.wikipedia.org/wiki/Okapi_BM25) (BM is an abbreviation of best matching) is a ranking function used by search engines to estimate the relevance of documents to a given search query. It is based on the probabilistic retrieval framework developed in the 1970s and 1980s by Stephen E. Robertson, Karen Spärck Jones, and others.
+
+>The name of the actual ranking function is BM25. The fuller name, Okapi BM25, includes the name of the first system to use it, which was the Okapi information retrieval system, implemented at London's City University in the 1980s and 1990s. BM25 and its newer variants, e.g. BM25F (a version of BM25 that can take document structure and anchor text into account), represent TF-IDF-like retrieval functions used in document retrieval.
+
+See a [usage example](../modules/indexes/retrievers/examples/elastic_search_bm25.ipynb).
+
+```python
+from langchain.retrievers import ElasticSearchBM25Retriever
+```

docs/integrations/evernote.md

@@ -0,0 +1,20 @@
+# EverNote
+
+>[EverNote](https://evernote.com/) is intended for archiving and creating notes in which photos, audio and saved web content can be embedded. Notes are stored in virtual "notebooks" and can be tagged, annotated, edited, searched, and exported.
+
+## Installation and Setup
+
+First, you need to install `lxml` and `html2text` python packages.
+
+```bash
+pip install lxml
+pip install html2text
+```
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/evernote.ipynb).
+
+```python
+from langchain.document_loaders import EverNoteLoader
+```

docs/integrations/facebook_chat.md

@@ -0,0 +1,21 @@
+# Facebook Chat
+
+>[Messenger](https://en.wikipedia.org/wiki/Messenger_(software)) is an American proprietary instant messaging app and 
+> platform developed by `Meta Platforms`. Originally developed as `Facebook Chat` in 2008, the company revamped its
+> messaging service in 2010.
+
+## Installation and Setup
+
+First, you need to install `pandas` python package.
+
+```bash
+pip install pandas
+```
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/facebook_chat.ipynb).
+
+```python
+from langchain.document_loaders import FacebookChatLoader
+```

docs/integrations/figma.md

@@ -0,0 +1,21 @@
+# Figma
+
+>[Figma](https://www.figma.com/) is a collaborative web application for interface design.
+
+## Installation and Setup
+
+The Figma API requires an `access token`, `node_ids`, and a `file key`.
+
+The `file key` can be pulled from the URL.  https://www.figma.com/file/{filekey}/sampleFilename
+
+`Node IDs` are also available in the URL. Click on anything and look for the '?node-id={node_id}' param.
+
+`Access token` [instructions](https://help.figma.com/hc/en-us/articles/8085703771159-Manage-personal-access-tokens).
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/figma.ipynb).
+
+```python
+from langchain.document_loaders import FigmaFileLoader
+```

docs/integrations/git.md

@@ -0,0 +1,19 @@
+# Git
+
+>[Git](https://en.wikipedia.org/wiki/Git) is a distributed version control system that tracks changes in any set of computer files, usually used for coordinating work among programmers collaboratively developing source code during software development.
+
+## Installation and Setup
+
+First, you need to install `GitPython` python package.
+
+```bash
+pip install GitPython
+```
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/git.ipynb).
+
+```python
+from langchain.document_loaders import GitLoader
+```

docs/integrations/gitbook.md

@@ -0,0 +1,15 @@
+# GitBook
+
+>[GitBook](https://docs.gitbook.com/) is a modern documentation platform where teams can document everything from products to internal knowledge bases and APIs.
+
+## Installation and Setup
+
+There isn't any special setup for it.
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/gitbook.ipynb).
+
+```python
+from langchain.document_loaders import GitbookLoader
+```

docs/integrations/google_bigquery.md

@@ -0,0 +1,20 @@
+# Google BigQuery
+
+>[Google BigQuery](https://cloud.google.com/bigquery) is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data.
+`BigQuery` is a part of the `Google Cloud Platform`.
+
+## Installation and Setup
+
+First, you need to install `google-cloud-bigquery` python package.
+
+```bash
+pip install google-cloud-bigquery
+```
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/google_bigquery.ipynb).
+
+```python
+from langchain.document_loaders import BigQueryLoader
+```

docs/integrations/google_cloud_storage.md

@@ -0,0 +1,26 @@
+# Google Cloud Storage
+
+>[Google Cloud Storage](https://en.wikipedia.org/wiki/Google_Cloud_Storage) is a managed service for storing unstructured data.
+
+## Installation and Setup
+
+First, you need to install `google-cloud-bigquery` python package.
+
+```bash
+pip install google-cloud-storage
+```
+
+## Document Loader
+
+There are two loaders for the `Google Cloud Storage`: the `Directory` and the `File` loaders.
+
+See a [usage example](../modules/indexes/document_loaders/examples/google_cloud_storage_directory.ipynb).
+
+```python
+from langchain.document_loaders import GCSDirectoryLoader
+```
+See a [usage example](../modules/indexes/document_loaders/examples/google_cloud_storage_file.ipynb).
+
+```python
+from langchain.document_loaders import GCSFileLoader
+```

docs/integrations/google_drive.md

@@ -0,0 +1,22 @@
+# Google Drive
+
+>[Google Drive](https://en.wikipedia.org/wiki/Google_Drive) is a file storage and synchronization service developed by Google.
+
+Currently, only `Google Docs` are supported.
+
+## Installation and Setup
+
+First, you need to install several python package.
+
+```bash
+pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib
+```
+
+## Document Loader
+
+See a [usage example and authorizing instructions](../modules/indexes/document_loaders/examples/google_drive.ipynb).
+
+
+```python
+from langchain.document_loaders import GoogleDriveLoader
+```

docs/integrations/google_search.md

@@ -1,4 +1,4 @@
-# Google Search Wrapper
+# Google Search
 
 This page covers how to use the Google Search API within LangChain.
 It is broken into two parts: installation and setup, and then references to the specific Google Search wrapper.

docs/integrations/google_serper.md

@@ -1,4 +1,4 @@
-# Google Serper Wrapper
+# Google Serper
 
 This page covers how to use the [Serper](https://serper.dev) Google Search API within LangChain. Serper is a low-cost Google Search API that can be used to add answer box, knowledge graph, and organic results data from Google Search. 
 It is broken into two parts: setup, and then references to the specific Google Serper wrapper.

docs/integrations/google_vertex_ai.md

@@ -0,0 +1,24 @@
+# Google Vertex AI
+
+>[Vertex AI](https://cloud.google.com/vertex-ai/docs/start/introduction-unified-platform) is a machine learning (ML) 
+> platform that lets you train and deploy ML models and AI applications. 
+> `Vertex AI` combines data engineering, data science, and ML engineering workflows, enabling your teams to 
+> collaborate using a common toolset.
+ 
+## Installation and Setup
+
+
+```bash
+pip install google-cloud-aiplatform
+```
+
+See the [setup instructions](../modules/models/chat/integrations/google_vertex_ai_palm.ipynb)
+
+
+## Chat Models
+
+See a [usage example](../modules/models/chat/integrations/google_vertex_ai_palm.ipynb)
+
+```python
+from langchain.chat_models import ChatVertexAI
+```

docs/integrations/gutenberg.md

@@ -0,0 +1,15 @@
+# Gutenberg
+
+>[Project Gutenberg](https://www.gutenberg.org/about/) is an online library of free eBooks.
+
+## Installation and Setup
+
+There isn't any special setup for it.
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/gutenberg.ipynb).
+
+```python
+from langchain.document_loaders import GutenbergLoader
+```

docs/integrations/hacker_news.md

@@ -0,0 +1,18 @@
+# Hacker News
+
+>[Hacker News](https://en.wikipedia.org/wiki/Hacker_News) (sometimes abbreviated as `HN`) is a social news 
+> website focusing on computer science and entrepreneurship. It is run by the investment fund and startup 
+> incubator `Y Combinator`. In general, content that can be submitted is defined as "anything that gratifies 
+> one's intellectual curiosity."
+
+## Installation and Setup
+
+There isn't any special setup for it.
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/hacker_news.ipynb).
+
+```python
+from langchain.document_loaders import HNLoader
+```

docs/integrations/huggingface.md

@@ -47,7 +47,7 @@ To use a the wrapper for a model hosted on Hugging Face Hub:
 ```python
 from langchain.embeddings import HuggingFaceHubEmbeddings
 ```
-For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/huggingfacehub.ipynb)
+For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/huggingface_hub.ipynb)
 
 ### Tokenizer
 

docs/integrations/ifixit.md

@@ -0,0 +1,16 @@
+# iFixit
+
+>[iFixit](https://www.ifixit.com) is the largest, open repair community on the web. The site contains nearly 100k 
+> repair manuals, 200k Questions & Answers on 42k devices, and all the data is licensed under `CC-BY-NC-SA 3.0`.
+
+## Installation and Setup
+
+There isn't any special setup for it.
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/ifixit.ipynb).
+
+```python
+from langchain.document_loaders import IFixitLoader
+```

docs/integrations/imsdb.md

@@ -0,0 +1,16 @@
+# IMSDb
+
+>[IMSDb](https://imsdb.com/) is the `Internet Movie Script Database`.
+> 
+## Installation and Setup
+
+There isn't any special setup for it.
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/imsdb.ipynb).
+
+
+```python
+from langchain.document_loaders import IMSDbLoader
+```

docs/integrations/mediawikidump.md

@@ -0,0 +1,31 @@
+# MediaWikiDump
+
+>[MediaWiki XML Dumps](https://www.mediawiki.org/wiki/Manual:Importing_XML_dumps) contain the content of a wiki 
+> (wiki pages with all their revisions), without the site-related data. A XML dump does not create a full backup 
+> of the wiki database, the dump does not contain user accounts, images, edit logs, etc.
+
+
+## Installation and Setup
+
+We need to install several python packages.
+
+The `mediawiki-utilities` supports XML schema 0.11 in unmerged branches.
+```bash
+pip install -qU git+https://github.com/mediawiki-utilities/python-mwtypes@updates_schema_0.11
+```
+
+The `mediawiki-utilities mwxml` has a bug, fix PR pending.
+
+```bash
+pip install -qU git+https://github.com/gdedrouas/python-mwxml@xml_format_0.11
+pip install -qU mwparserfromhell
+```
+
+## Document Loader
+
+See a [usage example](../modules/indexes/document_loaders/examples/mediawikidump.ipynb).
+
+
+```python
+from langchain.document_loaders import MWDumpLoader
+```