merge

docs/additional_resources.rst

@@ -0,0 +1,17 @@
+Additional Resources
+==========================
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Additional Resources
+   :name: resources
+
+   LangChainHub <https://github.com/hwchase17/langchain-hub>
+   getting_started/tutorials.md
+   Gallery <https://github.com/kyrolabs/awesome-langchain>
+   ./additional_resources/deployments.md
+   ./additional_resources/tracing.md
+   ./additional_resources/model_laboratory.ipynb
+   Discord <https://discord.gg/6adMQxSpJS>
+   ./additional_resources/youtube.md
+   Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>

docs/ecosystem.rst

@@ -0,0 +1,11 @@
+Ecosystem
+==========================
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+   :caption: Ecosystem
+   :name: ecosystem
+
+   ./integrations.rst
+   ./dependents.md

docs/get_started.rst

@@ -0,0 +1,10 @@
+Get Started
+==========================
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Get Started
+   :name: get_started
+
+   getting_started/installation.md
+   getting_started/getting_started.md

docs/index.rst

@@ -1,202 +1,28 @@
-Welcome to LangChain
+Introduction
 ==========================
 
+Overview
+----------------
+
 | **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
+1. **Data-aware**: Connect a language model to other sources of data
+2. **Agentic**: Allow a language model to interact with its environment
 
 | The LangChain framework is designed around these principles.
 
-| 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
-----------------
-
-| How to get started using LangChain to create an Language Model application.
-
-- `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: 2
-   :caption: Getting Started
-   :name: getting_started
-   :hidden:
-
-   getting_started/getting_started.md
-   getting_started/concepts.md
-   getting_started/tutorials.md
-
-
-Modules
------------
-
-| 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.
-
-| The docs for each module contain quickstart examples, how-to guides, reference docs, and conceptual guides.
-
-| The modules are (from least to most complex):
-
-- `Models <./modules/models.html>`_: Supported model types and integrations.
-
-- `Prompts <./modules/prompts.html>`_: Prompt management, optimization, and serialization.
-
-- `Memory <./modules/memory.html>`_: Memory refers to state that is persisted between calls of a chain/agent.
-
-- `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.
-
-- `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.
+.. note::
+    This is the Python specific portion of the documentation. See here for the `Conceptual documentation <https://docs.langchain.com/docs/>`_ and here for the `JavaScript documentation <https://js.langchain.com/docs/>`_.
 
 .. toctree::
    :maxdepth: 1
-   :caption: Modules
-   :name: modules
+   :caption: LangChain
+   :name: langchain
    :hidden:
 
-   ./modules/models.rst
-   ./modules/prompts.rst
-   ./modules/memory.md
-   ./modules/indexes.md
-   ./modules/chains.md
-   ./modules/agents.md
-   ./modules/callbacks/getting_started.ipynb
-
-Use Cases
-----------
-
-| 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.
-
-- `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>`_: 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>`_: 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>`_: Language models love to chat, making this a very natural use of them.
-
-- `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>`_: Recommended reading if you want to use language models to analyze code.
-
-- `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>`_: Compressing longer documents. A type of Data-Augmented Generation.
-
-- `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::
-   :maxdepth: 1
-   :caption: Use Cases
-   :name: use_cases
-   :hidden:
-
-   ./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/extraction.md
-   ./use_cases/summarization.md
-   ./use_cases/evaluation.rst
-
-
-Reference Docs
----------------
-
-| 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
-   :name: reference
-   :hidden:
-
-   ./reference/installation.md
+   ./get_started.rst
+   ./modules.rst
+   ./ecosystem.rst
    ./reference.rst
+   ./additional_resources.rst
 
 
-Ecosystem
-------------
-
-| 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.
-
-
-.. toctree::
-   :maxdepth: 2
-   :glob:
-   :caption: Ecosystem
-   :name: ecosystem
-   :hidden:
-
-   ./integrations.rst
-   ./dependents.md
-   ./ecosystem/deployments.md
-
-
-Additional Resources
----------------------
-
-| 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.
-
-- `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.
-
-- `Tracing <./additional_resources/tracing.html>`_: A guide on using tracing in LangChain to visualize the execution of chains and agents.
-
-- `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 <./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.
-
-
-.. toctree::
-   :maxdepth: 1
-   :caption: Additional Resources
-   :name: resources
-   :hidden:
-
-   LangChainHub <https://github.com/hwchase17/langchain-hub>
-   Gallery <https://github.com/kyrolabs/awesome-langchain>
-   ./additional_resources/tracing.md
-   ./additional_resources/model_laboratory.ipynb
-   Discord <https://discord.gg/6adMQxSpJS>
-   ./additional_resources/youtube.md
-   Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>

docs/modules.rst

@@ -0,0 +1,15 @@
+Modules
+==========================
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Modules
+   :name: modules
+
+   ./modules/models.rst
+   ./modules/prompts.rst
+   ./modules/indexes.md
+   ./modules/memory.md
+   ./modules/chains.md
+   ./modules/agents.md
+   ./modules/callbacks/getting_started.ipynb

docs/reference.rst

@@ -9,8 +9,8 @@ API References
 
    ./reference/models.rst
    ./reference/prompts.rst
-   ./reference/indexes.rst
    ./reference/modules/memory.rst
+   ./reference/indexes.rst
    ./reference/modules/chains.rst
    ./reference/agents.rst
    ./reference/modules/utilities.rst

docs/use_cases.rst

@@ -0,0 +1,19 @@
+Use Cases
+==========================
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Use Cases
+   :name: use_cases
+
+   ./use_cases/autonomous_agents.md
+   ./use_cases/personal_assistants.md
+   ./use_cases/question_answering.md
+   ./use_cases/chatbots.md
+   ./use_cases/tabular.rst
+   ./use_cases/apis.md
+   ./use_cases/code.md
+   ./use_cases/extraction.md
+   ./use_cases/summarization.md
+   ./use_cases/evaluation.rst
+   ./use_cases/agent_simulations.md